Systems & Infrastructure

Important both as a researcher and as an institution builder whose long-running agenda tied deep RL, multimodal systems, and scientific AI into one coherent lab strategy.

A foundational figure in modern sequence modeling whose work on the Transformer changed the technical direction of language and multimodal systems.

A key open-model ecosystem builder whose work matters because it combines research, public infrastructure, and field-level coordination rather than isolated paper output alone.

Foundational less for any single public paper than for shaping the infrastructure, engineering culture, and systems thinking that make frontier-model research possible.

A foundational computer-vision researcher whose work on representations and architectures still shapes modern pretraining and perception systems.

One of the most important architecture-level thinkers in modern AI, with influence spanning Transformers, efficient scaling, and mixture-of-experts systems.

High-signal for the seam between machine learning and hardware systems, especially where learned optimization methods begin affecting the actual compute infrastructure underneath frontier models.

A strong person to follow for the point where machine learning research starts shaping the compute stack itself, especially in chip placement and systems-aware optimization.

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.