Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Our Ph.D. graduates have each expanded the frontiers of AI research and are now ready to embark on new adventures in academia, industry, and beyond.
These fantastic individuals bring with them a wealth of knowledge, fresh ideas, and a drive to continue contributing to the advancement of AI. Their work at BAIR, ranging from deep learning, robotics, and natural language processing to computer vision, security, and much more, has contributed significantly to their fields and has had transformative impacts on society.
This website is dedicated to showcasing our colleagues, making it easier for academic institutions, research organizations, and industry leaders to discover and recruit from the newest generation of AI pioneers. Here, you’ll find detailed profiles, research interests, and contact information for each of our graduates. We invite you to explore the potential collaborations and opportunities these graduates present as they seek to apply their expertise and insights in new environments.
Join us in celebrating the achievements of BAIR’s latest PhD graduates. Their journey is just beginning, and the future they will help build is bright!
Thank you to our friends at the Stanford AI Lab for this idea!
Email: salam_azad@berkeley.edu
Website: https://www.azadsalam.org/
Advisor(s): Ion Stoica
Research Blurb: My research interest lies broadly in the field of Machine Learning and Artificial Intelligence. During my PhD I have focused on Environment Generation/ Curriculum Learning methods for training Autonomous Agents with Reinforcement Learning. Specifically, I work on methods that algorithmically generates diverse training environments (i.e., learning scenarios) for autonomous agents to improve generalization and sample efficiency. Currently, I am working on Large Language Model (LLM) based autonomous agents.
Jobs Interested In: Research Scientist, ML Engineer
Email: aliciatsai@berkeley.edu
Website: https://www.aliciatsai.com/
Advisor(s): Laurent El Ghaoui
Research Blurb: My research delves into the theoretical aspects of deep implicit models, beginning with a unified “state-space” representation that simplifies notation. Additionally, my work explores various training challenges associated with deep learning, including problems amenable to convex and non-convex optimization. In addition to theoretical exploration, my research extends the potential applications to various problem domains, including natural language processing, and natural science.
Jobs Interested In: Research Scientist, Applied Scientist, Machine Learning Engineer
Email: catherine22@berkeley.edu
Website: https://cwj22.github.io
Advisor(s): Masayoshi Tomizuka, Wei Zhan
Research Blurb: My research focuses on machine learning and control algorithms for the challenging task of autonomous racing in Gran Turismo Sport. I leverage my background in Mechanical Engineering to discover how machine learning and model-based optimal control can create safe, high-performance control systems for robotics and autonomous systems. A particular emphasis of mine has been how to leverage offline datasets (e.g. human player’s racing trajectories) to inform better, more sample efficient control algorithms.
Jobs Interested In: Research Scientist and Robotics/Controls Engineer
Email: chawin.sitawarin@gmail.com
Website: https://chawins.github.io/
Advisor(s): David Wagner
Research Blurb: I am broadly interested in the security and safety aspects of machine learning systems. Most of my previous works are in the domain of adversarial machine learning, particularly adversarial examples and robustness of machine learning algorithms. More recently, I am excited about emerging security and privacy risks on large language models.
Jobs Interested In: Research scientist
Email: eko@berkeley.edu
Website: https://www.elizakosoy.com/
Advisor(s): Alison Gopnik
Research Blurb: Eliza Kosoy works at the intersection of child development and AI with Prof. Alison Gopnik. Her work includes creating evaluative benchmarks for LLMs rooted in child development and studying how children and adults use GenAI models such as ChatGPT/Dalle and form mental models about them. She’s an intern at Google working on the AI/UX team and previously with the Empathy Lab. She has published in Neurips, ICML, ICLR, Cogsci and cognition. Her thesis work created a unified virtual environment for testing children and AI models in one place for the purposes of training RL models. She also has experience building startups and STEM hardware coding toys.
Jobs Interested In: Research Scientist (child development and AI), AI safety (specializing in children), User Experience (UX) Researcher (specializing in mixed methods, youth, AI, LLMs), Education and AI (STEM toys)
Email: fangyuwu@berkeley.edu
Website: https://fangyuwu.com/
Advisor(s): Alexandre Bayen
Research Blurb: Under the mentorship of Prof. Alexandre Bayen, Fangyu focuses on the application of optimization methods to multi-agent robotic systems, particularly in the planning and control of automated vehicles.
Jobs Interested In: Faculty, or research scientist in control, optimization, and robotics
Email: frances@berkeley.edu
Website: https://www.francesding.com/
Advisor(s): Jacob Steinhardt, Moritz Hardt
Research Blurb: My research focus is in machine learning for protein modeling. I work on improving protein property classification and protein design, as well as understanding what different protein models learn. I have previously worked on sequence models for DNA and RNA, and benchmarks for evaluating the interpretability and fairness of ML models across domains.
Jobs Interested In: Research scientist
Email: kathyjang@gmail.com
Website: https://kathyjang.com
Advisor(s): Alexandre Bayen
Research Blurb: My thesis work has specialized in reinforcement learning for autonomous vehicles, focusing on enhancing decision-making and efficiency in applied settings. In future work, I’m eager to apply these principles to broader challenges across domains like natural language processing. With my background, my aim is to see the direct impact of my efforts by contributing to innovative AI research and solutions.
Jobs Interested In: ML research scientist/engineer
Email: nikhil_ghosh@berkeley.edu
Website: https://nikhil-ghosh-berkeley.github.io/
Advisor(s): Bin Yu, Song Mei
Research Blurb: I am interested in developing a better foundational understanding of deep learning and improving practical systems, using both theoretical and empirical methodology. Currently, I am especially interested in improving the efficiency of large models by studying how to properly scale hyperparameters with model size.
Jobs Interested In: Research Scientist
Email: oliviawatkins@berkeley.edu
Website: https://aliengirlliv.github.io/oliviawatkins
Advisor(s): Pieter Abbeel and Trevor Darrell
Research Blurb: My work involves RL, BC, learning from humans, and using common-sense foundation model reasoning for agent learning. I’m excited about language agent learning, supervision, alignment & robustness.
Jobs Interested In: Research scientist
Email: rcao@berkeley.edu
Website: https://rmcao.net
Advisor(s): Laura Waller
Research Blurb: My research is on computational imaging, particularly the space-time modeling for dynamic scene recovery and motion estimation. I also work on optical microscopy techniques, optimization-based optical design, event camera processing, novel view rendering.
Jobs Interested In: Research scientist, postdoc, faculty
Email: ryanhoque@berkeley.edu
Website: https://ryanhoque.github.io
Advisor(s): Ken Goldberg
Research Blurb: Imitation learning and reinforcement learning algorithms that scale to large robot fleets performing manipulation and other complex tasks.
Jobs Interested In: Research Scientist
Email: sdt@berkeley.edu
Website: https://www.qxcv.net/
Advisor(s): Stuart Russell
Research Blurb: My research focuses on making language models secure, robust and safe. I also have experience in vision, planning, imitation learning, reinforcement learning, and reward learning.
Jobs Interested In: Research scientist
Email: shishirpatil2007@gmail.com
Website: https://shishirpatil.github.io/
Advisor(s): Joseph Gonzalez
Research Blurb: Gorilla LLM – Teaching LLMs to use tools (https://gorilla.cs.berkeley.edu/); LLM Execution Engine: Guaranteeing reversibility, robustness, and minimizing blast-radius for LLM-Agents incorporated into user and enterprise workflows; POET: Memory bound, and energy efficient fine-tuning of LLMs on edge devices such as smartphones and laptops (https://poet.cs.berkeley.edu/).
Jobs Interested In: Research Scientist
Email: spetryk@berkeley.edu
Website: https://suziepetryk.com/
Advisor(s): Trevor Darrell, Joseph Gonzalez
Research Blurb: I work on improving the reliability and safety of multimodal models. My focus has been on localizing and reducing hallucinations for vision + language models, along with measuring and using uncertainty and mitigating bias. My interests lay in applying solutions to these challenges in actual production scenarios, rather than solely in academic environments.
Jobs Interested In: Applied research scientist in generative AI, safety, and/or accessibility
Email: xingyu@berkeley.edu
Website: https://xingyu-lin.github.io/
Advisor(s): Pieter Abbeel
Research Blurb: My research lies in robotics, machine learning, and computer vision, with the primary goal of learning generalizable robot skills from two angles: (1) Learning structured world models with spatial and temporal abstractions. (2) Pre-training visual representation and skills to enable knowledge transfer from Internet-scale vision datasets and simulators.
Jobs Interested In: Faculty, or research scientist
Email: yyu@eecs.berkeley.edu
Website: https://yaodongyu.github.io/
Advisor(s): Michael I. Jordan, Yi Ma
Research Blurb: My research interests are broadly in theory and practice of trustworthy machine learning, including interpretability, privacy, and robustness.
Jobs Interested In: Faculty