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! Abdus Salam Azad 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 Alicia Tsai 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 Catherine Weaver 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 Chawin Sitawarin 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 Dhruv Shah