Detailed list of skills, education, and experiences.
Education
PhD in Physical Chemistry
2020-present
Department of Chemistry, University of Toronto
Supervisor: Alán Aspuru-Guzik
MSc in Physics
2017-2020
Department of Physics, University of British Columbia
Supervisor: Sarah A. Burke
BSc in Physics First-class Honours
2013-2017
Minor in Interdiscplinary Life Sciences Department of Physics, McGill University
Supervisor: Peter Grütter
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.
Experience
May 2020–present
PhD graduate researcher, Aspuru-Guzik Matter Lab
Creating a software package for probabilistic prediction and classification on small datasets of molecules using TensorFlow. Examining the calibration of uncertainty in deep learning models. Manuscript in preparation.
Implementing string-based generative models in PyTorch for a molecular design benchmarking project. Manuscript in preparation.
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.
Aug 2017–Apr 2020
MSc graduate researcher, 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 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.