Honors undergraduate in Computer Science & Engineering at Penn State
IEEE AITest 2025 · LCTES 2026 · Patishnock Undergraduate Research Award
Driven by a passion for research, engineering, and open science.
A network operator pays far more for a capacity shortfall than for spare headroom, so this trains the traffic forecaster on that real cost instead of on RMSE.
Handing a small model from binary rewards to partial credit partway through training lifts its syntax-valid output from 18% to 63%.
Training a model on written explanations of Java performance bugs, rather than on labels alone, raises detection accuracy from 67% to 84%.
An AI code reviewer that may only report a bug when it can hand you a test that fails on the broken code and passes on the fix, and stays silent when it cannot.
An industrial inspection agent that sends a frame to the cloud based on what a mistake would cost, not on how confident the model happens to feel.
Turns a Wikipedia rabbit hole into a shareable map, where the path you actually clicked stays bright and every other link fades into context.
Founding engineer on an industrial copilot, running multi-agent workflows over live plant-floor SCADA, PLC, and MQTT data.