Kaye Husbands Fealing is the Assistant Director of the Social, Behavioral and Economic Sciences at the National Science Foundation (NSF) and co-chair of the Subcommittee on Social and Behavioral Sciences of the Committee on Science of the National Science & Technology Council (NSTC). She is the former Dean of the Ivan Allen College of Liberal Arts at the Georgia Institute of Technology and a former Chair of the School of Public Policy Georgia Tech, where she currently holds the title professor. She specializes in science of science and innovation policy, the public value of research expenditures, and broadening participation in STEM fields and the workforce.

Prior to her positions at Georgia Tech, Husbands Fealing taught at the Humphrey School of Public Affairs, University of Minnesota, and she was a study director at the National Academy of Sciences. Prior to the Humphrey School, she was the William Brough professor of economics at Williams College, where she began her teaching career in 1989. She developed and was the inaugural program director for NSF's Science of Science and Innovation Policy program and co-chaired the Science of Science Policy Interagency Task Group, chartered by the Social, Behavioral and Economic Sciences Subcommittee of the NSTC. At NSF, she also served as an Economics Program director. Husbands Fealing was a visiting scholar at Massachusetts Institute of Technology’s Center for Technology Policy and Industrial Development, where she conducted research on NAFTA’s impact on the Mexican and Canadian automotive industries, and research on strategic alliances between aircraft contractors and their subcontractors.

Husbands Fealing is an elected member of the American Academy of Arts and Sciences, is an Elected Fellow of the National Academy of Public Administration, an Elected Fellow of the American Association for the Advancement of Science (AAAS). She was awarded the 2023 Carolyn Shaw Bell Award from the American Economic Association's Committee on the Status of Women in the Economics Profession, and the 2017 Trailblazer Award from the National Medical Association Council on Concerns of Women Physicians. She is a member of the International Women’s Forum-Georgia Chapter, and member of the YWCA's Academy of Women Achievers. She serves as a member on AAAS' Executive Board, the National Academy of Public Administration's board, the trustee board for the R. Howard Dobbs Jr. Foundation, and the Society for Economic Measurement's board. She has served on several committees and panels, including: AAAS committees; National Academies’ panels; Council of Canadian Academies panels; American Academy of Arts and Sciences working groups; NSF’s Social, Behavioral, and Economic Sciences Advisory Committee, STEM Education Advisory Committee, and the Committee on Equal Opportunities in Science and Engineering; NIH’s National Institute of General Medical Sciences Council; General Accountability Office’s Science, Technology Assessment, and Analytics Polaris Council; and American Economic Association’s Committee on the Status of Women in the Economic Profession. At Georgia Tech, she co-chaired the Arts@Tech Institute Strategic Planning committee, and she has served on the Institute for Data Engineering and Science Council, the Intellectual Property Advisory Board, and other committees.

Husbands Fealing holds a Ph.D. in economics from Harvard University, and a B.A. in mathematics and economics from the University of Pennsylvania.

Dr. Justin Romberg is the Schlumberger Professor and the Associate Chair for Research in the School of Electrical and Computer Engineering and the Associate Director for the Center for Machine Learning at Georgia Tech.

Dr. Romberg received the B.S.E.E. (1997), M.S. (1999) and Ph.D. (2004) degrees from Rice University in Houston, Texas. From Fall 2003 until Fall 2006, he was a Postdoctoral Scholar in Applied and Computational Mathematics at the California Institute of Technology. He spent the Summer of 2000 as a researcher at Xerox PARC, the Fall of 2003 as a visitor at the Laboratoire Jacques-Louis Lions in Paris, and the Fall of 2004 as a Fellow at UCLA's Institute for Pure and Applied Mathematics. In the Fall of 2006, he joined the Georgia Tech ECE faculty. In 2008 he received an ONR Young Investigator Award, in 2009 he received a PECASE award and a Packard Fellowship, and in 2010 he was named a Rice University Outstanding Young Engineering Alumnus. He is currently on the editorial board for the SIAM Journal on the Mathematics of Data Science, and is a Fellow of the IEEE.

His research interests lie on the intersection of signal processing, machine learning, optimization, and applied probability.

Dr. Duke has led over $21 million in funded research for industry, government, and foundation partners. Dr. Duke’s research focuses on advancing techniques for identifying patients of interest from diverse data sources with applications spanning research, quality, and clinical domains. He led the Merck-Regenstrief Partnership in Healthcare Innovation and was a founding member of OHDSI, an open-source international health data analytics collaborative. In addition to numerous peer-reviewed publications, his work has been featured in the lay media including the New York Times, NPR, and MSNBC. Dr. Duke completed his medical degree at Harvard Medical School and a master's in human-computer interaction at Indiana University.

John received the B.S. degree in Mathematics at Bucknell University in Lewisburg, Pennsylvania (1983) and Sc.M. and Ph.D. degrees in Computer Science at Brown University in Providence, Rhode Island (1985 and 1989). He joined the faculty at Georgia Tech in 1989, and he is presently a Regents Professor in the School of Interactive Computing (IC) in the College of Computing. From 2021-2022, he served as the Interim School Chair of IC as well. John is additionally an Adjunct Faculty member of the School of Computing Instruction at GT, as he regularly teaches one of the large CS intro courses. In 2013, John was named an Honorary Professor in the School of Computer Science at the University of St. Andrews in Scotland. 

John's primary research areas are data and information visualization, approaching each from a human-computer interaction perspective. In 2013, he served as General Chair of the IEEE VIS conference, the flagship academic conference for his research area, when it was held in Atlanta. John received the 2012 IEEE VGTC Visualization Technical Achievement Award, and he was inducted into the ACM CHI Academy in 2016 and the IEEE VIS Academy in 2019. John was named an IEEE Fellow in 2014 and an ACM Fellow in 2022. On the instructional side, John has twice received the College of Computing's annual gus baird Teaching Award. 

John is Director of the Information Interfaces Research Group whose mission is to help people take advantage of information to enrich their lives. As the amount of data available to people and organizations has skyrocketed over the past 10-20 years, largely fueled by the growth of the internet, insufficient methods for people to benefit from this flood of data have been developed. A central focus of many of the group's projects is the creation of information visualization and visual analytics tools to help people explore, analyze, understand, and communicate data sets. In particular, they are creating visual analytics systems to help people with "sense-making" activities on data sets such as large document collections. The group also has developed many techniques and systems for providing people with peripheral awareness of useful information. John's passion about research in, and the value of, data visualization is illustrated in his EuroVis 2014 Conference Capstone invited lecture. He describes his more recent research on designing flexible and natural interfaces for human-data interaction in this 2022 CSIG-VIS lecture.

Joel Sokol is the Harold E. Smalley Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He is also Director of the interdisciplinary Master of Science in Analytics degree (on-campus and online).

His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol's LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize and been a finalist for the Cozzarelli Prize.

Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, held the Fouts Family Associate Professorship for a three-year term, and is the recipient of Georgia Tech's highest awards for teaching. He served two terms as INFORMS Vice President of Education, and is a past Chair and founding officer of the INFORMS section on sports operations research.

Dr. Sokol's Ph.D. in operations research is from MIT, and his bachelor's degrees in mathematics, computer science, and applied sciences in engineering are from Rutgers University.

The research in our group bridges the gap between applied electronic structure theory and first-principles molecular simulation to enable predictive computational discovery of new materials and new chemistry. This research relies heavily on sophisticated high-performance and high-throughput computing paradigms, employing modern graphics processing unit (GPU) based computing. A primary focus is electrochemistry and electrochemical energy storage applications, and we seek to develop a fundamental understanding of how redox chemistry and other chemical and physical processes are modulated by strong electric fields. We are interested in chemical reaction mechanisms within highly ionic and heterogeneous environments, and are developing multi-scale modeling approaches to study chemical reactivity in the condensed phase. This method development includes novel QM/MM approaches and machine-learning reactive force fields, which are combined with enhanced sampling molecular dynamics/Monte Carlo techniques. Please see our research group website for more details!

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.

George Lan is an A. Russell Chandler III Professor of Industrial and Systems Engineering at Georgia Institute of Technology.  His research and teaching interests lie in theory, algorithms and applications of stochastic optimization and nonlinear programming.  Most of his current research concerns the design of efficient algorithms for solving challenging optimization problems, especially those arising from data analytics, machine learning, and reinforcement learning. He is actively pursuing the applications of these methodologies in healthcare and sustainability areas. Dr. Lan serves as the associate editor for Computational Optimization and Applications (2014 – present), Mathematical Programming (2016 – present) and SIAM Journal on Optimization (2016  – present). Dr. Lan is an associate director for the center of machine learning at Georgia Tech.

Dr. Gari Clifford is a tenured Professor of Biomedical Informatics and Biomedical Engineering at Emory University and the Georgia Institute of Technology, and the Chair of the Department of Biomedical Informatics (BMI) at Emory. His research focuses on the application of signal processing and machine learning to medicine to classify, track and predict health and illness. His focus research areas include critical care, digital psychiatry, global health, mHealth, neuroinformatics and perinatal health. After training in Theoretical Physics, he transitioned to AI and Engineering for his doctorate (DPhil) at the University of Oxford in the 1990’s. He subsequently joined MIT as a postdoctoral fellow, then Principal Research Scientist where he managed the creation of the MIMIC II database, the largest open access critical care database in the world. He later returned as an Associate Professor of Biomedical Engineering to Oxford, where he helped found its Sleep & Circadian Neuroscience Institute and served as Director of the Centre for Doctoral Training in Healthcare Innovation at the Oxford Institute of Biomedical Engineering. As Chair, Dr Clifford has established BMI as a leading center for critical care and mHealth informatics, and as a champion for open access data and open source software in medicine, particularly through his leadership of the PhysioNet/CinC Challenges and contributions to the PhysioNet Resource. Despite this, he is a strong supporter of commercial translation, working closely with industry, and serves as CTO of MindChild Medical, a spin out from his research at MIT.

Whether we’re driving a car, cooking dinner, performing a psychology experiment, or even watching television, we’re performing goal-directed behavior. We must keep track of our current goal (e.g., to cook dinner), so that we do not execute responses inappropriate for the present situation (e.g., sitting down to watch television). Yet, we must also flexibly adapt our goals to changing situations. For example, we must override our “cooking” goal with an “answering” one when we hear the doorbell ring. My research focuses on the mental processes required to carry out these and other types of goal-directed behavior.

A complicated set of mental processes are involved in behaviors like these. In addition to maintaining and updating our goals, we must attend to relevant stimuli, store relevant information in memory, and select and execute appropriate responses. What is the nature of these processes? How do they interact? What are their limitations? How do they change with training? And what are the neural mechanisms underlying them? These are the types of questions I investigate using a variety of experimental techniques: including behavioral testing, functional neuroimaging, and magnetic stimulation.