We live in the era Big Data. Its algorithms pervade our lives--shaping our purchases, our finances, our health care, our education, our communities, our public policy. Armed with phones, computers, and countless other devices, society has produced more data in the past two years—a zettabyte—than the prior span of human civilization. Yet the promise of Big Data lies not only in quantity, but in the quality of our analyses and the foresight of our applications. Is Big Data the future of scientific inquiry? Are we giving too much power to algorithms, seeking large-scale patterns, with the risk of losing the core of our humanity? Join us to explore the potential and perils of Big Data.
The Big Ideas Series is supported in part by the John Templeton Foundation.
Claudia Perlich leads the machine learning efforts that power Dstillery’s digital intelligence for marketers and media companies. With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world. Perlich is the past winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award and has been selected for Crain’s New York’s 40 Under 40 list, Wired magazine’s Smart List, and Fast Company’s 100 Most Creative People. Perlich holds multiple patents in machine learning. She has won many data mining competitions and awards at Knowledge Discovery and Data Mining (KDD) conferences, and served as the organization’s General Chair in 2014. Prior to joining Dstillery in 2010, she worked at IBM’s Watson Research Center, focusing on data analytics and machine learning. She holds a PhD in Information Systems from New York University (where she continues to teach at the Stern School of Business), and an MA in Computer Science from the University of Colorado.
Gary Marcus, scientist, bestselling author, and entrepreneur, is Professor of Psychology and Neural Science at NYU and CEO and Co-Founder of the recently-formed Geometric Intelligence, Inc. His research on language, computation, artificial intelligence, and cognitive development has been published widely, in leading journals such as Science and Nature. He is also the author of four books including The Algebraic Mind, Kluge: The Haphazard Evolution of the Human Mind, and The New York Times Bestseller, Guitar Zero, and contributes frequently to the The New Yorker and The New York Times. His recent book, The Future of the Brain: Essays By The World’s Leading Neuroscientists, features the 2014 Nobel Laureates May-Britt and Edvard Moser. His efforts to update the Turing Test have spurred a worldwide movement.
Cathy O’Neil earned a Ph.D. in math from Harvard, was a postdoc at the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then switched over to the private sector, working as a quant for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. She left finance in 2011 and started working as a data scientist in the New York start-up scene, building models that predicted people’s purchases and clicks. She wrote Doing Data Science in 2013 and launched the Lede Program in Data Journalism at Columbia in 2014. She is a weekly guest on the Slate Money podcast and is currently writing a book about the dark side of big data called Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Random House).
Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. He is also an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012, he was from 2000-2007 a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group. His research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics, the 2013 Lagrange-CRT Foundation Prize for Complexity Science, and the 2014 Everett Rogers Prize. He is also the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton, 2003); Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999); and most recently Everything is Obvious: Once You Know The Answer (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the Department of Systems Biology, the executive committee of the Data Science Institute, and the Institute’s education and entrepreneurship committees. He is also an affiliate of Columbia’s Department of Statistics and a founding member of Columbia’s Center for Computational Biology and Bioinformatics (C2B2). Wiggins is a co-founder and co-organizer of hackNY, a nonprofit which since 2010 has organized once a semester student hackathons and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his PhD at Princeton University (1993-1998) in theoretical physics. In 2014 he was elected Fellow of the American Physical Society and is a recipient of Columbia’s Avanessians Diversity Award.