Electronic mail: csnell22@berkeley.edu
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Advisor(s): Dan Klein

Analysis Blurb: My work goals to grasp when and the way the completely different LLM scaling paradigms will be traded off and interchanged. Specifically, test-time scaling treats every immediate independently, drawing lengthy chains of inferences after which forgetting them completely between prompts. This differs critically from pretraining, which as a substitute learns a compressed illustration from a big dataset. I consider bridging the hole between these strategies of scaling computation, presents a key open problem within the area: how can we develop strategies which flip the inferences drawn at test-time again into realized representations that the mannequin can maintain onto throughout interactions.

Electronic mail: efleisig@berkeley.edu
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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 a purpose to practice 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 cut back downstream dangers when fashions are deployed to customers with completely different wants. Mixed, these interventions facilitate constructing LLMs that reduce societal harms, and maximize advantages to a wider vary of real-world customers.

What’s subsequent: Postdoctoral fellow at Princeton CITP

Electronic mail: graceluo@berkeley.edu
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Advisor(s): Trevor Darrell

Analysis Blurb: My analysis is on decoding and controlling generative fashions. For instance, I’ve labored on re-purposing picture mills for pc imaginative and prescient duties, and meta-modeling language activations for higher LLM probing and steering.

What’s subsequent: Analysis scientist in trade

Electronic mail: hanlinzhu@berkeley.edu
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Advisor(s): Stuart Russell, Jiantao Jiao

Analysis Blurb: My analysis facilities on understanding and bettering the reasoning capabilities of enormous language fashions (LLMs).

What’s subsequent: Member of Technical Workers at OpenAI

Electronic mail: hqi@berkeley.edu
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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

Electronic mail: zamfi@berkeley.edu
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Advisor(s): Bjoern Hartmann

Analysis Blurb: My analysis focuses on efficient human-AI co-design. I research the boundaries of language interfaces as a medium for interacting with AI, creating programs that mix language-focused interactions with structured consumer interfaces that draw on completely different ranges of abstraction. I concentrate on language-oriented applied sciences, like LLMs and text-to-image fashions, which are highly effective mediators of design processes. These applied sciences allow people to explain their wishes at nearly any degree of abstraction, from high-level targets vaguely specified (“I’d like a recreation to assist my child study 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

Electronic mail: jiachenlian@berkeley.edu
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Advisor(s): Gopala Anumanchipalli

Analysis Blurb: My analysis focuses on human-centered AI throughout speech, healthcare, and programs.

On the lookout for: Search for AI skills to affix our startup

Electronic mail: minwoo_kang@berkeley.edu
Web site:

Advisor(s): John Canny

Analysis Blurb: I research 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

Electronic mail: junhao_xiong@berkeley.edu
Web site:

Advisor(s): Jennifer Listgarten, Yun Music

Analysis Blurb: Junhao (Bear) Xiong is a PhD candidate at UC Berkeley, suggested by Jennifer Listgarten and Yun S. Music. 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.

On the lookout for: Analysis scientist

Electronic mail: kaylo_littlejohn@berkeley.edu
Web site:

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 customized speech, and a high-fidelity “digital speaking avatar” (Nature 2023, Nature Neuroscience 2025). I’m additionally tech lead for voice modeling at Roblox.

On the lookout for: Analysis Scientist / Engineer

Electronic mail: kentkchang@berkeley.edu
Web site:

Advisor(s): David Bamman

Analysis Blurb: I work on NLP and multimodal machine studying, with a concentrate on evaluating giant language fashions and constructing multimodal programs for understanding dialogue, narrative, and social interplay. My analysis consists of benchmarks for LLM memorization, multimodal datasets sourced from characteristic movies and tv, and research of mannequin conduct. I am keen on bridging computational strategies with questions from the humanities and social sciences about whose voices get represented in AI programs, and about AI’s broader impression. My work has appeared at EMNLP and ACL, amongst others.

On the lookout for: (educating) school, Analysis Scientist, ML/AI SWE

Electronic mail: kvablack@berkeley.edu
Web site:

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 actual world!

What’s subsequent: Analysis Scientist of Bodily Intelligence

Electronic mail: kunheyang@berkeley.edu
Web site:

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

Electronic mail: longlian@berkeley.edu
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Advisor(s): Trevor Darrell, Adam Yala

Analysis Blurb: My analysis primarily focuses on creating real-time multi-modal multi-agent programs and parallel reasoning programs via end-to-end RL.

What’s subsequent: Member of Technical Workers at Pondering Machines Lab

Electronic mail: maulikbhatt@berkeley.edu
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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 allow brokers purpose concerning the intent and conduct 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 aim of creating multi-agent coordination strong, interpretable, and deployable in the actual world.

What’s subsequent: Becoming a member of Toyota Woven’s end-to-end autonomous driving staff.

Electronic mail: psenka@berkeley.edu
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Advisor(s): Aditi Krishnapriyan

Analysis Blurb: Work in numerous 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 aim, molecular dynamics of proteins between floor states). My thesis took a variational method (suppose calculus of variations) immediately from deep generative fashions of the surroundings, framing path-finding as minimizing a useful induced by the realized mannequin itself (its rating, its critic, or its dynamics). By my analysis I’ve gained perception on correctly deal with dynamics in deep studying programs, and I plan to proceed creating programs which are dynamic and adaptive.

What’s subsequent: Lead Analysis Scientist at Baseten

Electronic mail: nthakkar@berkeley.edu
Web site:

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.

On the lookout for: Analysis scientist

Electronic mail: nmehandru@berkeley.edu
Web site:

Advisor(s): Ahmed Alaa and David Bamman

Analysis Blurb: My analysis develops and applies machine studying strategies for medical reasoning and illness development modeling utilizing unstructured textual content and time sequence information from digital well being data. In collaboration with physicians at UCSF, I bridge technique growth and medical validation with the intention to construct dependable, interpretable AI programs in drugs.

On the lookout for: Analysis Scientist

Electronic mail: nlauffer@berkeley.edu
Web site:

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

Electronic mail: qcli@berkeley.edu
Web site:

Advisor(s): Sergey Levine

Analysis Blurb: Latest progress in robotic manipulation coverage studying has been largely pushed by (1) the growing availability of large-scale prior datasets and (2) the success of motion chunking, the place the coverage predicts a brief sequence of future actions relatively than a single one. Nonetheless, most motion chunking insurance policies are educated 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 might leverage prior information to optimize action-chunking insurance policies with RL, combining empirical outcomes with theoretical insights.

On the lookout for: Submit-doc/analysis scientist for RL in robotics and LLMs!

Electronic mail: sampada_deglurkar@berkeley.edu
Web site:

Advisor(s): Prof Claire Tomlin

Analysis Blurb: My analysis is in offering security assurances for AI-enabled autonomous programs, starting from robots to autonomous autos to aviation programs. For this, I’ve labored with uncertainty quantification for machine studying fashions, decision-making below uncertainty algorithms, and instruments for producing probabilistic ensures on system operation.

On the lookout for: Analysis scientist, Analysis engineer

Electronic mail: vbenara@berkeley.edu
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Advisor(s): Ion Stoica

Analysis Blurb: My analysis focuses on LLM post-training, together with information curation, RLHF, RLVR with VLMs, evaluations, reasoning, agentic workflows, and interpretability. I even have robust experience in programs infrastructure for distributed computing.

On the lookout for: Analysis scientist / Analysis Engineer

Electronic mail: vongani_maluleke@berkeley.edu
Web site:

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 the moment 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 Guide at Deloitte, awarded Distinctive Performer two consecutive years, main AI system growth throughout media, telecommunications, retail, and monetary providers.

On the lookout for: Analysis scientist

Electronic mail: weijer_chang@berkeley.edu
Web site:

Advisor(s): Masayoshi Tomizuka

Analysis Blurb: My analysis focuses on creating secure and clever autonomous programs 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 conduct, and long-tail safety-critical situations at scale.

On the lookout for: Analysis Scientist, Utilized Scientist, Roboticist

Electronic mail: xiuyu@berkeley.edu
Web site:

Advisor(s): Kurt Keutzer

Analysis Blurb: My analysis focuses on creating scalable and self-improving giant language mannequin brokers, with emphasis on coding brokers for complicated, long-horizon duties. This course 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 Workers at xAI

Electronic mail: yichenxie0928@gmail.com
Web site:

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 goal to develop unified representations throughout modalities, enabling AI programs to purpose over area, time, and dynamics towards general-purpose embodied intelligence.

What’s subsequent: Analysis Scientist at Luma AI

Electronic mail: erginbas@berkeley.edu
Web site:

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 suggestion, 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)

Electronic mail: yhli@berkeley.edu
Web site:

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

Electronic mail: zhefu@berkeley.edu
Web site:

Advisor(s): Alexandre Bayen

Analysis Blurb: My analysis focuses on physics-informed studying and management for mixed-autonomy programs, with purposes in transportation. I design physics-informed neural networks to study options of nonlinear partial differential equations, enabling correct and data-efficient prediction of 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 area experiments. Extra broadly, I goal to advance reliable, interpretable AI for decision-making in complicated, real-world programs.

What’s subsequent: I might be an Vitality Fellow at Stanford after commencement. Additionally searching for College, or analysis scientist positions in AI, management, and autonomy.