Spring 2013 Events

Here are the Spring 2013 Events that the CUNY Data Mining Initiative will be hosting. All events are open and free to the public. If you would like to attend any event, please RSVP by emailing datamining@gc.cuny.edu.

FORTHCOMING EVENTS

FEBRUARY 15, 2013, 12:00-2:30PM, Rm. 6112 CUNY Graduate Center

“Educational Data Mining: Predict the Future, Change the Future”

Speaker: Ryan Baker, Teacher’s College

Bio: Ryan Shaun Joazeiro de Baker is the Julius and Rosa Sachs Distinguished Lecturer at Teachers College, Columbia University. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor’s Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students’ motivation, meta-cognition, affect, and robust learning.

MARCH 15, 2013, 12:00-2:30PM, Rm. 6112 CUNY Graduate Center

“Neural Networks: From Basics to New Developments”

Speaker: Tony Babinec, President of AB Analytics

The talk will show the use of neural networks in applied settings involving classification, numeric prediction, and clustering.

Bio:  Tony Babinec teaches statistics and data mining classes for IBM clients. He also presents classes for statistics.com. He has given talks and workshops at the AMA’s Advanced Research Techniques Forum, Statistical Modeling Week, the Joint Statistical Meetings, and the Conference on Statistical Practice. Tony is a past President of the Chicago Chapter of the American Statistical Association, and currently is their Workshops VP.

MARCH 21, 2013, 9:30-2:30PM, Rm. 4102 (Science Center) CUNY Graduate Center

“Automated Behavior Detection From Log Files: A Workshop on Educational Data Mining”

Workshop leader: Ryan Baker, Teacher’s College

Bio: Ryan Shaun Joazeiro de Baker is the Julius and Rosa Sachs Distinguished Lecturer at Teachers College, Columbia University. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University, and was a post-doctoral fellow in the Learning Sciences at the University of Nottingham. He earned his Bachelor’s Degree (Sc.B.) in Computer Science from Brown University. Dr. Baker has been Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute. He previously served as the first Technical Director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding President of the International Educational Data Mining Society, and as Associate Editor of the Journal of Educational Data Mining. His research combines educational data mining, learning analytics and quantitative field observation methods in order to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, and educational games. In recent years, he and his colleagues have developed strategies to make inferences in real-time about students’ motivation, meta-cognition, affect, and robust learning.

APRIL 26, 2013, 12:00-2:30PM, Rm. 6112 CUNY Graduate Center

“Causality and Statistical Learning”

Speaker: Andrew Gelman, Columbia University

Bio: Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, Joe Bafumi, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina).

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.