2026 BAIR Graduate Showcase

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.

Their work spans the breadth of modern AI – robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more. Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for the better.

Now they are headed everywhere ideas travel: to faculty and postdoctoral positions, to industry research labs, and to startups of their own founding – and several are still exploring what comes next and would love to hear from you.

Please join us in celebrating the achievements of these wonderful graduates. We are proud of everything they have accomplished at Berkeley, and we can’t wait to see what they do next!

Thank you to our friends at the Stanford AI Lab for this idea!

Baifeng Shi

**Email:**[email protected]

**Website:**https://bfshi.github.io/

**Advisor(s):**Trevor Darrell

**Research Blurb:**I work on building generalist vision and robotic models.

**What's next:**Member of Technical Staff at Physical Intelligence

Charlie Snell

**Email:**[email protected]

**Website:**https://sea-snell.github.io

**Advisor(s):**Dan Klein

**Research Blurb:**My work aims to understand when and how the different LLM scaling paradigms can be traded off and interchanged. In particular, test-time scaling treats each prompt independently, drawing long chains of inferences and then forgetting them entirely between prompts. This differs critically from pretraining, which instead learns a compressed representation from a large dataset. I believe bridging the gap between these methods of scaling computation, presents a key open challenge in the field: how can we develop methods which turn the inferences drawn at test-time back into learned representations that the model can hold onto across interactions.

Devin Guillory

**Email:**[email protected]

**Website:**https://devinguillory.com

**Advisor(s):**Trevor Darrell

**Research Blurb:**Accounting for data shifts in computer vision models

**What's next:**Building collaborative AI systems, looking for conspirators.

Eve Fleisig

**Email:**[email protected]

**Website:**https://efleisig.com

**Advisor(s):**Dan Klein

**Research Blurb:**I design language models to work reliably and fairly for the broad range of real LLM users. First, my research leverages disagreement among user preferences as signal, in order to train and evaluate LLMs for entire populations of users. Second, I work on designing rigorous evaluations to extricate challenging LLM harms that diverse users face. Finally, I work on core technical failures of LLMs, like miscalibrated confidence, to reduce downstream risks when models are deployed to users with different needs. Combined, these interventions facilitate building LLMs that minimize societal harms, and maximize benefits to a wider range of real-world users.

**What's next:**Postdoctoral fellow at Princeton CITP

Grace Luo

**Email:**[email protected]

**Website:**https://graceluo.net

**Advisor(s):**Trevor Darrell

**Research Blurb:**My research is on interpreting and controlling generative models. For example, I've worked on re-purposing image generators for computer vision tasks, and meta-modeling language activations for better LLM probing and steering.

**What's next:**Research scientist in industry

Hanlin Zhu

**Email:**[email protected]

**Website:**https://hanlinzhu.com/

**Advisor(s):**Stuart Russell, Jiantao Jiao

**Research Blurb:**My research centers on understanding and improving the reasoning capabilities of large language models (LLMs).

**What's next:**Member of Technical Staff at OpenAI

Haozhi Qi

**Email:**[email protected]

**Website:**https://haozhi.io/

**Advisor(s):**Jitendra Malik, Yi Ma

**Research Blurb:**Dexterous Manipulation and Robot Learning

**What's next:**Research scientist at Amazon; Faculty at University of Chicago

J.D. Zamfirescu-Pereira

**Email:**[email protected]

**Website:**https://zamfi.net

**Advisor(s):**Bjoern Hartmann

**Research Blurb:**My research focuses on effective human-AI co-design. I study the boundaries of language interfaces as a medium for interacting with AI, creating systems that blend language-focused interactions with structured user interfaces that draw on different levels of abstraction. I focus on language-oriented technologies, like LLMs and text-to-image models, that are powerful mediators of design processes. These technologies enable humans to describe their desires at almost any level of abstraction, from high-level goals vaguely specified (“I’d like a game to help my kid learn to read”) to low-level corrections of undesired outputs (“Don’t say ‘I know because I’ve tasted it’ when about a recipe substitution's taste”).

**What's next:**Assistant Professor, Computer Science, UCLA

Jiachen Lian

**Email:**[email protected]

**Website:**https://jlian2.github.io

**Advisor(s):**Gopala Anumanchipalli

**Research Blurb:**My research focuses on human-centered AI across speech, healthcare, and systems.

**Looking for:**Look for AI talents to join our startup

Josh Kang

**Email:**[email protected]

**Website:**https://joshuaminwookang.github.io/

**Advisor(s):**John Canny

**Research Blurb:**I study language modeling and related topics in NLP; specific interests are human user simulation and building conversational, collaborative AI agents.

**What's next:**AI Scientist at Mistral AI

Junhao (Bear) Xiong

**Email:**[email protected]

**Website:**https://www.linkedin.com/in/junhao-bear-xiong

**Advisor(s):**Jennifer Listgarten, Yun Song

**Research Blurb:**Junhao (Bear) Xiong is a PhD candidate at UC Berkeley, advised by Jennifer Listgarten and Yun S. Song. His work focuses on machine learning methods for biology, with an emphasis on generative modeling for proteins. Previously, he studied Applied Math and Computer Science at Johns Hopkins.

**Looking for:**Research scientist

Kaylo Littlejohn

**Email:**[email protected]

**Website:**https://kaylolittlejohn.com

**Advisor(s):**Gopala Anumanchipalli

**Research Blurb:**My research is focused on speech modeling and natural language processing. I co-led the development of multimodal AI tools to accurately translate brain activity into text, audible personalized speech, and a high-fidelity "digital talking avatar" (Nature 2023, Nature Neuroscience 2025). I am also tech lead for voice modeling at Roblox.

**Looking for:**Research Scientist / Engineer

Kent Chang

**Email:**[email protected]

**Website:**https://kentkc.org

**Advisor(s):**David Bamman

**Research Blurb:**I work on NLP and multimodal machine learning, with a focus on evaluating large language models and building multimodal systems for understanding dialogue, narrative, and social interaction. My research includes benchmarks for LLM memorization, multimodal datasets sourced from feature films and television, and studies of model behavior. I'm interested in bridging computational methods with questions from the humanities and social sciences about whose voices get represented in AI systems, and about AI's broader impact. My work has appeared at EMNLP and ACL, among others.

Looking for:(teaching) faculty, Research Scientist, ML/AI SWE

Kevin Black

**Email:**[email protected]

**Website:**https://kevin.black

**Advisor(s):**Sergey Levine

**Research Blurb:**I work on large-scale robot learning: including imitation learning, reinforcement learning, generative modeling, real-time control, and whatever else it takes to make robots work