[User] Please transcribe this text

[Assistant] Uhm, but also then, you know, there's no code for you to be able to run it. How do we do all of that? And so, there's a lot of statistics that's still to be discovered, uhm, and it really is just in response to the world's, the world evolving and the world of clinical trials evolving.


[User] Please transcribe this text

[Assistant] The same sort of fundamental tool. So when you're at university, you learn topics such as statistical inference, which is if you've got a data set, how do you pull out, you know, what the average is and things like that? How do you pull out what is the effect of age on this outcome or gender on this outcome?


[User] Please transcribe this text

[Assistant] You learn those fundamentals and those fundamentals are constant throughout but the intricacies of the individual models is what changes and you then have to sort of apply these fundamentals to these new models that you develop to pull out the different numbers that you need.


[User] Please transcribe this text

[Assistant] And it's little steps each time and it's little steps in response to the world changing. You know, the world of big data and AI, you know, as computing power increases, that changes what we're able to do and how much data we're able to process and that then


[User] Please transcribe this text

[Assistant] Leads us in a direction of where we need to go with our future, you know, future research. And I think it is the sort of small incremental


[User] Please transcribe this text

[Assistant] You know, if you take three day or seven day rolling averages, that gives you a better idea of what the curve really looks like. And you, you kind of saw these things evolving. There's still some things that I'd like to see maybe being done a little bit better. So for example, when, uh, that,


[User] Please transcribe this text

[Assistant] Remember when they had the exponential growth curve where they said if cases were doubling every seven days by the middle of October we'd have 50,000 cases and they showed that curve? That wasn't presented with any kind of uncertainty or anything like that, you know, projections into the future.


[User] Please transcribe this text

[Assistant] They're not shown with uncertainty and confidence bars or anything like that. So I think...


[User] Please transcribe this text

[Assistant] You know, there's still some things that we could do to better communicate with the general public, but I think, I think it's been okay. I think at some points we saw too much information and I think we were showing information for the sake of it. So when we were in the first lockdown and we had the daily briefings,


[User] Please transcribe this text

[Assistant] Did we really need to see every single day how many people were travelling by car or by bus or by train? I'm not sure that we really did. Uhm, I think one of the lessons that I hope has been learnt is


[User] Please transcribe this text

[Assistant] Don't overwhelm people with it. But I have been encouraged by the fact that people have been engaging with statistics. You know, there's been a lot of, uh, couch statisticians, uhm, they may not always be doing it right.


[User] Please transcribe this text

[Assistant] But at least they're engaging with it and it means that someone like me, I've got a great opportunity to talk to people about it and talk to people who may in the past have been completely shut down by it.


[User] Please transcribe this text

[Assistant] Yeah, and I'm not sure I even really know who I'm blaming, and I think that's why I always sort of tee it up as I'm giving the public the tools they need to be able to ask the right questions of what they're seeing.


[User] Please transcribe this text

[Assistant] I think that sometimes an editor will want a grabbing headline so that they get, you know, they get the page views or they get the newspapers sold. I think sometimes press officers are a little bit to blame, you know, they want the newspapers to pick up their work and report about it and so


[User] Please transcribe this text

[Assistant] They, they make the results seem as sensationalised as they can to try and get people to pick it up. You know universities as well, they come under so much pressure for their work to have impact.


[User] Please transcribe this text

[Assistant] So they themselves, you know, they want to have their work picked up because, you know, if you're thinking about if, if you're going to get future research funding, well if the last thing that you did got covered by the BBC,


[User] Please transcribe this text

[Assistant] That's amazing, you know, that's great impact and, and so there's a pressure on researchers to have their work published. There's a pressure then on press officers to make sure their work gets picked up and


[User] Please transcribe this text

[Assistant] There's a pressure from, you know, within the, the journalistic world for, for their work to get page views and I think that


[User] Please transcribe this text

[Assistant] It's maybe a real deep rooted problem that we're maybe not ever going to be able to solve. And so that's why I do really like having the opportunity to try and educate people of the things that they're seeing because


[User] Please transcribe this text

[Assistant] You know, I mean, I think things have got better and I definitely get more calls from journalists now saying, we've had this press release, can you just talk me through it? So I think that there is a little bit of a change and I do genuinely really appreciate that. But I think there's probably going to be


[User] Please transcribe this text

[Assistant] Some way to go, and I think we probably will never get 100% there, so I think


[User] Please transcribe this text

[Assistant] The headline may still be the big relative risk, you know, the impressive sounding figure, but you're starting to see the absolute risks now sneaking into the actual text. And the same with uncertainty. You know, you might get just the one single figure point estimate in your headline, but if you go on and read further, you might see a confidence interval.


[User] Please transcribe this text

[Assistant] Those sorts of things are starting to sneak in. Not sure they'll ever make the headlines, but uhm, hopefully they make the text a lot more than they used to.


[User] Please transcribe this text

[Assistant] Do you know what, I think, yeah, I mean, maybe my public shaming...


[User] Please transcribe this text

[Assistant] Makes people think a little bit more, uh, but I think sometimes, you know, as statisticians we also


[User] Please transcribe this text

[Assistant] I do try and, uh, award people who do well. So, uhm, you know, my work with the Royal Statistical Society, uh, this is my last year of chairing the Journalism Award. So, we do actually award excellence in, in journalism and


[User] Please transcribe this text

[Assistant] People are really proud when they get one of these awards and they see it as a really good thing, you know, this stamp of approval from the Royal Statistical Society that I do good statistical reporting. Uhm, and so I think maybe, uh,


[User] Please transcribe this text

[Assistant] A mix of giving people a slap on the wrists when they do it really badly, but also making sure that we really highlight the people that do it well and use them as an example for other people and something to aspire to.


[User] Please transcribe this text

[Assistant] I can appreciate that and, um, you know, clinical trials are a lot more complicated than just seeing a daily count on, on a, you know, on a plot on your screen. Uh, you know, there's all the intricacies of,


[User] Please transcribe this text

[Assistant] You know, they've had different trial designs and the designs have been called into question and things like that and it is really complicated and I do, you know, I'm trained in this so I understand it more but for anybody else I really do feel for anyone trying to...


[User] Please transcribe this text

[Assistant] Uhm, you know, one that anybody shouldn't take lightly. Uhm, I couldn't believe it. I, I gave a webinar yesterday.


[User] Please transcribe this text

[Assistant] Uhm, and it was the, the 9th of November, so just a month before, was when we got the first announcement from Pfizer, from their interim analysis. And in that month, we had


[User] Please transcribe this text

[Assistant] You know, interim analyses from Moderna, interim analyses from AstraZeneca Oxford. We had.


[User] Please transcribe this text

[Assistant] Final analysis from the Pfizer-BioNTech, final analysis from Moderna, and we had the first vaccine being put in someone's arm. It's been an absolutely crazy, crazy month.


[User] Please transcribe this text

[Assistant] Yeah, I am. I've been absolutely astounded by the results. The results have been amazing. If you look at all the study designs and all of that in a lot more detail, what they were hoping that they would achieve compared to what they did actually achieve is actually quite astounding. We've got three vaccines right now that not only work,


[User] Please transcribe this text

[Assistant] There's not zero risk every time you take a paracetamol, you know, there's nothing is ever zero risk, but I'm confident that


[User] Please transcribe this text

[Assistant] Everything that should have been done has been done, and everything that we could possibly know, we do know.


[User] Please transcribe this text

[Assistant] Uhm, and I am confident as well that the monitoring that's gonna carry on in the future is gonna be very...


[User] Please transcribe this text

[Assistant] Uh, and I'm, I'm more than happy to, to put this vaccine in my body and, you know, I have, uhm, I've got three grandparents left who are over the age of 80 and will be in that first wave and I've told all of them to, to please go get this vaccine as, as soon as it's offered.


[User] Please transcribe this text

[Assistant] So it's the, it's the vaccine effectiveness or sometimes it's called the vaccine efficacy. So these are the, the, it tells you how well does this vaccine work. So for the Pfizer-BioNTech, we, we saw a 95%


[User] Please transcribe this text

[Assistant] This vaccine would have prevented 95% of them. So if that same 100 people had been vaccinated, only five of them would have got coronavirus. And that's the number that I really look for to start off with. Now, when all of these studies were being designed,


[User] Please transcribe this text

[Assistant] They said that they wanted to be able to get a vaccine, uhm, that had a vaccine effectiveness of greater than 30%. That was the number that they were targeting. If they had a vaccine effectiveness of greater than 30%, then this would be a good result.


[User] Please transcribe this text

[Assistant] Uhm, and to have a vaccine effectiveness of 95%, that, that's huge. That's, that's so much more than we ever...


[User] Please transcribe this text

[Assistant] So, so it would have been, they wanted a true underlying efficacy of 30%. What that would have meant for the clinical trials when you take into account, you know, the uncertainty and the confidence bars. They were actually targeting an estimated vaccine effectiveness in the studies of 50%.


[User] Please transcribe this text

[Assistant] Uh, there was one year a few years ago where it went down to about 15%. So, cause you know, they do slightly different flu vaccines every year because of the different strains and things like that. So, yeaah, so 30%, to me, I agree with you, it does seem, it does seem quite low. I guess...


[User] Please transcribe this text

[Assistant] There is also the question of though, if, if we've got something that is going to help, it's better than doing nothing at all. You know, especially when we're in this situation.


[User] Please transcribe this text

[Assistant] Yeah, quite, exactly, yeah. You'd definitely feel a lot more confident about a 95% effectiveness than 30%.


[User] Please transcribe this text

[Assistant] I mean, I'm not a virologist, I don't know the ins and outs of vaccines, but I know that, you know, the Pfizer-BioNTech and the Moderna, that was using the genetic sequencing.


[User] Please transcribe this text

[Assistant] Uhm, and I believe that that's the first time that this has ever been used. All of that work that's been going on in the background for, you know, tens of twenties years has now come to fruition and we're really seeing


[User] Please transcribe this text

[Assistant] We're just really lucky that actually this new way of doing things turns out to be really good.


[User] Please transcribe this text

[Assistant] You can't statistically explain these big numbers. The numbers are what they are because of the vaccines being so good.


[User] Please transcribe this text

[Assistant] Yeah, so uhm, uncertainty becomes less of an issue when you've got such a big effect because it's so far away from that sort of 30 or 50% target. Uhm, so when you've got such a big effect you don't worry as much about


[User] Please transcribe this text

[Assistant] So they've not just been looking at cases of Covid, but they've also been looking at


[User] Please transcribe this text

[Assistant] Severe definition wise rather than it actually being a severe side effect. So the two grade three side effects that happened in more than 2% of people were fatigue and headache.


[User] Please transcribe this text

[Assistant] So in real terms, they're quite mild side effects, but in sort of clinical trial world, their clusters are grade three severe side effects. So I start looking at, you know, what were the safety profiles of them? So, you know, you don't,


[User] Please transcribe this text

[Assistant] You know, in, in its entirety. So I start looking at all of those sorts of things. Uh, it's been interesting looking at the different ways that they defined COVID cases and trying to then get an idea of, oh, what else, what might we be able to get from this study? So for example, the Pfizer BioNTech.


[User] Please transcribe this text

[Assistant] And the Moderna study, their definition of a case was 14 days after the second dose.


[User] Please transcribe this text

[Assistant] So the Pfizer-BioNTech, because it looked at people after the first dose, it meant you'd be able to have a look at that data in more detail and see if just one dose provided some protection and then two doses provided full protection.


[User] Please transcribe this text

[Assistant] You wouldn't necessarily be able to get that information from the Moderna study because they only class cases if they happened after the second dose. So I start looking at all these little intricacies and think,


[User] Please transcribe this text

[Assistant] So everything would have to go through an independent regulator, whether that's the, the Food and Drug Administration, so the FDA out in the US, or for example, we heard from the MHRA, earlier on,


[User] Please transcribe this text

[Assistant] So we have all these independent regulators and regulators get involved in every single stage of a clinical trial.


[User] Please transcribe this text

[Assistant] Um, there's lots of different phases of clinical trials. So phase one is your first in human studies. So it's the first time that a human gets it and you're really just looking at the way the body reacts to it. Uh, then your phase two is your sort of dose finding study where you try and figure out what's the optimal dose that you can give someone whilst


[User] Please transcribe this text

[Assistant] So when you're designing it, that has to get, you know, signed off by the regulators and say, yep, that's a good trial. Uh, you've designed that properly. We're happy with your outcomes. You, your sample size is big enough. You can go ahead and you can do it then.


[User] Please transcribe this text

[Assistant] Uhm, and then at the end, the full results, uhm, all have to be approved by regulators, uhm, and they're the ones that get the final say as to...


[User] Please transcribe this text

[Assistant] We save the minutes from every single meeting that's happened and the regulators would review all of that. So they really do go through it all with a fine-tooth comb. And that's why there's a bit of a delay between the results coming out and getting approval.


[User] Please transcribe this text

[Assistant] Yeah, so, you know, a lot of the time when these studies, uh, are happening, they're all done blind, so...


[User] Please transcribe this text

[Assistant] Because people don't know whether or not people have got the treatment or not, that kinda takes away that motivation to fudge the numbers and things like that, so.


[User] Please transcribe this text

[Assistant] People sometimes think, oh, it's in everybody's best interest to just get a vaccine out there really quickly. And that's not the case. It's in everybody's interest to get a vaccine out there as quickly as possible. That is safe. Because, yea, nobody wants that negative press.


[User] Please transcribe this text

[Assistant] We don't just wanna put a vaccine out, we wanna put a safe vaccine out.


[User] Please transcribe this text

[Assistant] I think these people who are saying these things are a little bit suspicious of the time scale and they're a bit suspicious of the fact that this seems to have happened so quickly and so corners must have been cut.


[User] Please transcribe this text

[Assistant] And that absolutely isn't the case here.


[User] Please transcribe this text

[Assistant] When we run trials normally, all these different phases, so I was talking about the phase one, two, three, uh, the designing of them, the analysis of them, it tends to happen in a sequential manner.


[User] Please transcribe this text

[Assistant] You know, it wouldn't be in anybody's best interest to be a sort of a waste of time or money. But because, you know, with coronavirus, every single day is however many deaths.


[User] Please transcribe this text

[Assistant] We really want to do everything that we can to speed up that process and, you know, everybody's just been able to throw as much money and as many resources at these things as possible that


[User] Please transcribe this text

[Assistant] People have been designing their phase two study whilst their phase one study is already happening and those sorts of efficiencies have all filtered through.


[User] Please transcribe this text

[Assistant] Exactly, and well, it's an expensive thing to do if it turns out that it doesn't work, because you've wasted a lot of time. You know, they were manufacturing the vaccine before they even knew whether or not it worked. Usually, that's a big risk. That's a huge waste of money if it turns out it doesn't work. But because it's, you know, coronavirus,


[User] Please transcribe this text

[Assistant] Sod the money, let's just try and get this as quickly as possible. You know, I use the analogy of building a house. When you build a house, you could do everything sequentially or there's some things that can be done at the same time without damaging the integrity of the structure.


[User] Please transcribe this text

[Assistant] And that's the same here. We can do some of the things at the same time.


[User] Please transcribe this text

[Assistant] Absolutely no corners have been cut. Every single step that needs to be taken in order to license a new drug, a new vaccine, any kind of new treatment, every single step


[User] Please transcribe this text

[Assistant] I think some people are worried about the fact that, you know, we don't have enough safety data, we haven't been watching people long enough.


[User] Please transcribe this text

[Assistant] Is exactly the same. Just because it's not been as long, you know, calendar length, everybody's been in the study for the amount of time that they would be when it would normally get licensed and


[User] Please transcribe this text

[Assistant] One of the things that I'd also like to stress is once a vaccine is licensed, that's not the end of the story. We don't just then, you know, let it out into the world and that's it over and done with.


[User] Please transcribe this text

[Assistant] We have phase four studies, which are surveillance studies and confirmation studies where they want to check, okay, that effectiveness or efficacy that we did see, do we still see it? And are we continuing to see side effects? You know, if you've got a side effect that's a one in a million person event,


[User] Please transcribe this text

[Assistant] You're never going to see that in any clinical trial and you're going to need to, you know, carry on monitoring people afterwards before you see it. You know, there's never going to be zero risk in any.


[User] Please transcribe this text

[Assistant] No, uhm, and I think I would agree with you there. One of the things that's really frustrated me is, uhm, we've had this sort of science by press release where we get this press release and it's got very limited, uh, information in it and people have been filling in the gaps.


[User] Please transcribe this text

[Assistant] People were asking questions of the design of the study before we'd even seen the design of the study and I think sometimes trying to


[User] Please transcribe this text

[Assistant] harmful because we do have skeptics out there. We do have people who are uncertain about whether or not to take the vaccine and filling


[User] Please transcribe this text

[Assistant] The, the empty spaces with this potentially unhelpful noise I don't think has really been that fantastic. You know, I mean I've done a lot of interviews, my phone hasn't stopped ringing and I'm just, I feel like


[User] Please transcribe this text

[Assistant] Now I've got a real role to play in this and I've got a part to play in trying to communicate to people what is the clinical trial process. Can they?


[User] Please transcribe this text

[Assistant] You know, can they have confidence in, in what they're putting inside their bodies? And I, yeah, I think I've just got to keep...


[User] Please transcribe this text

[Assistant] Yeah, it is. And I think, uh, statistics suffered a little bit for a while because, uhm, you know, artificial and intelligence and data science and machine learning, they were all cool. And classical statistics was a bit, you know, in the dark ages and it wasn't very cool.


[User] Please transcribe this text

[Assistant] Hardly any data, and uhm, I think this has really been an opportunity for the statistician to show their worth and really


[User] Please transcribe this text

[Assistant] That we do, and uhm, yeaah, maybe we'll have lots more budding statisticians join the field, which would be, which would be amazing. It would be a really good thing to come out of a really horrible and tough year.


[User] Please transcribe this text

[Assistant] Uh, no, we, we count both. Uh, yeah, I basically have gone by Walt professionally a bit. It was easier to get some, uh, some URL and usernames early on in my career, but I go by either. All right. Or what do I call you? I'd say you're Walt on the book. Yes, we'll go with that.


[User] Please transcribe this text

[Assistant] Yeah, I'm from the United States. I grew up in, uh, in New York, uh, just north of the city. And, uh, yeah, I had, you know, high school in New Jersey, college in Virginia, and, uh, I've been in Queens ever since, in New York City, so.


[User] Please transcribe this text

[Assistant] Uh, I saw Jurassic Park and I saw that there was this extremely cool representation of a very, uh, interesting dynamic and kind of fun mathematician in there.


[User] Please transcribe this text

[Assistant] And all of a sudden I was like, alright, maybe this is a job, maybe I can do this. And obviously I'm not, you know, touring parks these days and evaluating the viability of a dinosaur based entertainment thing. But it definitely got me pursued and interested in how to, you know, this could potentially be a thing that I actually do. So I majored in it in college and kind of the rest is history.


[User] Please transcribe this text

[Assistant] Park is a movie about a group of professionals from the real world who are brought to a magical place where they have invented dinosaurs. And the point of this expedition is to get them to sign off on the park and give them basically an expert's thumbs up. Uhm, they bring a paleontologist, a paleobotanist, they bring the lawyer for the park.


[User] Please transcribe this text

[Assistant] And one of the guys that they bring is this guy Ian Malcolm. And Ian Malcolm is a chaos theory guy.


[User] Please transcribe this text

[Assistant] functionally what that means is that he's a mathematician who thinks about probabilities and I think that it's heavily implied, you know, in the fullness of time that I realize it.


[User] Please transcribe this text

[Assistant] He saunters in there like a leopard and is just fantastically interesting. He has incisive questions. He flirts.


[User] Please transcribe this text

[Assistant] Everyone, regardless of man, woman, everyone. Dinosaur, for the love of God. Uh, he is just an interesting, dynamic, steam-sealing guy who clocked immediately what the problems with this park were going to be.


[User] Please transcribe this text

[Assistant] Was Cassandra like, uh, unheralded? And uh, and then eventually, you know, is one of the people who


[User] Please transcribe this text

[Assistant] Through various acts of heroism and various acts of, you know, just kinda chilling out and looking hot in the basement of a bunker, uh, it really steals the movie. I'm simply saying that life finds a way.


[User] Please transcribe this text

[Assistant] You have a notion potentially of, of, you know, math as being a thing that is, that is nerdy at times and you have this notion of math as a thing that is done just with chalkboards and closed offices and I think that having that kind of representation of a guy who, here is somebody who applied


[User] Please transcribe this text

[Assistant] He is just someone that was cool and someone that really kind of broke a lot of the stereotypes about what a person who has a career in math does and looks like. I think that I really liked how he explained stuff. I think that that was a really cool thing. Like there's obviously this really


[User] Please transcribe this text

[Assistant] Exciting and very charged scene in a car where he's explaining, well, here's the idea of chaos theory and probability and involves like putting a little drops of water on the back of somebody's hand and how they all go in different ways. And you know, as a person who like, you know, sometimes got math concepts that peers didn't or, or, you know, tutored people before or things like that.


[User] Please transcribe this text

[Assistant] That is a kind of a fun experience of it where you have an opportunity to kind of convey a thing that you know, uh, to folks who maybe would like to know more about it and being able to convey that.


[User] Please transcribe this text

[Assistant] The rest of the world had built up in my head about what, what it is that, that math people do day in day out.


[User] Please transcribe this text

[Assistant] I think it's more of the latter if only because I think I had a knack for it. Again, like it was the kind of thing where, you know, teachers were like, yeah, you should pursue this. This is, this is good. You're good at this thing. And, uh, and, and so I, uh, I think it definitely opened the doors for that kind of stuff. And I think.


[User] Please transcribe this text

[Assistant] Uh, you know, there's a couple of things in the book that I basically talk about how people find their professions often through representations in media. And I think it can just be a situation where it just kind of knocks down the barrier to entry.


[User] Please transcribe this text

[Assistant] It can give you an opportunity to consider something that maybe just isn't.


[User] Please transcribe this text

[Assistant] On some level it might inspire people to actually, you know, I want to be an astronaut now or I want to, you know, serve in the military and fly planes off of boats. At the same time, I think it can also just make it just something more realistic and make it so that it's not just concepts that are doing these things, but actual flesh and blood people.


[User] Please transcribe this text

[Assistant] I mean, he foretells the exact way that this goes down. He's the first and only person to actually warn these folks. He was clearly given a bunch of money by this organization, and when you're given a bunch of money by an organization, of course you're just gonna try to say the pap that they want you to say, but he at least says, no, I think that what you guys are doing is actually going to cause a serious problem. You ought to be more concerned about that, and to his credit, you know, I think that


[User] Please transcribe this text

[Assistant] In any kind of film, you always want to root for the heroes. I think that if a Tyrannosaurus rex attacked me for the first time, I would probably not know exactly how its vision worked and I would probably not know exactly what was going on. I think that he was daring and selfless in that moment if


[User] Please transcribe this text

[Assistant] You know, hey, listen, you take a run at a problem for the first time. You don't always get it right. And, and, and, you know, maybe in his world, there are consequences for that. Excellent.


[User] Please transcribe this text

[Assistant] The idea, like, you know, I had always kind of been keen on expressing myself in that regard, and so I had always been very excited by the idea of, you know.


[User] Please transcribe this text

[Assistant] How do you communicate to other people and how do you argue with other people and how do you get your points across? And so I think that like, you know, while I was definitely like,


[User] Please transcribe this text

[Assistant] Integrated and tied together, that writing and expressing yourself could work actually rather well with data and learning about your world in that way. I think, you know, it was very much, uh, kind of, you know, I started high school in 2008.


[User] Please transcribe this text

[Assistant] Uhm, I'm sorry, I graduated high school in 2008, and I started in a graduate college in 2012, and over that time,


[User] Please transcribe this text

[Assistant] You saw things like Nate Silver's 538 really, you know, start to take off. You saw a lot of just kind of the way the internet started talking about, you know, numbers, math, data and statistics.


[User] Please transcribe this text

[Assistant] Really, you know, from where it was in 2006 when I like was in the middle of high school.


[User] Please transcribe this text

[Assistant] To where it was when I was just graduating college and starting in journalism, it is, it is just night and day. Uh, and so it was, eh, you know, very much a kind of a transitional time of realizing that these were not skills that were in conflict with one another.


[User] Please transcribe this text

[Assistant] As I think a lot of people around me had argued, right? Like a lot of folks will just kind of tell you, well you gotta kind of pick one. But the ability to kind of express yourself, make arguments, and kind of be persuasive. And then the ability to kind of do your data analysis, do your math work, and kind of making sure that you have a


[User] Please transcribe this text

[Assistant] Very excellent foundation upon which to stand when you make this argument. Uh, that was kind of a gradual evolution of two things towards one another that, uh, took a couple years.


[User] Please transcribe this text

[Assistant] I wish I was so, uh, you know, devoted towards, towards the larger, more theoretical goals of our field, but no, I grew up in the vicinity of New York City and I went to a Catholic boy school where a lot of people's pit dads were rich off Wall Street and I was like, well, worst case scenario, I can just


[User] Please transcribe this text

[Assistant] You know, math actually kinda goes pretty mainstream after that, uh, when it comes to how we actually talked about how that was working. You saw, I remember I wrote a, like, you know, I wrote a,


[User] Please transcribe this text

[Assistant] Mathematics and how forecasting and how uhm, the ability to predict or not predict or fail to predict or hedge or fail to hedge.


[User] Please transcribe this text

[Assistant] And so I think that that was kind of a, uh, an interesting time for me just because, you know, I'd gone to start this embarking idea on the idea that perhaps I would go work for Wall Street or a financial institution, and then fairly immediately, you know, several large Wall Street financial institutions begin failing, and the problem


[User] Please transcribe this text

[Assistant] Was math at very many times. Whether it was understanding it, comprehending it, using it in the right way. Uhm, but basically they had built


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[Assistant] Set the spark for what became the global financial crisis was a really


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[Assistant] Kinda cool way to see like, okay, there are people who are doing this, there are people who have a head for this kind of stuff, who are able to express it and talk about it in a way, and their job isn't to work for the bank, it's to report about the bank.


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[Assistant] But like linear algebra was so cool. It was just an opportunity to kind of see a side, uh, like the matrix analysis stuff was just, it was mind opening in a way that nothing else had really had been. And so even long after I was like kind of deeper, deeper into the, um,


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[Assistant] Into the applied stuff, I was doing some operations research stuff as well too.


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[Assistant] Uhm, which I really, really enjoyed. I kept on really, really enjoying the theoretical side of it, the proof-based stuff of it.


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[Assistant] Um, it really, like the matrix analysis stuff was just, uh, it peeled open my brain in a way that I've never been able to really replicate. Not to say that the statistics and probability stuff wasn't challenging, but I think that there is something about when we're talking eigenvalues and eigenvectors, like all of a sudden, like you're almost, you are speaking a different language. And so it is.


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[Assistant] It was something that I really, really, really love and I got a chance to pursue it and I'm really happy I did.


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[Assistant] While I was in college, obviously, you know, I enjoyed, you know, even back into high school, writing, communicating, things like that. So I worked for the student newspaper. In part, just, it's a good, it's a good counterpoint to the math, right? It's a good, uhm, counterweight and it gives a different set of skills to kind of work.


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[Assistant] Uh, in my junior year at college, I took an internship in Washington DC and it was for an organization called the Center for Responsive Politics.


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[Assistant] And the Center for Responsive Politics operates the Open Secrets website, and their main thing is that they inventory every dollar in America that is donated and spent on elections. So you are able to basically track where the money goes in American politics, and you know, draw arguments and conclusions as a result of it. So who's oil and gas funding? Who's the unions funding? All that kind of stuff.


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[Assistant] Uh, we were able to do some really cool reporting off of that. I think that that was my first kind of, you know, really jumping in and being like, data journalism is a viable thing. I can do data as part of journalism. There are people in journalism who are impressed by that, who don't have those skills, who like to work with guys like that.


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[Assistant] And so that kind of was the spark that was like, I can do this. My senior year of college, uh, I messed around with a few like, kind of just fun stories. I think my favorite story that I wrote in college and is, uh, if there's any people who are in college and work for the newspaper and are interested in an excellent data story.


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[Assistant] Uhm, I freed up the Information Act, requested every parking ticket issued on campus for a year. I then sent a bunch of, uh, interns and reporters around to count up every parking space on campus. And then through, you know, simple division, I was able to figure out where the most effective place to park illegally is. And we put that on the front page of the newspaper. It's news that you can use. Kids love it.


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[Assistant] And, uh, kind of from there I realized that, you know, you can do things with this that you can't do, you know, just through, through gumshoes. You, you can do things with this that, that, you know, I could never, you know, call the, the William & Mary Police Department and say, hey, can you tell me where the best place to park illegally is? I have to figure that out on my own. You can break stories that otherwise can't break.


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[Assistant] Uhm, eventually got hired on in, uh, in that fall, uh, as, uh, around the politics, uh, the political election at that time.


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[Assistant] But then after that, you know, we were just kind of like, well, what do we write about now? And it was a lot of just kind of fun things, like, you know, talking about steady state probabilities and, and monopoly, right? So one of the most effective places to try to land on the board, uh, highlighting a lot of like data journalism and, and map based coverage, um, that the, from that time.


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[Assistant] And we just found that there was just like, you know, part of it was that BI had a fairly wall street audience, but we found that there was.


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[Assistant] You know, more demand than we had anticipated for smart, quant kind of coverage that, you know, maybe, you know, you didn't have to go all the way over the top with it, but uhm, people appreciate it. People, I think, you know, the media has a bit of a trust issue.


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[Assistant] And I think that if you're coming to bear with data and if you're coming to bear with evidence and you're coming to bear with, here's the exact process that I took to get to this conclusion,


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[Assistant] It bridges that trust issue just a little bit, and it makes it a little bit easier for


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[Assistant] Uh, Nate Silver had just left the New York Times, started up the 538 jingle at ESPN, and I was one of the first hires. And he brought me on to basically do what was then called lifestyle coverage.


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[Assistant] Which was everything that wasn't politics, sports, economics or, or, uh, or science. And, uh, and eventually through, you know, the power of just writing about stuff and, and seeing what the audience enjoyed and seeing what we enjoyed and seeing what we felt like we could contribute to, uh, we came to rename basically culture coverage. And, and that kind of dove in on, on movies, televisions, books.


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[Assistant] Uh, all these fun, exciting topics that I think that we spend so much of our lives doing and so much of our, our, you know, our hearts go out to this kind of stuff and so much of our world is molded by it, but doesn't really kind of get the, uh, the coverage and acclaim that I think a lot of other folks, uh, do.


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[Assistant] I was always a movies guy, I was always a tvd guy, I was always, you gotta remember like, I think a lot of data journalism in particular, so like, you know.


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[Assistant] Sports has always been a sports statistics thing. I remember when I was in college, what was the example always? It was always baseball statistics. Why? Because there's a very obvious connection between things like home field advantage and things, so it's a great way to kind of show


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[Assistant] Not much. And so there's a little bit of a bar there, right? There's a little bit of a hurdle that you have to hop over in the sense that you do need to find a way


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[Assistant] For me, I mean, I had a blast because it was just such a target rich environment. It was like such a great opportunity to really get out there. And...


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[Assistant] Uh, and it was, uh, it was a real good time. I was there for about five years. Uhm, I really enjoyed it. I, you know, got to meet a lot of extremely smart people and I think the cross pollination there was really phenomenal, like,


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[Assistant] I would, like, you know, one of my very good friends from there is this guy, Neil Payne, and he, uh, he's a sports guy. And so he, you know, he and I would just talk all this stuff all the time and


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[Assistant] One thing that he particularly lent me that was super informative was just like, you know, he introduced me to things like ELO ratings. He introduced me to things like maximum likelihood. He introduced me to things like how, like how people in the sports world


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[Assistant] Evaluate things and how potentially we can apply that towards other stuff. So it was just a really dynamic environment full of very, very, very smart people.


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[Assistant] Yeah, so that's where I got to culture. Uhm, I was there for about five years and I'd done their newsletter, uh, for, for most of that, the daily morning newsletter. And then it came to a point where basically,


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[Assistant] ESPN was selling them and they were selling to ABC.


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[Assistant] I, uh, basically was like, I'm gonna start my own daily morning newsletter. I think that that's the future of a lot of these things. And they were very, very supportive of it. Uh, they let me promote it within significant digits. And so my newsletter since then has been called Numblock. Uh, it is a daily morning newsletter, highlighting fascinating numbers in the news. And, uh, I've been doing that for the past like five years now. And,


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[Assistant] I eventually returned to BEI, uh, as an editor, uh, where I work on both, like, our visual and comic-based storytelling, um, that, uh, we were very, very proud of that, and as well as, uh, I do edit, um, some poli- some political stories. It's nice, yeah, I, you know, I ran their election for them on 2020, so that was fun.


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[Assistant] It's a good question. I think it's, uh, it's a mile wide but a couple inches deep. Like it's very much like you gotta know a lot of stuff. I think you have to be able to speak from a place of confidence in a lot of these things and having that kind of experience is really invaluable.


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[Assistant] Uh, that being said, the barrier to entry on this is lower than folks think. And whenever I'm trying to encourage folks if they want to give it a go, or if they want to experiment with it,


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[Assistant] Or if they're looking for potentially something new, like, you can really do some very powerful stuff just through division. And like, you know, you can really do powerful stories that I think even, you know, traditional journalists are intimidated by. If you go into it with the perspective of, let's find some data on this and let's back it up.


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[Assistant] Uhm, a lot of it just kind of comes down to process, a lot of it comes down to vetting were these numbers developed in the right way, are they trustworthy, should, uh, should I believe them.


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[Assistant] Uhm, but I would say that you're not really cracking at the big guns that often. The thing about journalism that I just very strongly believe is that like journalism exists for the sake of an audience, right? You do it so that it can be read by people and then they can understand their world in a slightly better way. And if you want to accomplish that, you do need to


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[Assistant] Be cognizant of what you're asking of them, right? And so if I'm trying, if I'm like, you know.


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[Assistant] I think a good, a good line to kind of talk about here is like, should you put the regression line in your post, right? And when we were at 538, the answer was often yes, because our audience had either enough statistical aptitude or enough statistical interest that oftentimes it made sense to try to put that in there.


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[Assistant] Potentially, like if you're trying to reach a large audience, that'll scare the crap out of somebody. That'll just intimidate them enough that they don't read anymore and then they don't learn what this wonderful data has to do.


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[Assistant] So you can then just have to think about how you express a regression line, right? You have to say, you know, we found that for every increase in this, there's an, tends to be an increase in that or things of that nature. And so it kind of requires you to kind of go back into


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[Assistant] Maybe a little bit of math education, kind of, to how best to express ourselves with this, but uhm, no, to answer your question, you're not really trying to get the big guns, but I am so grateful that I have the depth of experience that I did, because it, you know, it helps me understand a big gun when I see it.


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[Assistant] I mean, first of all, thank you for noticing. Uh, like, I'm not gonna lie, I put a lot of work into this stuff. Uh, I take this very, very seriously. This project was a couple years in the making. Uhm, so, sometimes, like, that, like, you know, some of my favorite projects are just me watching a movie with a clipboard, you know? Uhm, stopwatch, things like that. There are some basic tools of the trade that you can do in that regard. There's a lot of stuff in the book that I really love with that.


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[Assistant] The, the section that you're referring to, I think, is there's a lot in chapter one where basically chapter one's all about how movies affect your body.


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[Assistant] And so there's a couple really cool things in here. Some of this data is from some academic research that was done by others, like some, there's some research there from people who study


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[Assistant] Basically, uhm, volatile organic compounds, which are the things that living things exhale or excrete that are not oxygen, right? And basically, their argument is that you can actually trace reliably between different screenings of a film.


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[Assistant] Spikes and people exhaling different chemicals and this can be things like everybody flinching at the same time and as a result when you flinch you activate your muscles


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[Assistant] A chemical reaction that produces isoprene as a waste product and so you can see a spike of isoprene every time that you show the same movie in the same cinema which shows that there's


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[Assistant] A biological thing beyond simply just sight and sound going on there. There's another one in that chapter that I really really like that basically measured people's blood before and after watching horror movies compared to other films and essentially found that


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[Assistant] The human body prepares itself to be wounded by releasing a coagulation factor over the course of watching a horror movie. In that your brain takes this thing seriously. Your brain takes the threat of injury seriously during a horror movie and that's why you feel it. That's why it's not just a thing that you see, it's a thing that you feel.


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[Assistant] That chapter though contains some of my favorite stuff, which is, uhm, I built a fleet of galvanic skin devices, uhm, and basically these are, uh, galvanic skin, uhm, so your body,


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[Assistant] At all times has a little bit of sweat on it, uhm, on your hands. And, uh, and, you know, when you feel a little bit of emotional intensity, that slight beads of sweat within the pores on your hands increases. I want you to imagine that I'm holding your hands in a car on Jurassic Park and I'm just dripping water down, right? And so basically on the palms of your hands, uh, you are essentially, you know,


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[Assistant] But having a certain amount or more or less small beads of sweat there. And what we found, you know, a couple hundred years of research I found I should say, is that, you know, you can run an electrical current


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[Assistant] Through your hand, a small one to be clear, but you can run a lot of current through your hand with one finger and then have the other end, have the other lead on the other finger and then you figure out how much of that current makes it to the other side.


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[Assistant] And then that is actually a proxy for how much resistance electrically your hand has, which in turn will allow us to basically measure how much electrical current is getting through


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[Assistant] Second by second. And what that'll allow us to do is that'll allow us to show, okay, here's the emotionally intense scene of Jaws. We can tell because the sweat on your hand from a conductivity perspective spiked. And so it's one of the things that is used in a lie detector test. The polygraph test, one of the polygraphs is a GSR.


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[Assistant] And what it basically is is like a real time reading of what's going on.


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[Assistant] In your nervous system and you know, not your intentional nervous system, but this is really just kind of how you feel and how your body's preparing, whether it's fight or flight, whether it's bodies preparing emotional intensity.


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[Assistant] But so basically this involved me building about nine of these devices, uh, and then, you know, during the pandemic, I, you know, I loaded up some movies onto Plex and I sent them, I mailed these devices around the country just for my friends.


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[Assistant] And we're like, please, uh, you know, here's when you click start, here's when you click stop and, uh, you know, it records it and then just send it on back. So it was the aggregate work of, uh, that one must have been about 15, 20 of my friends.


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[Assistant] Uh, all watching movies for me, uh, over the course of, you know, a very, uh, locked in year. So, you know, I tried to, tried to be valuable with my friend's time and that kind of stuff. And there was stuff I could do and stuff I couldn't do due to time constraints, but I'm very proud of what we got together.


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[Assistant] Yeah, I, uh, I really, you know, I myself worry about this complaint all the time whenever I embark on something. And like, the thing that I always come back to is that like,


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[Assistant] Damn it, if every time after I've done one of these kind of data analysis projects on a piece of art, I come away appreciating it more and understanding a little bit more, and there's nothing that I've ever personally ruined for myself by doing some arithmetic around it, right? Uh, I found that the thing about art and the thing about movies and the thing about television is that they are


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[Assistant] These are things that people obsessed over making, right? Like it, like, you know, if you want to talk about, like, you know, Star Wars, like, you know, I have a scene where basically I, there's a, another thing I do in the book is, is I use my tracking technology to do some neat stuff to basically evaluate how focused somebody is on, on a film based, as kind of a proxy for


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[Assistant] The director's doing. And like, if you just imagine, like, a ten second frame of Star Wars, do you have any idea how many people spent how many hours


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[Assistant] Like I think that one thing that I'd love people to take away from the book is just understanding how much care and effort goes into making these things. Because they really stand up to scrutiny, particularly the classics. And as a result, like, you know, I can't think of a situation in which


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[Assistant] Something or someone or some career that I've covered has been diminished in my views after doing the status work on it. I've always come away with a little bit more appreciation. Uh, even, even kinda like,


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[Assistant] Uhm, like taking like, you know, back in 538 I would do a thing called Hollywood Taxonomy where I would look at an actor's career basically and plot out, you know, I would start off with a chart of like just here's their Rotten Tomatoes score against their box office and that would allow us to cluster some stuff.


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[Assistant] And basically, writing those out, you get a better understanding of who these folks are. You know, warts and all. And I think that you can understand, like, I think that, you know, just taking a little bit of a deeper look at them.


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[Assistant] It doesn't really undermine the world around it, it kind of gives you a better understanding of it and it gives you a better appreciation for it. So, I don't know, I have worried about that 100% because I have seen what


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[Assistant] You know, Saber Metrics and Moneyball can do to baseball and how, you know, two decades on, they suddenly had to start changing the rules in order to accommodate what had become a slightly more boring game as a result of people, you know, breaking


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[Assistant] I've seen what it can do to politics and how it can make it, you know, very horse racy versus, you know, actually discussing the issues and stuff. But what I've, you know, what I've really found is that


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[Assistant] I don't ever really think less of a thing after I've tried to understand a little bit more, uh, which I think if anything is just a credit to the, to the, uh, you know, craftsmanship that went into making these things. And you gotta remember, like,


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[Assistant] Movies and television shows are the projects of hundreds of people, sometimes thousands of people, all working on the same thing. And the quality is gonna be there, if you kinda keep on rubbing away at it.


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[Assistant] If they're not, they will be at some point. Again, here's the thing about Hollywood. Hollywood loves data. Like Hollywood is lousy for data. They, they, test screenings, they have, uh, you know, uh, I live in a neighborhood in Queens that I think is just, uh, like has a very desirable, um, set of, you know, people who would see survey, who would do survey things were young, vibrant neighborhood. And, um,


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[Assistant] So we get test screenings all the time here and you just fill out a survey after and then that's how they're able to kind of figure out what their cinema score is and here's where they're gonna figure out how they're gonna advertise it. Even going back to the earliest days of Hollywood, like you gotta remember that some of the first thing that Gallup was polling when Gallup became a pollster was movie stars. Uhm, the, I remember there's this story from way back when that they would basically


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[Assistant] The fan mail was a data point that they used. And so depending on how much fan mail a studio got over an actress or an actor, that informed, okay, hey, maybe we should, maybe we should give this one a push. Maybe, maybe all of a sudden she's not just supporting anymore. Maybe she can carry a movie on her own because she's getting a lot of letters from Topeka.


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[Assistant] And so as a result, you know, data has always been heavily integrated into the history of Hollywood. And it's, it's, it's, it's like a bit naive to assume otherwise because I think that now it's like so heavily data infused and you see companies like Netflix.


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[Assistant] Make pretty compelling arguments that they're a data company first, and then, and then, you know, movies kinda second sometimes. But I think that like, realistically, data has always been incorporated into this, there's always been testing, there's always been market testing, this is a multi-billion dollar industry for a reason, they don't mess around with it.


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[Assistant] And I think that they do have some cool tech that I actually would have liked to get at my hands on, but I think that, you know, it's expensive to do this kind of market research.


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[Assistant] You know, typically they'll have like, there's a button that like, you hold it positive if you're enjoying the film and negative if you're not having a good time. And that can let them kind of know when the film drags a bit. I think uhm, the eye tracking technology is rad, uhm, again I, you know, obviously,


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[Assistant] You know, what's in the book is, is a fraction of, of the messing around that I did with it, but like as a person who really likes comics, it was fascinating because I could record my friend reading a comic book and understand here's where they're actually putting their eyes the whole time. Here's what the person who drew the comic book did particularly well. Here's where, here's where it could use some improvement and I think that like,


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[Assistant] In that kind of field, that could be a really interesting technology. Again, only as a growth tool, because I think that at a certain point, like, you don't need to market as comics that much in order to kind of...


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[Assistant] Get a viable product out there. It wouldn't shock me if this would be a really kind of fun thing to apply in schools as like, okay, you know, if you want feedback as how you did as a director on this.


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[Assistant] We did a test screening with folks wearing eye tracking goggles and so, you know, the reason I bring eye tracking goggles into the book is that there was this very cool study that was all about basically how you know, people


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[Assistant] They showed people a bunch of, you know, commercials with eye tracking goggles on and asked them a little while later, which commercials did you like? Which ones were the most memorable?


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[Assistant] And the ones that were the most memorable and most liked were the ones where everybody was looking at the same part of the screen at the same time for the most amount of time. The idea is how effective was a director at hurting eyeballs up and down and around the screen? Not necessarily just in dead center, but like, how effective was the person at making you look where he wanted or she wanted you to look?


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[Assistant] And that's direction, that's how you direct eyeballs. Spielberg's a master at that. So many of the folks that we, you know, hold up as the greatest directors of all time are just really good at keeping your eyes where they want them to be.


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[Assistant] What I, what I think could be very kind of fun is like, you know, feedback when it comes to art can always be fundamentally subjective, but like if you were to show a film student like, hey listen, here's where you lost everybody's eyeballs, here's where they, here's the shot that maybe didn't work as much, here's where you can do different next time.


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[Assistant] Or again, like sometimes you want to have it kind of be a diffuse thing, so I think it would just kind of allow people potentially more insight into whether or not


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[Assistant] Their art is accomplishing what they want it to do. But yeah, I don't foresee a day in which we are hooking up eye tracking goggles to robots and then also a GSR device to basically figure out if this movie's good. I think that's a reckless way to make art, but I think that it can be, it's got its uses, I'll tell you.


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[Assistant] Like, I'll give you a few examples, because there's no r- there's no, like, perfect way to make a movie. And like, there's a Jaws chart in there, and I love Jaws. Because Jaws, like, does things with tension that, like, I've never seen another movie do. And if you looked at that chart, you realize, well, it's because about every five or ten minutes, there's a staccato moment of terror. And then it's very chill in between. And so it's basically just the first half of the movie is like,


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[Assistant] You know, basically a slasher film and then you have the middle point at the 4th of July where the tension gets higher than anything that you've ever had before. And then after that it's three guys on a boat in occasional flashes of terror.


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[Assistant] And you know, then towards the end, you know, you do get like, you know, it's a pretty sustained action sequence to close it out.


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[Assistant] But then compare that to Mad Max Fury Road, which is also in there. And Mad Max Fury Road is fascinating just because, you know, it's intense and interesting and visually compelling for the first hour and ten minutes. And then for the last half, it just kicks it up. And it is just up and up the whole time. And it basically is using tension in a different way.


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[Assistant] There's another, uh, I think there's two more in there, they're not all Spielbergs, but two of them are Casablanca and The Spike.


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[Assistant] Lee film, uh, do the right thing. And those films look very, very similar as their charts, and they're not films that you would necessarily think


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[Assistant] But nevertheless, they have very similar structures in that the tension just builds and builds and builds and builds. And you can see it over the course of the film. And whether or not you are getting more worried personally, your body is. And your body's realizing the vibes are getting off, the things are getting off kilter.


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[Assistant] Things are changing very quickly now and people are gonna make decisions that will affect the rest of their lives. And the stakes just get higher and higher.


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[Assistant] And so, you know, there's lots of different ways to make these kinds of movies. And I think that, you know, I really try not to be perspectivist when it comes to this one's better or this one's worse. I think, you know, we just ought to learn from the masters while we can, you know? You've written a book.


[User] Please transcribe this text

[Assistant] I think so. So again, I've very much like, I've sent it around to a few people that I trust. Uh, I've sent it around to a few people that I...


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[Assistant] Like, no, have a couple different backgrounds when it comes to math because I want to make sure that it's hitting. Like, I really want to make sure that the folks who are like me who just love numbers can't get enough of them are happy. Like, there's, I'm releasing as much of the data as I feasibly can, you know, when the book is out.


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[Assistant] Uh, so that people can remix and use it on their own, and potentially if they think it's got uses for, you know, education, like, by all means. Uh, so, like, there's a thoroughness and a depth there that I wanted to make sure that, so when I reached out to a few friends to be like, hey, can you just kind of vet this for this, there was, there was positive there, as well as just folks who are completely new to it.


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[Assistant] It wasn't necessarily like a technique, it was a like, a moral that I read at one point, uhm, which was,


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[Assistant] You know, there was this idea at Pixar that, cause if you write a book about creative people doing creative things, you, you inherently are gonna like take some notes, right? And so there's this idea at Pixar that like a movie is never done, a movie just needs to be released.


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[Assistant] And I think particularly with data journalism, that is often, like the, the, the, the thing that, that is slowing you down is, is sometimes just not the work itself, but just like, is this done? Like can I not, do I not have another couple weeks to add more stuff to it? Do I not have another chance to go even deeper on it? Like at a certain point you have to, you have to call it, you have to say I'm content and I think that I've gotten,


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[Assistant] As much as I need to get in here, even though there's more out there, because there's always more out there, and you can always get more depth, and you can always get more variables, and you can always get more, you know, comparison points, and you can always get more, uh, there's a point where you have to say like, you know,


[User] Please transcribe this text

[Assistant] This is never gonna be done, it just has to be released. And I think that that is a very healthy mentality to have.


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[Assistant] About creative work that you're doing, and I think also about data work that you're doing, just because again, there's a reason that, that, that we call them rabbit holes, right? It's because you never know how deep they go. And at a certain point down the rabbit hole, you just need to, you need to write it up and post it. And then, you know, you're okay, you know, is there a second story and go a little bit deeper than that?


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[Assistant] But I think that that mentality I think has done wonders just for my career in general just because it has made


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[Assistant] The ability to get out work that obviously, you know, comparing it to the platonic ideal of the book that you have in your head is, uh, is, is, is, you know, shorter potentially. But, uh, I think it's just as effective and, and honestly just a better way to communicate things at times than it is to, uh,


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[Assistant] That said, I would have liked to travel to, I think there were two or three things that I wanted to kind of see, but I'm content with the book where it is. I think it's got enough IRL stuff to get by. Don't worry about that. I also got to interview so many people, uh, cause it was very easy to hop on a zoom. So I just interviewed a ton of people and got a chance to talk to so many cool people about stuff.


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[Assistant] Here's the fantasy analysis, people can take this, if you do it, tag me at Walthickey, I will want to read this, and I'll put it in my newsletter, I swear to god, because this is, this is, this was a bit of a white whale for me, and I had to cut bait on it because we had a deadline coming up.


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[Assistant] So like, I have had a conversation with a few friends that


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[Assistant] You know, one thing that, uh, there's a chapter in the book about pop culture and geopolitics, right? How countries like the United Kingdom, how countries like Japan, how countries like Korea have really been able to expand their soft power, reach larger groups of people, win people over to their sides, their causes, and their alliances.


[User] Please transcribe this text

[Assistant] Not through military strength, but rather through their pop culture. By making people like their pop culture and by making, uhm, you know, by exporting their pop culture in the form of their actors, their actresses, their plots, their stories, their daytime soaps, their comedy shows and their movies. They've been able to inure a lot of the world to their point of view and their values by doing so.


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[Assistant] You know, you can see classic examples of this from the UK and folks like James Bond, things like Monty Python, just, you know, the fact that, you know, every now and again, I think it's something like, I don't know, ten.


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[Assistant] Cultural esteem, it is, it is one of the coolest countries on earth in the American perception in no small part because people grew up playing Pokemon, playing the different Nintendo games and watching Toonami. And then Korea, very recently we've seen them.


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[Assistant] Really see the playbook of those two countries and very specifically from a state sanction perspective, try to imitate it.


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[Assistant] Uhm, specifically through, you know, you even have Academy Award-winning films like Parasite, you've got phenomenons like Squid Game, you've got obviously BTS, which says Juggernaut in and of its own right, but the Korean Wave is absolutely designed and kind of specifically encouraged by the government to do that.


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[Assistant] There's a country that I wanted to write about that I could not get enough good data fast enough to get it done. And that is Canada. Because Canada is very interesting. Because Canada is also


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[Assistant] You know, if you look a little bit closer, you realize, wait, actually, this is almost an entirely Canadian cast, but it's an American production, it's an American television network, and basically Canada is just so sufficiently integrated with the American film industry, not only as a shooting location in places like Toronto, but also, like, in just actor exports, folks like Ryan Reynolds, right? Folks like,


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[Assistant] You know, uhm, the entire cast of many CW shows. Uhm, like, like you just, you see these folks all over the place and one thing I wanted to do was I wanted to figure out which


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[Assistant] American production is secretly the most Canadian. Like, you know, it could be an X-Men movie, it could be any of these different things where basically it is just, uh, you know, a lot of people who live in Hollywood, act in Hollywood, are very famous in Hollywood, but come from the Great North. Uh, I wanted to figure that out, and I unfortunately could not get it done in time.


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[Assistant] But I think it's a very, very interesting question. I have a few ideas. One of them that kinda comes to mind is a show like Battlestar Galactica, which is one of those sneaky ones that were, again, made pretty much entirely in Canada, entirely Canadian cast for the most part, with a few exceptions, but uhm, I, uh, I'm eager to find the answer to that and would greatly appreciate it.


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[Assistant] Greatly appreciate anyone wanting to advance my, my Canadian studies.


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[Assistant] I have, I have, cause like, you know, you write a book and then the number of people that you, that actually read it before you send it off is probably like, you know, you can count on one or two hands.


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[Assistant] I got, I've been getting like a lot of fun reactions from folks just to have got a chance to get the early one. And like there was one guy who was like, oh, I read it in the course of like two days and like I just blew through it and I want to like reread it and go back to all the stuff.


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[Assistant] There's folks who have been like, I have been kind of looking chart to chart, I've been going section to section. I think it's like, I tried to write it in a way that, you know,


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[Assistant] You can really enter it at any point that you want to. You can use it as a reference. You can use it as, uh, something to settle a bar bet. You can use it, uhm, to just kind of, you know, read this, like, kind of long persuasive argument about how pop culture affects people. Again, I got a chance to work with Heather Jones. She's a really phenomenal, uh, data visualization professional and she, uh, she and I worked very, very closely to get all this kind of data into a very, very visual format.


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[Assistant] Uh, so I, uh, I'm very, very proud of how it came out. It's really fun and I think folks are going to dig it. I think that folks are going to like it.


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[Assistant] That's like an example of like, yeah, like, has that chart ever made before? No, I don't think it has. Uhm, but the one that I think like definitely typifies what you're asking is like, I have this, this two page spread.


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[Assistant] That is, uh, is Ash Ketchum a good Pokemon trainer? And basically I ran


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[Assistant] God, four billion simulations of Pokemon battles to figure out what the probability is that Ash would win with the team that he had going into a Pokemon battle.


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[Assistant] And then comparing that with the eventual outcome to basically figure out, uh, like, wins above replacement for Ash Ketchum. So, uhm, like, I would agree with you. Like, is that taking the piss? Yea, it's taking a little bit of the piss, but, uh, I, you know, I think if we have these tools, we may as well have as much fun and interest


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[Assistant] Oh man, I mean ask me again tomorrow and the next day I'll tell you something different. Uhm, I would say my favorite movie. Uh, I always come back to Empire Strikes Back. Uh, that is, that is my favorite, that is one of my favorite movies. I like, uh, I think Millennium Actress is one of my favorite movies. Uh, I really, really love that one. Uhm, yeaah. Oh man, favorite movie.


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[Assistant] Yeah, I mean, I don't know, I have a lot of favorite directors, my favorite movie genuinely changes time and time again. It is really, again, I'm very omnivorous when it comes to what I like and I just can't get enough of just kind of, you know, something new. 2001 A Space Odyssey, that really is good.


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[Assistant] I don't know. There's a lot of stuff that I rewatch constantly. There's a lot of stuff that I'll put down for years and then come back to. Uhm, but if I had to recommend a movie to people...


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[Assistant] Uhm, watch Millennium Actress. That's a movie that I think is probably the, the, the movie that is one of my favorites that is probably the least watched of those favorites. Uh, People Should Get a Whirl. Satoshi Kon, Japanese film. Uhm, it is amazing.


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[Assistant] Yeah, so I was born in Bogota. My family is, uh, from a bunch of little towns in, uh, the state of Santander, which is, uh, close to Venezuela. And yeah, they were from all those little towns and migrated into the city of Bucaramanga.


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[Assistant] My, my dad was trained as a civil engineer. So, you know, he, he did kind of hydrology and things like this. So he, he did that kind of thing, but he, he eventually got more excited about just people and he ended up just working in companies, just kind of thinking about how people work together and, and, uh, thinking about teams and, and these kinds of things. So he did a lot of, a lot of the kinds of things.


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[Assistant] Yeah. And then my mom did everything. So she, she was trained as a sociologist. She was, uh, she was actually part of the first generation of sociologists in Columbia when that was a new, a new department and it was actually considered too radical. So it got, it got shut down very quickly.


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[Assistant] What was the next way that she could try to make a better world, I think is what I would, what I would say. So, you know, she did a lot of work, for example, helping, uh, neighborhoods, uh, find, uh, kind of community justice processes, for example, dealing with, uh, issues of, uh,


[User] Please transcribe this text

[Assistant] Just neighborhood violence or domestic violence and just try to help communities solve these problems sometimes without the help of the police because the police was not trusted and so, and so she did things like that.


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[Assistant] For sure, for sure, for sure. A lot of education and I would say also a lot of passion about their work. They love their work, they thought it was really meaningful and that's a lesson that I learned very early on.


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[Assistant] Which people have a hard time believing because, you know, now there seems to be some public persona that I do, but, but in fact, I am still that shy person. I, I loved soccer. I was absolutely obsessed with, with football and, uh,


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[Assistant] The number is 1 plus 2 plus 3 plus 4 plus 5 until 10.


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[Assistant] And uh, he likes to tell that story because he was like, I don't know what kind of child you were that like, cause I guess I went like one plus 10.


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[Assistant] 11, 2 plus 9, 11, 3 plus 8, 11. So that's 5, 11, that's 55. So my dad loves telling that story, but I think one thing that I get from that story is just, you know, that's actually a very beautiful math problem to like sum the first 10 numbers. That's not somebody that, that's not like a problem that anybody will think of. So I think my dad like loved math and had a taste for a good math problem, you know?


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[Assistant] I actually grabbed my whole neighborhood and I started like doing drills with my whole neighborhood because I thought we all should learn how to have like good football technique and I was just obsessed with football. That was definitely my main obsession.


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[Assistant] You know, it's funny because my mom would always ask me this question, like, what are you Federico? I don't understand. Uhm, and, uh,


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[Assistant] I don't think I fit into any of these categories because I mean, I was definitely like, uh, very serious about math and that would put me kind of squarely in the nerdy camp. But at the same time, I was really good at football. And I think, for example, that kept me away from getting bullied, which, which is a big deal, actually. I mean, I went to, uh,


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[Assistant] Uh, an all-boys school, where there was a lot of bullying, where I think that actually would have shaped my life in a very difficult way if I had been subjected to that, but somehow, I think because I was, uh...


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[Assistant] A good footballista, and I think people found me generally agreeable, so I was kind of a shy kid that people thought was alright and left alone, and I kind of hit in the corner to also...


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[Assistant] I was not tall actually. I was not tall and I was also the youngest. And so that was one thing that actually shaped my path just because everybody was a little bit older than me and so I was, I think that led me to okay, I just gotta make sure I don't get bullied and so I just kind of was in the corner and I tried not to draw too much attention to myself.


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[Assistant] You know, I had a, I had a very traumatic football experience where like, we had a tournament where I did, I did quite well. I was the youngest player in the team and I, I did quite well. So when the final went to penalty kicks, they let me have the last penalty kick and I missed it.


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[Assistant] And that was the end of my football career for, for, for a long time. I think that was, that was a really, now I realize that like that, that's a really hard thing for a, for a young boy to, to, to deal with. And uhm. Where'd you put it? I hit the post.


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[Assistant] I didn't know, I didn't really know what mathematicians do. That was not kind of a career that was very clear. What were your parents saying? Were they pushing you in that direction or were they pulling you in another direction? You know, my parents were so...


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[Assistant] Let him be free and see what he, what he wanted to pursue. But I think, but I think it was kind of like let him be free, but also give an example of like being really, really passionate about things. There's all kinds of books and all kinds of things in the house. There's, there's just a lot of curiosity. There's amazing music in the house. I'm from a family of musicians also. So, so my sister is a pianist. And so I think there was just a lot of.


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[Assistant] A lot of room for like, uh, kind of intellectual and creative curiosity.


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[Assistant] In math, yes, but when I applied to college, I was about to fail out of high school actually. Because as I told you, I had a bit of an attitude problem and I, you know, when I didn't like a homework, I wouldn't do it.


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[Assistant] And then I ended up applying to a couple of schools in the US because a friend of mine thought that was interesting and convinced me to do that. I think I was very lucky that I didn't understand the whole process at all and I should have known that I wasn't going to get in with those grades. I mean, I was, I was really like nowhere near the top of the class. I really was failing the majority of the subjects when I, when I applied to college.


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[Assistant] But I think because I had this kind of mathematical career, I mean, I did have, you know, international medals at the International Math Olympiad and so, uh, and I ended up going to MIT, which is the kind of place that I think thought, oh, this must be one of these kids who only likes math and we like those kinds of kids.


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[Assistant] I mean, I think it must have been those metals at the IMO that did it. I didn't know what MIT was. I had never heard of it. And I think if I had known, I wouldn't have applied. I just would have known that I wasn't going to get in. So I think the kind of lack of access helped me.


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[Assistant] So the, so the math Olympiad was a, a savior for you, a good thing. Yeah.


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[Assistant] No, and, and, not only, not only in terms of mech, I make path, but I think in terms of just my life, you know, when I was...


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[Assistant] So I was in the Colombian national team as a high schooler for four years. So when I was 14, the first time I left Colombia is to go all across the world to Sweden to a math competition. I got to go to Turkey the next year, go to Argentina, go to Hong Kong, go to Russia.


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[Assistant] And, uh, and so there was a sense in which actually mathematics really opened up the world for me and it really made me realize, wow, like, I like this, I don't know what a career in this means, but this is definitely a path to access that I hadn't had before.


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[Assistant] I mean, it was my first time in the U.S., uh, but yeaah, I had traveled, I had somehow made some friends from abroad, and actually because I was in the Math Olympiads.


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[Assistant] A couple of the friends that I met in the IMO actually ended up at MIT, so I even had a couple of, uh, friends, uh, arriving. At the same time, it was a complete culture shock, uh, for a couple of reasons. And I think maybe, maybe one that I would say was actually that I think that in the U.S. it's more kind of socially acceptable to be very, very narrowly interested in something.


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[Assistant] And at MIT there were people who were very, very narrowly interested in mathematics or in physics or in, and somehow like their life was about that kind of academic achievement in that particular field.


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[Assistant] I mean, I don't want to oversimplify cultures or national cultures, but I think that Colombia is a much more social culture where like, you can be a math nerd, but you still have to go to your sister's quinceañera and dance with her. And, uh, there's a sense in which you have to just be involved in music. You have to be involved in, in a lot of socializing that.


[User] Please transcribe this text

[Assistant] And so, I was pretty shocked to find people who were just so defined by their academics. That's something that I had never seen before, even though I was in a... I came from a...


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[Assistant] From, you know, being surrounded with who were probably the, you know, some of the most, eh, better, best prepared mathematicians in Colombia. And we were nerds, but I think, I think there was a sense in which we had a lot of other things going on.


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[Assistant] You know, I actually was really put off by it.


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[Assistant] You know the undergraduate culture at MIT and I think I see this at many of the very competitive institutions. There's all this like bragging about how little you have slept, how much you have worked and I just knew that that wasn't going to work for me so I actually moved off campus and I kind of tried to do things.


[User] Please transcribe this text

[Assistant] At a place like MIT, everybody was the top of their class and all of a sudden they're failing classes and I think that's actually really, really difficult psychologically. I don't think we talk enough about that, but they were like very serious.


[User] Please transcribe this text

[Assistant] Yeaah, yeaah. And yeaah, I mean I do think that actually that kind of academic pressure is really hard on young mathematicians and I think that there is this idea that we need to be defined only by like what grades we get and I think that's actually really brutal in mathematical culture.


[User] Please transcribe this text

[Assistant] Which is like the Russian version of Federico that was kind of like to match her politics and she decided to spare me that and...


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[Assistant] But there is actually like, you know, many kids of my generation in Colombia that have Russian names for this reason. So I think that I knew, it was clear to me that I wanted to try to...


[User] Please transcribe this text

[Assistant] And I knew that I would love math, but I actually didn't even understand what this path of a PhD or professorship was. And I, even very late until my senior, until my last year of undergraduate, I still didn't know what I was doing. And it seemed that people told me, oh, you should apply to PhD. So I did, but I think my whole career, I haven't been very strategic.


[User] Please transcribe this text

[Assistant] And it was not very planned out. I think it was just kind of a matter of finding the next step that made sense.


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[Assistant] I was well prepared, and actually I did well in my math classes at MIT. You know, I'm not somebody who likes to brag, but I think we should talk about it.


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[Assistant] In the Putnam team at MIT, which was like the top three undergrads that would, would, would make that team. I mean, it was, it was somehow, I think academically I was just perfectly fine prepared.


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[Assistant] I think the one thing that I would say is that the Math Olympias made me really good at problem solving.


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[Assistant] And so, you know, no matter what homework problem I had, this says, okay, I can, I can solve problems. There are a couple of things that.


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[Assistant] And so what that meant is that actually, I mean, I wasn't particularly competitive when I was, when I was at MIT, but I did not know what it was like to, to work, to do math with others. I had never, eh... To collaborate. To collaborate in...


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[Assistant] And I spent nine years there and I think I had maybe two or three math conversations with my classmates the whole time.


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[Assistant] Because I came from this individual competition mentality and because I was very isolated, I think I really did feel a big culture shock that made me feel very isolated in my mathematical.


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[Assistant] So that's one thing that got in the way. The other thing is that mathematical research is a lot deeper than solving math problems. And so it took me a while to understand, oh, like, just because you can solve math problems does not mean that you've understood the topic.


[User] Please transcribe this text

[Assistant] One of the learnings that I did in my undergrad and especially in my PhD is, okay, let me learn to go deeper and say, okay, I solved the math problem. Now let me try to solve it again a different way. Now let me try to think about this. Let me try and think about the bigger picture. And that's not something that's encouraged in the Olympiads or in a lot of mathematical schooling, I would say. I think because it's easier to test people on small problems, we reward.


[User] Please transcribe this text

[Assistant] Smaller thinking, small in terms of size, I don't mean to say it's unimportant, but we don't reward kind of deeper, longer thinking, and that's something that I had to learn that felt very different from Math Olympia thinking.


[User] Please transcribe this text

[Assistant] You know, I actually think that it probably had to do with starting teaching actually because that was maybe the first time that I had to, you know, when I started teaching my own classes, then I had to hold the whole semester in my head and really think about the bigger picture in a way that because I told you that I had a bit of attitude about things.


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[Assistant] So it is at MIT, uh, I was teaching undergraduate classes since I was a junior I think, but also because of the culture shock that I was experiencing as soon as vacation started, I would fly to Colombia and I was teaching there and I was the coach of the national team.


[User] Please transcribe this text

[Assistant] And so then I started teaching a bunch of classes there and, and, uh, and there, even though it was kind of problem solving training, I still had to think, okay, you know, what am I going to do over the course of the six weeks of this, of this training camp?


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[Assistant] In some sense, I'm considered one of the combinatorics experts out there and I hated combinatorics and I thought it was impossibly hard. And, uh, you know, when I was in the math Olympiads, every time that I didn't win the gold medal or whatever, it was because I missed the combinatorics question. It was, it was, I really struggled with it. When I arrived to MIT, the first year there was this problem solving seminar that Richard Stanley taught and he is


[User] Please transcribe this text

[Assistant] One of the founders of modern combinatorics, I came to learn. I just loved his, his mathematical taste. I don't, I don't know how else to say it. I think he just had a...


[User] Please transcribe this text

[Assistant] And it turned out to be combinatorics and then I thought, okay, maybe, maybe I need to, I need to learn about this. So I asked to teach combinatorics back in Colombia and I asked to like learn it and I worked really hard at it. My last year before applying to grad schools, I ended up taking two graduate classes, one with Richard Stanley, one with Sergey Fomin, which were kind of the beginning graduates.


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[Assistant] Coming at tourist classes and I just, I just absolutely loved that stuff. And I had to work incredibly hard at it because it did not come easily to me. And I think that's what did it. I think that's when I ended up.


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[Assistant] It's a, it's a good question. I don't know the answer to be honest. I, I think, because I, because I also, I mean there are different flavors of Kamenotorix.


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[Assistant] And there's a certain kind of combinatorial problem that I, that I still am not drawn to and I still struggle with a lot. And I, I definitely, I'm much more interested in the way that combinatorics interacts with algebra, with geometry, with algebraic geometry, with tropical geometry, with different fields. And so I think, I think it has to do with that actually, that the combinatorics that I first was exposed to actually was


[User] Please transcribe this text

[Assistant] Kind of like, can you come up with this really clever argument, uh, and I couldn't. And I think that, uh, going into this, this more structured way of doing combinatorics, that in some ways I found it a lot harder actually.


[User] Please transcribe this text

[Assistant] But there was more structure to hold on to and, uh, and so that's, that's how I ended up going into that direction.


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[Assistant] Yeah, so it's interesting because as I was telling you, I was, I was kind of disoriented, a little clueless. When I applied to grad schools, I didn't really, I didn't really even have a sense of like what, what the good PhD programs were.


[User] Please transcribe this text

[Assistant] It wasn't, it was challenging. I mean, and, and I think, you know, if you asked me during the PhD, I would have told you that it was a very positive experience. Sometimes when you look back, you, you find that things are, things...


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[Assistant] Especially, I think in, I think, uh, Colombians are experts at positive thinking. I think, you know, when you, there was a lot of wild stuff going on when I was growing up and we learned to see the positive side of things.


[User] Please transcribe this text

[Assistant] And so I'm always somebody who sees the positive side of things. Later on, I realized that there were some hard things for sure. What was beautiful is that MIT was the top place for this kind of commentators I was interested in. Richard Stanley ended up being my PhD advisor and I still


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[Assistant] To this day, I really love his mathematical taste and I think it really shaped mine. So him and then, you know, Sergei Fomin, Sara Billy, Anna Schilling, eh, there were many, many great people that I learned a lot from.


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[Assistant] What I realized later though is that I really was extremely isolated. And, uh, I, you know, I don't think anybody should go through a PhD program and never talk to their classmates about mathematics. And I just didn't know any better. I didn't really understand how you participate in a PhD program.


[User] Please transcribe this text

[Assistant] Both, both, eh, I think more the infrastructure. I mean, I started blaming myself, but I think with time when I started realizing, oh, I started seeing that many of my, many of my students, eh, face the same experience and that this happens disproportionately to black students, to Latinx students, to, to women, to trans folks. And I do think that, eh, you know, there's kind of a,


[User] Please transcribe this text

[Assistant] A center of mathematical culture that, if you're somehow a little bit different from that, it's sometimes not so clear how you're supposed to participate. I was never excluded in an explicit way.


[User] Please transcribe this text

[Assistant] I think generally people thought that I was a good mathematician and people were, people were never unkind to me. But there was a sense in which the culture of doing mathematics there was very, always very foreign to me and I think that,


[User] Please transcribe this text

[Assistant] Also, it was not very well thought out of, you know, how might we welcome people who might come from different walks of life.


[User] Please transcribe this text

[Assistant] I think, I think this is one of the biggest problems in mathematics, one of the hardest math problems to solve and it's one that I spent, you know, 20 years thinking about actually because, because, I mean, I think I have to acknowledge, you know, I came as a Latino with, with a good amount of privilege, you know, and if you go to Colombia, a mathematician looks like me.


[User] Please transcribe this text

[Assistant] I was never, you know, when I was growing up, nobody doubted me. I was not in a minority that people thought was, was somehow not as, as well prepared.