\n
\n<\/p>\n

<\/p>\n

Congratulations to the Berkeley Synthetic Intelligence Analysis (BAIR) Lab class of 2026! This 12 months, 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.<\/p>\n

Their work spans the breadth of recent AI \u2014 robotics and embodied intelligence, giant language fashions and reasoning, pc imaginative and prescient, generative modeling, AI security, human-AI interplay, AI for science and healthcare, and far more. Alongside the best way, they’ve revealed influential analysis, constructed methods with real-world impression, mentored their friends, and formed the BAIR neighborhood for the higher.<\/p>\n

Now they’re headed in every single place concepts journey: to school and postdoctoral positions, to trade analysis labs, and to startups of their very own founding \u2014 and a number of other are nonetheless exploring what comes subsequent and would love to listen to from you.<\/p>\n

Please be part of us in celebrating the achievements of those great graduates. We’re happy with every thing they’ve achieved at Berkeley, and we are able to\u2019t wait to see what they do subsequent!<\/p>\n

<\/p>\n

Thanks to our associates on the Stanford AI Lab<\/a> for this concept!<\/i><\/small><\/p>\n\n

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\n\n
\n
\n $\"Charlie$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> csnell22@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/sea-snell.github.io<\/a><\/p>\n
Advisor(s):<\/strong> Dan Klein<\/p>\n
Analysis Blurb:<\/strong> My work goals to grasp when and the way the totally different LLM scaling paradigms might 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 an alternative 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 discovered representations that the mannequin can maintain onto throughout interactions.<\/p><\/div><\/div><\/div>\n\n\n
\n
\n $\"Eve$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> efleisig@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/efleisig.com<\/a><\/p>\n
Advisor(s):<\/strong> Dan Klein<\/p>\n
Analysis Blurb:<\/strong> I design language fashions to work reliably and pretty for the broad vary of actual LLM customers. First, my analysis leverages disagreement amongst person preferences as sign, in an effort to prepare and consider LLMs for complete 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 totally different wants. Mixed, these interventions facilitate constructing LLMs that decrease societal harms, and maximize advantages to a wider vary of real-world customers.<\/p>\n
What’s subsequent:<\/strong> Postdoctoral fellow at Princeton CITP<\/p><\/div><\/div><\/div>\n\n
\n
\n $\"Grace$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> graceluo@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/graceluo.web<\/a><\/p>\n
Advisor(s):<\/strong> Trevor Darrell<\/p>\n
Analysis Blurb:<\/strong> My analysis is on deciphering 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.<\/p>\n
What’s subsequent:<\/strong> Analysis scientist in trade<\/p><\/div><\/div><\/div>\n\n
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\n $\"Hanlin$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> hanlinzhu@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/hanlinzhu.com\/<\/a><\/p>\n
Advisor(s):<\/strong> Stuart Russell, Jiantao Jiao<\/p>\n
Analysis Blurb:<\/strong> My analysis facilities on understanding and bettering the reasoning capabilities of huge language fashions (LLMs).<\/p>\n
What’s subsequent:<\/strong> Member of Technical Workers at OpenAI<\/p><\/div><\/div><\/div>\n\n
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\n $\"Haozhi$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> hqi@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/haozhi.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Jitendra Malik, Yi Ma<\/p>\n
Analysis Blurb:<\/strong> Dexterous Manipulation and Robotic Studying<\/p>\n
What’s subsequent:<\/strong> Analysis scientist at Amazon; School at College of Chicago<\/p><\/div><\/div><\/div>\n\n
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\n $\"J.D.$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> zamfi@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/zamfi.web<\/a><\/p>\n
Advisor(s):<\/strong> Bjoern Hartmann<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on efficient human-AI co-design. I research the boundaries of language interfaces as a medium for interacting with AI, creating methods that mix language-focused interactions with structured person interfaces that draw on totally different ranges of abstraction. I deal with 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 needs at virtually any degree of abstraction, from high-level objectives vaguely specified (\u201cI\u2019d like a recreation to assist my child study to learn\u201d) to low-level corrections of undesired outputs (\u201cDon\u2019t say \u2018I do know as a result of I\u2019ve tasted it\u2019 when a few recipe substitution’s style\u201d).<\/p>\n
What’s subsequent:<\/strong> Assistant Professor, Pc Science, UCLA<\/p><\/div><\/div><\/div>\n\n
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\n $\"Jiachen$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> jiachenlian@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/jlian2.github.io<\/a><\/p>\n
Advisor(s):<\/strong> Gopala Anumanchipalli<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on human-centered AI throughout speech, healthcare, and methods.<\/p>\n
Searching for:<\/strong> Search for AI abilities to affix our startup<\/p><\/div><\/div><\/div>\n\n
\n
\n $\"Josh$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> minwoo_kang@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/joshuaminwookang.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> John Canny<\/p>\n
Analysis Blurb:<\/strong> I research language modeling and associated subjects in NLP; particular pursuits are human person simulation and constructing conversational, collaborative AI brokers.<\/p>\n
What’s subsequent:<\/strong> AI Scientist at Mistral AI<\/p><\/div><\/div><\/div>\n\n
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\n $\"Junhao$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> junhao_xiong@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/www.linkedin.com\/in\/junhao-bear-xiong<\/a><\/p>\n
Advisor(s):<\/strong> Jennifer Listgarten, Yun Tune<\/p>\n
Analysis Blurb:<\/strong> 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.<\/p>\n
Searching for:<\/strong> Analysis scientist<\/p><\/div><\/div><\/div>\n\n
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\n $\"Kaylo$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> kaylo_littlejohn@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/kaylolittlejohn.com<\/a><\/p>\n
Advisor(s):<\/strong> Gopala Anumanchipalli<\/p>\n
Analysis Blurb:<\/strong> 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.<\/p>\n
Searching for:<\/strong> Analysis Scientist \/ Engineer<\/p><\/div><\/div><\/div>\n\n
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\n $\"Kent$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> kentkchang@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/kentkc.org<\/a><\/p>\n
Advisor(s):<\/strong> David Bamman<\/p>\n
Analysis Blurb:<\/strong> I work on NLP and multimodal machine studying, with a deal with evaluating giant language fashions and constructing multimodal methods for understanding dialogue, narrative, and social interplay. My analysis consists of benchmarks for LLM memorization, multimodal datasets sourced from function movies and tv, and research of mannequin habits. I am desirous about bridging computational strategies with questions from the humanities and social sciences about whose voices get represented in AI methods, and about AI’s broader impression. My work has appeared at EMNLP and ACL, amongst others.<\/p>\n
Searching for:<\/strong> (educating) school, Analysis Scientist, ML\/AI SWE<\/p><\/div><\/div><\/div>\n\n
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\n $\"Kevin$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> kvablack@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/kevin.black<\/a><\/p>\n
Advisor(s):<\/strong> Sergey Levine<\/p>\n
Analysis Blurb:<\/strong> 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!<\/p>\n
What’s subsequent:<\/strong> Analysis Scientist of Bodily Intelligence<\/p><\/div><\/div><\/div>\n\n
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\n $\"Kunhe$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> kunheyang@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/www.kunheyang.com\/<\/a><\/p>\n
Advisor(s):<\/strong> Nika Haghtalab<\/p>\n
Analysis Blurb:<\/strong> 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 knowledge transmission, drawing on instruments from machine studying principle and computational economics.<\/p>\n
What’s subsequent:<\/strong> Postdoc Analysis at Stanford<\/p><\/div><\/div><\/div>\n\n\n
\n
\n $\"Long$ <\/a><\/p>\n
\n
\n Electronic mail:<\/strong> longlian@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/tonylian.com\/<\/a><\/p>\n
Advisor(s):<\/strong> Trevor Darrell, Adam Yala<\/p>\n
Analysis Blurb:<\/strong> My analysis primarily focuses on growing real-time multi-modal multi-agent methods and parallel reasoning methods by means of end-to-end RL.<\/p>\n
What’s subsequent:<\/strong> Member of Technical Workers at Pondering Machines Lab<\/p><\/div><\/div><\/div>\n\n
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\n $\"Maulik$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> maulikbhatt@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/maulikb.com<\/a><\/p>\n
Advisor(s):<\/strong> Negar Mehr<\/p>\n
Analysis Blurb:<\/strong> My analysis develops autonomous robots that may safely coordinate with people and different robots in shared environments. I construct scalable algorithms grounded in recreation principle and diffusion fashions that allow brokers motive 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 purpose of creating multi-agent coordination sturdy, interpretable, and deployable in the actual world.<\/p>\n
What’s subsequent:<\/strong> Becoming a member of Toyota Woven’s end-to-end autonomous driving staff.<\/p><\/div><\/div><\/div>\n\n
\n
\n $\"Michael$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> psenka@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/www.michaelpsenka.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Aditi Krishnapriyan<\/p>\n
Analysis Blurb:<\/strong> Work in varied domains (reinforcement studying, world fashions, AI+bio\/chem), usually engaged on longer-horizon and out-of-distribution issues in planning and interpolation (e.g. robotic manipulation from begin state to purpose, molecular dynamics of proteins between floor states). My thesis took a variational strategy (suppose calculus of variations) immediately from deep generative fashions of the setting, framing path-finding as minimizing a practical induced by the discovered mannequin itself (its rating, its critic, or its dynamics). By my analysis I’ve gained perception on correctly deal with dynamics in deep studying methods, and I plan to proceed growing methods which might be dynamic and adaptive.<\/p>\n
What’s subsequent:<\/strong> Lead Analysis Scientist at Baseten<\/p><\/div><\/div><\/div>\n\n\n
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\n $\"Neerja$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> nthakkar@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/neerja.me\/<\/a><\/p>\n
Advisor(s):<\/strong> Jitendra Malik<\/p>\n
Analysis Blurb:<\/strong> 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.<\/p>\n
Searching for:<\/strong> Analysis scientist<\/p><\/div><\/div><\/div>\n\n
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\n $\"Nikita$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> nmehandru@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/n-mehandru.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Ahmed Alaa and David Bamman<\/p>\n
Analysis Blurb:<\/strong> My analysis develops and applies machine studying strategies for scientific reasoning and illness development modeling utilizing unstructured textual content and time collection information from digital well being information. In collaboration with physicians at UCSF, I bridge technique growth and scientific validation with the intention to construct dependable, interpretable AI methods in medication.<\/p>\n
Searching for:<\/strong> Analysis Scientist<\/p><\/div><\/div><\/div>\n\n
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\n $\"Niklas$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> nlauffer@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/niklaslauffer.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Stuart Russell and Sanjit Seshia<\/p>\n
Analysis Blurb:<\/strong> 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.<\/p>\n
What’s subsequent:<\/strong> Analysis Scientist at Google Deepmind<\/p><\/div><\/div><\/div>\n\n
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\n $\"Qiyang$ <\/a><\/p>\n
\n
\n Electronic mail:<\/strong> qcli@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/colinqiyangli.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Sergey Levine<\/p>\n
Analysis Blurb:<\/strong> 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 reasonably than a single one. Nevertheless, most motion chunking insurance policies are educated through supervised imitation studying, as a result of environment friendly on-line self-improvement with reinforcement studying (RL) stays difficult\u2014limiting 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.<\/p>\n
Searching for:<\/strong> Put up-doc\/analysis scientist for RL in robotics and LLMs!<\/p><\/div><\/div><\/div>\n\n
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\n $\"Sampada$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> sampada_deglurkar@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/sdeglurkar.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Prof Claire Tomlin<\/p>\n
Analysis Blurb:<\/strong> 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 underneath uncertainty algorithms, and instruments for producing probabilistic ensures on system operation.<\/p>\n
Searching for:<\/strong> Analysis scientist, Analysis engineer<\/p><\/div><\/div><\/div>\n\n
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\n $\"Vinamra$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> vbenara@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/cs.berkeley.edu\/~vbenara<\/a><\/p>\n
Advisor(s):<\/strong> Ion Stoica<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on LLM post-training, together with information curation, RLHF, RLVR with VLMs, evaluations, reasoning, agentic workflows, and interpretability. I even have sturdy experience in methods infrastructure for distributed computing.<\/p>\n
Searching for:<\/strong> Analysis scientist \/ Analysis Engineer<\/p><\/div><\/div><\/div>\n\n
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\n $\"Vongani$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> vongani_maluleke@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/individuals.eecs.berkeley.edu\/~vongani_maluleke\/<\/a><\/p>\n
Advisor(s):<\/strong> Jitendra Malik and Angjoo Kanazawa<\/p>\n
Analysis Blurb:<\/strong> 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 era framework that helps a variety of movement era 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 Advisor at Deloitte, awarded Distinctive Performer two consecutive years, main AI system growth throughout media, telecommunications, retail, and monetary companies.<\/p>\n
Searching for:<\/strong> Analysis scientist<\/p><\/div><\/div><\/div>\n\n
\n
\n $\"Wei-Jer$ <\/a><\/p>\n
\n
\n Electronic mail:<\/strong> weijer_chang@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/weijer-chang.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Masayoshi Tomizuka<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on growing secure and clever autonomous methods for advanced, human-centered environments. I work on the intersection of machine studying, generative fashions, and reinforcement studying, with functions in autonomy. My work addresses challenges in multi-agent interplay, interactive human habits, and long-tail safety-critical eventualities at scale.<\/p>\n
Searching for:<\/strong> Analysis Scientist, Utilized Scientist, Roboticist<\/p><\/div><\/div><\/div>\n\n
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\n $\"Xiuyu$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> xiuyu@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/xiuyuli.com\/<\/a><\/p>\n
Advisor(s):<\/strong> Kurt Keutzer<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on growing scalable and self-improving giant language mannequin brokers, with emphasis on coding brokers for advanced, 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.<\/p>\n
What’s subsequent:<\/strong> Member of Technical Workers at xAI<\/p><\/div><\/div><\/div>\n\n
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\n $\"Yichen$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> yichenxie0928@gmail.com<\/a>
\n Web site:<\/strong> https:\/\/yichen928.github.io\/<\/a><\/p>\n
Advisor(s):<\/strong> Masayoshi Tomizuka<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on constructing multimodal basis fashions and world fashions that perceive and work together with advanced bodily environments. I intention to develop unified representations throughout modalities, enabling AI methods to motive over area, time, and dynamics towards general-purpose embodied intelligence.<\/p>\n
What’s subsequent:<\/strong> Analysis Scientist at Luma AI<\/p><\/div><\/div><\/div>\n\n
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\n $\"Yigit$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> erginbas@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/www.linkedin.com\/in\/erginbas\/<\/a><\/p>\n
Advisor(s):<\/strong> Kannan Ramchandran, Thomas A. Courtade<\/p>\n
Analysis Blurb:<\/strong> My PhD analysis spans two threads: on-line studying in large-scale markets, and interpretability of huge machine studying fashions. Within the first, I work on sequential decision-making with functions 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 knowledge principle. Extra just lately, I’ve been exploring principled methods to judge the faithfulness of mannequin self-explanations.<\/p>\n
What’s subsequent:<\/strong> Researcher at Hudson River Buying and selling’s AI Labs (HAIL)<\/p><\/div><\/div><\/div>\n\n
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\n $\"Yiheng$ <\/a><\/p>\n
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\n Electronic mail:<\/strong> yhli@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/Yihengli.com<\/a><\/p>\n
Advisor(s):<\/strong> Masayoshi Tomizuka<\/p>\n
Analysis Blurb:<\/strong> I’m engaged on imaginative and prescient world modeling, with prior expertise in diffusion mannequin’s effectivity in addition to in autonomous driving.<\/p>\n
What’s subsequent:<\/strong> Analysis Scientist at Waymo<\/p><\/div><\/div><\/div>\n\n
\n
\n $\"Zhe$ <\/a><\/p>\n
\n
\n Electronic mail:<\/strong> zhefu@berkeley.edu<\/a>
\n Web site:<\/strong> https:\/\/fu-zhe.com\/<\/a><\/p>\n
Advisor(s):<\/strong> Alexandre Bayen<\/p>\n
Analysis Blurb:<\/strong> My analysis focuses on physics-informed studying and management for mixed-autonomy methods, with functions in transportation. I design physics-informed neural networks to study 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 area experiments. Extra broadly, I intention to advance reliable, interpretable AI for decision-making in advanced, real-world methods.<\/p>\n
What’s subsequent:<\/strong> I shall be an Vitality Fellow at Stanford after commencement. Additionally on the lookout for School, or analysis scientist positions in AI, management, and autonomy.<\/p><\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"
Congratulations to the Berkeley Synthetic Intelligence Analysis (BAIR) Lab class of 2026! This 12 months, 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 \u2014 robotics and embodied intelligence, giant language fashions […]<\/p>\n","protected":false},"author":2,"featured_media":16344,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[311,2567,310,110,2568,312,193,4429],"class_list":["post-16342","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-artificial","tag-bair","tag-berkeley","tag-blog","tag-graduate","tag-intelligence","tag-research","tag-showcase"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/16342","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16342"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/16342\/revisions"}],"predecessor-version":[{"id":16343,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/16342\/revisions\/16343"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/16344"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16342"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16342"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}