Saurabh Sinha received his Ph.D. in Computer Science from the University of Washington, Seattle, in 2002, and after post-doctoral work at the Rockefeller University with Eric Siggia, he joined the faculty of the University of Illinois, Urbana-Champaign, in 2005, where he held the positions of Founder Professor in Computer Science and Director of Computational Genomics in the Carl R. Woese Institute for Genomic Biology until 2022. He joined Georgia Institute of Technology in 2022, as Wallace H. Coulter Distinguished Chair in Biomedical Engineering, with joint appointments in Biomedical Engineering and Industrial & Systems Engineering. Sinha’s research is in the area of bioinformatics, with a focus on regulatory genomics and systems biology. Sinha is an NSF CAREER award recipient and has been funded by NIH, NSF and USDA. He co-directed an NIH BD2K Center of Excellence and was a thrust lead in the NSF AI Institute at UIUC. He led the educational program of the Mayo Clinic-University of Illinois Alliance, and co-led data science education for the Carle Illinois College of Medicine. Sinha has served as Program co-Chair of the annual RECOMB Regulatory and Systems Genomics conference and served on the Board of Directors for the International Society for Computational Biology (2018-2021). He was a recipient of the University Scholar award of the University of Illinois, and selected as a Fellow of the AIMBE in 2018.

Dyer’s research interests lie at the intersection of machine learning, optimization, and neuroscience. Her lab develops computational methods for discovering principles that govern the organization and structure of the brain, as well as methods for integrating multi-modal datasets to reveal the link between neural structure and function.

Our work centers on understanding how the brain represents information and intention, and using this knowledge to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders. We take a dynamical systems approach to characterizing the activity of large populations of neurons, combined with rigorous systems engineering (signal processing, machine learning, and real-time systems) to advance the performance of brain-machine interfaces and neuromodulatory devices.

May Dongmei Wang, Ph.D., is The Wallace H Coulter Distinguished Faculty Fellow, professor of BME, ECE and CSE, Director of Biomedical Big Data Initiative, and Georgia Distinguished Cancer Scholar. She is also Petit Institute Faculty Fellow, Kavli Fellow, Fellow of AIMBE, Fellow of IEEE, and Fellow of IAMBE. She received BEng from Tsinghua University China and MS/PhD from Georgia Institute of Technology (GIT). Dr. Wang’s research and teaching are in Biomedical Big Data and AI-Driven Biomedical Health Informatics and Intelligent Reality (IR) for predictive, personalized, and precision health. She has published over 270 referred journal and conference proceeding articles (13,500+ GS-Citations) and delivered over 280 invited and keynote lectures. Dr. Wang’s research has been supported by NIH, NSF, CDC, GRA, GCC, VA, Children’s Healthcare of Atlanta, Enduring Heart Foundation, Wallace Coulter Foundation, Carol Ann and David Flanagan Foundation, Shriner’s Hospitals, Microsoft Research, HP, UCB, and Amazon.

Dr. Wang chairs IEEE Engineering in Medicine and Biology Society (EMBS) BHI-Technical Community and ACM Special Interest Group in Bioinformatics (SIGBio), and is the Senior Editor of IEEE Journal of Biomedical & Health Informatics (IF=7.02), and Associate Editor for IEEE Transactions on BME, and IEEE Review of BME. She was IEEE EMBS Distinguished Lecturer and PNAS (Proceeding of National Academy of Sciences) Emerging Area Editor. During the past decade, Dr. Wang has been a standing panelist for NIH Study Sections, NSF Smart and Connect Health, and Brain Canada, and has co-chaired and helped organize more than 10 conferences by IEEE Engineering in Medicine and Biologics  Gordon Research Conferences, ACM Special Interest Groups in Bioinformatics, and IEEE Future Directions.

Dr. Wang received GIT Outstanding Faculty Mentor for Undergrad Research Award and Emory University MilliPub Award for a high-impact paper cited over 1,000 times. She was selected into 2022 Georgia Tech LeadingWomen Program and 2021 Georgia Tech Provost Emerging Leaders Program. Previously, she was Carol Ann and David Flanagan Distinguished Faculty Fellow, GIT Biomedical Informatics Program Co-Director in ACTSI, and Bioinformatics and Biocomputing Core Director in NIH/NCI-Sponsored U54 Center for Cancer Nanotechnology Excellence.

Jaydev P. Desai, Ph.D, is currently a Professor and BME Distinguished Faculty Fellow in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech. Prior to joining Georgia Tech in August 2016, he was a Professor in the Department of Mechanical Engineering at the University of Maryland, College Park (UMCP). He completed his undergraduate studies from the Indian Institute of Technology, Bombay, India, in 1993. He received his M.A. in Mathematics in 1997, M.S. and Ph.D. in Mechanical Engineering and Applied Mechanics in 1995 and 1998 respectively, all from the University of Pennsylvania. He was also a Post-Doctoral Fellow in the Division of Engineering and Applied Sciences at Harvard University. He is a recipient of several NIH R01 grants, NSF CAREER award, and was also the lead inventor on the "Outstanding Invention of 2007 in Physical Science Category" at the University of Maryland, College Park. He is also the recipient of the Ralph R. Teetor Educational Award. In 2011, he was an invited speaker at the National Academy of Sciences "Distinctive Voices" seminar series on the topic of "Robot-Assisted Neurosurgery" at the Beckman Center. He was also invited to attend the National Academy of Engineering's 2011 U.S. Frontiers of Engineering Symposium. He has over 150 publications, is the founding Editor-in-Chief of the Journal of Medical Robotics Research, and Editor-in-Chief of the Encyclopedia of Medical Robotics (currently in preparation). His research interests are primarily in the area of image-guided surgical robotics, rehabilitation robotics, cancer diagnosis at the micro-scale, and rehabilitation robotics. He is a Fellow of the ASME and AIMBE.

Dr. Borodovsky and his group develop machine learning algorithms for computational analysis of biological sequences: DNA, RNA and proteins. Our primary focus is on prediction of protein-coding genes and regulatory sites in genomic DNA. Probabilistic models play an important role in the algorithm framework, given the probabilistic nature of biological sequence evolution.