GT MAP Seminar: Prof. Le Song (GT CSE)

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This is a part of the GT MAP activities on Optimal Transport.  GT MAP is a place for research discussion and collaboration. We welcome participation of any researcher interested in discussing his/her project and exchange ideas with Mathematicians.

There will be light refreshments through out the event. This seminar will be held in Skiles 005 and refreshments at Skiles Atrium.


A couple of members of Prof. Song's group will present their research

3:00 PM - 3:45PM Prof.  Le Song will give a talk on ``Efficient Prediction of User Activity using Mass Transport Equation"

3:45PM -- 4:00PM Break with Discussions

4:00PM - 4:25PM Second talk by Xinshi on ``sequential Monte Carlo problem with mass transportation"

4:25PM - 5PM Discussion of open problems stemming from the presentations.

Title: Efficient Prediction of User Activity using Mass Transport Equation

Abstract: Point processes such as Hawkes processes are powerful tools to model user activities and have a plethora of applications in social sciences. Predicting user activities based on point processes is a central problem which is typically solved via sampling. In this talk, I will describe an efficient method based on a differential-difference equation to compute the conditional probability mass function of point processes. This framework is applicable to general point processes prediction tasks, and achieves marked efficiency improvement in diverse real-world applications compared to existing methods.



Prof. Song obtained B.S. degree in computer science from the South China University of Technology, Guanzhou, China in 2002, received my Master's degree in 2004, and Ph.D. degree in 2008 both in computer science from the University of Sydney, Australia. Prof. Song was also a Ph.D. student with the Statistical Machine Learning Program at NICTA, and his thesis advisor is Alex Smola. Since Summer 2008, Prof. Song was a postdoc fellow at Carnegie Mellon Univeristy, working on machine learning and computational biology projects with Eric Xing, Carlos Guestrin, Geff Gordon and Jeff Schneider. Right before he joined Georgia Tech, he spent some time as a research scientist at Fernando Pereira's group at Google Research.



  • Workflow Status: Published
  • Created By: Sung Ha Kang
  • Created: 10/26/2018
  • Modified By: Sung Ha Kang
  • Modified: 11/19/2018

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