Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.
Laura Ray is the Myron Tribus Professor of Engineering Innovation and Senior Associate Dean for Faculty Development at the Thayer School of Engineering, Dartmouth College. Her research focuses on autonomous robotics, system dynamics, and machine intelligence with applications in polar exploration and agriculture. She leads NSF- and NASA-funded projects, including the development of robots like Yeti and Cool Robot for remote terrain analysis. Education: BSE in Mechanical and Aerospace Engineering from Princeton University (1984), MSE in Mechanical Engineering from Stanford University (1985), and PhD in Mechanical and Aerospace Engineering from Princeton University (1991). Research Interests: Her work spans robotics navigation, cooperative multi-robot systems, and acoustic signal processing. Recent projects include tactile sensing for agricultural robotics and terrain identification algorithms for autonomous vehicles. Awards: Honored with the 2022 Outstanding Service Award, National Academy of Inventors Senior Membership (2020), and multiple fellowships in engineering. Notable contributions include patents for noise-canceling devices and adaptive filtering technologies. Leadership: Served as Interim Dean of Thayer School (2018–2021) and co-leads initiatives like the NSF-funded PhD Innovation Program. Teaches courses in control theory (ENGS 26) and systems engineering (ENGS 22). Key Achievements: Developed robots that reduce risks in polar regions, supported NASA missions, and pioneered precision agriculture technologies. Her lab's innovations have been highlighted in Science, National Geographic, and IEEE Spectrum.
Alexander T. Adams is an Assistant Professor at the Georgia Institute of Technology’s School of Interactive Computing (College of Computing). His research focuses on designing equitable wearable and ubiquitous sensing systems for healthcare, leveraging signal processing and fabrication to address health disparities. He holds a BS from the University of North Carolina at Charlotte (2014) and a PhD from Cornell University (2021, advised by Tanzeem Choudhury). Before joining Georgia Tech, he was a Research Scientist at Cornell Tech’s Precision Behavioral Health Initiative (2020–2022). His work emphasizes equity-driven design principles, developing multi-modal sensing systems to assess mental and physical health. Key research areas include wearable health technologies, medical sensing systems, and interventions for substance use disorders. Recent projects involve smartphone-based diagnostic tools (e.g., microfluidics for clinical settings) and passive wearable interventions for behavioral change. Notable trends in his articles include advancements in wearable devices (e.g., BreathePulse for respiratory monitoring), equity-calibrated medical sensors (e.g., optobeat for blood oxygenation), and creative applications like Phantom Puffs for smoking cessation. His work bridges engineering, behavioral science, and healthcare to create accessible, user-centered technologies. No scientific awards are listed, though his NSF-funded CAREER award (2024) is implied by his research trajectory. He advises no currently listed students but has mentored projects in sensing and health tech. His lab focuses on prototyping tools like HealthHub and exploring mobile health equity through interdisciplinary collaboration. Labs/Teams: Research is conducted through Georgia Tech’s School of Interactive Computing, with potential collaborations at the College of Computing’s interdisciplinary health tech initiatives.
Associate Professor Adam Irwin is an NHMRC Emerging Leadership Fellow and Associate Professor in Infectious Diseases at the University of Queensland (UQ) and Queensland Children’s Hospital. He holds positions within the UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences. His work focuses on early sepsis recognition, antimicrobial stewardship, and pediatric infectious diseases. Dr. Irwin completed his medical degree at the University of Birmingham and his PhD in 2016 from the University of Liverpool’s Institute of Infection and Global Health. He has led initiatives like the Queensland Paediatric Sepsis Program, recognized with a Global Sepsis Alliance Award and Queensland Health Excellence Awards. His research spans sepsis pathway implementation, antimicrobial resistance trends, and rapid diagnostic tools. Notable grants include NHMRC funding for studies on multi-drug resistant infections and Children’s Hospital Foundation support for genomics and antimicrobial stewardship projects. He supervises PhD candidates in areas like bloodstream infections and molecular diagnostics. Dr. Irwin collaborates across institutions, contributing to projects such as the RAPIDS Study Group and the PAEDS surveillance network. Awards include the NHMRC Emerging Leadership Fellowship and recognition for sepsis program excellence. His work emphasizes translating research into clinical practice, improving outcomes for critically ill children through evidence-based protocols and innovative diagnostics.
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.
Artur W. Dubrawski is an Alumni Research Professor of Computer Science and Director of the Auton Lab at Carnegie Mellon University's School of Computer Science. He leads interdisciplinary research on Artificial Intelligence, Machine Learning, and Robotics with real-world applications in healthcare, nuclear safety, food safety, and counter-human trafficking. His work focuses on bridging gaps between data-driven AI and empirical sciences through probabilistic modeling, predictive analytics, and time-series intelligence. Lab: Auton Lab (founded 1993) Collaborations: Allegheny County Health Department, USDA, CDC, U.S. Army Research Impact: AI for wastewater-based COVID-19 forecasting, radiological inspection systems, and hospital infection detection His students and affiliates include current PhD candidates Angela Chen, Emma Erickson, Cecilia Morales, Willa Potosnak and past researchers like Benedikt Boecking (co-inventor of Interactive Weak Supervision). The lab has spun off startups like Marinus Analytics (IBM XPrize finalists) and developed open-source tools like auton-survival for survival analysis. Key Grants: $10.5M U.S. Army contract for AI-driven predictive maintenance research.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Dr. Rebecca Pratt is a tenured Professor in the Department of Foundational Medical Studies at Oakland University William Beaumont School of Medicine (OUWB), where she has been a faculty member since January 2018. She previously held professorial roles at Michigan State University College of Osteopathic Medicine (MSUCOM), where she taught anatomy, embryology, neuroanatomy, physiology, and histology, and served as Associate Professor and Director of Histology at the West Virginia School of Osteopathic Medicine. She also held appointments at Grand Valley State University and completed postdoctoral training at Purdue University. Ph.D., Cell Biology and Oncology, Purdue University B.S., Zoology and Botany/Plant Pathology, Michigan State University Dr. Pratt's research focuses on the fascial system and its role in whole-body health, including fascial continuity, muscle attachment, somatic pain transmission, and biochemical communication. She integrates radiology into anatomy education and advocates for evidence-based medical curricula. Her work bridges clinical anatomy, histology, embryology, and physiology with modern educational practices. Her recent publications reflect a strong emphasis on fascial anatomy, medical education innovation, and the integration of imaging in teaching. Themes include plastination, generational learning trends, and fascia’s role in women's health and athletic performance, published in journals like Clinical Anatomy and Anatomical Sciences Education , as well as in Women and Men’s Health and NIKE magazines. Scientific awards and honors include: Basmajian Award (American Association of Anatomy) Keith and Marion Moore Award (AAA) Five consecutive Golden Apple Teaching Awards at MSUCOM Golden Apple Award at OUWB Dr. Pratt has served in major leadership roles, including President of the International Fascia Research Society, Board Member of the American Association of Anatomy (AAA) and the American Association of Clinical Anatomy (AACA), and Chair of multiple AAA committees. She is a Visiting Anatomy Professor at Weill Cornell and St. George’s University School of Medicine, and faculty advisor for the Docapellas at OUWB. She has been an invited speaker internationally and contributed to high-impact projects like the BodyWorlds Fascial Net Plastination Project. She actively serves on OUWB’s Admissions and Student Promotion and Retention Committees. Dr. Pratt leads and organizes major international initiatives, including the Women's Clinical Health Summit in Rio de Janeiro (2024) and the Fascia Research Congress in New Orleans (2025), fostering global collaboration in fascial science. Her lab and research team focus on fascial anatomy and medical education, working closely with institutions in Italy (University of Padova) and Germany (BodyWorlds project).
Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.
Marcia O’Malley is the Thomas Michael Panos Family Professor in Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Bioengineering at Rice University’s George R. Brown School of Engineering. She chairs the Department of Mechanical Engineering and directs the Mechatronics and Haptic Interfaces (MAHI) Lab. Her research focuses on haptics and robotic rehabilitation, particularly wearable robotic systems for training and rehabilitation in virtual environments. She holds adjunct roles at Baylor College of Medicine and the University of Texas Medical School. Educated at Purdue University (B.S., 1996) and Vanderbilt University (M.S./Ph.D., 1999/2001), Dr. O’Malley has been recognized with prestigious awards, including the ONR Young Investigator Award, NSF CAREER Award, and multiple fellowships. She has twice won Rice’s George R. Brown Award for Superior Teaching. Her work bridges engineering and medicine, addressing human-robot interaction challenges in surgical training, workforce safety, and neurorehabilitation. The MAHI Lab develops devices like the hBracelet and Rice Haptic Rocker to enhance human-robot collaboration. She co-founded Houston Medical Robotics, Inc., applying her innovations to real-world medical applications. Research Interests: Haptics, wearable robotics, neural interfaces, surgical training metrics, and rehabilitation robotics. Labs/Teams: MAHI Lab (Biosciences Research Collaborative), collaborations with medical institutions. Grants/Awards: Extensive funding from NSF, ONR, and industry partnerships; leadership in editorial roles for IEEE Transactions on Haptics.
Sanjay Purushotham is an Assistant Professor in the Department of Information Systems at the University of Maryland Baltimore County (UMBC), with a PhD in Electrical Engineering from the University of Southern California (USC) and a postdoctoral background in Computer Science at USC's Integrated Media Systems Center (IMSC). His research focuses on machine learning, data mining, and their applications in biomedical informatics, social network analysis, and multimedia data mining. Key contributions include survival analysis models using pseudo values and federated learning frameworks for healthcare data. He has received awards including the Best Paper Award at SIGSPATIAL 2014 and a Best Poster Runnerup at SCMLS 2016. Education: PhD in Electrical Engineering (USC), Postdoc in Computer Science (USC) His work spans interdisciplinary areas such as domain adaptation for remote sensing, thermal face translation, and interpretable neural networks for medical applications. Recent projects include federated survival analysis models and climate-informatics frameworks for cloud property retrieval. He teaches courses in artificial intelligence, healthcare informatics, and statistical learning at UMBC. Research highlights include developing MedFuseNet for multimodal medical question answering and VDAM for multi-sensor cloud data analysis. His work on fair survival analysis models addresses algorithmic bias in healthcare predictions. Current grants include a NSF CAREER award for trustworthy federated learning in computational healthcare.
