My work is on excitable media, complex systems, and pattern formation, using a combined approach of theory, experiments, and computer simulations.

Interested in: Complex Systems, Experimental physiology, Biophysics, High performance computing and GPU.

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.

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!

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.

Susan Lozier is a physical oceanographer and the dean of the Georgia Institute of Technology's College of Sciences. Previously, she was the Ronie-Richelle Garcia-Johnson Professor of Earth and Ocean Sciences in the Nicholas School of the Environment at Duke University in Durham, North Carolina. Her research focuses on large-scale ocean circulation, the ocean's role in climate variability, and the transfer of heat and fresh water from one part of the ocean to another.

Lozier received her Bachelor of Science degree from Purdue University in 1979, and her Master of Science (1984) and Doctor of Philosophy (1989) degrees from the University of Washington.

Lozier was a post-doctoral fellow at the Woods Hole Oceanographic Institution before joining the faculty at Duke University. She is a principal investigator for the Overturning in the Subpolar North Atlantic Program (OSNAP), responsible for coordinating its international and national projects. She was the first woman to graduate from the University of Washington's physical oceanography doctoral program, and is active in the community mentoring program, MPOWIR (Mentoring Physical Oceanography Women to Increase Retention). In 2020 she was elected to the American Academy of Arts and Sciences.

Lozier was the featured speaker for the 16th Annual Roger Revelle Annual Commemorative Lecture, sponsored by the National Academies and held at the National Museum of Natural History in Washington, D.C., on March 4, 2015, presenting her lecture on Overturning Assumptions: Past, Present, and Future Concerns about the Ocean's Circulation. She started a two-year term as president of the American Geophysical Union in 2021.

Hannah Choi is an Assistant Professor in the School of Mathematics at Georgia Tech. Her research focuses on mathematical approaches to neuroscience, with primary interests in linking structures, dynamics, and computation in data-driven brain networks at multiple scales. Before coming to Georgia Tech, she was a postdoctoral fellow at the University of Washington and also a visiting scientist at the Allen Institute for Brain Science, and spent one semester at the Simons Institute for the Theory of Computing at the University of California, Berkeley as a Patrick J McGovern Research Fellow. She received her Ph.D. in Applied Mathematics from Northwestern University and her BA in Applied Mathematics from the University of California, Berkeley.

Professor Nagi Gebraeel is the Georgia Power Early Career Professor and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his MS and PhD from Purdue University in 1998 and 2003, respectively.

Dr. Gebraeel's research interests lie at the intersection of Predictive Analytics and Machine Learning in IoT enabled maintenance, repair and operations (MRO) and service logistics. His key focus is on developing fundamental statistical learning algorithms specifically tailored for real-time equipment diagnostics and prognostics, and optimization models for subsequent operational and logistical decision-making in IoT ecosystems. Dr. Gebraeel also develops cyber-security algorithms intended to protect IoT-enabled critical assets from ICS-type cyberattacks (cyberattacks that target Industrial Control Systems). From the standpoint of application domains, Dr. Gebraeel has general interests in manufacturing, power generation, and service-type industries. Applications in Deep Space missions are a recent addition to his research interests, specifically, developing Self-Aware Deep Space Habitats through NASA's HOME Space Technology Research Institute.

Dr. Gebraeel leads Predictive Analytics and Intelligent Systems (PAIS) research group at Georgia Tech's Supply Chain and Logistics Institute. He also directs activities and testing at the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute. Formerly, Dr. Gebraeel served as an associate director at Georgia Tech's Strategic Energy Institute (from 2014 until 2019) where he was responsible for identifying and promoting research initiatives and thought-leadership at the intersection of Data Science and Energy applications. He was also the former president of the Institute of Industrial and Systems Engineers (IISE) Quality and Reliability Engineering Division, and is currently a member of the Institute for Operations Research and the Management Sciences (INFORMS), and IISE (since 2005).

Omar I. Asensio is an Associate Professor in the Jimmy and Rosalynn Carter School of Public Policy and the Director of the Data Science & Policy Lab at Georgia Tech. During the 2023-2024 academic year, he was a fellow at the Institute for Business in Global Society at Harvard Business School. Professor Asensio’s research focuses on climate and electrification strategies at the intersection of technology, AI, and sustainability. He employs large-scale data, field experiments, and human-in-the-loop AI systems to address innovation challenges in energy systems, transportation, and human mobility. He contributed to the zero emission vehicles (ZEV) policy guidance for COP26 and the Glasgow Climate Pact.

Prof. Asensio is a member of the U.S. National Academies of Sciences, Engineering, and Medicine (NASEM) New Voices 2021 cohort, which recognizes early- to-mid career leaders for exceptional contributions to science, engineering and medicine. He is a two-time former chair of the Natural Resource, Energy, and Environmental Policy section of APPAM, and is the recipient of the 2023 Faculty Excellence in Research Award from the Ivan Allen College. At Georgia Tech, he is a Brook Byers Institute for Sustainable Systems (BBISS) Fellow and a faculty affiliate of the Institute for Data Engineering & Science (IDEaS), the Machine Learning Center, and the Strategic Energy Institute (SEI).

Professor Asensio has received multiple awards for his research, including the National Science Foundation CAREER Award, the Alliance for Research on Corporate Sustainability (ARCS) Emerging Scholar Award, and the Research Impact on Practice Award (RIPA) from the Academy of Management’s Organizations & the Natural Environment Division (ONE-NBS). His work has been published in leading journals such as Nature Energy, Nature Sustainability, and PNAS. 

Professor Asensio’s research and teaching have been supported by awards from the National Science Foundation, Microsoft, ESRI, the U.S. State Department’s Diplomacy Lab, and the U.S. Department of Energy. His work has informed policy advisory communications for the U.S. National Academy of Sciences, the UK government, the United Nations Economic Commission for Latin America and the Caribbean, and the IndiaAI initiative. His research has been featured in popular press, including Bloomberg, Scientific American, Motor Trend, Fast Company, NPR’s All Things Considered, Yahoo! News, The Huffington Post, and the Washington Post.

Dr. Asensio serves as Associate Editor of Data & Policy journal published by Cambridge University Press. He earned his doctorate in Environmental Science & Engineering with specialties in Economics from UCLA.

Tom Conte holds a joint appointment in the Schools of Electrical & Computer Engineering and Computer Science at the Georgia Institute of Technology. He is the founding director of the Center for Research into Novel Computing Hierarchies (CRNCH). His research is in the areas of computer architecture and compiler optimization, with emphasis on manycore architectures, microprocessor architectures, back-end compiler code generation, architectural performance evaluation and embedded computer system architectures.

Edmond Chow is a Professor in the School of Computational Science in the College of Computing. He previously held positions at D. E. Shaw Research and Lawrence Livermore National Laboratory. His research is in developing and applying numerical methods and high-performance computing to solve large-scale scientific computing problems and seeks to enable scientists and engineers to solve larger problems more efficiently using physical simulation. Specific interests include numerical linear algebra (preconditioning, multilevel methods, sparse matrix computations) and parallel methods for quantum chemistry, molecular dynamics, and Brownian/Stokesian dynamics.  Chow earned an Honors B.A.Sc. in systems design engineering from the University of Waterloo, Canada, in 1993, and a Ph.D. in computer science with a minor in aerospace engineering from the University of Minnesota in 1997. Chow was awarded the 2009 ACM Gordon Bell prize and the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE).