CV
Biotech R&D - AI/ML - Comp Bio
With a PhD in Physics and over 4 years of industry experience in AI and biotech, I am committed to advancing science and technological innovation. I bring strong quantitative skills, paired with a solid track record in scientific communication, data visualization, and mentoring across disciplines. I’m passionate about contributing to projects that have a positive impact on society.
Education
- Ph.D. in Computational Biophysics, University of Toronto, 2024
- M.Sc. in Computational Chemistry, University of Massachusetts Lowell, 2017
- B.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology, 2014
Work Experience
- ML Scientist II, Fable Therapeutics; Toronto, ON, Canada — Apr 2025 – Now
- Supporting a computational drug design platform in preclinical R&D.
- Integrating sequence- and structure-based generative models for therapeutic design and optimization.
- Coordinating cross-disciplinary (in silico + in vitro) research initiatives.
- ML Researcher, Neurooptic Dx; Toronto, ON, Canada — Dec 2024 – Mar 2025
- Developing computer-vision + time-series models to analyze physiological data.
- Building a platform for impairment detection and diagnostics of neurological conditions from micro eye movements (patent pending).
- Junior AI Researcher, Denti.AI; Toronto, ON, Canada — Apr 2020 – May 2021
- Building algorithms for projecting 3D computer tomography scans to 2D panoramic images.
- Developing computer vision models for automated diagnostics from X-ray dental images and heuristic algorithms to improve AI predictions.
- Research Intern, Menten.AI; Toronto, ON, Canada — Dec 2019 – Dec 2020
- Benchmarking of classical vs. quantum combinatorial optimization methods for strategic decisions on technology adoption.
- Implementing GPU-accelerated combinatorial optimization algorithms.
- Development of an automated pipeline for drug target identification based on protein database.
- Graduate Research Assistant, University of Toronto; Toronto, ON, Canada — Sept 2017 – Sept 2024
- Delivering conference presentations and university lectures on advanced computational physics.
- Developing computational and ML pipelines for analysis of protein structural data, resulting in peer-reviewed publications.
- Running molecular simulations of complex protein systems (enzymes, cancer proteins) aimed to understand structure-dynamics-function paradigm.
- Graduate Research and Teaching Assistant, University of Massachusetts Lowell; Lowell, MA, USA — Sept 2015 – May 2017
- ML analysis of the simulated data to find the critical parameters of the dynamic process.
- Developing a CUDA-accelerated scientific software package for simulating microtubule dynamics, a critical chemotherapeutic target (peer-reviewed publication).
- Research Assistant, Moscow Institute of Physics and Technology; Moscow, Russia — Sept 2014 – May 2015
- Bioinformatics analysis of protein databases to extract clusters of protein contacts.
- Developing multiscale computational models of fibrin oligomers.
Skills
- Programming: Python, NumPy, C/C++, Parallel Computing (CUDA, MPI, OpenMP)
- Biotechnologies: Protein Language Models, AlphaFold, Molecular Dynamics Simulations, Rosetta, GROMACS, ChimeraX, VMD, BioPython, MDAnalysis
- Machine Learning: PyTorch, scikit-learn, Computer Vision, W&B, Data Visualization, Pandas
- Quantum Computing: Quantum Annealer (D-Wave), Quantum Circuit (PennyLane)
- Quantitative Skills: Statistics, Linear Algebra, Graph analysis, Statistical Physics
- Others: Cloud Computing (ComputeCanada, AWS), Git, JIRA, Project Management, Mentoring, Scientific Communication
Publications
Jack B Maguire, Daniele Grattarola, Vikram Khipple Mulligan, Eugene Klyshko, Hans Melo (2021). "XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers." PLoS computational biology. 9 (17).
Evgenii Kliuchnikov, Eugene Klyshko, Maria S. Kelly, Artem Zhmurov, Ruxandra I. Dima, Kenneth A. Marx, Valeri Barsegov (2022). "Microtubule assembly and disassembly dynamics model: Exploring dynamic instability and identifying features of Microtubules’ Growth, Catastrophe, Shortening, and Rescue." Computational and Structural Biotechnology Journal. 20(1).
E Klyshko, JSH Kim, S Rauscher (2022). "LAWS: Local Alignment for Water Sites — tracking ordered water in simulations." Biophysical Journal. 122 (14), 2871-2883
Christopher J Nunn, Eugene Klyshko, Sid Goyal (2023). "petiteFinder: An automated computer vision tool to compute Petite colony frequencies in bakers yeast." BMC Bioinformatics 24, 50.
Klyshko E, Kim JS-H, McGough L, Valeeva V, Lee E, Ranganathan R, Rauscher S (2024) "Functional protein dynamics in a crystal." Nature Communications 15(1):3244
Talks and Posters
February 19, 2020
Poster at San-Diego Convention Center, San-Diego CA, USA
July 08, 2019
Talk at RIKEN Center, Kobe, Japan
May 29, 2019
Poster at University of Toronto Missisauga, Kaneff Building, Missisauga, ON, Canada
May 06, 2019
Workshop Poster at McGill University, New Residence Hall, Montreal, QC, Canada
May 01, 2019
Talk at University of Toronto Missisauga, IB150, Missisauga, Ontario, Canada
March 05, 2019
Poster at Baltimore Conventional Center, Baltimore MD, USA
May 05, 2018
Poster at University of Toronto, Earth Sciences Centre, Toronto, ON, Canada
May 06, 2017
Talk at Whitehead Institute, Cambridge MA, USA
May 07, 2016
Talk at Whitehead Institute, Cambridge MA, USA
Teaching
