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.
Generative AI and Large Language Models
The capabilities and behavior of modern generative and large language models. Covers how LLMs are pretrained, in-context learning and few-shot prompting, generative model families (autoregressive, diffusion), context windows, and tokenization and sampling. Emphasizes understanding what generative models can and cannot do and how they differ from discriminative ML.
Prompt Engineering
Designing prompts and instructions that steer large language models toward reliable outputs. Covers zero- and few-shot prompting, chain-of-thought and structured reasoning prompts, system/role instructions, output formatting and constraints, and iterative prompt refinement. Focuses on getting consistent, controllable behavior from hosted and open models without changing their weights.