Machine Learning Meets Causal Inference

#machine #learning #artificial #intelligence #graph #embedding #enrichment #neural #networks #recommender #systems #representation
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Webex link for meeting: https://tinyurl.com/ybob6fxt

Meeting password: IEEE-ATL-CS-2020

  • Date: 25 Jun 2020
  • Time: 05:00 PM to 06:00 PM

 

Webex meeting. Please join us for a presentation by Dr. Sheng Li.
 
Talk Abstract: Causal inference is a critical research topic across many domains, such as statistics, education, political science and economics, for decades. Estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this talk, I will introduce how machine learning helps infer causality from observational data, and introduce classical causal inference methods as well as the recent deep learning based causal inference methods. Domain applications and future research directions will also be discussed.
 
Bio: Dr. Sheng Li is an Assistant Professor of Computer Science at the University of Georgia since 2018. He was a Research Scientist at Adobe Research from 2017 to 2018. Prior to that, he obtained the Ph.D. degree in computer engineering from Northeastern University in 2017. Dr. Li's research interests include graph based machine learning, deep learning, user behavior modeling, visual intelligence, and causal inference. He has published over 90 papers at peer-reviewed conferences and journals, and has received over 10 research awards, such as the Adobe Data Science Research Award, Baidu Research Fellowship, and SDM Best Paper Award. He serves as Associate Editor of five international journals including IEEE Computational Intelligence Magazine and Neurocomputing. He has also served as senior program committee member for AAAI, and program committee member for NeurIPS, ICML, KDD, IJCAI, ICCV, CVPR and ICLR. He is a senior member of IEEE.

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  • Date: 25 Jun 2020
  • Time: 09:00 PM UTC to 10:00 PM UTC
  • Add_To_Calendar_icon Add Event to Calendar
  • Webex link for meeting: https://tinyurl.com/ybob6fxt
  • Meeting password: IEEE-ATL-CS-2020
  • Atlanta, Georgia
  • United States
  • Contact Event Host
  • Starts 15 May 2020 01:00 PM UTC
  • Ends 25 June 2020 01:00 PM UTC
  • No Admission Charge

  Speakers

Dr. Sheng Li of University of Georgia, Department of Computer Science

Topic:

Knowledge-Guided Representation Learning on Graphs

Graph-structured data are ubiquitous, which are extensively used in domains like social networks and recommender systems. In this talk, I will present our recent work on graph representation learning, and discuss the applications in different domains. We aim to address the following research questions: (1) How to obtain effective embeddings for both nodes and edges in graphs? (2) How to enrich graph embeddings by using external knowledge? Novel graph neural networks for graph embedding and graph enrichment will be presented, with extensive evaluations on real-world datasets. Finally, I will discuss future work on graph embedding and graph reasoning.

Biography:

Dr. Sheng Li is an Assistant Professor of Computer Science at the University of Georgia since 2018. He was a Research Scientist at Adobe Research from 2017 to 2018. Prior to that, he obtained the Ph.D. degree in computer engineering from Northeastern University in 2017. Dr. Li's research interests include graph based machine learning, deep learning, user behavior modeling, visual intelligence, and causal inference. He has published over 90 papers at peer-reviewed conferences and journals, and has received over 10 research awards, such as the Adobe Data Science Research Award, Baidu Research Fellowship, and SDM Best Paper Award. He serves as Associate Editor of five international journals including IEEE Computational Intelligence Magazine and Neurocomputing. He has also served as senior program committee member for AAAI, and program committee member for NeurIPS, ICML, KDD, IJCAI, ICCV, CVPR and ICLR. He is a senior member of IEEE.




Agenda

5:00 pm -- Webex meeting starts

6:00 pm -- Meeting ends