B.S. Computer Science, Minors in Physics and AI · Schreyer Honors College · GPA 3.71 / 4.00 · University Park, PA
Research Experience
SEP 2026– NOW
Do Residual-Stream Probes Survive Degrading Chain-of-Thought Legibility?
Supervised Program for Alignment Research (SPAR) · Mentee Researcher · mentor: Marios Tsatsos
To test whether activation probes still work when chain-of-thought turns illegible, sampled 1,816 Qwen3-4B traces on 400 borderline prompts (XSTest, OR-Bench) the model both complies with and refuses, so a probe must read the reasoning, not the prompt
Labelled comply/refuse with two LLM judges (88% agreement, κ = 0.76), since keyword checks misread soft refusals
Traced the per-token offset that buries the J-lens's readout of a model's activations to the model's own word frequency (Spearman 0.48); subtracting it, the obvious fix, makes readouts 3.5–7×worse
Z-scoring took secret-word elicitation from zero at every layer to the only working method on Qwen3-1.7B (pre-registered)
Explained why one small transformer in a published memorization challenge could barely learn: its ReLU units died in training and cut off the gradient, a training failure, not a capacity limit
Confirmed it with one controlled experiment (a bias after the hidden layer restored full capacity, 1024 facts; before it, none, 488) and hand-built weights storing 672 facts; overturned the authors' explanation
Penn State Multi-Campus REU · advised by Prof. Asif ud-Doula · Python
To measure the error from NICER's equal-glow assumption, simulated X-ray photons scattering through a neutron star's atmosphere
Validated it against an exact analytic solution (reduced χ² = 0.70) and published waveforms (0.11% peak error)
Showed the error shifts between two observables; first-author paper under review at Astrophysics and Space Science
Experience
DEC 2025– APR 2026
Nittany AI Advance Internship Program
Application Specialist · Team of 5 · Client: Penn State Office of the Physical Plant · State College, PA
Built a tool that pulls records out of scanned documents (OCR, LLM, regex); processed 1,000 client documents, with human review
Raised accuracy up to 10% by merging LLM and regex results and benchmarking Azure models to pick GPT-5; 5× faster in parallel
Projects
FEB 2026– NOW
CrashAI — Crash-Risk Analysis Platform
Founder & Technical Lead · Team of 3 · 1st of 40 teams, Nittany AI Challenge 2026 · Scoped with PennDOT
Built a crash-severity model (XGBoost, 81 road and driver features) with per-site explanations and what-if simulation; led a team building a 33-tool agent, retrieval over engineering manuals, and an eval harness
Skills
Machine Learning Python, PyTorch, HuggingFace Transformers, TransformerLens, NumPy, scikit-learn, Monte Carlo simulation, Git
Mechanistic Interpretability Circuits, OV/QK analysis, activation and path patching, direct logit attribution, Jacobian and logit lenses