Jonathan Star is a meeting designer and facilitator, specializing in scenario planning, a technique which uses stories about the future to change the minds and actions of teams so that they are better prepared for tomorrow. He worked at GBN, Monitor and Deloitte Consulting before starting his own independent practice, Scenario Insight, in 2014. He helps clients explore issues such as the future of healthcare, higher education and climate change. He has also lectured at UC Berkeley, where he taught on a Masters program in Data Science. Earlier in his career, Jonathan worked at London Business School, conducting research into business strategy and long-term corporate success. He holds degrees in Business Economics from the University of Nottingham and the University of Warwick in the UK.
Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers.
Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.
Mike Dusenberry is an engineer at the IBM Spark Technology Center, creating a deep learning library for SystemML and solving for performant deep learning at scale. He was on his way to an M.D. and a career as a physician in his home state of North Carolina when he teamed up with professors on a medical machine learning research project. Two years later in San Francisco, Mike is contributing to Apache SystemML as a committer and researching medical applications for deep learning.
Steven Hillion has been leading large engineering and analytics projects for fifteen years. Before joining Alpine Data, he founded the analytics group at Greenplum, leading a team of data scientists and also designing and developing new open-source and enterprise analytics software. Before that, he was Vice President of Engineering at M-Factor, Inc. (acquired by DemandTec) where he built analytical applications that became a global standard for demand modeling. Earlier, at Kana Communications, Steven led the engineering group during the two largest releases of its flagship product. At Scopus Technology (later Siebel Systems) he co-founded development groups for finance, telecom and other verticals. He received his Ph.D. in mathematics from the University of California, Berkeley, and was a King Charles I Scholar at Oxford University.
After receiving her BA from NYU and her MA from King's College London, Madison J Myers started her career in political science where she focused on food policy and social justice. Shortly after, she became interested in data science and now works at IBM’s Spark Technology Center as a data science intern while also studying as a MIDS graduate student at UC Berkeley. Madison currently focuses on Apache SystemML and Apache Spark, developing use cases in the medical and social science domains.
Thomson Nguyen is the Head of Data Science for Square Capital, where his team focuses on mitigating loan default risk, business and operations optimization, and moonshot data product development. He is also a Visiting Scholar at the Courant Institute for Mathematical Sciences at NYU, where his research is in malicious behavior and parallelized decision trees. He was the Founder/CEO of Framed Data, a Y Combinator-backed predictive analytics startup acquired by Square in 2016. He has fond memories of working on math problem sets in 1015 Evans as a Cal undergrad.
Sivan is a Director of Data Science for the Data Science Center of Excellence for The Climate Corporation. In this capacity, Sivan and her team are supporting the development of innovative data-driven solutions to help growers optimize their operations across the globe. In addition, as part of her role Sivan is helping to develop and adopt best-practices for leading Data Science teams.
Sivan began her career in the Israeli military serving as an instructor for an anti-tank missile unit. She then transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion, Israel Institute of Technology. She later moved to the U.S. to complete a Ph.D. degree in Statistics from The Wharton School, University of Pennsylvania.
Sivan’s experiences from the military, academia and private industry shaped her leadership style. She is an enthusiastic disagreeable giver and a constant empirical driven learner. Sivan is also a proud mother of two adorable boys.
Paolo Parigi, Associate Director of Computational Social Science at IRiSS and Lead Trust Scientist at Uber, has studied the relationship between risk and trust in the sharing economy for several years. In 2013, Paolo received funding from the National Science Foundation to lead research in this area. With an interdisciplinary team of social scientists and computer scientists, Paolo has developed novel methodological approaches to harness the power of online data and has explored innovative ways for collaborating between academia and industry. In partnership with Airbnb and other sharing economy companies, Paolo and his team explored the multifaceted nature of the relationship between risk, trust, and reputation. Paolo’s more theoretical work has touched on the impact of technology on relationships.
Paolo has an MA in Quantitative Methods and a PhD in Sociology from Columbia University. Prior to his current positions, Paolo had an appointment in the Sociology department at Stanford as an assistant professor.
AnnaLee Saxenian (Anno) is professor and dean of UC Berkeley’s School of Information and has a joint faculty appointment with the Department of City and Regional Planning. Her latest book, The New Argonauts: Regional Advantage in a Global Economy (Harvard University Press, 2006) explores how and why immigrant engineers from Silicon Valley are transferring their technology entrepreneurship to emerging regions in their home countries—Taiwan, Israel, China and India in particular—and launching companies far from established centers of skill and technology. The “brain drain,” she argues, has now become “brain circulation” — a powerful economic force for the development of formerly peripheral regions that is sparking profound transformations in the global economy. Saxenian is also author of the widely acclaimed Regional Advantage: Culture and Competition in Silicon Valley and Route 128 (Harvard University Press, 1994). Other publications include Silicon Valley’s New Immigrant Entrepreneurs (Public Policy Institute of California, 1999), and Local and Global Networks of Immigrant Professionals in Silicon Valley (PPIC, 2002). She holds a P.D in political science from MIT, a master’s in regional planning from UC Berkeley, and a BA in economics from Williams College.
Jennifer Shin is a Senior Principal Data Scientist at The Nielsen Company and the Founder of 8 Path Solutions, a data science, analytics, and technology company. As an experienced data scientist and management consultant, Jennifer has led complex, large scale, and high profile projects for corporate, public, and private clients, including GE Capital, the Carlyle Group, Fortress Investment Group, and Columbia University.