CV
Education
- M.S. in Data Science, ETH Zurich, Sept. 2025 - present
- B.S. in Computational Physics with High Honors, University of California San Diego, May 2025
- GPA: 3.9/4.0, San Diego, CA
- Relevant Coursework:
- Upper Division Physics: Classical Mechanics; Quantum Mechanics I & II; Electromagnetism I, II, & III; Statistical & Thermal Physics; Advanced Circuits Lab; Computational Physics II
- Math & CS: Linear Algebra; Differential Equations; Statistical Methods; Graduate Deep Learning; Data Structures; Machine Learning in Physics; Neuromorphic Computing
- High School, Boston University Academy, Sep. 2017 – June 2021
- Completed 12 undergraduate courses at Boston University focusing on Physics and Computer Science, Boston, MA
Activities
- Duarte Particle Physics Laboratory, UC San Diego, Jan. 2023 - present, San Diego, CA
- Developing automated neural architecture search (NAS) and optimization pipeline for deploying efficient machine learning models for low latency hardware like FPGAs
- Using cutting edge NAS techniques with compression methods like quantization and iterative magnitude pruning
- Synthesized models onto Vivado FPGA’s for minimum latency model inference
- High Energy Physics Lab (Excellence Research Internship Program), École Polytechnique Fédérale de Lausanne (EPFL), June 2023 - Sep. 2023, Lausanne, Switzerland
- Created classification program to detect heavy neutral leptons at the CMS experiment at CERN
- Used Graph Neural networks and multilayer perceptrons to find optimal architecture for signal detection
- HYQU Quantum Physics Laboratory, ETH Zurich, June 2022 - Sep. 2022, Zurich, Switzerland
- Designed a vibration isolation stage for a cryogenic cavity experiment by deriving predicted eigenmodes and validating with COMSOL; now installed and operating at micro-kelvin temperatures
- MIT Beaver Works Summer Institute, Massachusetts Institute of Technology, Lincoln Laboratories, Jul 2020, (online) Cambridge, MA
- Developed software packages that integrated processing of satellite images to access damage and analyzed road maps for finding optimal evacuation routes during natural disasters
- A3D3 Trainee, Accelerated AI Algorithms for Data-Driven Discovery Institute, June 2024 – present
Publications/Presentations
- Surrogate Neural Architecture Codesign Package (SNAC-Pack), Dec. 2025, NeurIPS 2025 Machine Learning and the Physical Sciences Workshop
- Integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment without requiring time-intensive synthesis. https://arxiv.org/abs/2512.15998
- wa-hls4ml: Benchmark and Surrogate Models for hls4ml Resource Estimation, Nov. 2025, ACM Transactions on Reconfigurable Technology and Systems (TRETS)
- Tutorial: Super Neural Architecture Codesign Package (SNAC-Pack), Sep. 2025, Fast Machine Learning for Science Conference 2025, ETH Zurich
- Led a 1.5-hour hands-on tutorial guiding participants through the complete SNAC-Pack workflow, from dataset preparation to hardware deployment and optimization.
- Neural Architecture Codesign for Fast Physics Applications, Jan. 2025, Machine Learning Science and Technology Journal
- Neural Architecture Codesign for Fast Bragg Peak Analysis, Feb. 2024, 2024 AAAI Workshop on AI to Accelerate Science and Engineering, Vancouver, Canada
- Automated pipeline to streamline neural architecture codesign for fast, real-time Bragg peak analysis in high-energy diffraction microscopy. https://arxiv.org/abs/2312.05978
- Fast ML Conference Lightning Talk & Poster, Oct. 2024, Fast Machine Learning for Science Conference, West Lafayette, Indiana, USA
Awards/Recognitions
- UC San Diego Physical Sciences Dean’s Undergraduate Award for Excellence, Mar 2025, UC San Diego, San Diego, CA
- Hackathon Winner for “Model Performance” and “Best Judged Model”, Jan 2025, Climate Event Identification NSF HDR ML Challenge Hackathon, San Diego, CA
- Chair’s Challenge Award, Oct 2024, UC San Diego Physics, San Diego, CA
- Physics Honors Program, Apr. 2024 - present, UC San Diego, San Diego, CA
- UCSD Physical Sciences Summer Research Award, June 2024 - Sept. 2024, UC San Diego, San Diego, CA
- Excellence Research Internship Program, June 2023 - Sept. 2023, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Provost Honors, Sep. 2021 - present, UC San Diego, San Diego, CA
- Boston Herald All-Scholastic Athlete, May 2019, Boston Herald, Boston, MA
Skills and Interests
- Computer Languages: Python, Java, C/C++, HTML
- Computer Tools: Tensorflow, Pytorch, Kubernetes, Circuit Building
- ML topics: Computer Vision, Geometric Deep Learning, Fast ML, Network Pruning, Simulated Annealing, Neural Architecture Search
- World Languages: native speaker: English, Russian; B1-level German
Additional Projects
- Chess Review MCP: AI Chess Coach, 2026
- Built an AI chess coach that reviews any game (Lichess, Chess.com, or any PGN) with Stockfish and explains each mistake in plain words, grounded in real engine lines rather than guesses
- Runs both as an MCP server inside Claude Code and as an interactive web board with an eval bar, win graph, move arrows, and an in-browser AI coach; cross-game history rolls up into a per-player coaching profile
- Open source (MIT). See the project website and the GitHub repository
- AI Moving Lamp, Fall 2023, UC San Diego, San Diego, CA
- DC-powered lamp that moves along a table, uses computer vision to detect notebooks, stops, and turns the light until notebook is no longer detected
- The lamp uses a Raspberry Pi and a Tensorflow Lite custom trained machine learning model. Designed and 3D printed all the parts of the lamp
- More info can be seen in this video link
- SQUID Effect Derivation from Maxwell’s Equations, Feb. 2024, UC San Diego, San Diego, CA
- Derived the key equations underlying a Superconducting Quantum Interference Device (SQUID) starting from the most fundamental laws: Maxwell’s and Schrodinger’s Equations
- Derivations were simplified to an undergraduate level from advanced graduate textbooks that cover this topic
- The derivation paper can be viewed here (PDF)
- Car Racing Simulation using Reinforcement Learning, Apr. 2024, UC San Diego, San Diego, CA
- Applied Deep-Q Networks (DQN) and Proximal Policy Optimization (PPO) models to optimize performance in the OpenAI Car Racing game environment
- Explored reward augmentation strategies like grass detection, speed rewards, and acceleration rewards to enhance training efficiency and model performance
- View the presentation and detailed paper
