I am a machine learning engineer in San Jose, CA, building LLM systems and AI agents, computer vision pipelines and production ML infrastructure. As an AI Trainer Fellow at Handshake AI I build evaluation environments for frontier models. I am co-first author of Interpreting Latent CoT Reasoning as Dynamical Systems (arXiv 2607.09698, poster at the ICML 2026 FoGen Workshop). I hold an M.S. in Artificial Intelligence from WPI (May 2026, GPA 3.97). My work spans mechanistic interpretability of latent chain of thought, TensorRT-optimized 3D perception, and multi-agent LLM pipelines that ship.

Before WPI, I spent two years as a Lead DevOps Engineer at Persistent Systems (Top Talent 2024, Bravo Individual Award), owning CI/CD and AWS/Azure infrastructure across 20+ production projects with a 95% deploy-speed improvement. That production instinct is the thing I bring to research: pipelines that do not break, checkpoints that do not get lost, results that are reproducible 28 out of 28 times.

Reproduced CODI self-distillation and the COCONUT 4-stage curriculum as LoRA adapters on GPT-2, then ran a full DMD + PHATE + UMAP dynamical-systems analysis on 768-dim latent trajectories. Found opposite stability signatures. CODI behaves as a stable attractor and COCONUT as an unstable expanding system (DMD unstable-mode fraction 41% vs 85%), preserved across GSM8K and MATH.

Spatial probing of physical reasoning in Qwen2.5-VL, InternVL 2.5, and LLaVA-OneVision. Physics saliency maps across encoder, projection, and LLM layers, plus a 5-condition factorial LoRA ablation to find the leverage point for physical intuition.

Adapted FairEM (VLDB 2023) to annotator-level fairness. Discovered Non-Native annotators biased on hard questions (Fisher p=0.031, 6 of 10 FairEM metrics unfair). Robust across 5 cleaning strategies, 10K-shuffle permutation tests, BH-FDR-corrected.

3D perception pipeline for autonomous vehicles with TensorRT FP16 + layer fusion optimization, profiled via Nsight Systems and Nsight Compute. Composes MiDaS, YOLO3D, OSX, Mask R-CNN, and Detic into a Blender-rendered 3D scene.

CycleGAN + diffusion to synthesize paired healthy and diseased MRI and segment unsupervised. 65,072 BraTS2020 images processed with greater than 90% segmentation precision, without labels.

Confidence-aware LLM framework with dual-channel human-in-the-loop validation for ESI scoring: +25% triage accuracy, 40% fewer false escalations, 50% less miscommunication, 2.3x better patient-provider alignment.

Procedural game-asset generator combining Stable Diffusion + LoRA, CVAE-GAN, and Wave Function Collapse for spatially consistent biome maps. Unity prefab integration shipped.

Implements Mildenhall et al. NeRF (ECCV 2020) end to end. Trains on posed images, renders 360 degree orbits of Lego, Hotdog, and Ship scenes. Hierarchical sampling with positional encoding.

Auto-commits and pushes your changes with AI-generated commit messages via GitHub Copilot. Smart file filtering, debounced commits, status-bar control. Published on the VS Code Marketplace.

Multi-agent system using LangGraph + Amazon Bedrock RAG, AWS Step Functions, Lambda, S3, Textract. Production deployed: attorney review reduced from multi-day to ten minutes.