PhD Defense by Nan Du

Event Details
  • Date/Time:
    • Friday December 4, 2015
      2:00 pm - 4:00 pm
  • Location: KACB 1315
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Summary Sentence: Learning, Inference, and Optimization of High-Dimensional Asynchronous Event Data

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Title : Learning, Inference, and Optimization of High-Dimensional Asynchronous Event Data

 

Nan Du

School of Computational Science and Engineering College of Computing Georgia Institute of Technology

 

Date : Friday, December 04, 2015

Time : 10:00 AM to 12:00 PM EST

Location : KACB 1315

 

Committee

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Dr. Le Song (Advisor), School of Computational Science and Engineering, Georgia Institute of Technology Dr. Hongyuan Zha, School of Computational Science and Engineering, Georgia Institute of Technology Dr. Yu (Jeffrey) Hu, Scheller College of Business, Georgia Institute of Technology Dr. Christos Faloutsos, School of Computer Science, Carnegie Mellon University Dr. Evgeniy Gabrilovich, Senior Staff Research Scientist, Google Research

 

Abstract

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The increasing availability and granularity of temporal and spatial event data induced from user activities in on-line media, social networks and medical informatics provide new opportunities and challenges to model and understand user behaviors. In addition to study the macroscopic patterns on the population level, such type of data further enable us to investigate user interactions in a more fine-grained scale to address the "who will do what by when and where ?" question with new exploratory and predictive models. On the other hand, because these myriads of microscopic event data, such as publishing a post, forwarding a tweet, purchasing a product, checking in a place, often arise asynchronously and interdependently, they require new representing and analyzing methods far beyond those based on independent and identically distributed data models. In this dissertation, we present a novel probabilistic framework for modeling and reasoning about repeated sequences of event data. Within the proposed framework, we introduce a pipeline of newly developed statistical models, state-of-the-arts learning algorithms and a general purpose software library for multivariate point process to tackle several canonical problems in practice, including : (1) latent network structure recovery, (2) continuous-time influence estimation and maximization, (3) time-sensitive recommendation and (4) temporal document clustering, in the field of social network analysis, recommendation systems, and document modeling.

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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 25, 2015 - 8:35am
  • Last Updated: Oct 7, 2016 - 10:15pm