About me

I am a PhD student and Research Assistant at the University of Toronto Clean Energy Lab. My research is focused on integrating machine learning and quantum chemistry methods to accelerate the discovery of novel materials and medicines. Before joining the Clean Energy Lab at UofT, I earned a Bachelor of Science degree in Physics from Taras Shevchenko National University of Kyiv. My undergraduate research focused on experimental work with nanomaterials as well as computational high-energy physics.

My Research

I work on AI for materials and drug discovery, including Graph Neural Networks, Foundation Models, and Active Learning methods. In my research, I use Python (including libraries like PyTorch, Pandas, Numpy, Scipy, etc) to develop machine learning models and analyze chemical and biological data. I also extensively use Quantum Chemistry methods (Density Functional Theory, Semiempirical Tight-Binding) to simulate materials and molecules (Molecular Dynamics, Electronic Structure calculations, etc).

  • Graph Neural Network for Materials Density of States prediction

  • Navigate Materials Space with ML-generated Electronic Fingerprints

  • Efficient Biological Data Acquisition through Inference Set Design

Selected Publications

Conference Presentations

Talks

  • Accelerating discovery of phosphorescent blue OLED emitters using chemical foundation models
    Canadian Societies for Chemistry and Chemical Engineering Conference X2026, Toronto, Canada, 2026
  • Accelerated discovery of battery materials using ML-predicted Density of States
    Climate Positive Energy Research Day, Toronto, Canada, 2023
  • Navigating Material Space with ML-Generated Electronic Fingerprints
    Canadian Chemistry Conference and Exhibition, Vancouver, Canada, 2023
  • Machine learning methods for predicting density of states
    MRS Fall Meeting & Exhibit, Boston, United States, 2022
  • Machine learning methods for predicting density of states
    Canadian Chemistry Conference and Exhibition, Calgary, Canada, 2022
  • Spatio-temporal correlation between Gamma-ray bursts and High-energy neutrino
    Week of Doctoral Students, Prague, Czech Republic, 2020

Posters

  • Efficient High-Throughput Compound Library Screens with Active Learning
    Accelerate Conference, Toronto, Canada, 2025
  • Efficient Biological Data Acquisition through Inference Set Design
    International Conference on Learning Representations (ICLR), Singapore, 2025
  • Navigating Material Space with ML-Generated Electronic Fingerprints
    Materials for Sustainable Development Conference (MATSUS24), Barcelona, Spain, 2024
  • Navigating Material Space with ML-Generated Electronic Fingerprints
    Accelerate Conference, Toronto, Canada, 2023
  • Machine learning methods for predicting density of states
    Accelerate Conference, Toronto, Canada, 2022
  • Machine learning methods for predicting density of states
    The Canadian Symposium on Theoretical and Computational Chemistry, Kelowna, Canada, 2022

Awards & Scholarships

  • Climate Positive Energy Graduate Scholarship

  • Connaught Scholarship for Doctoral Students

Side Projects

  • Ainfer

    A web application for research paper analysis with Large Language models.

  • Satellite tracker

    Code for live tracking of satellites and space debris. Developed for NASA Space Apps hackathon