Education
- 2019 - current
- Ph.D., Physics
- University of California, Berkeley
- 2018 - 2019
- M.S., Theoretical Physics
- Perimeter Institute/University of Waterloo
- Thesis: ”Machine (Un)Learning in Phases Classification of Lattice Models”
- 2014 - 2018
- B.S., Physics and Astronomy
- Stony Brook University
- Summa Cum Laude, Honors College
- Physics Thesis: “Local Measurement of the Material Budget in the CMS Tracker”. Advisor: Dr. Klaus Dehmelt
- Astronomy Thesis: “Measuring Small‐Scale Dark Matter with High‐Resolution CMB Lensing”. Advisor: Dr. Neelima Sehgal
Experience
- 06 - 08/2021
- Graduate Student Research Assistant: AI for quantum control
- Lawrence Berkeley National Laboratory
- Developed an OpenAI-compatible gym to model the dynamics of transmon qubits under control pulses, and implemented multiple Q-learning algorithms to learn both discrete and continuous pulse amplitudes.
- Achieved a 3x reduction in gate duration for single-qubit operations, while sustaining a fidelity of 99.9%.
- 06 - 08/2018
- Student Researcher: Model-fitting with \(\lambda\)-statistics for pulsar search
- Perimeter Institute, Canada
- Investigated a \(\lambda\)-statistics-based model-fitting algorithm on time-series data.
- Validated the algorithm’s effectiveness using a simplified toy model featuring 2D timestreams, laying the foundation for its application to the full pulsar search problem.
- 06 - 08/2017
- Student Researcher: Validation of material budget in the CMS tracker
- CERN, Geneva, Switzerland
- Modified existing C++ code and added Python scripts to extract azimuthal coordinate information for radiation length analysis in the Compact Muon Solenoid (CMS) tracker.
- Identified non-constant radiation length values in the tracker’s edge modules, offering initial insights for further improving the accuracy of the material budget estimation.
Teaching
- Graduate Student Instructor @ University of California, Berkeley
- Spring 2024
- CHEM 277B. Machine Learning Algorithms
- Facilitated weekly discussions on implementing various optimization and machine learning algorithms using Python, with a focus on Pandas and PyTorch.
- Provided assignments focused on core optimization methods, followed by deep learning models like CNNs, RNNs, VAEs, and GNNs, tailored to molecular science.
- Spotlighted recent developments in machine learning, including sequence-to-sequence learning and transformers.
- Spring 2021
- PHYS C21. Physics and Music
- Fall 2020
- CS C191. Quantum Information Science and Technology
- Fall 2019/Spring 2020
- PHYS 8A/8B. Introductory Physics
Service and leadership
2021 Splash! Teacher, Introduction to Neural Networks, UC Berkeley
2016 - 2018 Faculty Director Advisory Board, College of Science and Society, SBU
2015 - 2018 Resident Assistant, Mount College, Campus Residences, SBU
2015 - 2016 Undergraduate College Fellow, College of Science and Society, SBU