Fremont, California, United States
I build scalable AI systems grounded in physics and mathematics. At Lam Research, I engineer computational models that evaluate multi-million-dollar R&D investments and accelerate technology decisions. At Los Alamos National Laboratory, I modeled defense-grade materials using agent-based simulations for the Weapons Physics Directorate. At MIT, I built machine learning frameworks that improved real-time decision systems by 25%. My focus: reinforcement learning, AI evaluation, and deploying intelligent systems that work in the real world. MS Applied Data Science, USC. BA Physics, Hunter College.
• Engineer and optimize computational models used to evaluate multi-million-dollar semiconductor R&D investments, reducing time-to-insight for capital allocation decisions across next-generation technology programs. • Build integrated valuation and simulation frameworks in Python that improved ROI forecasting accuracy, directly informing executive-level decisions on equipment scaling and technology roadmaps. • Design and deploy end-to-end data pipelines in partnership with cross-functional engineering and science teams, accelerating model deployment cycles and driving measurable gains in operational efficiency. • Apply probabilistic modeling and Monte Carlo methods to quantify uncertainty in high-stakes investment scenarios, enabling more robust risk-adjusted decision-making.
• Developed machine learning models to evaluate high-value semiconductor capital investments, reducing time-to-solution by double-digit percentages and improving capital allocation efficiency. • Built quantitative frameworks for R&D prioritization using Python (NumPy, Pandas, scikit-learn), enhancing forecasting accuracy for equipment scaling and strategic planning. • Designed predictive tools that optimized client-facing KPIs around manufacturing yield, cost reduction, and operational throughput.
• Selected as 1 of 55 fellows from 10,000+ applicants (0.55% acceptance rate) and 1 of only 3 physics candidates (0.03%) for Princeton’s competitive doctoral preview program. • Engaged with Princeton Physics and Princeton Plasma Physics Laboratory (PPPL) faculty on research spanning plasma physics, computational modeling, and semiconductor applications. • Participated in graduate-level workshops and faculty-led research discussions on topics including simulation methods, high-performance computing, and AI applications in physical sciences.
• Conducted materials science research on lithium-ion battery systems, using NMR spectroscopy to characterize electrode-electrolyte interactions and optimize performance metrics. • Designed and executed experiments in electrode fabrication and electrolyte formulation, generating quantitative insights into energy efficiency, cycle life, and system durability. • Collaborated across interdisciplinary teams of chemists, physicists, and engineers on milestone results in clean energy storage and sustainable technology R&D.
• Conducted theoretical research in supersymmetric gauge theory, developing mathematical frameworks that advanced understanding of symmetry structures in particle physics. • Applied advanced modeling techniques in quantum field theory, deepening insights into the intersection of abstract mathematics and computational physics. • Collaborated with interdisciplinary research teams, strengthening expertise in applied mathematics, formal logic, and mathematical modeling.
• Managed and analyzed a $200M+ portfolio of commercial real estate assets for institutional clients including Fortune 500 firms, with focus on risk assessment, valuation modeling, and market analytics. • Produced data-driven market intelligence that directly supported three high-value transactions in Manhattan and Brooklyn, applying quantitative analysis to investment decision-making. • Ranked in the top 5% of 80+ brokers in a competitive Upper East Side office, driven by analytical rigor and client engagement strategies.