About
I'm Jamie Fong, an undergraduate at Vanderbilt University pursuing a B.S. in Computer Science and Mathematics. My work sits at the intersection of machine learning and formal methods — I'm interested in what we can actually prove about the systems we deploy, not just what we can measure on a test set.
Most recently I was a summer research intern in Vanderbilt's VeriVITAL lab under Dr. Taylor Johnson, where I built the first formal robustness verification benchmark for surgical skill-assessment neural networks. Before that I wrote a paper extending the BehaVerify behavior-tree verification tool to natively support multi-agent modeling.
Alongside research, I work as a software engineer and a teaching assistant for Vanderbilt's data structures course. I like building things that are both rigorous and genuinely useful.
Education
Vanderbilt University
B.S. in Computer Science and Mathematics
Relevant Coursework
Selected Work
All projects →Formal Verification Benchmark for Surgical Robots
The first formal robustness verification benchmark for surgical skill-assessment neural networks, applying Star-set reachability and bound propagation to a grouped 1D FCN trained on da Vinci robotic kinematics (JIGSAWS dataset).
Python · n2v · α,β-CROWN · ONNX · VNN-LIB
Composing Multi-Agent Behavior Trees
A DSL extension for BehaVerify that lets users model and verify multi-agent behavior-tree scenarios without hand-coding each agent.
Python · BehaVerify · nuXmv · DSL design
Both featured projects came out of my research with Dr. Taylor Johnson at Vanderbilt. Full write-ups and papers are on the Research page.