Srinivas Aluru is executive director of the Institute for Data Engineering and Science (IDEaS) and professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He co-leads the NSF South Big Data Regional Innovation Hub which nurtures big data partnerships between organizations in the 16 Southern States and Washington D.C., and the NSF Transdisciplinary Research Institute for Advancing Data Science. Aluru conducts research in high performance computing, large-scale data analysis, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. An early pioneer in big data, Aluru led one of the eight inaugural mid-scale NSF-NIH Big Data projects awarded in the first round of federal big data investments in 2012. He has contributed to NITRD and OSTP led white house workshops, and NSF and DOE led efforts to create and nurture research in big data and exascale computing. He is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, the John. V. Atanasoff Discovery Award from Iowa State University, and the Outstanding Senior Faculty Research Award, Dean's award for faculty excellence, and the Outstanding Research Program Development Award at Georgia Tech. He is a Fellow of AAAS, IEEE, and SIAM, and is a recipient of the IEEE Computer Society Golden Core and Meritorious Service awards.

Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE), Georgia Institute of Technology. She was a Postdoctoral Research Fellow at the Center for Theoretical Neuroscience, the Zuckerman Mind Brain Behavior Institute, Columbia University. She received her Ph.D. degree in Computational and Quantitative Neuroscience and a graduate certificate in Statistics and Machine Learning from Princeton University. Anqi was selected for the 2018 MIT Rising Star in EECS, 2022 DARPA Riser, and 2023 Alfred P. Sloan Fellow. Her research interest is to develop scientifically-motivated Bayesian statistical models to characterize structure in neural data and behavior data in the interdisciplinary field of machine learning and computational neuroscience. She has a general interest in building data-driven models to promote both animal and human studies in the system and cognitive neuroscience.

Helen Xu comes to Georgia Tech from Lawrence Berkeley National Laboratory where she was the 2022 Grace Hopper Postdoctoral Scholar. She completed her Ph.D. at MIT in 2022 with Professor Charles E. Leiserson. Her main research interests are in parallel and cache-friendly algorithms and data structures. Her work has previously been supported by a National Physical Sciences Consortium fellowship and a Chateaubriand fellowship. She has interned at Microsoft Research, NVIDIA Research, and Sandia National Laboratories. 

Bo Dai is a tenure-track assistant professor at Georgia Tech's School of Computational Science and Engineering. Prior to joining academia, he worked as a Staff Research Scientist at Google Brain. Bo Dai completed his Ph.D. in the School of Computational Science and Engineering at Georgia Tech, where he worked from 2013 to 2018 with Professor Le Song. His research focuses on developing principled and practical machine learning techniques for real-world applications. Bo Dai has received numerous awards for his work, including the best paper award at AISTATS 2016. He regularly serves as a (senior) area chair at major AI/ML conferences, such as ICML, NeurIPS, AISTATS, and ICLR.

Nisha Chandramoorthy is an assistant professor in the School of Computational Science and Engineering at Georgia Tech. Her research involves mathematical analyses and development of rigorous computational methods for better understanding and engineering nonlinear, possibly chaotic, dynamical systems. Some themes from her research are statistical response to perturbations, probability measure transport and high-dimensional Bayesian inference, and generalization of learning algorithms. These are motivated by fundamental scientific questions about nonlinearity as well as computational problems surrounding nonlinear systems. Both aims feed each other to improve our collective understanding of complex nonlinear processes, including in systems biology, climate studies and machine learning.

Prior to joining Georgia Tech, Nisha was a postdoctoral researcher at the Institute for Data, Systems and Society at MIT. She received her Ph.D. and master’s degrees from MIT in 2021 and 2016 respectively, and her bachelor’s degree from Indian Institute of Technology, Roorkee, in 2014.

Nabil Imam works on topics in machine learning and theoretical neuroscience with the goal of understanding general principles of neural coding and computation, and their technological applications.

Prof. Imam joined Georgia Tech faculty in January 2022.

Suresh Marru is a research professor dedicated to advancing science and engineering through AI and cyberinfrastructure. Over the past two decades, he has focused on accelerating and democratizing computational science. His work includes the development of science gateways and the pioneering of the Apache Airavata distributed systems framework.

In his current role as the Director of Georgia Tech's ARTISAN Center, his team is at the forefront of pioneering efforts to integrate AI into diverse scientific domains. His group is dedicated to bridging the gap between theory, experimentation, and computation by fostering open-source integration frameworks. These frameworks automate research processes, optimize complex models, and integrate disparate scientific data with simulation engines.

Collaboration is at the heart of Suresh’s ethos. He has had the privilege of working alongside brilliant scientists and technologists, contributing to groundbreaking research in domains such as geosciences, neuroscience, and molecular dynamics. These collaborations have not only accelerated scientific discovery but have also offered valuable insights into the potential of AI in scientific innovation.

Beyond his professional endeavors, Suresh is deeply passionate about open science and open-source software. He also believes in building synergies between academia and industry. He has played an instrumental role in a series of tech startups. Currently, he serves as the Chief Technology Officer at Folia, a company dedicated to unleashing the power of annotations.

Dr. Haesun Park is a Regents' Professor and Chair in the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, U.S.A. She was elected as a SIAM Fellow in 2013 and IEEE Fellow in 2016 for her outstanding contributions in numerical computing, data analysis, and visual analytics. She was the Executive Director of Center for Data Analytics 2013-2015 and was the director of the NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics) Center 2008-2014. She has published extensively in the areas of numerical computing, large-scale data analysis, visual analytics, text mining, and parallel computing. She was the conference co-chair for SIAM International Conference on Data Mining in 2008 and 2009 and an editorial board member of the leading journals in computational science and engineering such as IEEE Transactions on Pattern Analysis and Machine Intelligence, SIAM Journal on Matrix Analysis and Applications, and SIAM Journal on Scientific Computing. She was the plenary keynote speaker at major international conferences including SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011. Before joining Georgia Tech, she was a professor in Department of Computer Science and Engineering, University of Minnesota, Twin Cities 1987- 2005 and a program director in the Computing and Communication Foundations Division at the National Science Foundation, Arlington, VA, U.S.A., 2003 - 2005. She received a Ph.D. and an M.S. in Computer Science from Cornell University, Ithaca, NY in 1987 and 1985, respectively, and a B.S. in Mathematics from Seoul National University, Seoul, Korea in 1981 with the Presidential Medal for the top graduate.

Dr. Chen is an Assistant Professor in the School of Computational Science and Engineering. Previously he was a Research Scientist at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. Dr. Chen’s research is in the multidisciplinary fields of computational mathematics, data science, scientific machine learning, and parallel computing with various applications in materials, energy, health, and natural hazard. Specifically, his research focuses on developing fast, scalable, and parallel computational methods for integrating data and models under high-dimensional uncertainty to make (1) statistical model learning via Bayesian inference, (2) reliable system prediction with uncertainty quantification, (3) efficient data acquisition through optimal experimental design, and (4) robust control and design by stochastic optimization.