About Me

I am a PhD Stdent at Intelligent System Laboratory (ISL), Rensselaer Polytechnic Institute (RPI). My advisor is Professor Qiang Ji . I will be joining IBM as a Research Staff Member at T. J. Watson Research Center.

My research interests include Machine Learning and Computer Vision, specifically:

Graphical Models: - Local Structure Learning of Probabilistic Graphical Models; - Causal Discovery
Machine Learning: - Feature Extraction and Feature Selection
Applications: - Computer Vision: Human Pose Estimation and Action Recognition; - Affective Computing: Human Emotion Recognition and Facial Expression Recognition

Education

2010 - 2016

Ph.D.

Rensselaer Polytechnic Institute

Electrical Engineering

2006 - 2009

B.S., magna cum laude

Rensselaer Polytechnic Institute

Electrical Engineering, minor in Electronic Art

Research

Markov Blanket Discovery

We developed new efficient algorithms to learn local structures in the graphical models, in terms of Markov Blankets. These local structures provide an efficient approach of structure learning.
Specifically, my work involves:

Discovered a new coexistence property between possible false positive parent-child set and the spouses in a Markov Blanket.

Developed new efficient constraint-based and score-based algorithms that improve the state-of-the-art.

Feature Selection

We apply the Markov Blanket algorithms to feature selection. By considering structured information among variables, this approach demonstrates several optimalities for feature selection.
Specifically, my work involves:

Proved several optimalities of structured feature selection using Markov Blanket.

Developed algorithms for large-scale and hierarchical feature selection.

Paper Link

Causal Discovery

We developed a new algorithms to efficiently learn the direct causes and effects of a target variable by using Markov Blankets. The proposed local causal discovery algorithm drastically improves the efficiency from the state-of-the-art.
Specifically, my work involves:

Discovered that independence relationship changes among neighbours of the target can help detect direct causes and effects.

Designed a new algorithm for local causal discovery for direct causes and effects.

Paper Link

Latent Markov Blanket Discovery

We investigated the Markov Blanket discovery under the presence of latent variables. By finding these local latent variables, we can discover the previously unknown factors, get a more compact model with better generalization, and improve the classification results.
Specifically, my work involves:

developed an algorithm to discover the latent variables local to one target variable.

Other

Human Emotion Detection

Spring 2014 ~ Summer 2014, Collaboration with Cognitive Science Department, Rensselaer Polytechnic Institute, Troy, NY

Studied emotion similarities and differences among various human subjects while undergoing similar emotional experience.

Implemented and extracted signal features from a number of human physiological signals, including electromyogram, photoplethysmogram, and Galvanic skin responses.

Developed new feature selection techniques for within- and cross-subject emotion prediction.

Human Body Tracking and Gesture Recognition

Fall 2011 ~ Fall 2012, NSF Triple Helix Graduate Fellowship, Rensselaer Polytechnic Institute, Troy, NY

Developed a human gesture recognition system with Microsoft Kinect camera, using dynamic graphical models.

Developed a human pose estimation framework from depth image sequences to recognize upper body parts.

Designed and co-developed two education software with the Microsoft Kinect camera with computer vision and machine learning algorithms, providing middle school students with additional STEM learning tools.

Video Link

Publications

Journals: - Tian Gao, and Qiang Ji. "Efficient Markov Blanket Discovery and Its Applications", IEEE Transactions on Cybernetics. To Appear. 2016; - Tian Gao, and Qiang Ji. "Efficient Score-Based Markov Blanket Discovery" Int. J. Approx. Reason., 2016. Submitted.
Conferences: - Tian Gao, and Qiang Ji. "Constrained Local Latent Variable Discovery", International Joint Conference on Artificial Intelligence (IJCAI), 2016. Accepted.; - Tian Gao, and Qiang Ji. "Local Causal Discovery for Direct Causes and Effects", Neural Information Processing Systems (NIPS), 2015.; - Tian Gao, Ziheng Wang, and Qiang Ji. "Structured Feature Selection", International Conference on Computer Vision (ICCV), 2015.; - Ziheng Wang, Tian Gao, and Qiang Ji. "Learning with Hidden Information Using a Max-Margin Latent Variable Model". 22nd International Conference on Patter Recognition (ICPR), 1389-1394. IEEE, 2014.

Useful Resources

Probabilistic Graphical Models

Software by Kevin Murphy and students

Causal Discovery

Judea Pearl: Causal Analysis in Theory and Practice

The TETRAD Project

Other Machine Learning Tools

the UC Irvine Machine Learning Datasets Repository

Tian Gao

About Me

Research

Other

Publications

Useful Resources