About Me
I'm a Stats & CS student at UIUC working at the intersection of ML Systems, AI Infrastructure, and Applied ML across computational biology, financial markets, and quantum systems. I research discrete diffusion LLM inference under Prof. Fan Lai, and protein-protein and gene-environment interactions via evolutionary game theory and multi-agent reinforcement learning at MIT CSAIL under the Kellis Lab, where I also work on MantisAI for agentic reasoning. I'm broadly interested in deep learning, computer vision, and decision intelligence. In my personal time I like to watch anime, bowl (NJ state champion), listen to music, and explore Japanese culture. Feel free to reach out for research or internship opportunities.
University of Illinois Urbana-Champaign
B.S. Statistics & Computer Science (2025-2028)
Experience
- Selected as a top technical builder for YC's invite-only 2-day AI Startup School in SF
- Attending YC startup company recruiting sessions and small-group talks with YC partners and alumni
- Accessing $25K+ in compute credits across OpenAI, Anthropic, Cursor, xAI, AWS, and Azure
- Enrolled in intensive two-week training covering quantum information science and computing architectures
- Studying mathematical foundations of quantum gates and circuit execution via the Qiskit SDK
- Exploring cloud-based quantum infrastructure and integration of quantum workflows with classical HPC
- Building discrete diffusion LM inference pipeline with throughput-latency profiling for text generation
- Implementing probability flow controllers in diffusion transformers with adaptive masking and step-wise denoising
- Prototyping flow matching for scalable sequence generation as production-ready alternative to discrete diffusion
- Building MantisAI Platform for modelling single-cell genomics and protein interactions under Prof. Kellis
- Modelling protein-protein and gene-environment interactions via evolutionary game theory and MARL
- Optimizing back-end ML models and embeddings for environment aware knowledge graphs
- Built time-series forecasting pipeline for PAHO modeling dengue/malaria supply chains across Latin America
- Engineered ETL pipelines aggregating multi-country epidemiological data with seasonality and outbreak features
- Delivered actionable supply recommendations to 300M+ person health systems under Dr. Ian Brooks at UIUC
- Architected high-throughput trading infrastructure ingesting live Binance orderbook data via WebSockets
- Engineered high-performance vectorized backtesting engine in NumPy/pandas to validate 50+ strategies
- Containerized end-to-end pipeline with Docker, optimizing system architecture for sub-100ms latency
- Built a Supabase backend with real-time PostgreSQL replication and edge functions supporting 5K+ users
- Implemented Bloom filters for constant-time membership verification, reducing per-request overhead
- Optimized query performance via Row Level Security and composite indexing, cutting peak-load latency by 40%
- Engineered a RAG-based transformer pipeline for automated investment research and client-facing query handling
- Implemented a parallelized Monte Carlo simulation engine stress-testing portfolio returns across 50+ scenarios
- Built data pipelines to extract and analyze real estate market signals using Python and statistical modeling