Congratulations to the Berkeley Synthetic Intelligence Analysis (BAIR) Lab class of 2026! This yr, BAIR celebrates one other exceptional group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of synthetic intelligence and machine studying.
Their work spans the breadth of recent AI — robotics and embodied intelligence, massive language fashions and reasoning, laptop imaginative and prescient, generative modeling, AI security, human-AI interplay, AI for science and healthcare, and rather more. Alongside the best way, they’ve revealed influential analysis, constructed methods with real-world influence, mentored their friends, and formed the BAIR neighborhood for the higher.
Now they’re headed in all places concepts journey: to school and postdoctoral positions, to business analysis labs, and to startups of their very own founding — and a number of other are nonetheless exploring what comes subsequent and would love to listen to from you.
Please be part of us in celebrating the achievements of those fantastic graduates. We’re happy with all the things they’ve completed at Berkeley, and we are able to’t wait to see what they do subsequent!
Thanks to our mates on the Stanford AI Lab for this concept!
E-mail: csnell22@berkeley.edu
Web site: https://sea-snell.github.io
Advisor(s): Dan Klein
Analysis Blurb: My work goals to grasp when and the way the totally different LLM scaling paradigms may be traded off and interchanged. Specifically, test-time scaling treats every immediate independently, drawing lengthy chains of inferences after which forgetting them solely between prompts. This differs critically from pretraining, which as a substitute learns a compressed illustration from a big dataset. I imagine bridging the hole between these strategies of scaling computation, presents a key open problem within the subject: how can we develop strategies which flip the inferences drawn at test-time again into discovered representations that the mannequin can maintain onto throughout interactions.
E-mail: efleisig@berkeley.edu
Web site: https://efleisig.com
Advisor(s): Dan Klein
Analysis Blurb: I design language fashions to work reliably and pretty for the broad vary of actual LLM customers. First, my analysis leverages disagreement amongst consumer preferences as sign, with the intention to prepare and consider LLMs for total populations of customers. Second, I work on designing rigorous evaluations to extricate difficult LLM harms that various customers face. Lastly, I work on core technical failures of LLMs, like miscalibrated confidence, to scale back downstream dangers when fashions are deployed to customers with totally different wants. Mixed, these interventions facilitate constructing LLMs that decrease societal harms, and maximize advantages to a wider vary of real-world customers.
What’s subsequent: Postdoctoral fellow at Princeton CITP
E-mail: graceluo@berkeley.edu
Web site: https://graceluo.web
Advisor(s): Trevor Darrell
Analysis Blurb: My analysis is on deciphering and controlling generative fashions. For instance, I’ve labored on re-purposing picture mills for laptop imaginative and prescient duties, and meta-modeling language activations for higher LLM probing and steering.
What’s subsequent: Analysis scientist in business
E-mail: hanlinzhu@berkeley.edu
Web site: https://hanlinzhu.com/
Advisor(s): Stuart Russell, Jiantao Jiao
Analysis Blurb: My analysis facilities on understanding and enhancing the reasoning capabilities of enormous language fashions (LLMs).
What’s subsequent: Member of Technical Employees at OpenAI
E-mail: hqi@berkeley.edu
Web site: https://haozhi.io/
Advisor(s): Jitendra Malik, Yi Ma
Analysis Blurb: Dexterous Manipulation and Robotic Studying
What’s subsequent: Analysis scientist at Amazon; College at College of Chicago
E-mail: zamfi@berkeley.edu
Web site: https://zamfi.web
Advisor(s): Bjoern Hartmann
Analysis Blurb: My analysis focuses on efficient human-AI co-design. I examine the boundaries of language interfaces as a medium for interacting with AI, creating methods that mix language-focused interactions with structured consumer interfaces that draw on totally different ranges of abstraction. I concentrate on language-oriented applied sciences, like LLMs and text-to-image fashions, which might be highly effective mediators of design processes. These applied sciences allow people to explain their wishes at nearly any stage of abstraction, from high-level objectives vaguely specified (“I’d like a recreation to assist my child be taught to learn”) to low-level corrections of undesired outputs (“Don’t say ‘I do know as a result of I’ve tasted it’ when a few recipe substitution’s style”).
What’s subsequent: Assistant Professor, Pc Science, UCLA
E-mail: jiachenlian@berkeley.edu
Web site: https://jlian2.github.io
Advisor(s): Gopala Anumanchipalli
Analysis Blurb: My analysis focuses on human-centered AI throughout speech, healthcare, and methods.
Searching for: Search for AI skills to affix our startup
E-mail: minwoo_kang@berkeley.edu
Web site: https://joshuaminwookang.github.io/
Advisor(s): John Canny
Analysis Blurb: I examine language modeling and associated matters in NLP; particular pursuits are human consumer simulation and constructing conversational, collaborative AI brokers.
What’s subsequent: AI Scientist at Mistral AI
E-mail: junhao_xiong@berkeley.edu
Web site: https://www.linkedin.com/in/junhao-bear-xiong
Advisor(s): Jennifer Listgarten, Yun Tune
Analysis Blurb: Junhao (Bear) Xiong is a PhD candidate at UC Berkeley, suggested by Jennifer Listgarten and Yun S. Tune. His work focuses on machine studying strategies for biology, with an emphasis on generative modeling for proteins. Beforehand, he studied Utilized Math and Pc Science at Johns Hopkins.
Searching for: Analysis scientist
E-mail: kaylo_littlejohn@berkeley.edu
Web site: https://kaylolittlejohn.com
Advisor(s): Gopala Anumanchipalli
Analysis Blurb: My analysis is targeted on speech modeling and pure language processing. I co-led the event of multimodal AI instruments to precisely translate mind exercise into textual content, audible personalised speech, and a high-fidelity “digital speaking avatar” (Nature 2023, Nature Neuroscience 2025). I’m additionally tech lead for voice modeling at Roblox.
Searching for: Analysis Scientist / Engineer
E-mail: kentkchang@berkeley.edu
Web site: https://kentkc.org
Advisor(s): David Bamman
Analysis Blurb: I work on NLP and multimodal machine studying, with a concentrate on evaluating massive language fashions and constructing multimodal methods for understanding dialogue, narrative, and social interplay. My analysis contains benchmarks for LLM memorization, multimodal datasets sourced from function movies and tv, and research of mannequin habits. I am all for bridging computational strategies with questions from the humanities and social sciences about whose voices get represented in AI methods, and about AI’s broader influence. My work has appeared at EMNLP and ACL, amongst others.
Searching for: (instructing) college, Analysis Scientist, ML/AI SWE
E-mail: kvablack@berkeley.edu
Web site: https://kevin.black
Advisor(s): Sergey Levine
Analysis Blurb: I work on large-scale robotic studying: together with imitation studying, reinforcement studying, generative modeling, real-time management, and no matter else it takes to make robots work in the true world!
What’s subsequent: Analysis Scientist of Bodily Intelligence
E-mail: kunheyang@berkeley.edu
Web site: https://www.kunheyang.com/
Advisor(s): Nika Haghtalab
Analysis Blurb: My analysis focuses on the theoretical foundations of designing and evaluating AI algorithms in environments formed by human incentives and AI company. My work spans human-centric coverage studying, incentive-aware analysis, and multi-agent collaboration and data transmission, drawing on instruments from machine studying idea and computational economics.
What’s subsequent: Postdoc Analysis at Stanford
E-mail: longlian@berkeley.edu
Web site: https://tonylian.com/
Advisor(s): Trevor Darrell, Adam Yala
Analysis Blurb: My analysis primarily focuses on growing real-time multi-modal multi-agent methods and parallel reasoning methods by end-to-end RL.
What’s subsequent: Member of Technical Employees at Considering Machines Lab
E-mail: maulikbhatt@berkeley.edu
Web site: https://maulikb.com
Advisor(s): Negar Mehr
Analysis Blurb: My analysis develops autonomous robots that may safely coordinate with people and different robots in shared environments. I construct scalable algorithms grounded in recreation idea and diffusion fashions that permit brokers cause concerning the intent and habits of others round them. My work spans real-time multi-agent trajectory planning and imitation studying within the presence of multi-modality. I’ve validated these strategies on {hardware} platforms starting from quadrotors to manipulators, with the objective of creating multi-agent coordination sturdy, interpretable, and deployable in the true world.
What’s subsequent: Becoming a member of Toyota Woven’s end-to-end autonomous driving staff.
E-mail: psenka@berkeley.edu
Web site: https://www.michaelpsenka.io/
Advisor(s): Aditi Krishnapriyan
Analysis Blurb: Work in varied domains (reinforcement studying, world fashions, AI+bio/chem), typically engaged on longer-horizon and out-of-distribution issues in planning and interpolation (e.g. robotic manipulation from begin state to objective, molecular dynamics of proteins between floor states). My thesis took a variational method (suppose calculus of variations) immediately from deep generative fashions of the atmosphere, framing path-finding as minimizing a useful induced by the discovered mannequin itself (its rating, its critic, or its dynamics). Via my analysis I’ve gained perception on the way to correctly deal with dynamics in deep studying methods, and I plan to proceed growing methods which might be dynamic and adaptive.
What’s subsequent: Lead Analysis Scientist at Baseten
E-mail: nthakkar@berkeley.edu
Web site: https://neerja.me/
Advisor(s): Jitendra Malik
Analysis Blurb: My analysis focuses on scaling predictive world fashions to deal with the complexity of in-the-wild movement. Utilizing autoregressive and diffusion frameworks, I develop higher representations for real-world prediction and suggest strategies to effectively adapt these fashions to new domains.
Searching for: Analysis scientist
E-mail: nmehandru@berkeley.edu
Web site: https://n-mehandru.github.io/
Advisor(s): Ahmed Alaa and David Bamman
Analysis Blurb: My analysis develops and applies machine studying strategies for scientific reasoning and illness development modeling utilizing unstructured textual content and time collection knowledge from digital well being data. In collaboration with physicians at UCSF, I bridge methodology improvement and scientific validation with the intention to construct dependable, interpretable AI methods in medication.
Searching for: Analysis Scientist
E-mail: nlauffer@berkeley.edu
Web site: https://niklaslauffer.github.io/
Advisor(s): Stuart Russell and Sanjit Seshia
Analysis Blurb: Niklas’s analysis is targeted on AI security and reinforcement studying, significantly within the space of multi-agent interplay and LM brokers. He is labored on enabling adversarial studying in cooperative and mixed-motive settings, fixing problems with covariate shift in coaching LM brokers on long-horizon duties, in addition to evaluating security dangers posed by LM brokers in multi-agent settings.
What’s subsequent: Analysis Scientist at Google Deepmind
E-mail: qcli@berkeley.edu
Web site: https://colinqiyangli.github.io/
Advisor(s): Sergey Levine
Analysis Blurb: Current progress in robotic manipulation coverage studying has been largely pushed by (1) the rising availability of large-scale prior datasets and (2) the success of motion chunking, the place the coverage predicts a brief sequence of future actions fairly than a single one. Nonetheless, most motion chunking insurance policies are skilled by way of supervised imitation studying, as a result of environment friendly on-line self-improvement with reinforcement studying (RL) stays difficult—limiting real-world applicability. My PhD analysis studied how we may leverage prior knowledge to optimize action-chunking insurance policies with RL, combining empirical outcomes with theoretical insights.
Searching for: Put up-doc/analysis scientist for RL in robotics and LLMs!
E-mail: sampada_deglurkar@berkeley.edu
Web site: https://sdeglurkar.github.io/
Advisor(s): Prof Claire Tomlin
Analysis Blurb: My analysis is in offering security assurances for AI-enabled autonomous methods, starting from robots to autonomous autos to aviation methods. For this, I’ve labored with uncertainty quantification for machine studying fashions, decision-making beneath uncertainty algorithms, and instruments for producing probabilistic ensures on system operation.
Searching for: Analysis scientist, Analysis engineer
E-mail: vbenara@berkeley.edu
Web site: https://cs.berkeley.edu/~vbenara
Advisor(s): Ion Stoica
Analysis Blurb: My analysis focuses on LLM post-training, together with knowledge curation, RLHF, RLVR with VLMs, evaluations, reasoning, agentic workflows, and interpretability. I even have robust experience in methods infrastructure for distributed computing.
Searching for: Analysis scientist / Analysis Engineer
E-mail: vongani_maluleke@berkeley.edu
Web site: https://individuals.eecs.berkeley.edu/~vongani_maluleke/
Advisor(s): Jitendra Malik and Angjoo Kanazawa
Analysis Blurb: Vongani Maluleke is a PhD candidate at UC Berkeley (BAIR, suggested by Jitendra Malik and Angjoo Kanazawa), the place she led the event of MAGNet, a unified multi-agent movement technology framework that helps a variety of movement technology duties with out retraining or architectural adjustments, outperforming task-specialized state-of-the-art baselines. She is at present extending this work by deploying it on a Unitree G1 humanoid to make it embody social intelligence. Earlier than her PhD, she was a Senior AI Marketing consultant at Deloitte, awarded Distinctive Performer two consecutive years, main AI system improvement throughout media, telecommunications, retail, and monetary companies.
Searching for: Analysis scientist
E-mail: weijer_chang@berkeley.edu
Web site: https://weijer-chang.github.io/
Advisor(s): Masayoshi Tomizuka
Analysis Blurb: My analysis focuses on growing protected and clever autonomous methods for complicated, human-centered environments. I work on the intersection of machine studying, generative fashions, and reinforcement studying, with purposes in autonomy. My work addresses challenges in multi-agent interplay, interactive human habits, and long-tail safety-critical eventualities at scale.
Searching for: Analysis Scientist, Utilized Scientist, Roboticist
E-mail: xiuyu@berkeley.edu
Web site: https://xiuyuli.com/
Advisor(s): Kurt Keutzer
Analysis Blurb: My analysis focuses on growing scalable and self-improving massive language mannequin brokers, with emphasis on coding brokers for complicated, long-horizon duties. This route builds on my work in parallel reasoning, and on broader experience in making generative fashions extra environment friendly in coaching and inference throughout language and imaginative and prescient.
What’s subsequent: Member of Technical Employees at xAI
E-mail: yichenxie0928@gmail.com
Web site: https://yichen928.github.io/
Advisor(s): Masayoshi Tomizuka
Analysis Blurb: My analysis focuses on constructing multimodal basis fashions and world fashions that perceive and work together with complicated bodily environments. I intention to develop unified representations throughout modalities, enabling AI methods to cause over house, time, and dynamics towards general-purpose embodied intelligence.
What’s subsequent: Analysis Scientist at Luma AI
E-mail: erginbas@berkeley.edu
Web site: https://www.linkedin.com/in/erginbas/
Advisor(s): Kannan Ramchandran, Thomas A. Courtade
Analysis Blurb: My PhD analysis spans two threads: on-line studying in large-scale markets, and interpretability of enormous machine studying fashions. Within the first, I work on sequential decision-making with purposes to advice, pricing, and assortment choice. My focus is on designing algorithms with provable ensures for welfare maximization, income maximization, and stability. Within the second, I develop scalable attribution strategies that exploit the sparse, low-degree construction of real-world interactions, utilizing instruments from sign processing and data idea. Extra lately, I’ve been exploring principled methods to guage the faithfulness of mannequin self-explanations.
What’s subsequent: Researcher at Hudson River Buying and selling’s AI Labs (HAIL)
E-mail: yhli@berkeley.edu
Web site: https://Yihengli.com
Advisor(s): Masayoshi Tomizuka
Analysis Blurb: I’m engaged on imaginative and prescient world modeling, with prior expertise in diffusion mannequin’s effectivity in addition to in autonomous driving.
What’s subsequent: Analysis Scientist at Waymo
E-mail: zhefu@berkeley.edu
Web site: https://fu-zhe.com/
Advisor(s): Alexandre Bayen
Analysis Blurb: My analysis focuses on physics-informed studying and management for mixed-autonomy methods, with purposes in transportation. I design physics-informed neural networks to be taught options of nonlinear partial differential equations, enabling correct and data-efficient prediction of site visitors dynamics. Constructing on these fashions, I develop each model-based and learning-based management methods that coordinate automated autos to enhance system-level efficiency. My work bridges machine studying, management, and real-world deployment, and has been validated in large-scale subject experiments. Extra broadly, I intention to advance reliable, interpretable AI for decision-making in complicated, real-world methods.
What’s subsequent: I can be an Power Fellow at Stanford after commencement. Additionally searching for College, or analysis scientist positions in AI, management, and autonomy.
