Georgia Institute of Technology

Aug 19, 2019

The Machine Learning Center at Georgia Tech (ML@GT) continues to grow each year, boasting over 120 faculty members from across all six colleges. In 2019-20, the center will add six new members to its faculty, including three women.

“ML@GT continues to grow and we are thrilled to welcome these new faculty members to our community. Their commitment to progress and service is important as we continue to expand as an institution and the fields of machine learning and artificial intelligence continue to evolve,” said Irfan Essa, director of ML@GT.

Fall 2019

 

Diyi Yang

Assistant Professor

College of Computing – School of Interactive Computing

Interested in computational social science, natural language processing, and machine learning, Yang’s goal is to understand human communication and build intelligent systems that support human to human interaction and human to computer interaction.

Yang earned a Best Paper honorable mention at 2019 SIGCHI, 2016 ICWSM, and 2012 International Conference on Web Intelligence. In 2012 she was a KDD Cup Champion.

Yang was previously a postdoc at Google AI and. Facebook PhD Fellow. She has also interned at Microsoft Research Asia and Redmond, Stanford University, and Wikimedia Foundation. She earned a Ph.D. from the Language Technologies Institute at Carnegie Mellon University.

 

Judy Hoffman

Assistant Professor

College of Computing – School of Interactive Computing

Joining the institute from Facebook AI Research, Hoffman brings a wealth of knowledge at the intersection of computer vision and machine learning. Her research tackles real-world variation and scale while minimizing human supervision. Hoffman develops learning algorithms that facilitate the transfer of information through semi-supervised and unsupervised model generalization and adaptation. Her thesis focused on transferrable representation learning for visual recognition.

Hoffman was previously a postdoctoral researcher at UC Berkeley after earning a Ph.D. in electrical engineering and computer engineering from UC Berkeley as well. She is a recipient of the NSF Graduate Fellowship and the Rosalie M. Stern Fellowship.

When she is not in the lab, Hoffman enjoys being outside, traveling, and hiking. Some recent favorite destinations are the Swiss Alps and Glacier National Park in Alaska.

 

Chao Zhang

Assistant Professor

College of Computing – School of Computational Science and Engineering

Zhang joins ML@GT after earning his Ph.D. in computer science from UIUC where he was advised by Jiawei Han. His research focuses on machine learning for unstructured text data and its applications with an emphasis on improving label efficiency and robustness of learning algorithms.

Zhang has received numerous awards including 2015 ECML/PKDD Best Student Paper Award Runner-Up and the 2013 Chiang Chen Overseas Graduate Fellowship.

 

Xiuwei Zhang

Assistant Professor

College of Computing – School of Computational Science and Engineering

Zhang’s research interests include applying machine learning and optimization skills in method development and data analysis for single-cell RNA-Seq data. She also analyzes and applies the evolution of biological data such as protein structures and biological networks.

Zhang earned her Ph.D. from the Ècole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Prior to moving to the United States, Zhang was a postdoc researcher at the European Bioinformatics Institute (EBI) and Wellcome Trust Sanger Institute in Cambridge, UK. She was also a 2016 Simons Institute research fellow in their program on Algorithmic Challenges in Genomics.

 

Debankur Mukherjee

Assistant Professor

College of Engineering - H. Milton Stewart School of Industrial and Systems Engineering

Prior to his arrival at Georgia Tech, Mukherjee was a Prager Assistant Professor at Brown University’s Division of Applied Mathematics. He received a B.Sc. from the University of Calcutta in 2012, an M. Stat. from the Indian Statistical Institute in May 2014, and a Ph.D. from the Netherlands' Eindhoven University of Technology in 2018.

Mukherjee’s research spans the areas of applied probability and stochastic networks, with applications in queueing theory, performance analysis, random graphs, and randomized algorithms. His primary focus is to address fundamental theoretical challenges that arise in data centers and cloud networks and provide key insights in understanding various trade-offs in designing efficient systems. Specifically, his current research interests include analyzing large-scale structured systems with limited flexibility and developing mean-field theoretical foundation for multi-layer neural networks.

Mukherjee received the Best Student Paper Award at ACM SIGMETRICS 2018 for introducing a stochastic comparison framework to study the impact of underlying network topologies on the performance of load balancing schemes in large-scale systems.

 

Spring 2020

 

Sehoon Ha

Assistant Professor

College of Computing – School of Interactive Computing

Ha is currently a research scientist at Google Brain and will join Georgia Tech in Spring 2020. Before joining Google, Ha worked at Carnegie Mellon University as a postdoctoral researcher and Disney Research as an associate research scientist.

He received his Ph.D. degree in Computer Science from Georgia Institute of Technology. Ha’s research interests lie at the intersection between computer graphics and robotics, including physics-based animation, deep reinforcement learning, and computational robot design.

His work has been published at top-tier venues including ACM Transactions on Graphics, IEEE Transactions on Robotics, and International Journal of Robotics Research. Ha has been nominated as the best conference paper (Top 3) in Robotics: Science and Systems, and featured in the popular media press such as IEEE Spectrum, MIT Technology Review, PBS News Hours, and Wired.

 

Srijan Kumar

Assistant Professor

College of Computing – School of Computational Science and Engineering

Kumar is currently a postdoctoral researcher at Stanford University and will be joining Georgia Tech in Spring 2020. He earned his bachelor’s degree in computer science and engineering from the Indian Institute of Technology, Kharagpur, and his Ph.D. in computer science from the University of Maryland, College Park.

Kumar creates network science, data science, and social computing models with the goal of enabling useful, safe, and trustful cyberspaces. His trust, safety, and integrity models are being used at major tech companies, including Flipkart, Reddit, and Wikipedia.  His research has been widely covered by the popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. 

He was the runner-up for the 2018 ACM SIGKIDD Doctoral Dissertation Award and 2017 WWW Best Paper Award. Kumar was also the recipient of the 2017 Larry S. Davis Doctoral Dissertation Award and the 2013 Dr. BC Roy Gold Medal.