Selected for research fellowship to develop data systems and ML models analyzing consumer behavior patterns and supernormal stimuli.
Contributed to an Ultra-Advanced GRPO system for enterprise support automation, using multi-objective neural reward modeling, advanced data logging, and real-time performance analytics.
Developed ESM Transformer models to classify protein sequences with 92% accuracy. Used PyTorch and Hugging Face for model training and evaluation.
Provide one-on-one tutoring for high schoolers in algebra, calculus, and American Math Compeititon (AMC).
Built an energy simulation app modeling consumption patterns with actionable recommendations. Used C++/Python backend and HTML/JS/CSS frontend.
Applied statistical modeling to analyze genomic data, improving models by 22%. Used Linux for data processing and MATLAB for simulations.
B.S. in Computer Science and Business, Economics, and Management (BEM)
Minor in Information and Data Sciences (IDS)
Relevant coursework: Data Structures and Algorithms, Software Design, Probability and Statistics, Statistical Inference, Introduction to Computational Science and Engineering