Peter Kasson is an international leader in the study of biological membrane structure, dynamics, and fusion, with particular application to how viruses gain entry to cells. His group performs both high-level experimental and computational work – a powerful combination that is critical to advancing our understanding of this important problem. His publications describe inventive approaches to the measurement of viral fusion rates and characterization of fusion mechanisms, and to the modeling of large-scale biomolecular and lipid assemblies. He has applied these insights to the prediction of pandemic outbreaks and drug resistance, with particular attention to Zika, SARS-CoV-2, and influenza pathogens in recent years. See https://kassonlab.org/ for more information.

Wei Xu is an associate professor in the School of Interactive Computing at the Georgia Institute of Technology. Xu received her Ph.D. in Computer Science from New York University, and her B.S. and M.S. from Tsinghua University. Her research interests are in natural language processing, machine learning, and social media. Her recent work focuses on text generation, stylistics, information extraction, robustness and controllability of machine learning models, and reading and writing assistive technology. She is a recipient of the NSF CAREER Award, CrowdFlower AI for Everyone Award, Criteo Faculty Research Award, and Best Paper Award at COLING'18. She has also received funds from DARPA and IARPA and is part of the Machine Learning Center and NSF AI CARING Institute at Georgia Tech.

Lu Gan joined the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology as an Assistant Professor in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab at Georgia Tech, and is on the core faculty of the Institute for Robotics and Intelligent Machines. Her research interests include robot perception, robot learning, and autonomous navigation. Her group explores the use of computer vision, machine learning, estimation, probabilistic inference, kinematics and dynamics to develop autonomous systems in ground, air, and space applications.

She holds a B.S. in Automation from the University of Electronic Science and Technology of China, an M.S. in Control Engineering from Beihang University, and received her M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor. Before joining Georgia Tech, she had a two-year appointment as a Postdoctoral Scholar at the Graduate Aerospace Laboratories of the California Institute of Technology and the Center for Autonomous Systems and Technologies at Caltech.

Larry P. Heck is a Professor with a joint appointment in the Schools of Electrical and Computer Engineering and Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Distinguished Chair of Advanced Computing Concepts and is a Georgia Research Alliance Eminent Scholar. His received the BSEE from Texas Tech University (1986), and MSEE and PhD EE from the Georgia Institute of Technology (1989,1991). He is a Fellow of the IEEE, inducted into the Academy of Distinguished Engineering Alumni at Georgia Tech and received the Distinguished Engineer Award from the Texas Tech University. He was a Senior Research Engineer with SRI (1992-98), VP of R&D at Nuance (1998-2005), VP of Search and Advertising Sciences at Yahoo! (2005-2009), Chief Scientist of the Microsoft Speech products and Distinguished Engineer in Microsoft Research (2009-2014), Principal Scientist with Google Research (2014-2017), CEO of Viv Labs and SVP at Samsung (2017-2021).

Our goal is to contribute to the fundamental understanding of the Earth's biogeochemical cycling in the present and past climate, to conduct research in Ecosystem and Biogeochemistry, Ocean Carbon Cycle, Global Climate Change, and Ocean Deoxygenation using computational modeling, observations and AI/machine learning approaches. 

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.

Nate Damen is a Research Engineer I with Aerospace, Transportation and Advanced Systems Laboratory of Georgia Tech Research Institute. Damen’s work at ATAS has focused on Mixed Reality applications, robotics, the automation of CAR-T cellular expansions, and bioreactor design. Before joining GTRI, Damen conducted research into the manipulation of textiles with Softwear Automation and the design of deformable parcel manipulation systems with Dorabot. His creative work ATLTVHEAD with the Atlanta Beltline Inc., includes the creation of several wearable electronic systems for remote computing and novel interactions between wearable systems and live user input from those walking the Atlanta Beltline. 

Overview:
Our brain not only processes sensory signals but also makes predictions about the world. Generating and updating predictions are essential for our survival in a rapidly changing environment. Multiple brain regions including the cerebellum and the cortex are thought to be involved in the processing of prediction signals (aka predictive processing). However, it is not clear what circuit mechanisms and computations underlie predictive processing in each region, and how the cortical and cerebellar prediction signals interact to support cognitive and sensorimotor behavior. Our lab is interested in figuring out these questions by using advanced experimental and computational techniques in systems neuroscience.

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.

Animesh Garg is a Stephen Fleming Early Career Assistant Professor at School of Interactive Computing at Georgia Tech. He leads the People, AI, and Robotics (PAIR) research group. He is on the core faculty in the Robotics and Machine Learning programs. Animesh is also a Senior Researcher at Nvidia Research. Animesh earned a Ph.D. from UC Berkeley and was a postdoc at the Stanford AI Lab. He is on leave from the department of Computer Science at University of Toronto and CIFAR Chair position at the Vector Institute.

Garg earned his M.S. in Computer Science and Ph.D. in Operations Research from UC, Berkeley. He worked with Ken Goldberg at Berkeley AI Research (BAIR). He also worked closely with Pieter Abbeel, Alper Atamturk & UCSF Radiation Oncology. Animesh was later a postdoc at Stanford AI Lab with Fei-Fei Li and Silvio Savarese.

Garg's research vision is to build the Algorithmic Foundations for Generalizable Autonomy, that enables robots to acquire skills, at both cognitive & dexterous levels, and to seamlessly interact & collaborate with humans in novel environments. His group focuses on understanding structured inductive biases and causality on a quest for general-purpose embodied intelligence that learns from imprecise information and achieves flexibility & efficiency of human reasoning.