CV
Education
- B.S. Candidate in Computational Physics, University of California San Diego, Expected May 2025
- GPA: 3.9/4.0, San Diego, CA
- Relevant Coursework:
- Upper Division Physics: Classical Mechanics I, Quantum Mechanics I & II; Electromagnetism I, II, & III; Advanced Circuits Lab; Statistical & Thermal Physics I; Computational Physics II
- Math: Calculus; Linear Algebra; Analytical Geometry; Differential Equations; Statistical Methods
- Other: Graduate CSE Deep Learning; Data Structures; Machine Learning in Physics; German I-IV
- 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
- Created a vibration isolation stage for their cryogenic cavity experiment inside a dilution refrigerator cryostat
- Derived the predicted eigenmodes of the system using differential equations and confirmed them with COMSOL
- The setup has been installed onto the experiment and has had great success especially at micro-kelvin temperatures
- MIT Beaver Works Summer Institute, 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, June 2024 – present
- Accelerated AI Algorithms for Data-Driven Discovery Institute
- Society of Physics Students, Sep 2022 – present, UC San Diego Chapter, San Diego, CA
Publications/Presentations
- 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, Oct. 2024, Fast Machine Learning for Science Conference, West Lafayette, Indiana, USA
Awards/Recognitions
- 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
- All-Scholastic Athlete of the Year, May 2019, Boys Varsity Tennis, 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
- 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, Spring 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, Winter 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
- 2D Ising Model with Markov Chain Monte Carlo, Winter 2024, UC San Diego, San Diego, CA
- Implemented MCMC simulation using the Metropolis-Hastings algorithm to study the 2D Ising model of ferromagnetism
- Analyzed phase transitions, magnetization, and the effects of temperature and external magnetic fields
- Optimized computationally intensive parts using C++
- The project paper can be accessed here
