Events Calendar
This feed is a curated compilation of entrepreneurship events around the university.
Columbia Data Science Institute
DEADLINE: Data Science Day Demo and Poster Submissions
The Data Science Institute is excited to begin soliciting demos and posters from CU faculty and their students to be shown at our upcoming Data Science Day @ Columbia University: WHEN: Wednesday, April 5, 2017 WHERE: Roone Arledge Auditorium, Lerner Hall TIME: Invited Speakers: 9AM-2PM | Demos + Posters: 2PM-5PM SUBMISSION DEADLINE: Wednesday, March […]
YES, You Really Can Use This! — Applying Data Science to Real-World Problems
Davis Auditorium Schapiro, New York City, United StatesABSTRACT: Booz Allen Hamilton’s Dan Liebermann and Ben Arancibia will cover what it takes to get data science done in the real world. They will be sharing stories from the trenches – covering experiences and lessons learned from turning data science theory into reality when the problem (and the solution) are far from known. The […]
Third Annual Data Science Day
AGENDA Keynote Speaker: Alfred Spector Chief Technology Officer and Head of Engineering at Two Sigma "Opportunities and Perils in Data Science" Lightning Talk Sessions: Our Connected World Suman Jana, Assistant Professor of Computer Science Susan McGregor, Assistant Professor of Journalism; Assistant Director, Tow Center for Digital Journalism Hollie Russon-Gilman, Lecturer in International and Public Affairs Applications […]
Recent Advances in Post-Selection Statistical Inference
Davis Auditorium Schapiro, New York City, United StatesThe Joint Colloquium Series, hosted by the Data Science Institute and the Department of Statistics, presents Recent Advances in Post-Selection Statistical Inference" with Professor Robert Tibshirani of Stanford University. In this era of big data and complex statistical modeling, scientists use sophisticated computational tools to search through a large number of models, looking for meaningful […]
Data Science Institute-Industry-Innovation Seminar: Surveillance and Social Situational Awareness
CEPSR 750 New York CityThis talk will describe a variety of methods that have been developed for the purposes of understanding group level social behaviors using stand-off video surveillance methods. Three main topics are […]
Data Science Institute-Industry-Innovation Seminar: Peter Marx, General Electric
Schapiro Building Room 750 530 West 120th Street, New York City, United StatesPeter Marx, Vice President, Advanced Projects, GE Digital Adjunct Professor, USC TITLE: How the New Availability of Urban and Industrial Data are Impacting Our World from Public Safety to Jet […]
Improving Health-Care: Challenges and Opportunities for Reinforcement Learning
Davis Auditorium Schapiro, New York City, United StatesReinforcement learning offers a powerful paradigm for automatically discovering and optimizing sequential treatments for chronic and life-threatening diseases. In particular, we will focus on how data collected in multi-stage sequential trials can be used to automatically generate treatment strategies that are tailored to patient characteristics and time-dependent outcomes. We will also examine promising methods to […]
Data Science Institute Colloquium: Kevin Murphy | Research Scientist, Google (Keynote)
OPEN TO ALL - NO REGISTRATION REQUIRED Towards Machines that Perceive and Communicate ABSTRACT: I will summarize some recent work related to visual scene understanding and "grounded" language understanding. In […]
Data, Ethics and Decision-Making Lecture Series: Dr. Jeanette M. Wing, Avanessians Director of the Data Science Institute
OPEN TO ALL ABSTRACT: The Data, Ethics, and Decision-making Speaker Series hosted by the Institute for Social and Economic Research and Policy (ISERP) presents Dr. Jeannette M. Wing, Avanessians Director of the Data Science Institute and Professor of Computer Science in at Columbia University, on "Using Data for Good: What does it mean?". Professor Wing […]
Data Science Institute Colloquium Series Event: What Can Deep Learning Learn from Linear Regression
OPEN TO ALL ABSTRACT When training large-scale deep neural networks for pattern recognition, hundreds of hours on clusters of GPUs are required to achieve state-of-the-art performance. Improved optimization algorithms could potentially enable faster industrial prototyping and make training contemporary models more accessible. In this talk, I will attempt to distill the key difficulties in optimizing […]