Instructors

Yan Zhang, the director of SPARC, is an assistant professor in mathematics (specializing in combinatorics) at San Jose State University after receiving his PhD from MIT. He wants to help people (everywhere, not just SPARC) become more human and awesome. He has mentored students at the Mathematical Olympiad Summer Program (where he represented the U.S. at the International Mathematics Olympiad with a silver medal), RSI, and MIT-PRIMES. He received the Undergraduate Math Association’s Distinguished Undergraduate Teaching Award for his postdoctoral position at UC Berkeley.

Jacob Steinhardt recently finished a PhD at Stanford University and will be joining UC Berkeley as an assistant professor of Statistics. His research focuses on designing machine learning systems that are reliable and easy to reason about. Other interests include mathematics, robotics, and cognitive science. He is a fan and technical advisor of the Open Philanthropy Project. He received a silver medal at the International Olympiad in Informatics and has coached for the USA Computing Olympiad since 2009. In his free time he likes playing ultimate frisbee and rock-climbing.

Paul Christiano works at OpenAI, a non-profit AI research lab. His research focuses on communicating goals to AI systems. He recently finished a PhD at UC Berkeley in statistical learning theory, working on algorithms for quickly deciding who to trust in large networks. In 2008, he represented the U.S. at the International Mathematics Olympiad.

Andrew Critch received his PhD in mathematics from UC Berkeley, where he specialized in algebraic statistics, and particularly its applications to machine learning and causal inference. He was awarded a three-year research faculty position at the NSF-sponsored Mathematical Biosciences Institute, but soon after decided to learn about the stock market and began working as an algorithmic trader for Jane Street Capital in New York. He loves thinking about causality, cognitive science, markets, and apparent philosophical paradoxes, and wants to convince you that free will and determinism are compatible. He also likes helping people to understand their goals and adopt new and productive habits.

Qiaochu Yuan is an instructor and researcher at the Center for Applied Rationality, a non-profit that runs workshops dedicated to giving people tools to improve their thinking and their lives broadly. He was formerly an NSF Graduate Fellow in the mathematics department at UC Berkeley, and authors the math blog Annoying Precision. His dominant current interest is in understanding how to facilitate personal growth in himself and others.

Chelsea Voss works as a software engineer for Pilot.com. She studied computer science with a minor in mathematics at MIT, and enjoys doing deep dives into complexity theory, type theory, and biochemistry. During her master’s thesis research, she learned how to apply logical inference with SMT solvers to problems in systems biology. In the past, she has worked at Wave.com and Khan Academy, taught as an instructor at USABO and USACO, presented computational biology research at the Intel STS, and competed on the USA teams to the International Biology Olympiad and International Linguistics Olympiad.

Michael Webb is a PhD student in Economics at Stanford University. His research focuses on automation and its effects on the economy, the relationship between education and productivity, and causality. He previously served as a correspondent for The Economist, as senior economic aide to a British legislator, and as a research economist at the Institute for Fiscal Studies. He holds degrees from Oxford and MIT.

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Jaan Altosaar is a physics PhD student at Princeton University, focusing on machine learning. He studied mathematics and physics at McGill; his work is supported by the Natural Sciences and Engineering Research Council of Canada. In the past he has interned at Google Brain and DeepMind. Jaan founded usefulscience.org and to unplug he saunas, makes music and dances.

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Dylan Cable is a NSF graduate fellow in Computer Science at MIT. He is currently interested in bridging the gap between mathematics/machine learning and computational biology. Previously as an undergraduate at Stanford, Dylan has done research in probability theory, computational neuroscience, and mechanism design. Outside of academics, Dylan enjoys learning guitar and improvisational theater.

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Katie Dunn is a recent graduate of MIT, where she studied astronomy & education. She enjoys literature, well-designed games, learning/trying new things, and helping others do the same.

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Damon Sasi is a licensed Marriage and Family Therapist in Florida, where he works as a therapist by day and writes Pokemon rational fiction by night. He’s a strong believer in the power of rationality to improve therapeutic practices, as well as stories as a catalyst for growth, and is working to combine all three in as many ways as possible.