I’m a Ph.D. student exploring techniques that make us faster, more resilient, and better informed. I like turning math and data into systems clients and stakeholders can and want to use. I work at the intersection of decision sciences, reinforcement learning, and human-aware AI.
I’m currently seeking AI/ML or data-science internships and research collaborations.
Topics include multiple regression and correlation methods, model building and validation processes, analysis of variance, logistic regression and regularized regression techniques.
Explorations into program optimizations for emerging massively parallel architectures while building foundations in the polyhedral model for future portability and research.
Machine learning for embedded and edge systems, focusing on hardware/software codesign, model optimization, accelerators, applications, and ethical considerations.
Introduction to neural networks and machine learning methods, covering decision trees, Bayesian and genetic algorithms, reinforcement learning, and hands-on system design.
Introduction to distributed file systems and map-reduce, with a focus on performance tuning, large-scale data mining algorithms, and applications in clustering, classification, and machine learning.
A balanced introduction to linear algebra as both a problem-solving tool and an abstract mathematical structure, with applications, computing, and advanced topics like spectral theory and matrix decompositions.
Introduction to real analysis, focusing on convergence, continuity, differentiation, integration, series of functions, and the development of proof-based reasoning.
Introduction to time series analysis and forecasting, covering ARMA/Box-Jenkins models, seasonality, volatility modeling, and financial risk management applications.
Development of quantitative trading frameworks, covering market mechanics, trading strategies, high-frequency data modeling, portfolio optimization, execution, and performance evaluation.
Introduction to identification and methods to address endogeneity, with emphasis on commonly applied techniques in empirical research.
Graduate course on quantum computing algorithms, focusing on designing and executing programs on simulators and real quantum hardware, with emphasis on contrasts to classical algorithms.
Graduate course in optimization methods, covering unconstrained and constrained optimization theory and algorithms (e.g. quasi-Newton, genetic algorithms) with applications in control, systems, and decision-making.
Interactive Streamlit app that visualizes the efficient frontier, Capital Market Line, and portfolios under variable risk aversion γ. Built with PyPortfolioOpt, CVXPY, and Matplotlib.
Browser-based experiment (Firebase + Qualtrics + JS) measuring how AI assistance affects perceived competence, sincerity, and selfishness.
Transfer-learning pipeline for canine behavior recognition on constrained hardware; explored quantization and compression.
Vanilla HTML/CSS/JS, no build step, GitHub Pages-ready. Interactive filters, dark mode, and smooth animations.
Help students build intuition for systems, networking, and memory hierarchies; emphasize clarity, curiosity, and confidence.
Guiding students in fundamental CS topics as well as statistics, math, and data science.