Rishi Kundi is an Associate Professor of Surgery at the University of Maryland School of Medicine and Chief of Endovascular in Trauma. His clinical and research focus is vascular trauma management and endovascular techniques. Research Interests: Specializes in REBOA, ECMO for trauma, vascular injury grading systems, and bioengineered grafts, with emphasis on evidence-based protocols for blunt cerebrovascular injuries. Publications: Recent work advances understanding of pediatric vs. adult vascular trauma outcomes, REBOA applications in transplant complications, and anticoagulation strategies for trauma-ECMO patients.
Dr. Mirna Becevic is Assistant Professor in Dermatology at University of Missouri, focusing on telemedicine applications to improve healthcare access for rural and underserved populations. As lead evaluator for Show-Me ECHO, she develops virtual learning networks for primary care providers. Education includes doctoral training in health informatics. Research evaluates telehealth implementations for dermatology, pediatric care, and chronic disease management. Recent projects examine AI applications for skin lesion classification and telehealth impacts on opioid prescribing patterns. Awards include Ann K. Covington mentoring award (2021) and MU Top Faculty Achiever (2017). Research outcomes inform telehealth policy and implementation frameworks for improving specialty care access.
Anh Nguyen is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, affiliated with the Samuel Ginn College of Engineering. His work focuses on deep learning, computer vision, and explainable AI. He holds a Ph.D. from the University of Wyoming and a B.S. from Assumption University. Research highlights include developing methods to improve AI robustness, analyzing biases in large language models, and creating tools for visual correspondence in image processing. He leads the Center for Artificial Intelligence and Cybersecurity Engineering and directs Auburn's first K-6 AI education program through his AI Club after-school initiative. Recipient of a $460,736 NSF CAREER Award for AI innovation Developed the AI@AU lecture series and multiple benchmark datasets (e.g., ImageNet-Hard) Collaborates with industry partners on real-world AI applications Recent projects explore multimodal model limitations (Zerobench), medical imaging (LiteGPT for chest X-rays), and interactive AI systems that incorporate human feedback. His work bridges theoretical advancements with practical implementations in healthcare, wildlife monitoring, and education.
Robert Fovell is a Professor of Atmospheric and Environmental Sciences at the University at Albany, SUNY. He holds a PhD from the University of Illinois (1988) under Prof. Yoshi Ogura. Previously, he served at UCLA from 1991 to 2015. His research focuses on mesoscale and convective meteorology using numerical models, covering topics like tropical cyclones, boundary layer dynamics, and climate classification. His work is funded by NSF, NASA, NOAA, and private sector entities. He has received teaching awards including the UCLA Eby Award (2005) and AMS Teaching Excellence Award (2012). Fovell teaches courses such as Numerical Weather Prediction and Mesoscale Modeling. He has authored educational materials, including the video course Meteorology: An Introduction to the Wonders of the Weather (2010). Education: PhD, 1988, University of Illinois (Atmospheric Sciences) Research Interests: Mesoscale and convective-scale numerical modeling Squall lines, tropical cyclones, sea breezes Boundary layer rolls and gravity waves Climate classification methodologies Awards and Honors: UCLA Distinguished Teaching Award (2005) American Meteorological Society (AMS) Teaching Excellence Award (2012) Current Courses (Fall 2024): ATM 419/563: Numerical Weather Prediction ATM 562: Numerical Methods and Modeling Professional Service: Former Chair of UCLA College of Letters and Science Faculty (2007–2009) AMS Committee on Mesoscale Processes Chair (2009–2011) Co-chair of AMS Michio Yanai Symposium (2011)
Ala'a Al-Habashna is an Adjunct Professor at Carleton University's School of Computer Science within the Faculty of Engineering and Design. He holds a PhD from Carleton University and focuses on advanced wireless communication systems, 5G/6G networks, machine learning applications, and discrete-event simulation. His research integrates AI-driven solutions for network optimization, spectrum management, and smart urban infrastructure analysis. Education: PhD in Computer Science, Carleton University Research Interests: 5G/6G Networks, Machine Learning for Communications, Cognitive Radio Systems, IoT Integration, and Urban Modeling via Computer Vision Focus on resource allocation algorithms, channel modeling, and AI-driven network architectures Key Article Trends: Recent work emphasizes AI integration in next-gen networks (e.g., RIS-assisted MIMO, GenAI-6G), 5G energy efficiency, and urban analytics using street-view imagery. Earlier contributions include DEVS-based simulation frameworks for fire modeling and D2D video streaming optimization. Collaborations & Funding: Active partnerships include Ericsson Canada for projects like Channel Reconstruction for LTE/NR and Spectrum Sharing with Machine Learning . A funded PhD position is available in Wireless Communication and 5G Networks requiring expertise in Mathematical Optimization. Labs & Teams: Engaged in Ericsson-Carleton Partnership initiatives and leads interdisciplinary teams addressing 6G challenges. Research leverages Carleton's advanced wireless and AI facilities.
Azeez Abdul Azeez is a researcher at Tampere University, specializing in AI-driven condition monitoring systems for mechanical and hydraulic components. His work focuses on integrating artificial intelligence with fluid power systems to enhance fault detection and predictive maintenance. He holds a Doctoral Degree in Mechanical Engineering from Tampere University (2025). Key research areas include gear pump simulation, hydraulic valve diagnostics, and electric drive structures. Research Interests His research interests revolve around the application of AI in mechanical systems, including: Condition Monitoring of Electro-Hydraulic Systems Simulation of Gear Pump Performance under Varying Complexity Sensorless Fault Classification in Hydraulic Valves Energy Efficiency in Off-Road Mobile Machines Main Findings His recent work highlights the importance of model complexity in gear pump simulations (2023), the impact of electric drive structures on hydraulic valve diagnostics (2023), and the potential of AI in non-road machine applications (2025). Collaborations include studies on multi-physics co-simulations of electric vehicles (2023) and offshore renewable energy feasibility in UAE (2021). Labs and Teams His research is conducted within Tampere University's Automation Technology and Mechanical Engineering department, often in collaboration with industry partners focusing on fluid power systems and sustainable energy.
Jitka Annen is a Postdoctoral Researcher at the Coma Science Group within the Faculty of Medicine at the University of Liège, Belgium. Her work focuses on multimodal neuroimaging approaches to study brain structure-function relationships in disorders of consciousness and space neuroscience applications. Education: PhD in Biomedical Sciences and Pharmaceutics (Multimodal Neuroimaging in Patients with Disorders of Consciousness), University of Liège, Belgium (2019) MSc in Biomedical Sciences, Neurobiology, University of Amsterdam, The Netherlands (2014) BSc in Psychobiology, Neurobiology, University of Amsterdam, The Netherlands (2012) Her research integrates neuroimaging and neurophysiology across spatio-temporal scales to investigate consciousness mechanisms, brain connectivity in pathological states, and the effects of extreme environments like zero gravity on neural systems. She employs complementary data acquisition techniques to study disorders of consciousness, including coma, unresponsive wakefulness syndrome, minimally conscious state, and locked-in syndrome, with recent work extending to cosmonaut brain adaptations. Analysis of her publication record reveals consistent focus on advancing neuroimaging methodologies for consciousness assessment, developing computational models of brain dynamics in pathological states, and exploring novel therapeutic interventions including neuromodulation and psychedelic compounds. Her work bridges clinical applications with fundamental neuroscience questions about consciousness mechanisms. Research Leadership: Active member of Coma Science Group at GIGA research institute Collaborator on international space neuroscience projects studying brain changes in cosmonauts Contributor to development of standardized assessment protocols for disorders of consciousness
Mark D. Anderson, MD is an Associate Professor in the Department of Neurology at the University of Mississippi Medical Center's School of Medicine. Board certified in Neurology by the American Board of Psychiatry and Neurology and in Neuro-Oncology by the United Council of Neurologic Subspecialties, he serves on the Cancer Institute Clinical Leadership Committee and co-chairs the Multidisciplinary Tumor Board for CNS tumors. Education: MD in Medicine, University of Alabama-Birmingham (2007) BS and MS in Biomedical Engineering, University of Miami (2003) Neuro-Oncology Fellowship, The University of Texas M.D. Anderson Cancer Center Neurology Residency, Vanderbilt University Dr. Anderson's research centers on neuro-oncology with emphasis on central nervous system tumors, glioblastoma therapeutics, and cancer-related neurological complications. His clinical work focuses on tumor board leadership and developing targeted therapies for malignant brain tumors, particularly investigating drug delivery systems for glioblastoma treatment. He actively contributes to understanding viral complications in neuro-oncology patients and rare tumor pathologies. His publication record (2013-2018) demonstrates sustained focus on neuro-oncology, with recurring themes in glioma biology, treatment resistance mechanisms (particularly bevacizumab discontinuation), viral interactions with brain tumors, and molecular diagnostics including IDH1 mutation analysis. The research spans clinical case reports, therapeutic studies, and molecular investigations across CNS tumor types. Grants and Advising: Principal Investigator: NCI-funded study Toward Changing Glioblastoma Outcomes (2018-2020) on targeted drug delivery systems Neurology Residency Advisor since 2018 Regular lecturer on CNS tumors for Cancer Registrars and Mississippi Neurosurgical Society Dr. Anderson co-chairs the CNS Multidisciplinary Tumor Board and serves on multiple cancer committee panels, integrating clinical care with research protocols through the UMMC Cancer Institute. His work bridges laboratory findings with clinical applications in neuro-oncology practice.
Dr. Alexandra Gersing is an Associate Professor in Radiology at the University of California, San Francisco (UCSF). She holds concurrent roles as Director of Magnetic Resonance Imaging and AI Research at the University Hospital of Munich and Ludwig Maximilians University Munich. Her expertise lies in musculoskeletal imaging and AI-driven medical imaging technologies. Dr. Gersing completed her medical degree at the University of Hamburg and Technical University of Munich (TUM), followed by radiology residency and musculoskeletal imaging fellowship at TUM's University Hospital. She also served as a postdoctoral fellow at UCSF from 2014 to 2016. Affiliations: UCSF School of Medicine, University Hospital of Munich, Ludwig Maximilians University Munich Roles: Associate Professor, Director of MRI and AI Research Her research focuses on advancing MRI techniques and AI applications for musculoskeletal disorders, particularly osteoporosis and bone tumors. She has published extensively in top journals like Radiology and European Radiology , and her work includes developing deep learning models for fracture differentiation and tumor classification. Dr. Gersing has received notable awards including the 2023 Wilhelm-Conrad-Roentgen Prize and the International Skeletal Society Seed Grant. She mentors doctoral candidates, postdoctoral scholars, and junior faculty, and actively contributes to professional societies such as the European Society of Skeletal Radiology. Her work integrates multidisciplinary teams to leverage AI and advanced imaging for clinical diagnostics and treatment planning, emphasizing translational research with significant clinical impact.
Dr. Thomas M. Link serves as Professor and Division Chief of Musculoskeletal Radiology at the University of California, San Francisco (UCSF) in the Department of Radiology and Biomedical Imaging, with additional leadership roles as Director of the T32 Program, Clinical Director of the Musculoskeletal and Quantitative Imaging Research (MQIR) Group, and Co-Director of Clinical & Translational Musculoskeletal Imaging. His educational foundation includes an M.D. from Johannes Gutenberg University (1987), clinical training at Groote Schuur Hospital (University of Cape Town), multiple German radiology residencies, and a Ph.D. from University Hospital Muenster. Following a UCSF fellowship (1996) and Visiting Associate Professorship (1999-2001), he joined UCSF in 2003 after serving as Vice-Chair of Radiology at Technical University of Munich. Dr. Link's research centers on translational musculoskeletal imaging through three interconnected pillars: osteoporosis imaging (novel bone quality/density assessment), osteoarthritis and cartilage imaging (prevention of degeneration using high-field MRI), and interventional bone tumor techniques . His work leverages 3.0T/7.0T MRI and MR-guided focused ultrasound to bridge laboratory discoveries with clinical applications, emphasizing quantitative biomarkers for disease progression. His extensive publication record (400+ peer-reviewed articles) reveals current trends in adipose tissue's role in joint degeneration, AI-driven image analysis (particularly GPT-4 for report extraction), and multicenter validation of imaging biomarkers using Osteoarthritis Initiative data, with recent work increasingly incorporating machine learning for diagnostic precision. Scientific recognition includes election to AIMBE College of Fellows (2019), multiple mentoring awards (UCSF Outstanding Faculty Mentoring Award 2019), the Lodwick Award (Harvard 2019), and Distinguished Investigator Award (Academy of Radiology Research 2016), reflecting sustained contributions across research, education, and clinical innovation. As T32 Program Director, he oversees NIH-funded training for imaging scientists while leading MQIR's interdisciplinary team in developing clinical applications. His mentorship excellence is evidenced by the Pathways to Discovery Long-Term Mentor Award (2018) and sustained involvement in collaborative projects like the Osteoarthritis Initiative, demonstrating exceptional grant management across multi-institutional consortia. The MQIR Group under his direction integrates basic scientists and clinicians to translate imaging innovations into clinical practice, with current focus on quantitative MRI biomarkers for early disease detection and MR-guided therapeutic interventions for musculoskeletal disorders.
Dror Suhami, MD is an HS Clinical Instructor in the Department of Radiology at the University of California, San Francisco School of Medicine. His clinical and research work focuses on neuroradiology with particular expertise in stroke diagnosis and imaging techniques. Dr. Suhami's research interests include: Neuroradiology and stroke imaging Cerebral angiography techniques Lateral medullary syndrome diagnosis Vocal cord paralysis assessment Thrombolytic therapy applications Medical imaging with X-rays and CT His recent work has examined novel diagnostic approaches for neurological conditions using advanced imaging techniques, with a focus on practical clinical applications. Dr. Suhami has also contributed to research on AI applications in medical imaging, particularly for COVID-19 diagnosis. Dr. Suhami's scientific contributions include: Development of CTA as a diagnostic tool for lateral medullary syndrome Application of deep neural networks for X-ray image classification in COVID-19 His work connects neuroradiology with clinical neurology, focusing on improving diagnostic accuracy for stroke and related neurological conditions through advanced imaging techniques.
Ari Stern is a Professor of Mathematics at Washington University in St. Louis , specializing in Geometric Numerical Analysis . His work bridges geometry, applied analysis, and computational mathematics, focusing on numerical methods that maintain global accuracy for differential equations through modern geometric principles. He earned his B.A. and M.A. in Mathematics from Columbia University and a Ph.D. in Applied and Computational Mathematics from Caltech (2009), advised by Jerrold E. Marsden and Mathieu Desbrun. Prior to WashU (2012), he was a postdoc at UCSD with Michael Holst. Research Interests : Geometric integration, finite element exterior calculus, symplectic geometry, and applications to physics and machine learning. His recent publications address multisymplecticity, functional equivariance, and hybrid finite element methods. Collaborations span topics from Alzheimer’s disease modeling via machine learning to Hamiltonian mechanics and geometric electrodynamics. Awards : NSF Grant (2019). Teaching : Courses include Numerical Methods for Differential Equations, Measure Theory, and Honors Mathematics.
Walter R. Mebane, Jr. is a Professor of Political Science and Statistics at the University of Michigan's College of Literature, Science, and the Arts. He holds a Ph.D. from Yale University (Political Science), an M.A. from Yale, and a B.A. from Harvard University (Government). His research focuses on election forensics, statistical methodology, and voting systems, with a particular emphasis on detecting electoral fraud and analyzing voting technologies. Mebane has developed the eforensics R package for election fraud analysis and contributed to tools like RGENOUD for optimization. He teaches advanced courses on multivariate analysis, survey sampling, and election forensics. His work spans global elections, including analyses of anomalies in Kenya, Turkey, Venezuela, and the U.S. He integrates computational methods (e.g., neural networks, agent-based models) with traditional statistical techniques. Mebane’s research also addresses issues like voting machine accuracy, strategic voting, and the application of Benford’s Law. His contributions bridge political theory, statistical rigor, and practical election monitoring.
Dr. Ivan Kiskin is a Lecturer in AI for Multimodal Health Monitoring at the University of Surrey's Surrey Institute for People-Centred Artificial Intelligence (PAI), affiliated with the School of Biosciences. He holds a PhD in Machine Learning for Acoustic Mosquito Detection from the University of Oxford (2020) and a first-class MEng in Engineering Science (2015). His research focuses on machine learning for audio/signal processing, Bayesian deep learning, and AI applications in health monitoring. He leads the HumBug project, developing smartphone-based mosquito detection systems funded by the Bill and Melinda Gates Foundation. His work combines bioacoustic signal analysis with community-driven data collection strategies, particularly in malaria-prone regions. Key contributions include the HumBugDB dataset, few-shot learning frameworks for bioacoustic event detection, and evaluations of AI systems in pandemic response. He collaborates with the Centre for Vision, Speech and Signal Processing (CVSSP) and co-supervises PhD students in AI-driven health applications.
Lasse Løvstakken is a Professor at the Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU). His research focuses on medical ultrasound imaging, particularly in developing advanced techniques for blood flow analysis and cardiac imaging. Key projects include 3D ultrasound imaging of blood flow in pediatric and adult hearts, leveraging artificial intelligence and deep learning for automated measurements and diagnostic improvements. His work emphasizes the integration of AI into echocardiography, such as real-time guidance systems and automated strain analysis, to enhance reproducibility and reduce variability. Collaborative efforts span clinical validation of new imaging modalities and interdisciplinary applications, such as seabed classification using deep learning. He leads projects funded by institutions like NTNU and collaborates with international teams on innovations in cardiac mechanics, valve timing, and hemodynamic modeling. Løvstakken’s research also addresses translational challenges, including telemedicine applications of handheld ultrasound devices and automated quantification tools for clinical use. His contributions bridge biomedical engineering and clinical cardiology, aiming to improve diagnostic accuracy and patient care through cutting-edge imaging technologies.
