The Berkeley Artificial Intelligence Research Lab has announced its 2026 PhD graduating class, a group whose work spans the breadth of modern AI and whose next destinations reflect where the field's talent is flowing.
The graduates' research covers robotics and embodied intelligence, large language model reasoning, computer vision, generative modeling, AI safety, human-AI interaction, and AI applications in science and healthcare. Several have published influential work, built systems with real-world impact, and mentored peers within the BAIR community.
Their career paths illustrate the current distribution of AI talent. Multiple graduates are joining Physical Intelligence, the robotics startup, as members of technical staff. Others are heading to OpenAI, Google DeepMind, xAI, Mistral AI, and Thinking Machines Lab. Several are taking faculty positions, including at UCLA and the University of Chicago. A notable contingent is entering finance and quantitative research, with one joining Hudson River Trading's AI Labs and another becoming Chief Scientist at Yumi Health.
Some thesis topics are indicative of where the field is headed. One graduate worked on bridging test-time scaling and pretraining, treating the gap between drawing long inference chains and learning compressed representations as a core open challenge. Another focused on building language models that work reliably for diverse populations of users, leveraging preference disagreement as signal. A third developed scalable attribution methods for interpreting large machine learning models using tools from signal processing and information theory.
In robotics, graduates are working on dexterous manipulation, real-time multi-agent coordination through end-to-end reinforcement learning, and scaling predictive world models for in-the-wild motion. In AI safety, research spans multi-agent interaction, adversarial learning in cooperative settings, and probabilistic guarantees for autonomous systems ranging from robots to aviation.
The healthcare and biology contingent includes work on machine learning for proteins, clinical reasoning from electronic health records, and translating brain activity into speech and digital avatars. One graduate co-led the development of multimodal AI tools that convert neural signals into audible personalized speech, published in Nature and Nature Neuroscience.
BAIR itself remains one of the most prolific AI research labs globally, with graduates regularly shaping both academic and industry directions. The 2026 class continues that pattern, dispersing into roles that will influence everything from frontier model development to robotics deployment to financial systems.