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HomeArtificial Intelligence2024 BAIR Graduate Listing – The Berkeley Synthetic Intelligence Analysis Weblog

2024 BAIR Graduate Listing – The Berkeley Synthetic Intelligence Analysis Weblog



Yearly, the Berkeley Synthetic Intelligence Analysis (BAIR) Lab graduates a number of the most proficient and progressive minds in synthetic intelligence and machine studying. Our Ph.D. graduates have every expanded the frontiers of AI analysis and are actually able to embark on new adventures in academia, business, and past.

These implausible people convey with them a wealth of data, recent concepts, and a drive to proceed contributing to the development of AI. Their work at BAIR, starting from deep studying, robotics, and pure language processing to pc imaginative and prescient, safety, and way more, has contributed considerably to their fields and has had transformative impacts on society.

This web site is devoted to showcasing our colleagues, making it simpler for educational establishments, analysis organizations, and business leaders to find and recruit from the latest era of AI pioneers. Right here, you’ll discover detailed profiles, analysis pursuits, and make contact with info for every of our graduates. We invite you to discover the potential collaborations and alternatives these graduates current as they search to use their experience and insights in new environments.

Be a part of us in celebrating the achievements of BAIR’s newest PhD graduates. Their journey is simply starting, and the long run they are going to assist construct is shiny!

Thanks to our pals on the Stanford AI Lab for this concept!


Abdus Salam Azad


E mail: salam_azad@berkeley.edu
Web site: https://www.azadsalam.org/

Advisor(s): Ion Stoica

Analysis Blurb: My analysis curiosity lies broadly within the subject of Machine Studying and Synthetic Intelligence. Throughout my PhD I’ve targeted on Setting Era/ Curriculum Studying strategies for coaching Autonomous Brokers with Reinforcement Studying. Particularly, I work on strategies that algorithmically generates various coaching environments (i.e., studying eventualities) for autonomous brokers to enhance generalization and pattern effectivity. At the moment, I’m engaged on Giant Language Mannequin (LLM) primarily based autonomous brokers.
Jobs In: Analysis Scientist, ML Engineer


Alicia Tsai


E mail: aliciatsai@berkeley.edu
Web site: https://www.aliciatsai.com/

Advisor(s): Laurent El Ghaoui

Analysis Blurb: My analysis delves into the theoretical facets of deep implicit fashions, starting with a unified “state-space” illustration that simplifies notation. Moreover, my work explores numerous coaching challenges related to deep studying, together with issues amenable to convex and non-convex optimization. Along with theoretical exploration, my analysis extends the potential functions to varied drawback domains, together with pure language processing, and pure science.
Jobs In: Analysis Scientist, Utilized Scientist, Machine Studying Engineer


Catherine Weaver


E mail: catherine22@berkeley.edu
Web site: https://cwj22.github.io

Advisor(s): Masayoshi Tomizuka, Wei Zhan

Analysis Blurb: My analysis focuses on machine studying and management algorithms for the difficult job of autonomous racing in Gran Turismo Sport. I leverage my background in Mechanical Engineering to find how machine studying and model-based optimum management can create secure, high-performance management methods for robotics and autonomous methods. A selected emphasis of mine has been the way to leverage offline datasets (e.g. human participant’s racing trajectories) to tell higher, extra pattern environment friendly management algorithms.
Jobs In: Analysis Scientist and Robotics/Controls Engineer


Chawin Sitawarin


E mail: chawin.sitawarin@gmail.com
Web site: https://chawins.github.io/

Advisor(s): David Wagner

Analysis Blurb: I’m broadly eager about the safety and security facets of machine studying methods. Most of my earlier works are within the area of adversarial machine studying, notably adversarial examples and robustness of machine studying algorithms. Extra just lately, I’m enthusiastic about rising safety and privateness dangers on giant language fashions.
Jobs In: Analysis scientist



Eliza Kosoy


E mail: eko@berkeley.edu
Web site: https://www.elizakosoy.com/

Advisor(s): Alison Gopnik

Analysis Blurb: Eliza Kosoy works on the intersection of kid growth and AI with Prof. Alison Gopnik. Her work contains creating evaluative benchmarks for LLMs rooted in baby growth and finding out how kids and adults use GenAI fashions reminiscent of ChatGPT/Dalle and kind psychological fashions about them. She’s an intern at Google engaged on the AI/UX workforce and beforehand with the Empathy Lab. She has printed in Neurips, ICML, ICLR, Cogsci and cognition. Her thesis work created a unified digital setting for testing kids and AI fashions in a single place for the needs of coaching RL fashions. She additionally has expertise constructing startups and STEM {hardware} coding toys.
Jobs In: Analysis Scientist (baby growth and AI), AI security (specializing in kids), Person Expertise (UX) Researcher (specializing in combined strategies, youth, AI, LLMs), Schooling and AI (STEM toys)


Fangyu Wu


E mail: fangyuwu@berkeley.edu
Web site: https://fangyuwu.com/

Advisor(s): Alexandre Bayen

Analysis Blurb: Below the mentorship of Prof. Alexandre Bayen, Fangyu focuses on the appliance of optimization strategies to multi-agent robotic methods, notably within the planning and management of automated automobiles.
Jobs In: College, or analysis scientist in management, optimization, and robotics


Frances Ding


E mail: frances@berkeley.edu
Web site: https://www.francesding.com/

Advisor(s): Jacob Steinhardt, Moritz Hardt

Analysis Blurb: My analysis focus is in machine studying for protein modeling. I work on bettering protein property classification and protein design, in addition to understanding what totally different protein fashions be taught. I’ve beforehand labored on sequence fashions for DNA and RNA, and benchmarks for evaluating the interpretability and equity of ML fashions throughout domains.
Jobs In: Analysis scientist



Kathy Jang


E mail: kathyjang@gmail.com
Web site: https://kathyjang.com

Advisor(s): Alexandre Bayen

Analysis Blurb: My thesis work has specialised in reinforcement studying for autonomous automobiles, specializing in enhancing decision-making and effectivity in utilized settings. In future work, I am keen to use these rules to broader challenges throughout domains like pure language processing. With my background, my intention is to see the direct impression of my efforts by contributing to progressive AI analysis and options.
Jobs In: ML analysis scientist/engineer



Nikhil Ghosh


E mail: nikhil_ghosh@berkeley.edu
Web site: https://nikhil-ghosh-berkeley.github.io/

Advisor(s): Bin Yu, Track Mei

Analysis Blurb: I’m eager about growing a greater foundational understanding of deep studying and bettering sensible methods, utilizing each theoretical and empirical methodology. At the moment, I’m particularly eager about bettering the effectivity of enormous fashions by finding out the way to correctly scale hyperparameters with mannequin dimension.
Jobs In: Analysis Scientist


Olivia Watkins


E mail: oliviawatkins@berkeley.edu
Web site: https://aliengirlliv.github.io/oliviawatkins

Advisor(s): Pieter Abbeel and Trevor Darrell

Analysis Blurb: My work entails RL, BC, studying from people, and utilizing commonsense basis mannequin reasoning for agent studying. I’m enthusiastic about language agent studying, supervision, alignment & robustness.
Jobs In: Analysis scientist


Ruiming Cao


E mail: rcao@berkeley.edu
Web site: https://rmcao.web

Advisor(s): Laura Waller

Analysis Blurb: My analysis is on computational imaging, notably the space-time modeling for dynamic scene restoration and movement estimation. I additionally work on optical microscopy methods, optimization-based optical design, occasion digicam processing, novel view rendering.
Jobs In: Analysis scientist, postdoc, school


Ryan Hoque


E mail: ryanhoque@berkeley.edu
Web site: https://ryanhoque.github.io

Advisor(s): Ken Goldberg

Analysis Blurb: Imitation studying and reinforcement studying algorithms that scale to giant robotic fleets performing manipulation and different advanced duties.
Jobs In: Analysis Scientist


Sam Toyer


E mail: sdt@berkeley.edu
Web site: https://www.qxcv.web/

Advisor(s): Stuart Russell

Analysis Blurb: My analysis focuses on making language fashions safe, sturdy and secure. I even have expertise in imaginative and prescient, planning, imitation studying, reinforcement studying, and reward studying.
Jobs In: Analysis scientist


Shishir G. Patil


E mail: shishirpatil2007@gmail.com
Web site: https://shishirpatil.github.io/

Advisor(s): Joseph Gonzalez

Analysis Blurb: Gorilla LLM – Educating LLMs to make use of instruments (https://gorilla.cs.berkeley.edu/); LLM Execution Engine: Guaranteeing reversibility, robustness, and minimizing blast-radius for LLM-Brokers integrated into consumer and enterprise workflows; POET: Reminiscence sure, and vitality environment friendly fine-tuning of LLMs on edge gadgets reminiscent of smartphones and laptops (https://poet.cs.berkeley.edu/).
Jobs In: Analysis Scientist


Suzie Petryk


E mail: spetryk@berkeley.edu
Web site: https://suziepetryk.com/

Advisor(s): Trevor Darrell, Joseph Gonzalez

Analysis Blurb: I work on bettering the reliability and security of multimodal fashions. My focus has been on localizing and lowering hallucinations for imaginative and prescient + language fashions, together with measuring and utilizing uncertainty and mitigating bias. My pursuits lay in making use of options to those challenges in precise manufacturing eventualities, slightly than solely in tutorial environments.
Jobs In: Utilized analysis scientist in generative AI, security, and/or accessibility


Xingyu Lin


E mail: xingyu@berkeley.edu
Web site: https://xingyu-lin.github.io/

Advisor(s): Pieter Abbeel

Analysis Blurb: My analysis lies in robotics, machine studying, and pc imaginative and prescient, with the first purpose of studying generalizable robotic abilities from two angles: (1) Studying structured world fashions with spatial and temporal abstractions. (2) Pre-training visible illustration and abilities to allow information switch from Web-scale imaginative and prescient datasets and simulators.
Jobs In: College, or analysis scientist


Yaodong Yu


E mail: yyu@eecs.berkeley.edu
Web site: https://yaodongyu.github.io/

Advisor(s): Michael I. Jordan, Yi Ma

Analysis Blurb: My analysis pursuits are broadly in concept and apply of reliable machine studying, together with interpretability, privateness, and robustness.
Jobs In: College


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