Computer Science PhD student at Tufts. I build physics-informed ML systems, intelligent agents, and tools that accelerate scientific discovery.
Shaped by research in nuclear physics, scientific ML, and real-world systems engineering.
Numerical PDEs, super resolution, data-driven simulators aligned with conservation laws.
Autoencoders and contrastive methods for compression, anomaly detection, and structure discovery.
Agentic workflows to orchestrate experiments and validate theory with notebooks.
Transformer-based models (e.g., SnowflakeNet) for incomplete ATTPC track recovery.
Search, automation, and evaluation tooling that compounds iteration speed.
Education technology, data platforms, and accessible tooling for emerging communities.
Learning to enhance coarse numerical solutions while preserving physical fidelity and stability constraints.
Compact representations for high-throughput scientific signals and fields with controllable distortion.
An agentic system to automate theoretical physics validation workflows inside notebooks.
Anthony Kuchera, Warren Rogers, Tahmid Awal, Olivia Guarinello
Applying SnowflakeNet-like architectures to ATTPC data to reconstruct incomplete tracks from nuclear reactions. Core of my honors thesis at Davidson College Physics.
Supporting instruction, mentoring, and evaluation in Distributed Computing.
Organized seminars and outreach to expand physics learning and community.
Supported 20+ projects in analytics and ML; built resources for DS learning at Davidson.
Data science community with 500+ learners; launched Computational Astrophysics course (150+ students).
Maintained olympiad problem portal (5,000+ users); led Girls Math Camp with EMK Center.
Guided experiments and tutoring across core physics/CS courses and tools.
I’m always happy to chat about research, collaborations, or mentorship.
