Hannah Choi is an Assistant Professor in the School of Mathematics at Georgia Tech. Her research focuses on mathematical approaches to neuroscience, with primary interests in linking structures, dynamics, and computation in data-driven brain networks at multiple scales. Before coming to Georgia Tech, she was a postdoctoral fellow at the University of Washington and also a visiting scientist at the Allen Institute for Brain Science, and spent one semester at the Simons Institute for the Theory of Computing at the University of California, Berkeley as a Patrick J McGovern Research Fellow. She received her Ph.D. in Applied Mathematics from Northwestern University and her BA in Applied Mathematics from the University of California, Berkeley.

Professor AlRegib is currently a professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He is the director of the Multimedia and Sensors Lab (MSL) at Georgia Tech. In 2012, he was named the director of Georgia Tech’s Center for Energy and Geo Processing (CeGP). He is a faculty member of the Center for Signal and Information Processing (CSIP). He also serves as the director of Georgia Tech’s initiatives and programs in MENA. He has authored and co-authored more than 170 articles in international journals and conference proceedings. He has been issued several U.S. patents and invention disclosures. He is a Senior Member of the IEEE.

Professor AlRegib received the ECE Outstanding Graduate Teaching Award in 2001 and both the CSIP Research and the CSIP Service Awards in 2003. In 2008, he received the ECE Outstanding Junior Faculty Member Award. In 2017, he received the 2017 Denning Faculty Award for Global Engagement.

Professor AlRegib has participated in many service activities. He is an area chair for ICME 2016/17 and the tutorial chair for ICIP 2016. He is a voted member of the IEEE SPS Technical Committees on Multimedia Signal Processing (MMSP) and Image, Video, and Multidimensional Signal Processing (IVMSP). He was a member of the editorial board of the Wireless Networks Journal (WiNET), 2009-2016 and the IEEE Transaction on Circuits and Systems for Video Technology (CSVT), 2014-2016. Currently, he is a member of the the editorial board of the Elsevier journal Signal Processing: Image Communications, 2015-present. He served as the chair of the Special Sessions Program at ICIP’06; the area editor for Columns and Forums in the IEEE Signal Processing Magazine (SPM), 2009–12; the associate editor for IEEE SPM, 2007-09; the tutorials co-chair in ICIP’09; a guest editor for IEEE J-STSP, 2012; a track chair in ICME’11; the co-chair of the IEEE MMTC Interest Group on 3D Rendering, Processing, and Communications, 2010-12; the chair of the Speech and Video Processing Track at Asilomar 2012; and the technical program co-chair of IEEE GlobalSIP, 2014. He is leading a team that is organizing the IEEE VIP Cup, 2017.

His research group, which consists of more than 20 students and researchers, is working on projects related to machine learning, image and video processing, image and video understanding, seismic imaging, perception in visual data processing, healthcare intelligence, and video analytics.

Professor AlRegib has provided services and consultation to several firms, companies, and international educational and R&D organizations. He has been a expert witness in a number of patent infringement cases.

Dhruv Batra is an Associate Professor in the School of Interactive Computing at Georgia Tech. His research interests lie at the intersection of machine learning, computer vision, natural language processing, and AI, with a focus on developing intelligent systems that are able to concisely summarize their beliefs about the world with diverse predictions, integrate information and beliefs across different sub-components or `modules' of AI (vision, language, reasoning, dialog), and interpretable AI systems that provide explanations and justifications for why they believe what they believe. In past, he has also worked on topics such as interactive co-segmentation of large image collections, human body pose estIMaTion, action recognition, depth estIMaTion, and distributed optimization for inference and learning in probabilistic graphical models. He is a recipient of the Office of Naval Research (ONR) Young Investigator Program (YIP) award (2016), the National Science Foundation (NSF) CAREER award (2014), Army Research Office (ARO) Young Investigator Program (YIP) award (2014), Virginia Tech College of Engineering Outstanding New Assistant Professor award (2015), two Google Faculty Research Awards (2013, 2015), Amazon Academic Research award (2016), Carnegie Mellon Dean's Fellowship (2007), and several best paper awards (EMNLP 2017, ICML workshop on Visualization for Deep Learning 2016, ICCV workshop Object Understanding for Interaction 2016) and teaching commendations at Virginia Tech. His research is supported by NSF, ARO, ARL, ONR, DARPA, Amazon, Google, Microsoft, and NVIDIA. Research from his lab has been extensively covered in the media (with varying levels of accuracy) at CNN, BBC, CNBC, Bloomberg Business, The Boston Globe, MIT Technology Review, Newsweek, The Verge, New Scientist, and NPR. From 2013-2016, he was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, where he led the VT Machine Learning & Perception group and was a member of the Virginia Center for Autonomous Systems (VaCAS) and the VT Discovery Analytics Center (DAC). From 2010-2012, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed academic computer science institute located on the University of Chicago campus. He received his M.S. and Ph.D. degrees from Carnegie Mellon University in 2007 and 2010 respectively, advised by Tsuhan Chen. In past, he has held visiting positions at the Machine Learning Department at CMU, CSAIL MIT, Microsoft Research, and Facebook AI Research.

Professor Nagi Gebraeel is the Georgia Power Early Career Professor and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his MS and PhD from Purdue University in 1998 and 2003, respectively.

Dr. Gebraeel's research interests lie at the intersection of Predictive Analytics and Machine Learning in IoT enabled maintenance, repair and operations (MRO) and service logistics. His key focus is on developing fundamental statistical learning algorithms specifically tailored for real-time equipment diagnostics and prognostics, and optimization models for subsequent operational and logistical decision-making in IoT ecosystems. Dr. Gebraeel also develops cyber-security algorithms intended to protect IoT-enabled critical assets from ICS-type cyberattacks (cyberattacks that target Industrial Control Systems). From the standpoint of application domains, Dr. Gebraeel has general interests in manufacturing, power generation, and service-type industries. Applications in Deep Space missions are a recent addition to his research interests, specifically, developing Self-Aware Deep Space Habitats through NASA's HOME Space Technology Research Institute.

Dr. Gebraeel leads Predictive Analytics and Intelligent Systems (PAIS) research group at Georgia Tech's Supply Chain and Logistics Institute. He also directs activities and testing at the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute. Formerly, Dr. Gebraeel served as an associate director at Georgia Tech's Strategic Energy Institute (from 2014 until 2019) where he was responsible for identifying and promoting research initiatives and thought-leadership at the intersection of Data Science and Energy applications. He was also the former president of the Institute of Industrial and Systems Engineers (IISE) Quality and Reliability Engineering Division, and is currently a member of the Institute for Operations Research and the Management Sciences (INFORMS), and IISE (since 2005).

Faces of Research - Profile Article

Dr. Dickson is the Vassar Woolley Professor of Chemistry & Biochemistry and has been at Georgia Tech since 1998. He was a Senior Editor of The Journal of Physical Chemistry from 2010-2021, and his research has been continuously funded (primarily from NIH) since 2000. Dr. Dickson has developed quantitative bio imaging and signal recovery/modulation schemes for improved imaging of biological processes and detection of medical pathologies. His work on fluorescent molecule development and photoswitching of green fluorescent proteins was recognized as a key paper for W.E. Moerner’s 2014 Nobel Prize in Chemistry. Recently, Dr. Dickson’s lab has developed rapid susceptibility testing of bacteria causing blood stream infections. Their rapid recovery methods, coupled with rigorous multidimensional statistics and machine learning have led to very simple, highly accurate and fast methods for determining the appropriate treatment within a few hours after positive blood cultures. These hold significant potential for drastically improving patient outcomes and reducing the proliferation of antimicrobial resistance.

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.

Arriaga is a Human Computer Interaction (HCI) researcher in the School of Interactive Computing. She uses psychological concepts, theories and methods to address fundamental topics of HCI and Social Computing. Her current research interests are in the area of chronic care management and mental health. She designs mHealth systems that address gaps in chronic care and mental health management. The computational systems she designs: foster engagement, facilitate continuity of care, promote patient self-advocacy, and mediate communication between patient and healthcare providers.