Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
On-Device and Edge ML
Running models on resource-constrained and privacy-sensitive devices. Covers model optimization for mobile and embedded hardware, on-device inference and privacy architecture, and dedicated neural accelerators. Focuses on the size, latency, power, and privacy tradeoffs of moving inference off the server and onto the edge.
Computer Vision
Modeling and pipelines for image and video data. Covers image classification, detection and segmentation, image preprocessing and augmentation, and end-to-end vision model pipelines. Focuses on the fundamentals of applying learning to visual inputs and the practical constraints of vision systems.