Wei Xu is an associate professor in the School of Interactive Computing at the Georgia Institute of Technology. Xu received her Ph.D. in Computer Science from New York University, and her B.S. and M.S. from Tsinghua University. Her research interests are in natural language processing, machine learning, and social media. Her recent work focuses on text generation, stylistics, information extraction, robustness and controllability of machine learning models, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award, CrowdFlower AI for Everyone Award, Criteo Faculty Research Award, and Best Paper Award at COLING'18. She has also received funds from DARPA and IARPA and is part of the Machine Learning Center and NSF AI CARING Institute at Georgia Tech.

My research focuses on engineering-driven machine learning methodologies for real-world problems in manufacturing and healthcare. The objective is to develop new models that combine learning methods with domain knowledge, in order to improve (i) efficiency, performance, and interpretability of the learning models; and (ii) productivity, scalability, and security of the engineering systems. 

Research areas: Additive Manufacturing, Bio-manufacturing, Healthcare, Machine Learning, Statistics.

Location:
Jialei Chen, Ph.D.
Department of Statistics, University of Georgia 

Dr. Shihao Yang is an assistant professor in the School of Industrial & Systems Engineering at Georgia Tech. Prior to joining Georgia Tech, he was a post-doc in Biomedical Informatics at Harvard Medical School after finishing his PhD in statistics from Harvard University. Dr. Yang’s research focuses on data science for healthcare and physics, with special interest in electronic health records causal inference and dynamic system inverse problems.

Xu Chu is an assistant professor in the School of Computer Science at Georgia Tech. He obtained his Ph.D. degree from the University of Waterloo in late 2017, and joined Georgia Tech in Jan 2018. He is a recipient of the JP Morgan Faculty Research Fellow Award, the Microsoft Ph.D. fellowship award, and the David R. Cheriton fellowship award. 

He is broadly interested in data management systems and machine learning. In particular, he focuses on (1) how to leverage advanced machine learning techniques to solve hard and practical data management problems, such as large-scale data integration; and (2) how to build data management systems to tackle the common pain points in practical machine learning, such as the lack of high-quality labeled data.

Dr. Felix J. Herrmann is Georgia Research Alliance Eminent Scholar in Energy and a professor at the Georgia Institute of Technology with appointments in the Schools of Earth and Atmospheric Sciences, Computational Science and Engineering, and Electrical and Computer Engineering. Dr. Herrmann will be the 2019 Distinguished Lecturer of the Society of Exploration Geophysicists (SEG). 

Dr. Herrmann holds a M.Sc. and Ph.D. in Engineering Physics from the Delft University of Technology. He completed his postdoctoral studies at Stanford University and MIT before becoming a professor at the University of British Columbia's Department of Earth, Ocean, and Atmospheric Sciences. He joined the faculty of the Georgia Institute of Technology in October 2017. 

During his career, Dr. Herrmann has worked on the development of the next-generation of industrial acquisition and computational imaging technologies designed to improve the image quality in complex geological areas at vastly reduced costs and environmental impact. Aside from driving innovations, by leveraging recent developments in the mathematical and computational sciences, Dr. Herrmann has extensive experience working with industry. At the University of British Columbia, he was the founder and director of the Seismic Laboratory for Imaging and Modelling (SLIM), which hosted the industry Consortium SINBAD. Under his guidance, SLIM became a world leader in the successful integration of transformative scientific developments, such as compressive sensing, randomized linear algebra, and machine learning, into innovative approaches that tackle the most challenging imaging problems. With his move to the Georgia Institute of Technology, Dr. Herrmann plans to broaden his research program to include other imaging modalities. Dr. Herrmann was a long program participant at UCLA's Institute for Pure and Applied Mathematics in the Fall of 2004 and has been involved in public-private partnerships around the world. He serves on the editorial board of Geophysical Prospecting and on the SEG Research Committee.

Chris Gu is an Assistant Professor of Marketing in the Scheller College of Business at Georgia Institute of Technology. His research focuses on the quantitative study of the behaviors of individuals and organizations under various types of information constraints and economic structures, with the goal of improving their well-being. His current work focuses on understanding how consumers search for products under partially revealed information, how consumers adopt sustainable technologies under the influence of government policies, how companies decide about internal technology adoption and upgrade, and how social network connections influence individual crowdsourcing behaviors. He is an AMS Mary Kay Foundation Doctoral Dissertation Competition Finalist, and his research has received the ISMS Doctoral Dissertation Award.

Yao Xie is a Coca-Cola Foundation Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, which she joined in 2013 as an Assistant Professor. She also serves as Associate Director of Machine Learning and Data Science of the Center for Machine Learning. From September 2017 until March 2023 she was the Harold R. and Mary Anne Nash Early Career Professor. She was a Research Scientist at Duke University from 2012 to 2013. 

Her research lies at the intersection of statistics, machine learning, and optimization in providing theoretical guarantees and developing computationally efficient and statistically powerful methods for problems motivated by real-world applications. 

She is currently an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, Journal of the American Statistical Association: Theory and Methods, Sequential Analysis: Design Methods and Applications, INFORMS Journal on Data Science, and an Area Chair of NeurIPS and ICML.

Xiuwei Zhang is an Assistant Professor and J. Z. Liang Early Career Assistant Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology. Her research group works on applying machine learning and optimization skills in method development and data analysis for single-cell RNA-Seq data and other types of data on single cell level. The goal is to study cellular mechanisms during differentiation, development of cells and disease progression. 

Zhang was a postdoc researcher in Prof. Nir Yosef‘s group at UC Berkeley. She obtained a Ph.D. in computer science under the supervision of Prof. Bernard Moret in the Laboratory for Computational Biology and Bioinformatics, EPFL (École Polytechnique Fédérale de Lausanne), Switzerland. 

Before moving to the United States, she was a postdoc researcher in Dr. Sarah Teichmann’s group at the European Bioinformatics Institute (EBI) and Wellcome Trust Sanger Institute in Cambridge, UK. Zhang was supported by a Fellowship for Prospective Researchers and an Advanced Postdoc Mobility Fellowship from Swiss National Science Foundation (SNSF) from Aug. 2012 to Jul. 2015. She was a research fellow in the 2016 Simons Institute program on Algorithmic Challenges in Genomics. Her Erdös number is 3.

Tuo Zhao is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering and the school of Computational Science and Engineering (By Courtesy) at Georgia Tech. 

His research focuses on developing principled methodologies, nonconvex optimization algorithms and practical theories for machine learning (especially deep learning). He is also interested in natural language processing and actively contributing to open source software development for scientific computing. 

Tuo Zhao received his Ph.D. degree in Computer Science at Johns Hopkins University in 2016. He was a visiting scholar in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health from 2010 to 2012, and the Department of Operations Research and Financial Engineering at Princeton University from 2014 to 2016. 

He was the core member of the JHU team winning the INDI ADHD 200 global competition on fMRI imaging-based diagnosis classification in 2011. He received the Google summer of code awards from 2011 to 2014. He received the Siebel scholarship in 2014, the Baidu Fellowship in 2015-2016 and Google Faculty Research Award in 2020. He was the co-recipient of the 2016 ASA Best Student Paper Award on Statistical Computing and the 2016 INFORMS SAS Best Paper Award on Data Mining.

Prof. Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. His research develops data science solutions to address the high-stakes challenges on the web and in the society. He has pioneered the development of user models and network science tools to enhance the well-being and safety of people. Applications of his research widely span e-commerce, social media, finance, health, web, and cybersecurity. His methods to predict malicious users and false information have been widely adopted in practice (being used in production at Flipkart and Wikipedia) and taught at graduate level courses worldwide. He has received several awards including the ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and best paper awards from WWW and ICDM. His research has been the subject of a documentary and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He completed his postdoctoral training at Stanford University, received a Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.