PhD Proposal by Hang Wu

Event Details
  • Date/Time:
    • Thursday December 12, 2019 - Friday December 13, 2019
      9:00 am - 11:59 am
  • Location: Coda C1115 Druid Hills
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Summary Sentence: Machine Learning Thesis Proposal Presentation

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Hang Wu

Machine Learning Thesis Proposal Presentation


Date: December 12th, 2019

Time: 9:00 am - 11:00 am

Location: Coda C1115 Druid Hills 


Committee Members:

May D. Wang, PhD (Georgia Tech/Emory, Department of Biomedical Engineering) (Advisor)

Polo Duen Horng Chau, PhD (Georgia Tech, School of Computational Science & Engineering)

Justin Romberg, PhD (Georgia Tech, Department of Electrical Engineering)


Title: Adaptive Causal Inference using Learning-to-Learn Techniques



Causal inference appears in a wide range of domains, for example, causal relationships between molecules, the causal effect of a public policy, building invariant machine learning models. However, the limited sample size and the heterogeneity of causal models make it challenging to apply causal inference to real-world applications. While humans excel in learning from a few samples and quickly adapt to unseen tasks, can we build causal inference algorithms that have similar efficiency and flexibility? 


This proposal outlines our previous and proposed work for developing adaptive causal inference algorithms using learning-to-learn techniques. First, we present adaptive causal effect estimation algorithms, and demonstrate its applications in clinical decision support and recommendation systems. Second, we propose algorithms for quickly identifying multiple correlated causal graphs using learning-to-learn principles. Lastly, we present applications of causal inference in fairness of machine learning.


Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Public, Graduate students, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Dec 11, 2019 - 9:02am
  • Last Updated: Dec 11, 2019 - 9:02am