Colloquium Series

Colloquim Series Event - Dr. Hal Daume III

Wednesday, October 15, 2014 - 6:00pm to 7:00pm
Davis Auditorium, Room 412, Shapiro SEPSR
500 West 120th Street
New York, NY 10027
United States

The classic framework of machine learning is: example in, prediction out. This is great when examples are fully available at all times, and when all parts of an example are relevant for making a prediction. But it is very different from how humans reason. We get some information. We may make a prediction. Or we may decide we need to get more information.

Matching on-the-fly: Sequential Allocation with Higher Power & Efficiency by Adam Kapelner

Thursday, March 6, 2014 - 10:00am to 11:30am
750 Costa Engineering Commons – Interschool Lab
Columbia University
New York, NY 10027
United States

Speaker: Adam Kapelner - PhD Candidate in Statistics, Wharton School of the University of Pennsylvania

Title: Matching on-the-fly: Sequential Allocation with Higher Power & Efficiency

Abstract:

Teaching the Craft of Code by David Pritchard

Thursday, February 27, 2014 - 10:00am to 11:00am
750 Costa Engineering Commons – Interschool Lab
Columbia University
New York, NY 10027
United States

Speaker: David Pritchard

Title: Teaching the Craft of Code

Abstract: Programming is a skill that is best learned actively. I will discuss several websites that I have developed with this aim: Computer Science Circles (Python), Websheets (Java), and a Java execution visualizer.

Data Science Insitute - Colloquium Series Event

Monday, March 10, 2014 - 9:00am to 10:30am
United States

Abstract: The capacity of a communication channel is the maximum rate at which information can be reliably transmitted over the channel. In this work I consider the capacity of the binary deletion channel, where bits are deleted independently with a certain probability. This represents perhaps the simplest channel with synchronization errors but a characterization of its capacity remains an open question. I will present several techniques to lower bound the capacity, including Markov chain methods, Poisson-repeat channels, and ideas from renewal theory.

Urban Data & Transport Planning with Transfers by Manuela M. Veloso

Thursday, February 13, 2014 - 6:00pm
Davis Auditorium, Room 412, Shapiro SEPSR
500 West 120th Street
New York, NY 10027
United States

Abstract: In this talk, I will present a few urban data sets and discuss extracted patterns and models, and their variability. Based on vehicle routes of several data sets, I will then present a novel transport planning algorithm with transfers and show the corresponding resource savings. This work is in collaboration with students, in particular with Brian Coltin.

Biography: Manuela Veloso’s long-term research goal is the effective construction of autonomous agents where cognition, perception, and action are combined to address planning, execution, and learning tasks. Her vision is that multiple intelligent robots with different sets of complementary capabilities will provide a seamless synergy of intelligence. Manuela Veloso’s research focuses on the continuous integration of reactive, deliberative planning, and control learning for teams of multiple agents acting in adversarial, dynamic, and uncertain environments. Her multiagent and multirobot research interests have been motivated by and experimented in the domain of robot soccer. Since 2009, she has been investigating indoor mobile, service, companion robots, CoBots, such that robots and humans interact in a symbiotic relationship building upon individual strengths and limitations. Veloso created and directs her CORAL overarching research lab for the research on intelligent agents that Collaborate, Observe, Reason, Act, and Learn. As of 2010, she has ten PhD students and has graduated other twenty one PhD students, whose theses are available at her website www.cs.cmu.edu/~mmv. She thanks her students for the compelling research that they jointly pursue.

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