CV
Computational Biophysicist, Biotech R&D, AI/ML Specialist
As a Ph.D. in Physics with a strong industry background in biotech and data science (9+ years of combined experience), I’m seeking opportunities to apply my expertise to tackle complex problems, particularly in science, technology, and healthcare. In addition to strong quantitative skills, I have extensive experience in data visualization, scientific communication, and mentoring. 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
- Graduate Research Assistant, University of Toronto; Toronto, ON, Canada — Sept 2017 – Sept 2024
- Carried out molecular simulations to model complex protein systems (enzymes, cancer proteins), contributing to understanding functional protein dynamics.
- Developed computational and ML pipelines for parallel analysis of protein structural data, resulting in peer-reviewed publications.
- Designed computer vision tools for the automated analysis of lab experiments, enhancing the accuracy and throughput.
- Supervised and guided undergraduate and graduate-level research projects, focusing on ML applications in computational biology.
- Delivered conference presentations and university lectures on advanced computational physics.
- Coordinated cross-disciplinary research initiatives with experimental scientists from UChicago, Harvard, and UPenn.
- AI Researcher, Denti.AI; Toronto, ON, Canada — Apr 2020 – May 2021
- Designed computer vision models for automated diagnostics from X-ray dental images used in production.
- Developed heuristic algorithms to improve AI predictions.
- Implemented algorithms for projecting 3D computer tomography scans to 2D panoramic images.
- Intern Scientist, Menten.AI; Toronto, ON, Canada — Dec 2019 – Dec 2020
- Pioneered the development of an automated pipeline for drug target identification, successfully integrating it with large-scale protein structure databases.
- Implemented a GPU-accelerated quantum-inspired combinatorial optimization solver improving peptide design efficiency.
- Conducted comprehensive benchmarking of classical vs. quantum combinatorial optimization methods for strategic decisions on technology adoption.
- Graduate Research and Teaching Assistant, University of Massachusetts Lowell; Lowell, MA, USA — Sept 2015 – May 2017
- Developed a CUDA-accelerated scientific software package for simulating microtubule dynamics, a critical chemotherapeutic target (peer-reviewed publication).
- Carried out ML analysis of the simulated data to find the critical parameters of the dynamic process.
- Conducted multi-scale computer modeling of cellular division processes, providing insights into error correction mechanisms during mitosis.
- Led the deployment and maintenance of the GPU-cluster at the Massachusetts Green High Performance Computing Center, ensuring high availability and performance.
- Research Assistant, Moscow Institute of Physics and Technology; Moscow, Russia — Sept 2014 – May 2015
- Developed multiscale computational models of fibrin oligomers.
- Conducted bioinformatics analysis of protein databases to extract clusters of protein contacts.
Skills
- Programming: Python, NumPy, C/C++, Parallel Computing (CUDA, MPI, OpenMP)
- Biotechnologies: Molecular Dynamics Simulations, Free Energy Calculations, 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
