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.

Education

Masters of Science, Computer Science, Georgia Institute of Technology, 2022

Bachelors of Science, Mechanical Engineering, Georgia Institute of Technology, 2020

Research Expertise

Robot Planning and Control, Embodied Artificial Intelligence, Laboratory Automation, Software Engineering

Selected Publications

Bowles-Welch, A., Byrnes, W., Kanwar, B., Wang, B., Joffe, B., Casteleiro Costa, P., Armenta, M., Xu, J., Damen, N., Zhang, C., Mazumdar, A., Robles, F., Yeago, C., Roy, K., Balakirsky, S. (2021). Artificial Intelligence Enabled Biomanufacturing of Cell Therapies. Georgia Tech Research Institute Internal Research and Development (IRAD) Journal

Byrnes, W., Ahlin, K., Rains, G., & McMurray, G. (2019). Methodology for Stress Identification in Crop Fields Using 4D Height Data. IFAC-PapersOnLine, 52(30), 336–341. https://doi.org/10.1016/j.ifacol.2019.12.562

Byrnes, W., Kanwar, B., Damen, N., Wang, B., Bowles-Welch, A. C., Roy, K., & Balakirsky, S. (2023). Process Development and Manufacturing: A NEEDLE-BASED AUTOSAMPLER FOR BIOREACTOR CELL MEDIA COLLECTION. Cytotherapy, 25(6), S172.

Wang, B., Kanwar, B., Byrnes, W., Costa, P. C., Filan, C., Bowles-Welch, A. C., ... & Roy, K. (2023). Process Development and Manufacturing: DIGITAL TWIN-ENABLED FEEDBACK-CONTROLLED AUTOMATION WITH INTEGRATED PROCESS ANALYTICS FOR BIOMANUFACTURING OF CELL THERAPIES. Cytotherapy, 25(6), S206-S207.

Professional Activities

STEM@GTRI Program Mentor

IEEE Member

During his graduate work at UC, Berkeley, Simon sought to uncover general principles of animal locomotion that reveal control strategies underlying the remarkable stability and maneuverability of movement in nature. His work has demonstrated the importance animals’ natural dynamics for maintaining stability in the absence of neural feedback. His research emphasizes the importance of placing neural control in the appropriate dynamical context using mathematical and physical models. He has collaborated with researchers at four other institutions to transfer these principles to the design of the next generation of bio-inspired legged robots. 

Simon received his Ph.D. in Integrative Biology at UC, Berkeley and has been a Hertz Fellow since 2002. His work has led to fellowships and awards from the National Science Foundation, the University of California, the Woods Hole Marine Biological Institute, the American Physical Society, the Society of Integrative and Comparative Biology, and the International Association of Physics Students. He is also currently affiliated the new Center for Interdisciplinary Bio-Inspiration in Education and Research (CIBER) at Berkeley.

Yue Chen is an assistant professor in the Department of Biomedical Engineering, GT/Emory. He received his Ph.D. degree in Mechanical Engineering from Vanderbilt University, M.S. in Mechanical Engineering from Hong Kong Polytechnic University, and a B.S. in Vehicle Engineering from Hunan University. His research focused on designing, modeling, and control of continuum robots and apply them in medicine.

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.

Judy Hoffman is an associate  professor in the School of Interactive Computing at Georgia Tech, a member of the Machine Learning Center, and a Diversity and Inclusion Fellow. Her research lies at the intersection of computer vision and machine learning with specialization in domain adaptation, transfer learning, adversarial robustness, and algorithmic fairness. She has received numerous awards including the Samsung AI Researcher of the Year Award (2021), the NVIDIA female leader in computer vision award (2020), AIMiner top 100 most influential scholars in Machine Learning (2020), MIT EECS Rising Star in 2015, and is a recipient of the NSF Graduate Fellowship. In addition to her research, she co-founded and continues to advise for Women in Computer Vision, an organization which provides mentorship and travel support for early-career women in the computer vision community. Prior to joining Georgia Tech, she was a research scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016 after which she completed postdocs at Stanford University (2017) and UC Berkeley (2018).

David Hu is a fluid dynamicist with expertise in the mechanics of interfaces between fluids such as air and water. He is a leading researcher in the biomechanics of animal locomotion. The study of flying, swimming and running dates back hundreds of years, and has since been shown to be an enduring and rich subject, linking areas as diverse as mechanical engineering, mathematics and neuroscience. Hu's work in this area has the potential to impact robotics research. Before robots can interact with humans, aid in minimally-invasive surgery, perform interplanetary exploration or lead search-and-rescue operations, we will need a fundamental physical understanding of how related tasks are accomplished in their biological counterparts. Hu's work in these areas has generated broad interest across the fields of engineering, biology and robotics, resulting in over 30 publications, including a number in high-impact interdisciplinary journals such as Nature, Nature Materials, Proceedings of the National Academy of Sciences as well as popular journals such as Physics Today and American Scientist. Hu is on editorial board member for Nature Scientific Reports, The Journal of Experimental Biology, and NYU Abu Dhabi's Center for Center for Creative Design of Materials. He has won the NSF CAREER award, Lockheed Inspirational Young Faculty award, and best paper awards from SAIC, Sigma Xi, ASME, as well as awards for science education such as the Pineapple Science Prize and the Ig Nobel Prize. Over the years, Hu's research has also played a role in educating the public in science and engineering. He has been an invited guest on numerous television and radio shows to discuss his research, including Good Morning America, National Public Radio, The Weather Channel, and Discovery Channel. His ant research was featured on the cover of the Washington Post in 2011. His work has also been featured in The Economist, The New York Times, National Geographic, Popular Science and Discover His laboratory appeared on 3D TV as part of a nature documentary by 3DigitalVision, "Fire ants: the invincible army," available on Netflix.

My research integrates my work in complex fluids and granular media and the biomechanics of locomotion of organisms and robots to address problems in nonequilibrium systems that involve interaction of matter with complex media. For example, how do organisms like lizards, crabs, and cockroaches cope with locomotion on complex terrestrial substrates (e.g. sand, bark, leaves, and grass). I seek to discover how biological locomotion on challenging terrain results from the nonlinear, many degree of freedom interaction of the musculoskeletal and nervous systems of organisms with materials with complex physical behavior. The study of novel biological and physical interactions with complex media can lead to the discovery of principles that govern the physics of the media. My approach is to integrate laboratory and field studies of organism biomechanics with systematic laboratory studies of physics of the substrates, as well as to create mathematical and physical (robot) models of both organism and substrate. Discovery of the principles of locomotion on such materials will enhance robot agility on such substrates