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.

Chao Zhang is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology. His research area is data mining, machine learning, and natural language processing. His research aims to enable machines to understand text data in more label-efficient and robust way in open-world settings. Specific research topics include weakly-supervised learning, out-of-distribution generalization, interpretable machine learning, and knowledge extraction and reasoning. He is a recipient of Google Faculty Research Award, Amazon AWA Machine Learning Research Award, ACM SIGKDD Dissertation Runner-up Award, IMWUT distinguished paper award, and ECML/PKDD Best Student Paper Runner-up Award. Before joining Georgia Tech, he obtained his Ph.D. degree in Computer Science from University of Illinois at Urbana-Champaign in 2018.

While teaching textiles and fashion design studio classes at Savannah College of Art & Design, Zeagler realized his true passion lies in bridging the gap between the disciplines of Wearable design and Human-Centered Computing. A diverse background in fashion, industrial design, and textiles drive his research on electronic textiles and on-body interfaces with the Contextual Computing Group of the GVU center of Georgia Tech. As a Principal Research Scientist for the Georgia Tech Interactive Media Technology Center and Instructor for the Georgia Tech School of Industrial Design he teaches courses on Wearable Product Design and an ID section of Mobile and Ubiquitous Computing (MUC).  Zeagler enjoys working with corporations such as HP/Palm and Google to bring real-world experience into the classroom. He recently acquired a NASA Georgia Space Consortium grant to fund MUC student projects on wearable computing for space—a wonderful opportunity for undergraduate students. He is also a member of the NASA Wearable Technology Cluster a group of scientists and academics working together to give advice to those in NASA working on wearable computing or electronic textile projects. A deep understanding of the garment production process fosters innovation in his research. Zeagler’s company Pecan Pie Couture hand-dyed, embroidered, and screen-printed textiles and garments. Building upon that skillset, his recent research led to the creation of the Electronic Textile Interface Swatch Book (ESwatchBook) in collaboration with Thad Starner. The ESwatchBook is designed to help facilitate discussions between the skill and craft-based design disciplines (.i.e. fashion) and more technical disciplines (.i.e. computer science). To put the ESwatchBook’s capabilities to the test, he developed a series of workshops at multiple colleges with the purpose of bringing together designers with engineers/technology specialists. The workshops were funded by a National Endowment for the Arts grant, which he co-authored. Zeagler’s most recent endeavor FIDO: Facilitating Interactions for Dogs with Occupations is an exploration into using wearable electronics to enhance interactions between service dogs and their handler/owners.

I am currently a Senior Research Scientist at Georgia Tech working in the School of Computer Science in the College of Computing since 2015. Previously, I have worked as as a research scientist in the School of Computational Science and Engineering (CSE) from 2013 to 2015. This work focused on advanced user support and benchmarking for the Keeneland project and investigating architecture-related research topics for Dr. Jeff Vetter’s Future Technologies Group at Oak Ridge National Lab.

With a background in computer architecture, my main research interests are focused on the intersection of high-performance computing and novel accelerators including GPUs, Xeon Phi, FPGAs, and Arm SVE processors. I am currently working on a collaborative research program for near-memory computing with High Bandwidth Memory (HBM) for processors and GPUs, SuperSTARLU, which is funded by the NSF. I am co-director of Georgia Tech’s Center for High Performance Computing, and I am also the director of a novel architecture testbed, the CRNCH Rogues Gallery, that aims to simplify and democratize access to novel post-Moore accelerators in the neuromorphic, reversible, and novel networking spaces.

I defended my PhD in August 2013 in the area of computer architecture working under Dr. Sudhakar Yalamanchili. More information on this networks- and memory-related research can be found under the publications tab.

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.

Professor John Wise uses numerical simulations to study the formation and evolution of galaxies and their black holes. He is one of the lead developers of the community-driven, open-source astrophysics code Enzo and has vast experience running state-of-the-art simulations on the world’s largest supercomputers. He received his B.S. in Physics from the Georgia Institute of Technology in 2001. He then studied at Stanford University, where he received his Ph.D. in Physics in 2007. He went on to work at NASA’s Goddard Space Flight Center just outside of Washington, DC as a NASA Postdoctoral Fellow. Then in 2009, he was awarded the prestigious Hubble Fellowship which he took to Princeton University before arriving at Georgia Tech in 2011, coming back home after ten years roaming the nation.

My research focuses on three major areas: (a) understanding and improving worker well-being, (b) temporal dynamics in team contexts, and (c) research methods. Collectively, my research seeks to improve our understanding of optimal human functioning more generally, across time, and within specific contexts (e.g., organizational, teams).