Nuclear and high-energy physics undergraduate researcher focused on low-x QCD, diffraction, gluon saturation, and dark-boson searches at the Electron-Ion Collider. Uses Python, C++/ROOT, machine learning, and reproducible simulations to study particle collisions, while also developing gamma-ray-burst mission science for ѰSat through NASA Space Grant and exploring quantitative modeling in finance.
B.S. Physics candidate at Stony Brook University, expected May 2027. His academic work centers on the future Electron-Ion Collider, which will probe the structure of protons and nuclei through the quarks and gluons that make them up. His work helps build expectations for what the EIC should measure and how those measurements may appear.
Uses C++, ROOT, Python, Unix/Linux, numerical simulations, Monte Carlo event generators, and data analysis workflows for physics research.
Interested in paths that apply rigorous analytical methods to complex systems, including graduate research in nuclear physics and quantitative finance. Across both fields, his focus is on computational modeling, data analysis, and careful reasoning under uncertainty.
Projects connecting quantitative finance, machine learning, data analysis, and computational modeling with nuclear physics, gluon saturation, and high-energy collision research.
At high energies, gluons multiply until their growth may begin to saturate, revealing new behavior in nuclear matter. This research analyzes 10 million simulated electron–nucleus collisions using geometric scaling and spatially dependent nuclear structure to study where saturation occurs and how the Electron–Ion Collider could detect it.
EIC QCD Gluon Saturation nPDFDeveloped a weighted C++/ROOT simulation across 22 model scenarios and reproduced published benchmarks within 0.4-2.4%. Across 60 independently seeded comparisons, boosted decision trees improved sensitivity by 1.21-1.36%, equivalent to a 2.5-2.8% gain in detection significance or about 5% less required data, and outperformed the benchmark in 20/20 high-mass tests. Detector-level validation is the next step.
Machine Learning Dark Bosons EIC C++/ROOTThe ѰSat mission is a NASA CubeSat Initiative project centered on a 1U CubeSat designed to detect gamma rays from cosmic bursts. The work defines the mission's science goals and 30–300 keV detection strategy, communicates its objectives through technical presentations, and plans how future detections could be studied alongside gravitational-wave and other transient observations.
NASA Space Grant 1U CubeSat Gamma-Ray Bursts Multimessenger AstrophysicsCompleted end-to-end benchmark for classifying short-horizon limit-order-book movement on real FI-2010 data. The selected histogram gradient-boosting model reached 73.25% held-out three-class accuracy, 61.35% balanced accuracy, and 64.13% macro F1—an improvement of 16.31 percentage points and 37.88% lower error than the majority baseline. Chronological model selection, committed diagnostics, and checksummed data retrieval make the result fully reproducible.
Limit Order Books Gradient Boosting FI-2010 Python View results & code →Fellowships, presentation awards, travel grants, and academic recognition.
Photos from research programs, conferences, presentations, leadership, and academic experiences.
