cv

List of related skills, education, and experiences.

Updated 28/12/2023.

Education

  • 2020-
    PhD in Theoretical Physical Chemistry
    University of Toronto, Toronto, ON
    Department of Chemistry
    • Affiliated with the Vector Institute for Artificial Intelligence.
    • Supervisor: Alán Aspuru-Guzik.
  • 2017-2020
    MSc in Physics
    University of British Columbia, Vancouver, BC
    Department of Physics and Astronomy
    • Supervisor: Sarah A. Burke.
  • 2013-2017
    BSc (Hons.) in Physics
    McGill University, Montréal, QC
    Department of Physics
    • Graduated first-class honours.
    • Minor in Interdiscplinary Life Sciences
    • Supervisor: Peter Grütter.

Experience

  • 5/2020-
    PhD graduate researcher
    University of Toronto, Toronto, ON
    Matter Lab
    • Creating a software package, DIONYSUS for probabilistic prediction and classification on small datasets of molecules using TensorFlow. Examining the calibration of uncertainty in deep learning models. Results published in RSC Digital Discovery.
    • Compiled kernal-based prediction and classification ML package for chemistry called GAUCHE. Co-first author of results published in NeurIPS 2023.
    • Implementing string-based generative models in PyTorch for a molecular design benchmarking project, TARTARUS. Results published in NeurIPS 2023.
    • Developing variational autoencoders trained with Bayesian models for low-data inverse design of molecules.
    • Designing new molecular graph features based on electronic structures with the goal of improving performance of graph-based neural networks on chemical structures.
  • 5-8/2023
    AI PhD Resident
    SandboxAQ, Toronto (Remote), ON
    • Joined the drug development and chemical simulations team (AQBioSim). Worked on both internal research and client-facing projects.
    • Performed active learning guided high-throughput virtual screening of ~30,000 small organic compounds. Our virtual screening platform found almost 1000 possible drug candidates for our client's target protein.
    • Developed a 3D equivariant probabilistic graph neural network (GNN) and an efficient protein-ligand complex extraction algorithm for prediction of free energy perturbation calculations (FEP) binding affinities. Further demonstrated the model's effectiveness in active learning experiments for drug hit identification.
    • Work presented at the NeurIPS 2023 AI4D3 workshop, with a parallel submission planned for a peer reviewed chemistry journal. Work is part of a pending patent.
  • 2017-2020
    MSc graduate researcher
    University of British Columbia, Vancouver, BC
    Laboratory for Atomic Imaging (LAIR)
    • Studied the luminescence of single organic photovoltaic molecules using scanning tunneling microscopy (STM). Presented findings as an invited speaker at IVC 2019.
    • Supervised and provided guidance to a summer student in classifying molecules in STM images using computer vision and neural networks.
    • Performed DFT (Gaussian16) and quantum chemistry calculations on small molecules for comparison with experimental results.
    • Characterized novel organic deep-blue fluorescent molecules designed by the Hudson Group at UBC.
    • Developed MATLAB analysis script to study strain in graphene samples, characterized by STM. Results published in Science Advances.

Skills

  • Programming
    • Proficient in Python, MATLAB, IDL, and LabView. Experience with FORTRAN, Java, and C.
    • Familiar with Linux environment and the command line. Basic experience in high-performance computing (SLURM).
    • Experience with using Git version control. Strong LaTeX skills.
  • Machine learning and data science
    • Data wrangling, analysis, and visualization in MATLAB, IDL, and Python (numpy, matplotlib, pandas, and seaborn).
    • Highly experienced with common ML frameworks in Python: TensorFlow/Keras, PyTorch/PyTorch Lightning, and scikit-learn.
    • Experience with probabilistic frameworks such as GPyTorch, and TensorFlow-Probability.

Other Interests