Sridhar Narasimhan is Professor of IT Management and Co-Director -Business Analytics Center (BAC), Scheller College of Business. The BAC partners with its Executive Council companies in the analytics space and supports Scheller’s BSBA, MBA, and MS Analytics programs. Professor Narasimhan has developed and taught the MBA IT Practicum course. Since 2016, he has been teaching Business Analytics to undergraduate and MBA students at Scheller. 

Professor Narasimhan is the founder and first Area Coordinator of the nationally ranked Information Technology Management area. In fall 2010, he was the Acting Dean and led the College in its successful AACSB Maintenance of Accreditation effort. He was Senior Associate Dean from 2007 through 2015.

Shamkant B. Navathe is a noted researcher in the field of databases with more than 150 publications on different topics in the area of databases. 

He is a professor in the College of Computing at Georgia Institute of Technology and founded the Research Group in Database Systems at the College of Computing at Georgia Institute of Technology (popularly called Georgia Tech). He has been at Georgia Tech since 1990. He has been teaching in the database area since 1975 and his textbook Fundamentals of Database Systems (with Ramez Elmasri, published by Pearson, Seventh Edition, 2015) has been a leading textbook in the database area worldwide for the last 19 years. It is now in its seventh edition and is used as a standard textbook in India, Europe, South America, Australia and South-east Asia. The book has been translated into Spanish, German, French, Italian, Portuguese, Chinese, Korean, Greek, and in Arabic.

His research is in the area of bioinformatics. Navathe is working in advisory roles with Indian companies like Tata Consultancy Services (TCS), and Persistent Systems. He is also consultant for companies in information systems and software products design area and is an independent director of GTL Limited, a Mumbai-based telecommunications company.

Nepomuk Otte is a Georgia Tech Professor of Physics. When not working on his astrophysics research programs, his mind revolves around flying. His passion for flying started at a very early age but never turned into a rating. That was until 2020, when he joined the Yellow Jacket Flying Club (YJFC) and eight months later was an instrument-rated private pilot. Although he never saw himself instructing, his flight instructor and DPE encouraged him, and it turns out he loves every bit of it.

His main research is about understanding the acceleration of charged particles (cosmic rays) in pulsars and using gamma-rays as probes of Lorentz invariance violation but his group also deviates and does other interesting research in the VHE gamma-ray band. They are members of the VERITAS Cherenkov telescope array and participate in the development and construction of the next generation VHE instrument the Cherenkov Telescope Array CTA.

Ben Wang is Professor Emeritus in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. In addition, Dr. Wang previously served as the Executive Director of the Georgia Tech Manufacturing Institute. 

Dr. Wang's primary research interest is in applying emerging technologies to improve manufacturing competitiveness. He specializes in process development for affordable composite materials. Dr. Wang is widely acknowledged as a pioneer in the growing field of nanomaterials science. His main area of research involves a material known as "buckypaper", which has shown promise in a variety of applications, including the development of aerospace structures, improvements in energy and power efficiency, enhancements in thermal management of engineering systems, and construction of the next-generation of computer displays.

Dr. Wang served on the National Materials and Manufacturing Board (NMMB). NMMB is the principal forum at the U.S. National Academies for issues related to innovative materials and advanced manufacturing, and has oversight responsibility for National Research Council activities in these technology areas. Dr. Wang is a Fellow of the Institute of Industrial Engineers, the Society of Manufacturing Engineers, and the Society for the Advancement of Material and Process Engineering.

Because of his contributions to advanced manufacturing and materials, Dr. Wang was invited to deliver a presentation to the U.S. National Research Council Review Panel in support of the U.S. National Nanotechnology Initiative in 2005. In 2012, he was invited to give testimony before the National Academies Committee on Manufacturing Extension Partnership. In 2012 he was invited to participate in the Roundtable on Strengthening U.S. Advanced Manufacturing in Clean Energy in the White House.

In addition to authoring or co-authoring more than 240 refereed journal papers, he is a co-author of three books: Computer-Aided Manufacturing (Prentice-Hall, 1st Edition, 2nd Edition, and 3rd Edition), Computer-Aided Process Planning (Elsevier Science Publishers), and Computer Aided Manufacturing PC Application Software (Delmar Publishers).

Dr. Wang earned his bachelor's in industrial engineering from Tunghai University in Taiwan, and his master's in industrial engineering and Ph.D. from Pennsylvania State 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.

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!

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.

I am an engineer and neuroscientist focused on how the brain and body cooperate to allow us to move. Fundamental abilities like standing and walking appear effortless until we–or someone we love–loses that ability. Movement is impacted in a wide range of diseases because it involves almost all parts of the brain and body, and their interactions with the environment. How we move is also highly individualized, changing across our lifetimes as a function of our experiences, and adapting in different situations. As such, assessing and treating movement impairments remains highly challenging. My approach is to dissect the complexities of how we move in health and disease by bridging what may seem to be disparate fields across engineering, neuroscience, and physiology. Our current application areas are Parkinson’s disease, stroke, aging and cerebral palsy, and we are interested in extending our work toward mild cognitive impairment and concussion.

My lab uses robotics, computation, and artificial intelligence to identify new physiological principles of sensing and moving that are enabling researchers to personalize rehabilitation and medicine. Primarily, we study people in the lab, studying brain and muscle activity in relationship to the body’s biomechanics in standing and walking. We use and develop robotic devices for assessing and assisting human movement, while interpreting brain and muscle activity to personalize the interactions. Our novel computer simulations of muscle, neurons, and joints establish a virtual platform for predicting how movements change in disease and improve with interventions. Recently, we have demonstrated the critical role of cognitive function motor impairment that may increase fall risk, suggesting that how we move and how we think may be closely related. Current projects include developing physiologically-inspired controllers to enable exoskeletons to enhance user balance, identifing individual differences that predict response to gait rehabilitation in stroke survivors, and developing more precise and physiologically-based methods to interpret clinical motor test outcomes.

Nicoleta Serban is the Peterson Professor of Pediatric Research in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Dr. Serban's most recent research focuses on model-based data mining for functional data, spatio-temporal data with applications to industrial economics with a focus on service distribution and nonparametric statistical methods motivated by recent applications from proteomics and genomics. 

She received her B.S. in Mathematics and an M.S. in Theoretical Statistics and Stochastic Processes from the University of Bucharest. She went on to earn her Ph.D. in Statistics at Carnegie Mellon University.

Dr. Serban's research interests on Health Analytics span various dimensions including large-scale data representation with a focus on processing patient-level health information into data features dictated by various considerations, such as data-generation process and data sparsity; machine learning and statistical modeling to acquire knowledge from a compilation of health-related datasets with a focus on geographic and temporal variations; and integration of statistical estIMaTes into informed decision making in healthcare delivery and into managing the complexity of the healthcare system.