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.

Raheem Beyah, Ph.D., is associate chair for Strategic Initiatives and Innovation, and the Motorola Foundation Professor in the School of Electrical & Computer Engineering at the Georgia Institute of Technology. His research is at the intersection of the networking and security fields. He leads the Georgia Tech Communications Assurance and Performance Group (CAP), which develops algorithms that enable a more secure network infrastructure with computer systems that are more accountable and less vulnerable to attacks. Through experimentation, simulation, and theoretical analysis, CAP provides solutions to current network security problems and to long-range challenges as current networks and threats evolve. Dr. Beyah has served as guest editor and associate editor of several journals in the areas of network security, wireless networks, and network traffic characterization and performance. He received the National Science Foundation CAREER award in 2009 and was selected for DARPA's Computer Science Study Panel in 2010. He is a member of NSBE, ASEE, and is a senior member of IEEE and ACM. Beyah is a native of Atlanta, Georgia. He received his Bachelor of Science in Electrical Engineering from North Carolina A&T State University in 1998. He received his Master's and Ph.D. in Electrical and Computer Engineering from Georgia Tech in 1999 and 2003, respectively. Prior to returning to Georgia Tech, Dr. Beyah was a faculty member in the Department of Computer Science at Georgia State University, a research faculty member with the Georgia Tech Communications Systems Center (CSC), and a consultant in Andersen Consulting's (now Accenture) Network Solutions Group.

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.

Prof. AlRegib is currently the John and Marilu McCarty Chair Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. His group is the Omni Lab for Intelligent Visual Engineering and Science (OLIVES) at Georgia Tech. In 2012, he was named the Director of Georgia Tech’s Center for Energy and Geo Processing (CeGP). He is the director of the Center for Signal and Information Processing (CSIP). He also served as the Director of Georgia Tech’s Initiatives and Programs in MENA between 2015 and 2018. He has authored and co-authored more than 300 articles in international journals and conference proceedings. He has been issued several U.S. patents and invention disclosures. He is a Fellow of the IEEE.

Prof. 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. He and his students received the Beat Paper Award in ICIP 2019. He received the 2024 ECE Distinguished Faculty Achievement Award at Georgia Tech. He and his students received the Best Paper Award in ICIP 2019 and the 2023 EURASIP Best Paper Award for Image communication Journal.

Prof. AlRegib participated in a number of activities. He has served as Technical Program co-Chair for ICIP 2020 and ICIP 2024. He served two terms as a member of the IEEE SPS Technical Committees on Multimedia Signal Processing (MMSP) and Image, Video, and Multidimensional Signal Processing (IVMSP), 2015-2017 and 2018-2020. He was a member of the Editorial Boards of both the IEEE Transactions on Image Processing (TIP), 2009-2022, and the Elsevier Journal Signal Processing: Image Communications, 2014-2022. 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. He was an Area Chair for ICME 2016/17 and the Tutorial Chair for ICIP 2016. 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 lead a team that organized the IEEE VIP Cup, 2017 and the 2023 IEEEE VIP Cup. He delivered short courses and several tutorials at international events such as BigData, NeurIPS, ICIP, ICME, CVPR, AAAI, and WACV.

In the Omni Lab for Intelligent Visual Engineering and Science (OLIVES), he and his group work on robust and interpretable machine learning algorithms, uncertainty and trust, and human in the loop algorithms. The group studies interventions into AI systems to enhance their trustworthiness. The group has demonstrated their work on a wide range of applications such as Autonomous Systems, Medical Imaging, and Subsurface Imaging. The group is interested in advancing the fundamentals as well as the deployment of such systems in real-world scenarios. His research group is working on projects related to machine learning, image and video processing, image and video understanding, subsurface imaging, perception in visual data processing, healthcare intelligence, and video analytics. The primary applications of the research span from Autonomous Vehicles to Portable AI-based Ophthalmology and Eye Exam and from Microscopic Imaging to Seismic Interpretation. The group was the first to introduce modern machine learning to seismic interpretation.

In 2024, and after more than three years of continuous work, he co-founded Georgia Tech’s AI Makerspace. The AI Makerspace is a resource for the entire campus community to access AI. Its purpose is to democratize access to AI. Together with his team, they are developing tools and services for the AI Makerspace via a VIP Team called AI Makerspace Nexus. In addition, he created two AI classes from scratch with innovative hands-on exercises using the AI Makerspace. One class is the ECE4252/8803 FunML class (Fundamentals of Machine Learning) where students learn the basics of Machine Learning as well as eight weeks of Deep learning both mathematically and using hands-on exercises on real-world data. The second class is a sophomore-level AI Foundations class (AI First) that teaches any student from any college the basics of AI such as data literacy, learning, decision, planning, and ethics using theory and hands-on exercises on the AI Makerspace. Prof. AlRegib wrote two textbooks for both classes.

Prof. AlRegib has provided services and consultation to several firms, companies, and international educational and R&D organizations. He has been a witness expert in a number of patents infringement cases and Inter Partes Review (IRP) cases.

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.

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 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).

We develop chemical and biological tools for research in a wide range of fields. Some of them are briefly described below; please see our group web page for more details. Chemistry, biology, immunology, and evolution with viruses. The sizes and properties of virus particles put them at the interface between the worlds of chemistry and biology. We use techniques from both fields to tailor these particles for applications to cell targeting, diagnostics, vaccine development, catalysis, and materials self-assembly. This work involves combinations of small-molecule and polymer synthesis, bioconjugation, molecular biology, protein design, protein evolution, bioanalytical chemistry, enzymology, physiology, and immunology. It is an exciting training ground for modern molecular scientists and engineers. Development of reactions for organic synthesis, chemical biology, and materials science. Molecular function is what matters most to our scientific lives, and good chemical reactions provide the means to achieve such function. We continue our efforts to develop and optimize reactions that meet the click chemistry standard for power and generality. Our current focus is on highly reliable reversible reactions, which open up new possibilities for polymer synthesis and modification, as well as for the controlled delivery of therapeutic and diagnostic agents to biological targets. Traditional and combinatorial synthesis of biologically active compounds.We have a longstanding interest in the development of biologically active small molecules. We work closely with industrial and academic collaborators on such targets as antiviral agents, compounds to combat tobacco addiction, and treatments for inflammatory disease.

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.