Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.
Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.
Dr. Kelley Gabriel is a Professor and Associate Dean in the School of Public Health at the University of Alabama at Birmingham (UAB), with a primary appointment in the Department of Epidemiology. She holds additional roles as a Senior Scientist in multiple UAB research centers, including the Nutrition Obesity Research Center (NORC) and the Integrative Center for Aging Research. Her research focuses on optimizing physical activity and sedentary behavior measurement in large epidemiological studies, with a particular emphasis on cardiorespiratory fitness, chronic disease prevention, and life-course health trajectories. Dr. Gabriel earned her PhD in Public Health (Epidemiology) from the University of Pittsburgh, an MS in Exercise Physiology from Northeastern University, and a BS in Athletic Training and Sports Medicine from Ithaca College. Her research interests include developing methodological strategies for precise physical activity measurement, examining the timing of physical fitness and disease risk, and leveraging cohort studies like CARDIA and SWAN. She has secured grants from NIH, NHLBI, and other agencies to support studies on the 24-hour movement paradigm, cognitive health, and cardiovascular outcomes. Key collaborations involve large-scale epidemiological cohorts such as the Coronary Artery Risk Development in Young Adults (CARDIA) and the Multi-Ethnic Study of Atherosclerosis (MESA). Her over 200 peer-reviewed publications and four book chapters reflect expertise in physical activity epidemiology and translational research. Grants include NIH-funded projects on cognitive resilience to Alzheimer’s disease, cardiovascular health trajectories, and the role of activity patterns in aging populations. Her work emphasizes translational strategies to improve public health through evidence-based lifestyle interventions. Dr. Gabriel leads multidisciplinary teams at UAB, contributing to initiatives like the Osteoporosis in Men (MrOS) Study and the Cooper Center Longitudinal Study. Her research integrates wearable technologies, biomarker analysis, and advanced statistical methods to address global health challenges related to physical activity and chronic disease.
Meeyoung Cha is a Professor at KAIST and Scientific Director of the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. Her research focuses on Data Science for Humanity, encompassing computational social science, misinformation dynamics, and human-machine interaction. She holds a PhD in Computer Science from KAIST (2008) and previously served as Chief Investigator at the Institute for Basic Science and Visiting Professor at Facebook. Her work addresses societal challenges such as poverty mapping, fraud detection, and AI ethics. Key achievements include best paper awards and recognition like the Hong Jin-Ki Creator Award (2024) and Test-of-Time Awards (ACM IMC 2022, AAAI ICWSM 2020). Research interests span AI ethics, social media analysis, and interdisciplinary applications of machine learning. Notable projects include modeling climate risks via satellite imagery and analyzing chatbot interactions' societal impacts. She leads the MPI-SP's Data Science for Humanity Group, mentoring over 20 students across PhD and postdoc programs. Education: PhD in Computer Science (KAIST, 2008) Affiliations: MPI-SP (Germany), KAIST Key Awards: Hong Jin-Ki Creator Award, Korean Young Information Scientist Award, Test-of-Time Awards Her publications bridge computational methods with societal issues, including climate modeling, protein engineering, and algorithmic fairness. Current projects explore geospatial AI for economic development and ethical AI design frameworks.
Amir Asif is a Professor at the Lassonde School of Engineering, York University, and concurrently serves as Vice President, Research and Innovation. His academic leadership roles include Dean of the Gina Cody School of Engineering and Computer Science at Concordia University (2014-2020). He specializes in signal processing, communications, and their applications in healthcare, power grids, and distributed systems. Asif holds a PhD from Carnegie Mellon University and a Harvard certification in executive leadership. Education: PhD, Electrical and Computer Engineering, Carnegie Mellon University (1996) MS, Electrical and Computer Engineering, Carnegie Mellon University (1993) BSc, University of Engineering and Technology Lahore (1990) Harvard Certificate in Leadership for Senior Executives (2018) Research Interests: Asif’s work spans signal processing for medical imaging (e.g., ultrasound elastography), smart grid optimization, and cybersecurity in power systems. His recent publications address hydrogen energy systems, EMG-based gesture recognition, and resilient control frameworks against cyberattacks. Grants & Leadership: He leads NSERC-funded projects on federated learning and resilient algorithms. He chairs the Ontario Council of University Research and serves on TRIUMF Innovations and the Richmond Hill Board of Trade. His grants include SSHRC funding for equity initiatives and NSERC support for distributed signal processing. Teaching & Mentorship: Asif has supervised over a dozen graduate students and taught courses like Digital Communications and Statistical Signal Processing Theory. Notable advisees include Arash Mohammadi (PhD, 2014) and Nick Sajadi (PhD, 2017).
Patrick H. Madden is an Associate Professor and Director of the MS Information Systems program at the School of Computing, Binghamton University. His research focuses on combinatorial optimization, VLSI physical design automation, and algorithmic solutions for NP-Hard problems. He is also involved in interdisciplinary work in cryptography and biology-related optimization. Education: BS and MS from New Mexico Institute of Mining and Technology PhD from University of California, Los Angeles (UCLA) Professional Roles: Chair of ACM/SIGDA (Design Automation Special Interest Group) Chair of Design Automation Conference (DAC) Sponsors Committee Advisor to Binghamton ACM Student Chapter Coach for ICPC Programming Contest Teams Member of Watson School Graduation Committee Research Contributions: Leads the Optimality Research Group, developing optimization algorithms for VLSI CAD and medical software applications. Notable contributions include the Feng Shui placement tool and medical reference apps for iOS/Android platforms. Awards: SUNY Chancellor's Award for Excellence in Professional Service (2015) Grants & Collaborations: Collaborates with Prof. Monte McCollum (Cinema Department) on hybrid cinema projects and Dr. Joshua Steinberg (Physician) on medical software. Active in ACM committees and EDA conferences (DAC, ICCAD, ISPD). Labs/Teams: Directs the Optimality Research Group and oversees medical software collaborations through CS441/580 projects.
Rahim Rahimi is an Assistant Professor of Materials Engineering at Purdue University, associated with the College of Engineering. His research focuses on advanced materials for biomedical applications, environmental sensing, and flexible electronics. Key interests include developing smart sensors for healthcare, antibacterial coatings for medical implants, and sustainable agricultural monitoring systems. Research emphasizes targeted drug delivery systems via smart capsules, environmental sensor networks for water quality and soil health, and nanotechnology applications in wearable devices. Notable projects include oxygen-generating surgical meshes for wound healing and low-cost wireless sensors for precision agriculture. His work bridges materials science with clinical and environmental challenges, leveraging plasma deposition techniques and nanomaterial functionalization. Recent efforts focus on self-calibrating sensors and integrating machine learning for manufacturing optimization. No scientific awards are explicitly listed in the provided information. His advisory role and grant activities are inferred through his research outputs in materials engineering and biomedical innovation. Rahimi collaborates across disciplines within Purdue's engineering ecosystem, contributing to labs focused on bio-inspired materials and flexible electronics. Future work aims to advance implantable medical devices and scalable sensor technologies for global health applications.
Sveta Zinger is a Full Professor in context-informed dynamic image analysis for clinical decision support at Eindhoven University of Technology (TU/e). She holds affiliations with the Biomedical Diagnostics Lab, NeuroPlatform, and EAISI Health. Her research focuses on medical image/video analysis, temporal data analysis, and machine learning for clinical applications. She has led projects funded by ZonMw, NWO, Philips, and others. She is also Co-Editor-in-Chief of Computer Methods and Programs in Biomedicine and serves on the Vidi committee for NWO. Education: MSc (2000) from Dnepropetrovsk State University; PhD (2004) from École Nationale Supérieure des Télécommunications, France. Postdoctoral roles at the French Atomic Agency and University of Groningen. Research Projects: Includes FORSEE (video monitoring for adverse events in healthcare) and NEUROTREND (fMRI biomarkers for depression). Awards: Second place in the CAMELYON17 challenge for metastases detection. Her teaching includes courses on DSP fundamentals, medical image processing, and cognitive neuroscience. She collaborates with clinical and industrial partners to advance biomedical diagnostics and healthcare technology.
Rikky Muller is an Associate Professor of Electrical Engineering and Computer Sciences at UC Berkeley, holding the S. Shankar Sastry Professorship in Emerging Technologies. She is Co-director of the Berkeley Wireless Research Center (BWRC), a Core Member of the Center for Neural Engineering and Prostheses (CNEP), and an Investigator at the Chan-Zuckerberg Biohub. Her research focuses on implantable/wearable medical devices, low-power wireless systems, and neurotechnology for neurological applications. Education: PhD (2013), UC Berkeley; BS and M.Eng. (2004), MIT, all in EECS. Prior roles include IC designer at Analog Devices and co-founder of Cortera Neurotechnologies (acquired). Research interests include neural interfaces, closed-loop neuromodulation, and biomedical microelectronics. Notable contributions include Neural Dust (ultrasonic implants), wireless EEG systems, and seizure prediction hardware. Awards: MIT TR35 Innovator, NAE Gilbreth Lectureship, NSF CAREER Award, IEEE SSCS New Frontier Award Grants: Bakar Fellows, Hellman Fellowship, NSF CAREER Labs: Muller Lab (UC Berkeley EECS), Chan-Zuckerberg Biohub collaborations
Ruoqing Zhu is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, with a primary appointment in the College of Liberal Arts & Sciences. He also serves as an inaugural member of the Carle Illinois College of Medicine, a Faculty Fellow at the National Center for Supercomputing Applications, and an affiliated researcher with the Carl R. Woese Institute for Genomic Biology and the Center for Genomic Diagnostics. His roles include PhD Program Director and Advisory Board member of Prenosis Inc. Dr. Zhu holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2013), an MA in Statistics from Bowling Green State University (2008), and dual B.S. degrees in Mathematics and Financial Engineering from Nanjing University (2006, 2005). His postdoctoral training was at Yale University’s Department of Biostatistics (2013–2015). His research focuses on developing statistical methods for decision-making in personalized medicine and reinforcement learning, addressing challenges such as model interpretability, high-dimensional data, and distributional shifts. Key areas include uncertainty quantification, causal inference, and applications in bioinformatics, nutrition, and infectious diseases. He co-teaches courses at Carle Illinois, including Data Science Project and Foundations: Molecules to Populations , and contributes to interdisciplinary initiatives like the Personalized Nutrition Initiative. His recent work emphasizes trustworthy AI in healthcare, including sepsis prediction tools, metabolomic analysis, and biomarker discovery. He is actively involved in translational research, bridging computational methods with clinical and public health applications.
Vivek Boominathan is an Assistant Research Professor in the Department of Electrical and Computer Engineering at Rice University. He is affiliated with the GLEE lab (Geometry, Light, & Imaging lab). His research focuses on computational imaging, combining computer vision, machine learning, applied optics, and nanofabrication to develop innovative imaging systems for applications such as robotics, medical sensing, and virtual/augmented reality. He has contributed to projects like PhlatCam (a lensless camera) and NeuWS (neural wavefront shaping). His work bridges optics, algorithms, and materials science to overcome traditional limitations in imaging systems. Boominathan's research interests include lensless imaging, optical meta-devices, turbulence mitigation, and bio-inspired imaging systems. He has developed systems like Foveated thermal imaging prototypes and real-time lensless microscopes. His lab emphasizes interdisciplinary approaches, integrating hardware design with machine learning. Key projects include: NeuWS: Neural wavefront shaping for imaging through scattering media CoIR: Compressive implicit radar for sensing applications FlatCam and PhlatCam: Ultra-thin lensless imaging devices Bioluminescence imaging in marine species His work has been published in top venues like Science Advances, Optica, and IEEE TPAMI. He collaborates with institutions like NASA JPL and industry partners on applied imaging solutions. Current research trends emphasize sensor-algorithm co-design and high-speed imaging systems for AR/VR applications. Boominathan holds a PhD in Electrical Engineering and has extensive postdoctoral experience in computational imaging. He advises projects in the GLEE lab and mentors students in hardware-software co-design for imaging systems. His lab focuses on translating theoretical innovations into practical devices with commercial potential.
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Axel Schülzgen is a Professor of Optics at CREOL, The College of Optics and Photonics, University of Central Florida. He also holds an Adjunct Research Professor position at the University of Arizona's College of Optical Sciences. His research focuses on fiber fabrication, nano-structured fibers, nonlinear materials, and applications in fiber lasers, sensing, and communications. He earned a PhD in Physics from Humboldt-University of Berlin, Germany. His expertise spans optical fiber devices, components, and structures, with a strong emphasis on advancing high-power laser delivery and sensing technologies. Research Interests: - Development of hollow-core fibers for low-loss light transmission - Anti-resonant fiber designs for multi-mode guidance - Nonlinear optical materials for fiber lasers - Applications in fiber optic sensing and medical imaging - Disordered media imaging using optical fibers Awards & Honors: OSA Fellow (Optical Society of America) SPIE Fellow (International Society for Optics and Photonics) 2021 Excellence in Graduate Teaching Award 2015 CREOL Excellence in Research Award Advising & Labs: - Current advisees: Ameen Alhalemi, Caleb Dobias, Md Abu Sufian - Notable alumni: Xiaowen Hu (2022), Stefan Gausmann (2021), and Jian Zhao (2019) - Research Group: Focuses on fiber fabrication technology, nanotechnology in fibers, and photonics applications
Dr. Andrew Erwin is an Assistant Professor in Mechanical Engineering at the University of Cincinnati, focusing on robotics, human-robot interaction, and rehabilitation engineering. He holds a PhD and MS from Rice University (2018, 2014) and a BS from the University of Massachusetts Amherst (2012). Prior to UC, he was a postdoc at the University of Southern California and the Jet Propulsion Laboratory. His research explores how forces and movements are executed in healthy individuals, and how robotic devices can assist or restore function post-injury. Key areas include rehabilitation robotics, bio-inspired systems, haptic interfaces, and motor learning. He has received prestigious awards such as the NASA Postdoctoral Program Fellowship (2018) and the IEEE/ASME Transactions on Mechatronics Best Paper Award (2017). Dr. Erwin’s work integrates biomechanics, control systems, and neurophysiology. His lab develops devices like the SE-AssessWrist for wrist assessment and explores planetary seismometers for space missions. He maintains an active Google Scholar profile with over 25 publications. Education: PhD, Mechanical Engineering, Rice University, 2018 MS, Mechanical Engineering, Rice University, 2014 BS, Mechanical Engineering, University of Massachusetts Amherst, 2012 His current research emphasizes curriculum design for robotics learning, human-robot collaboration, and adaptive control systems. He offers a PhD position for Fall 2025 focusing on these areas.
Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.
