The entire program will take place on campus at BAIR Lab, where students participate in daily guest lectures and activities guided by BAIR Lab researchers, and select visits to research labs. Participants experience life on a college campus, including lunch provided daily at UC Berkeley dining halls and a campus tour.

The BAIR Lab High School Summer Program is made possible through the generous support of BAIR Labs and their ongoing partnership with the Lawrence Hall of Science. It is a unique opportunity designed for and offered to students who have had limited access to STEM mentors and experiences outside of school, who may be the first generation in their family to attend college, and who demonstrate financial need. 

The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, control, and robotics. BAIR includes over 50 faculty and more than 300 graduate students and postdoctoral researchers pursuing research on fundamental advances in many areas as well as cross-cutting themes, such as multimodal deep learning, human compatible AI, and connecting AI with other scientific disciplines and the humanities.

Participation in this program is by application and acceptance only. This program is not available for public purchase and there is no optional residential add-on available. We partner with local school STEM teachers, advisers, and administrations, as well as youth leadership and other out of school organizations to support recruitment efforts.

Our days begin with a guest lecturer, usually a UC Berkeley AI or Computer Science (CS)  professor, describing the research that they do with AI systems. These one-hour lectures usually focus on either providing a high-level overview of how certain AI systems work on a technical level and the problems they are trying to solve, or how those systems interact with society and social systems and the outcomes of such interactions. After these lectures and Q&A with the professors, we engage in a quick interactive and kinesthetic activity related to the day’s learning objectives that also gets us on our feet and engaging socially with one another. Then, we engage in a participatory lecture: that is, a lecture that is broken into smaller pieces with individual and group tasks interspersed to provide opportunities to engage with the content being lectured about. These participatory lectures cover the basics of the pertinent AI concepts that are foundational to the project work aspects of the program. After the lecture, we take an hour break for lunch and socialization; sometimes graduate and undergraduate students join us to mingle and talk with us about studying AI and CS at the college level. Upon returning from lunch, we turn our attention to either: (1) foundational technical skill development or (2) project work. Earlier in the week, most afternoons are spent on introductory Python programming knowledge, hands-on experience developing machine-learning models, and building an understanding of mathematical concepts underlying AI systems. Later in the week, these skills and knowledge sets are applied to real-world AI problems under the mentorship of AI graduate students. Participants will also visit robotics labs that focus on human-compatible AI and will meet with the respective faculty and graduate students in those labs.

Jishan Jiang

Jishan Jiang, a rising junior at UC Berkeley, is pursuing a double major in Legal Studies and Data Science, complemented by a minor in Education. She is interested in learning designs, curriculum development, data ethics, educational policies and K-12 STEM education. With aspirations to become an educator and researcher, Jishan aims to harness the transformative power of education to uplift and empower underserved communities.

Jishan grew up in Suzhou, China. In her free time, she enjoys spending time with her cat and cooking with her friends.

Naomi Yonas

Naomi Yonas is a rising Senior studying Electrical Engineering & Computer Science at UC Berkeley. Her specific interests are connecting artificial intelligence, education, and social justice to combat systemic biases. She believes technology should be equitable and accessible for everyone of all backgrounds.

In her free time, she loves to read books, take long outdoor walks, and ponder about anything.

Tim Hurt

Tim Hurt is a science curriculum developer and researcher at the Lawrence Hall of Science. He has been an educator for the past 10 years, has experience teaching all grades K–12, and has led UC Berkeley’s AI4ALL since 2019. He has developed curricula for grades K–12 with materials covering physics, engineering, and space science. He currently works on projects related to developing and researching AI exhibits for the Lawrence Hall of Science. Tim has both an undergraduate degree in Physics and a Masters in Information and Data Science, from the University of California, Berkeley.