Matthew Vaishnav's Portfolio

Matthew Vaishnav

Applied ML · Computational Pathology · Federated Oncology Learning

Whole-slide AI / PANDA Phikon features / TransnnMIL / PathologyFL / Dominance-aware federated robustness

Work

Matthew Vaishnav is an applied machine-learning and computational pathology research engineer based in Kitchener-Waterloo. His flagship work studies whole-slide pathology AI, federated oncology learning, failure-mode analysis, and reproducible medical-AI validation. Recent work identifies a FedAvg dominant-site failure mode on full PANDA Phikon slide features and evaluates cross-site blending plus dominance-aware detector switches under label noise and ordinal grading bias.

Flagship Research Latest Result

Flagship Research Platform

My main research project combines whole-slide pathology AI, federated learning, benchmark automation, and clinical-data integration components. The work includes a full PCam benchmark, PANDA prostate-grading experiments with Phikon features, TransnnMIL architecture work, PathologyFL simulated multi-site learning, and dominance-aware federated robustness analysis.

PCam benchmark: 85.26% test accuracy and 0.9394 AUC on the full 32,768-sample PCam test set.
PANDA scale: 10,611 readable PANDA-derived Phikon slide feature vectors, 768 dimensions.
Federated robustness: 15-seed full-PANDA studies of FedAvg under dominant-site label corruption and systematic ordinal threshold bias.
Key finding: cross-site blending improves robustness when the dominant simulated site becomes unreliable, especially under conservative grading shift.
Detector switch: clean-calibrated FedAvg diagnostics can trigger a switch away from sample-size dominance in unsafe regimes.
Models: TransnnMIL, AttentionMIL, CLAM-style and TransMIL-style workflows.

Research Metrics

PANDA Phikon Cache: 10,611; Readable slide feature vectors

Feature Dimension: 768; Mean-pooled Phikon embeddings

Validation Seeds: 15; Full-PANDA FL stress studies

PCam AUC: 0.9394; Full 32,768-sample test set

PCam Accuracy: 85.26%; Bootstrap confidence intervals

Research Focus: FL Robustness; Dominant-site failure detection

Bio

2006Born in Ontario, Canada.

2025 to presentStudying Computer Systems Technician at Conestoga College.

2025Built 18-node home lab with Security Onion and pfSense for security research.

2025 to presentBuilding computational pathology tooling, PCam benchmarks, TransnnMIL, PathologyFL, and federated robustness experiments.

2026Validated dominance-aware federated learning failure modes on 10,611 PANDA-derived Phikon slide features across 15-seed stress studies.

2026 to presentBuilding reproducible documentation, benchmark analysis, and validation reports for computational oncology research.

I ♥

Computational pathology, whole-slide image AI, federated learning, mathematical validation, security engineering, failure analysis, home labs, clean interfaces, coffee, music, art, and my dog.

On the web

Inspired by Takuya Matsuyama's homepage