Ph.D. Applied Physics

M.S. Computer Science

• Bay State Fellow
• Master's Project being completed in conjunction with first-year of PhD

B.S. Physics, B.S. Mathematics, B.S. Computer Science

• GPA: 3.94
• Cum Laude, Commonwealth Honors Scholar with Greatest Distinction, Phi Beta Kappa
• Thesis: Characterization of Drifting Charge Clusters in Liquid Xenon Produced by a Laser-Driven Photocathode for the nEXO Experiment

L. Barrett (Speaker)

nEXO Light Simulation Workshop, McGill University, Québec, CA

D. Cesmecioglu (Presenter), J. Bane, L. Barrett, K.S. Kumar, M. Loscar, A. Nolan, J. Zhu

Neutrino 2024, Milan, IT

L. Barrett (Presenter), J. Mott, K.S. Kumar

APS DNP 2023, Waikoloa, HI, US

UMass Amherst College of Information & Computer Sciences

• Applying modern reinforcement learning algorithms to optimize link generation between quantum memory registers with various noise models
• Used reinforcement learning to train a quantum circuit compiler to compile given sets of operations to a format suitable for a distributed quantum computer (DQC) minimizing teleportation operations.

University of Massachusetts Amherst

• Used Rust, parallelization, and optimized collision check algorithms to bring simulation time from several days to a couple of minutes. Presented in APS DNP CEU undergraduate poster session.
  • Checks 'two-bounce' conditions, ensuring there are no secondary sources of noise impacting the detectors. Used to validate collimator design.
• Set up, configured, and managed a small compute cluster suitable for Geant4 simulations.
• Developed algorithm to define smooth contour around electron beam profiles produced by simulations, considering geometric constraint.

University of Massachusetts Amherst

Under Prof. Krishna Kumar, worked on characterization of charge clusters drifting in liquid xenon produced by a laser-driven photocathode, in support of the development of an in-situ electron lifetime monitoring system.

• Developed noise-reduction algorithms based on digital modelling of a shaping circuit, fourier methods, and generative neural networks.
• Assembled, tested, and set up calibration pipeline for signal processing hardware.
• Work from the team presented in poster session at Neutrino 2024.

University of Massachusetts Amherst

Under Prof. Andrea Pocar, worked on studies using experimental data and GPU-accelerated Chroma simulations to study optical properties of materials in LXe, at the scintillation wavelength of Xe.

• Optimized and refactored simulation pipeline to allow for larger numbers of simulations with changing parameters to allow for 'sweeps'.
• Used parameter sweeps to identify hardware configurations that would allow for better measurements with lower error. (Presented in internal nEXO Collaboration meeting)
• Integrate Geant4 'unified model' to allow for more realistic reflective behavior (account for 'roughness').

UMass Amherst

• Developed a high-performance Julia simulation tool to simulate n-mode gaussian states.
• Implemented efficient representations and operations to study these states, and compared performance and error to existing libraries that only supported state-vector representations.

Cornell University College of Applied & Engineering Physics

• (F25) AEP3330 & AEP5330: Mechanics of Particles and Solid Bodies. ~20 student junior-level classical mechanics course
• (S26) PHYS3360 & AEP3630: Electronic Circuits.

UMass Amherst College of Information & Computer Sciences

• CS648: Quantum Information Systems. ~40 student graduate course with a focus on quantum algorithms and error correction.
• CS250: Introduction to Computation. -300 student core class introducing proofs and computational theory

UMass Amherst

• CS490Q: Quantum Information Science
• PHYS281: Computational Physics (x2)
• PHYS181: Introduction to Mechanics

Hotchkiss School

• Instructor for inaugural 'Euclid-to-Einstein' mathematics program for selected motivated high school students
• Designed curriculum components covering multivariable calculus, differential equations, and calculus of variations
• Held classes covering advanced math topics accessible to students who have taken up to AP Calculus

UMass Amherst

• Invited by both physics and CS department faculty to be paired with incoming freshman students as a peer mentor.
• Met regularly to provide social and academic support, particularly when involving course selection/planning, and getting involved in research (both with faculty on campus and applying to REUs)