Katie Millican

One of the clearest multimodal researchers to track if you want to understand how frontier labs turned vision-language work from narrow benchmarks into general-purpose model capability.

One of the more useful people to study for the Gemini era because his work spans both the text-core of multimodal frontier models and the optimization tricks that make those systems cheaper and more stable to train.

A high-signal researcher for understanding the modern scaling playbook, especially around compute-optimal training, retrieval-augmented language models, and the text side of Gemini-era multimodal systems.

Important for understanding how multilingual NLP, translation, and multimodal reasoning meet inside production-scale frontier systems rather than staying separate research tracks.

A good researcher to follow for the infrastructure side of frontier language models, especially mixture-of-experts scaling, instruction tuning, and the data systems that make very large models usable.

A high-signal reinforcement-learning researcher whose work sits on the path from AlphaGo-era planning systems to Gemini-era reasoning and post-training techniques.