Azadeh Ansari received the B.S. degree in Electrical Engineering from Sharif University of Technology, Iran in 2010. She earned the M.S and Ph.D. degrees in Electrical Engineering from University of Michigan, Ann Arbor in 2013 and 2016 respectively, focusing upon III-V piezoelectric semiconductor materials and MEMS devices and microsystems for RF applications. Prior to joining the ECE faculty at Georgia Tech, she was a postdoctoral scholar in the Physics Department at Caltech from 2016 to 2017. Ansari is the recipient of a 2017 ProQuest Distinguished Dissertation Award from the University of Michigan for her research on "Gallium Nitride integrated microsystems for RF applications." She received the University of Michigan Richard and Eleanor Towner Prize for outstanding Ph.D. research in 2016. She is a member of IEEE, IEEE Sensor's young professional committee and serves as a technical program committee member of IEEE IFCS 2018.

Glen Chou joined Georgia Tech as an assistant professor with a joint appointment in the School of Cybersecurity & Privacy and the School of Aerospace Engineering in November 2024. He directs the Trustworthy Robotics Lab, which designs principled algorithms that can enable general-purpose robots and autonomous systems to operate capably, safely, and securely, while remaining resilient to real-world failures and uncertainty. To achieve this, his research leverages control theory and machine learning, while connecting to optimization, computer vision, formal methods, planning, human-robot interaction, and statistics. Glen is interested in broad applications of autonomy, including robotic manipulation, vision-based navigation, aerospace, and large-scale cyber-physical systems more generally.

Glen is from Northern California. He holds dual B.S. degrees in EECS and ME from UC Berkeley, as well as an M.S. and Ph.D. in ECE from the University of Michigan. Prior to joining Georgia Tech, Glen was a postdoc at MIT CSAIL.

Alex Abramson is an assistant professor in the School of Chemical and Biomolecular Engineering at Georgia Tech. His research, which focuses on drug delivery and bioelectronic therapeutics, has been featured in news outlets such as The New York Times, NPR, and Wired. Abramson has received several recognitions for scientific innovation, including being named a member of the Forbes 30 Under 30 Science List and the MIT Technology Review Innovators Under 35 List. He is passionate about translating scientific endeavors from bench to bedside. Large pharmaceutical companies have exclusively licensed a portfolio of his patents to bring into clinical trials, and Abramson serves as a scientific advisor overseeing their commercialization. In addition to his scientific endeavors, Abramson plays an active role in his community by leading diversity and inclusion efforts on campus and volunteering as a STEM tutor to local students.

Abramson received a B.S. in chemical and biomolecular engineering from Johns Hopkins University and a Ph.D. in chemical engineering from MIT as an NSF Graduate Research Fellow under the direction of Professors Robert Langer and Giovanni Traverso. He conducted postdoctoral work at Stanford University as an NIH fellow with Professors Zhenan Bao and the late Sanjiv S. Gambhir.

The Abramson Lab develops ingestible, implantable, and wearable robotic therapeutic devices that solve key healthcare problems and provide measurable therapeutic outcomes. Our translationally focused research spans a multitude of areas, including (1) drug delivery devices for optimal drug adherence, (2) soft materials for bioelectronic sensors and therapeutics, and (3) preclinical drug screening technologies.

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 assistant 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).

Sang's research and art practice focuses on robotic and computational tools that work together with human users. His vision proposes extreme synergies between machine tools and humans, with technology essentially becoming a natural extension of our hands. This way, he challenges the fear and criticism around AI and automation that they replace human endeavors, by showing how symbiotic machines can unlock new human explorations and aesthetics. The impact of his research spans from publications in top tier HCI conferences such as CHI, TEI, and NIME, journals including Leonardo and IEEE Pervasive Computing, to design awards and art exhibitions. Several of his work were awarded the Fast Company Innovation by Design Award, and have been shown in art exhibitions at SIGGRAPH ASIA, CHI, TEI, and more. His work A Flying Pantograph was included in the Otherly Space / Knowledge exhibition at the Asia Culture Center along with some of the most prominent new media artists today. In 2014, He was an artist-in-residence at Microsoft Research Studio 99 where he created Remnance of Form - an interactive light and shadow installation. His work has received extensive media coverage from BBC, WIRED, Discovery, Fast Company and so on, and he was invited to national and international events including Sebasi+Pan, TEDx events, Seoul Digital Forum, and more. He is starting at Georgia Tech Industrial Design as an assistant professor. He has helped Artmatr in the development of a machine painting technology and its creative use through collaboration with some of today's most prominent painters. He received his Ph.D. from MIT Media Lab in 2018. Prior to that, he was a software engineer at Samsung Electronics where he led the software development of eyeCan, an open-source DIY eye-mouse designed for people with motor disability. This project became the foundation of Samsung's C-LAB. He received his Bachelor and Master of Science from KAIST, focusing on 3D Computer Vision and Machine Learning.

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).

Shreyas Kousik is an assistant professor in the George W. Woodruff School of Mechanical Engineering. Previously, Shreyas was a postdoctoral scholar at Stanford University, working in the ASL under Prof. Marco. Kousik completed a postdoc with Prof. Grace Gao in the NAV Lab. He received his Ph.D. in Mechanical Engineering at the University of Michigan, advised by Prof. Ram Vasudevan in the ROAHM Lab and received his undergraduate degree in Mechanical Engineering at Georgia Tech, advised by Prof. Antonia Antoniou.

Kousik’s research is focused on guaranteeing safety in autonomy via collision avoidance methods for robots. His lab’s goal is to translate safety in math to safety on real robots by exploring ways to model uncertainty from autonomous perception and estimation systems and ensure that these models are practical for downstream planning and control tasks