This seminar series is bringing data science researchers from Columbia University, NYU, Cornell Tech and Microsoft Research together. Our goal is to increase interactions within the broader New York data science community, to provide a new forum for discussions on data science research and to establish Microsoft Research as a leader in this field.
The events in this series start with a formal talk session (45 minutes). Invited speakers present short talks, providing their views on the opportunities and challenges in data science research. The second part of the event (2 hours) is a wine and cheese social designed to enable researchers to exchange ideas in a relaxed setting.
We will update you about the upcoming meeting very soon...
April 24, 2014 - Opening Event
You can view the talks and the panel discussion here.
Speaker: Duncan Watts
Bio: Prior to joining Microsoft, Duncan Watts was a Senior Principal Research Scientist at Yahoo! Research, where he directed the Human Social Dynamics group. Prior to joining Yahoo!, he was a full professor of Sociology at Columbia University, where he taught from 2000-2007. 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. He is also the author of three books, most recently Everything is Obvious (Once You Know The Answer) (Crown Business, 2011). He holds a B.Sc. in Physics from the Australian Defense Force Academy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.
Yann LeCun, NYU and Facebook
Title & Abstract: Yann presents a demo on deep learning and vision.
Bio: Yann is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.
Mor Naaman, Cornell Tech
Title: Data and People in Connective Media
Abstract: In five minutes or less, I will talk about how we use methods from social science, people-centered design, data science and machine learning to understand social media data large and small, and build new applications that help us make sense of the city from (public) social media data. I'll also say a word about Cornell Tech and our Connective Media hub. OK, six minutes may be needed to squeeze it all in.
Bio: Naaman is an associate professor at Cornell Tech's Jacobs Institute. He is also a co-founder and Chief Scientist at Seen.co, a startup founded to make sense of the real-time web and social media. Mor's research applies multidisciplinary methods to gain new insights about people and society from social media data, and to develop novel tools to make this data more accessible and usable in various settings. He gets awards, too, including the NSF Early Faculty CAREER Award, research awards from Google, Yahoo!, and Nokia, and three best paper awards.
Tony Jebara, Columbia University
Title: Learning From Network Connectivity and Mobile Phone Data
Abstract: Many real-world networks are described by both connectivity information as well as features for every node. While most network growth models are based on link analysis, we explore how an individual's data profile without any connectivity information can be used to infer their connectivity with other users. For example, in a class of incoming freshmen students with no known friendship connections, can we predict which pairs will become friends at the end of the year using only their profile information? Similarly, can we using co-location to predict communication? In other words, by observing only the mobile location data from users, can we predict what pairs of users are likely to communicate? To learn how to reconstruct these networks, we present structure-preserving metric learning and apply it to Facebook data, Wikipedia data, FourSquare data and mobile phone call detail records,
Bio: Tony is Associate Professor of Computer Science at Columbia University. He chairs the Center on Foundations of Data Science as well as directs the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, spatio-temporal data, vision and text. Jebara has founded or advised startups including Sense Networks (acquired by yp.com), AchieveMint, Agolo, and Bookt (acquired by RealPage NASDAQ:RP). He is the author of the book Machine Learning: Discriminative and Generative. In 2004, Jebara was the recipient of the Career award from the National Science Foundation.
Panel Topic: Opportunities and Challenges in Data Science Research
Panel Moderator: Jennifer Chayes, Managing Director, MSR New York City
Bio: Jennifer Tour Chayes is Managing Director of Microsoft Research New York City as well as the Microsoft Research New England lab in Cambridge. Before this, she was research area manager for Mathematics, Theoretical Computer Science and Cryptography at Microsoft Research Redmond. Chayes joined Microsoft Research in 1997, when she co-founded the Theory Group. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, and algorithmic game theory. She is the co-author of almost 100 scientific papers and the co-inventor of more than 20 patents.
Panel Members: Yann LeCun, Mor Naaman, Tony Jebara
Microsoft Research New York (641 Avenue of the Americas, New York)
Upon arrival to 641 Avenue of the Americas, you will need to check in at the front desk lobby by letting them know you are visiting Microsoft Research on the 7th floor. You will then take the elevator to the 7th floor where our Microsoft Receptionist will alert your host of your arrival.