Computational Research

Computational Research









Dark matter phenomenology, especially direct dark matter detection, local dark matter distribution, dark halos in cosmological simulations, and dark substructures; Astroparticle physics.

My research is focused on dark matter phenomenology, and more specifically on developing various strategies to significantly improve our knowledge of the dark matter distribution in our Galaxy. Understanding the nature and distribution of dark matter in the Milky Way is a fundamental problem in astroparticle physics, and has important implications for attempts to discovering dark matter. The long-term goal of my research is to identify the particle nature of dark matter, through its distinct signatures on the Galactic dark matter distribution. I use various approaches to probe the dark matter distribution in the Milky Way, taking advantage of state-of-the-art high resolution cosmological simulations and recent high precision astronomical data. In one approach, I study the implications of hydrodynamic simulations of galaxy formation for dark matter direct and indirect searches. This is done by identifying simulated galaxies that are similar to the Milky Way, extracting their dark matter density and velocity distribution, and using these distributions in the analysis of data from dark matter experiments. In another approach, I study the interaction of dark matter subhalos with stellar streams. Analyzing the features induced by these interactions in stellar streams can lead to a measurement of the dark matter subhalo mass spectrum, and provide important information on the particle nature of dark matter. The scope of my research interests also includes tackling other open problems in dark matter phenomenology, as well as exploring different topics in astroparticle physics.

Atomic and molecular collisions, especially involving positrons.


Modeling and forecasting of weather and climate.


Quantum field theory; Positron interactions; Quantum scattering theory.


Collider physics; search for long-lived particles; Higgs boson interactions with heavy quarks; neutrino detection and properties; machine learning for high-energy physics; detector development for high-energy physics experiments.

I am an experimental particle physicist, and as such I study the Universe through its tiniest, most fundamental constituents: the elementary particles. I am involved in two international collaborations: the ATLAS Experiment, which is one of the detectors of the Large Hadron Collider (LHC) at CERN, and the Deep Underground Neutrino Experiment, or DUNE, which is a future detector to be hosted by the US laboratory Fermilab near Chicago, Illinois.

The ATLAS Experiment is a five-storey high multi-purpose detector designed to record data from collisions happening at its center. The task for experimental particle physicists is to reconstruct the trajectories and identify the particles produced during these collisions. It enables the observation of new particles or new processes of subatomic physics. In the past, I was involved in searches for Supersymmetry and contributed to the discovery of a rare process: the Higgs boson production in association with a top quark pair (quarks are constituents of protons and neutrons). This complements the LHC’s big success in 2012 discovering the Higgs boson, the particle playing a key role in giving mass to the other elementary constituents of matter. Right now I am searching for “long-lived particles”. Numerous candidate theories attempting to expand our current theoretical framework are predicting particles whose life-times are longer than the ones already discovered. To spot them, we need to rethink algorithms used currently for pattern recognition. I am also active in detector development for high-energy physics. The LHC is preparing to deliver enhanced collisions starting in 2027 (called the High-Luminosity LHC Project). To survive the high rates and radiation levels, some sub-detectors of ATLAS need to be completely rebuilt. I am contributing to the future silicon inner tracker: the ITk. Several sensor units will be assembled and tested by several Canadian institutions.

The Deep Underground Neutrino Experiment (DUNE) is a future leading-edge experiment for neutrino science hosted by Fermilab in the United States. Planned to operate starting in 2026, DUNE will consist of two massive state-of-the-art neutrino detector complexes, one at Fermilab and the second 1300 km away from the first, at the Sanford Underground Research Facility in South Dakota, 1.5 km underground. The Long-Baseline Neutrino Facility (LBNF) will supply intense beams of neutrinos and antineutrinos with energy spectra broader than any other current experiment. DUNE is designed to study how neutrinos change over time (a phenomenon known as oscillations), both for big potential discoveries as well as for precision measurements. An exciting feature of DUNE is its sensitivity to neutrinos coming from the cosmos, especially stellar explosions (supernovae). The Canadian effort on DUNE has just started and the team is growing. As part of the DUNE computing group I am making sure that there is a coherent software framework for data analysis, using my experience on ATLAS. I am also planning to build a test bench for performing feasibility studies on the mechanical/optical parts of the ionization laser device, which will be one of DUNE’s crucial calibration systems.








Neutrino masses and mixing; Neutrino interactions with nuclei.

My research aims to understand the neutrino, the most abundant particle in the universe that has any mass. Because the neutrino almost never interacts, we know very little about it. However, that same characteristic also means that we can study them by making a beam of neutrinos and shooting them long distances through the earth and studying their composition after they have had time to evolve over time. These measurements can tell us more precisely what the mass differences are between different neutrinos, whether their masses are like the charged fermion mass spectra or the inverse, and whether neutrinos and antineutrinos evolve the same way over time. Because neutrinos have mass, they have a huge impact on galaxy formation in the universe, and if neutrinos and antineutrinos evolve differently, they may be the reason that the universe today is dominated by matter rather than simply being full of light from matter-antimatter annihilations.

The Deep Underground Neutrino Experiment (DUNE) combines an enormous fine-grained neutrino detector with an intense broad-band neutrino beam that has traveled over 1300 km by the time it reaches that detector. DUNE will enable us to test the neutrino oscillation framework at a level of detail and precision that is simply not possible with today’s suite of very capable narrow-band neutrino beam experiments. I will be working on DUNE’s near detector, because the best predictions for what the far detector will see come from an accurate (and tested) description of how neutrinos interact and from extensive measurements from the DUNE Near Detector Complex. My ultimate research plan is to do precision oscillation measurements on DUNE.

However, just as the detector and beamline for DUNE are not yet ready today, the field of neutrino physics as a whole is not yet ready to predict oscillated neutrino event rates over a broad range of energies at the required level of precision. In order to fulfill DUNE's promise of precision we not only have to build the detector and neutrino beamline, but we also have to understand the way neutrinos will interact in that detector. The MINERvA experiment, located at Fermilab, has started writing the book on neutrino interactions and how the nuclear environment affects those interactions. We have collected a huge sample of neutrino and antineutrino interactions on many different nuclei, and are able to shine a much brighter light on neutrinos than ever before. I am currently the co-spokesperson of MINERvA and I am making sure many more neutrino interaction measurements come from MINERvA to best prepare for DUNE.











Galactic archaeology; Galaxy formation; Data science for astrophysics; Machine learning; Astronomy education and outreach.

My research is "in and above the cloud", combining astrophysics, data science, cloud computing, planetary sciences, optical engineering, telescope operations and telescope observations. My primary astrophysical research focus is galactic archaeology. I have worked with instrumentation and telescopes around the world, and my experience enables me to provide technical leadership for York University's Allan I. Carswell Astronomical Observatory.

Another motivation for me is the promotion of astronomy education and research through public telescope activities and exploration. As a lecturer, trainer, and consultant, I innovate teaching methodologies for interdisciplinary audiences across the sciences as well as for businesses, students and the general public. Also, I am a certified Google Cloud Trainer and Google Cloud Engineer, and I have considerable experience as a trainer and consultant in data science in the private sector.












Quantum chromodynamics (QCD); Chiral perturbation theory.




Atomic processes, especially electron and positron scattering; Plasma physics.











Airglow; Aurorae; Atmospheric dynamics and composition; Space instrumentation.
















Visual cortex; Retina; Synaptic Plasticity; Learning; Perception; Information Theory; Machine Learning

I study the way the brain represents information about the outside world, and the way in which those representations are learned. My immediate goal is to build on my expertise in machine learning and sensory neuroscience to create a camera to brain translator that could restore sight to the blind, and could be used in computer vision systems. In parallel, I will develop new data science methods that will infer the brain’s learning rules from in vivo neural data, and use those methods to determine how behavioural context affects synaptic plasticity in the visual cortex. Next, I will use the brain’s learning rules to make next-generation machine learning algorithms that will be more flexible and efficient than the current state of the art. Finally, I will reveal how the interaction between different retinal ganglion cell types supports the communication of visual information from the eyes to the brain. That work may have strong implications for the development of next-generation retinal prosthetics.