ResearchMTEB-Gym: label-free embedding evaluationWhen can label-free embedding evaluation produce a trustworthy model ranking?Jun 2026 - PresentCo-developed MTEB-Gym, a label-free framework for ranking embedding models on datasets without human relevance annotations.Designed the judge-reliability study across 16 retrieval datasets and identified when LLM-based evaluation could and could not be trusted.Diagnosed task-definition mismatch as the framework's main failure mode and improved ranking quality by adding dataset-specific evaluation criteria.Contributed production-grade tooling to the MTEB ecosystem and built a public leaderboard that reports reliability alongside every model ranking.Current result: Nine merged upstream changes cover validation, uncertainty estimates, cache behavior, deterministic parallelism, and failure handling.Boundary: The active reliability study still needs a public result artifact.RSNA Knee Abnormality DetectionWhich validation choices materially change a knee-MRI model's reported AUC?Jul 2026 - Aug 2026Built a complete ML platform for the Radiological Society of North America's Knee Abnormality Detection competition, predicting 12 study-level findings from multi-plane MRI across 4,407 studies and 24,371 imaging series. I also completed an exact census of 819,078 DICOM files.Advanced the project's authenticated Kaggle public macro ROC-AUC from 0.618 to 0.912 across seven tracked submissions. A controlled ablation improved AUC from 0.618 to 0.764 by changing only report-derived supervision, identifying label quality as a primary modeling bottleneck.Implemented deterministic 2.5D ResNet-18 MIL and five-fold, three-plane EfficientNet-B3 training systems, then integrated DINO- and RadImageNet-based inference branches using rank fusion, label-specific fallback policies, and cross-label stacking.Engineered reproducible offline GPU execution with geometrically ordered MRI slices, hash-bound caches, mixed-precision training, RNG-complete checkpoint and resume, fresh-checkpoint inference replay, and atomic artifact publication. A private Kaggle T4 run featurized all 4,407 studies with zero failures.Current result: The authenticated Kaggle public macro ROC-AUC improved from 0.618 to 0.912 across seven tracked submissions.Boundary: The completed study used competition data and public leaderboard evaluation.AgentBreedHow much agent performance comes from the configuration space, and how much from the search operator?Mar 2026 - Jul 2026Designed a pre-registered study testing whether multi-agent performance depends more on the available configuration space or the search algorithm.Ran 700 reproducible evaluations across three domains using a deterministic simulator to separate true effects from LLM sampling noise.Used equivalence testing and sensitivity analysis to identify which agent-design choices meaningfully affected performance.Built a heavily tested research codebase and kept the study's conclusions explicitly limited to the deterministic simulator.Current result: In the 700-run deterministic synthetic pilot, pairwise differences among the tested search operators did not reach Holm-corrected significance.Boundary: The reported conclusions apply to the deterministic simulator used in the completed study.Procedural-Maze RL BaselinesWhy did modern reward-driven RL miss a policy a five-line heuristic could see?Feb 2026 - Apr 2026Benchmarked PPO, DQN, and A2C on procedurally generated mazes against simple heuristics and behavior cloning.Found that the learned agents had sufficient capacity but failed because of exploration and credit-assignment limitations.Built a reproducible experiment pipeline with roughly 3,500 traceable run artifacts.Corrected the original study's validation flaw by rebuilding the evaluation around a proper held-out protocol.Current result: The learned agents had sufficient capacity but failed because of exploration and credit-assignment limitations.Boundary: The final evaluation uses the corrected held-out protocol.Connectome Architecture BenchmarkDoes biological wiring still help after density, weights, graph realizations, and trainable components are controlled?Dec 2025 - Aug 2026Built a controlled benchmark testing whether biological connectomes provide better neural-network architectures than matched random graphs.Evaluated ten connectomes across six tasks while keeping the biological wiring fixed and training only the output layer.Found that biological structure sometimes helped, but degree distribution explained much of the advantage while exact biological wiring explained less.Tested the result against matched random graphs and controls for graph density, edge weights, and trainable components.Current result: Biological structure sometimes helped, but degree distribution explained much of the advantage while exact biological wiring explained less.Boundary: The completed analysis controlled graph density, edge weights, graph realizations, and trainable components.Regional Cabled Array expedition RR2607Can methane-bubble flux be estimated while the experiment is 2,900 meters underwater?Aug 2026 - PresentBuilding a field-to-analysis system for methane-bubble flux estimation from ROV video, sonar, hydrophone, CTD, and navigation data.The provenance layer keeps shipboard sources read-only and traces every derived artifact to source paths, hashes, and code versions.Current work covers computer vision, acoustic alignment, calibration, and held-out validation.Work in progress.Current result: Building a provenance-aware pipeline that aligns computer vision, sonar, and field metadata.Boundary: Work in progress; no final scientific result is claimed.Technical notesVLM Inference OptimizationAdvised by Professor Zhuang Liu at Princeton University.An AlphaEvolve-style search loop produced a best single-prompt Qwen3-VL configuration at 63.25% held-out accuracy on CharXiv chart QA; its difference from the 60.5% manual prompt was not significant (p = .42).MAP-Elites searched prompt, parsing, formatting, and inference-code variants across a behavioral archive.A separately designed per-question router reached 73.0%, compared with the 29.5% naive baseline.The router-to-naive paired comparison gave p = 2.4e-6, and every accepted mutation retained inspectable lineage.Single-GPU Model ServingAdvised by Professor Juncheng Yang at Harvard University.An 80-point vLLM matrix compared dense and mixture-of-experts models at roughly equal active-parameter budgets.The benchmark swept prompt length, generation length, concurrency, and serving configuration across Qwen3.5-9B and GLM-4.7-Flash.The dense/MoE throughput crossover appeared near concurrency three. At 49K-token generation, Qwen admitted 42 concurrent requests versus GLM's 10.A hardware audit caught a PCIe-throttled H100 before publication; GPQA accuracy moved by 20 points with token budget.Published recordThree peer-reviewed papers, 57 citations, and an h-index of 3 as of 2026-09.Design of systems for plasma activated water (PAW) for agri-food applications, Journal of Physics D: Applied Physics, 2024Design of a continuous plasma activated water (PAW) disinfection system for fresh produce industry, Innovative Food Science & Emerging Technologies, 2024Design and construction of a continuous industrial scale cold plasma equipment for fresh produce industry, Innovative Food Science & Emerging Technologies, 2024