Speakers

Ysis Wilson-Tarter is a staff data engineer at Absci, where deep learning AI and synthetic biology are harnessed to translate ideas into drugs. She leads the development of data platforms and pipelines for high-throughput biological data, as well as scientific tools for data analysis. Ysis is also the co-tech lead of the Bay Area chapter of Black Girls Code and teaches data visualization and analytics. She has lectured at several institutions, including UC Berkeley, and spoken at events such as DeveloperWeek, Open Data Science Conference, and the Global Synthetic Biology Summit. Tarter holds an MS in applied biomedical engineering from Johns Hopkins University and a BS in computer science from Stanford University where she specialized in biocomputation. She has published peer-reviewed articles in the fields of scalable neuroscience and synthetic biological design.

Deb Donig is an assistant professor of English at Cal Poly and a lecturer at the UC Berkeley’s School of Information. She is the co-founder of the Cal Poly Ethical Technology Initiative and the host of the “Technically Human” podcast. As a public scholar, she consults in the tech industry and in film and TV, including “Weight of the World,” a series about the memory of slavery, and Hunters, produced by Jordan Peele. She previously served as a policy expert for the Biden/Harris Higher Education Policy Strategy Team. Her first book project traces the evolution of comparative approaches to genocide and human rights.

Emily Franklin is an environmental engineer and atmospheric scientist from Boulder, Colorado. Emily earned her BS in Environmental Engineering at Yale University in 2017, where she conducted research in Dr. Drew Gentner’s research group and ran for the cross country and track and field teams. She then joined Dr. Allen Goldstein’s group at UC Berkeley, where she earned her MS in Environmental Engineering in 2018 and PhD in Environmental Engineering in 2022. Currently, Emily is an NSF-MPS Ascend Postdoctoral Fellow in Dr. Delphine Farmer’s group at Colorado State University. Her research investigates human impacts on aerosol formation and composition from remote to urban environments and includes development of machine learning-based models for enhanced characterization of complex organic mixtures. Outside of research, Emily has focused on preventing harassment during field research, teaching math and environmental science at San Quentin State Prison, and mitigating aerosol exposure in schools.

Dr. Jennifer King is the privacy and data policy fellow at the Stanford University Institute for Human-Centered Artificial Intelligence. An information scientist by training, Dr. King is a recognized expert and scholar in information privacy. Sitting at the intersection of human-computer interaction, law, and the social sciences, her research examines the public’s understanding and expectations of online privacy as well as the policy implications of emerging technologies. Most recently, her research explored alternatives to notice and consent (with the World Economic Forum), the impact of California’s new privacy laws, and dark patterns. Her past work includes projects focusing on social media, genetic privacy, mobile application platforms, the Internet of Things (IoT), and digital surveillance. Her scholarship has been recognized for its impact on policymaking by the Future of Privacy Forum, and she has been an invited speaker before the Federal Trade Commission at several Commission workshops. She has been featured in numerous publications and outlets, including The New York Times, the Washington Post, the Los Angeles Times, Wired, Recode, National Public Radio, CNBC, Bloomberg, CNET, Vox, Consumer Reports, NBC News, MIT Technology Review, among others. Dr. King completed her Ph.D. in information management and systems at the UC Berkeley School of Information.

Tanya Roosta is senior research scientist and manager at Amazon on the Alexa AI team. She also teaches the Statistics for Data Science course at the UC Berkeley School of Information. She has a Ph.D., MS., and B.S. in electrical engineering and computer science and additional master’s degrees in statistics and in financial engineering, all from UC Berkeley.

Stacia Wyman is a Computational Genomics Investigator at UC Berkeley’s Innovative Genomics Institute. Stacia holds a B.A. from Smith College, received her M.S. from the University of Wisconsin at Madison, and a Ph.D. in Computer Science from the University of Texas, Austin. Her postdoctoral work involved developing algorithms for identifying miRNAs as cancer biomarkers and analyzing whole genome sequences of tumors at the Fred Hutchinson Cancer Research Center. Stacia came to IGI in June of 2017 from Gladstone Institutes, where she worked on identifying modifiers of Parkinson’s Disease through whole genome sequencing of families. At IGI, she works on all thing computational related to CRISPR: editing outcomes, off-target effects, and CRISPR screens.

Jennifer Chayes is Associate Provost of Computing, Data Science, and Society, and Dean of the School of Information, at UC Berkeley, as well as Professor in four UC Berkeley departments and schools: Electrical Engineering and Computer Sciences, Information, Mathematics, and Statistics. For 23 years, she was at Microsoft Research where she was Technical Fellow and Managing Director of three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal.

Chayes has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Lecture Award of the Society for Industrial and Applied Mathematics (the highest honor of SIAM), the ACM Distinguished Service Award, and honorary doctorates from Bard College and Leiden University. Chayes is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. She is a fellow of the American Association for the Advancement of Science, the American Mathematical Society, the Association of Computing Machinery, the Association of Women in Mathematics, and the Fields Institute.

Chayes’ research areas include phase transitions in computer science, structural and dynamical properties of networks including graph algorithms, and applications of machine learning. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including applications in cancer immunotherapy, ethical decision making, and climate change. Chayes is deeply committed to increasing racial and gender diversity in STEM.