Find Academic Supervisors

Aleksandar JeremicView profile

Associate Professor

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +7 more

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.

Dennis SäringView profile

Professor

• Deep Learning
• Medical Image Analysis
• Artificial Intelligence
• +5 more

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.

Maciej A MazurowskiView profile

Associate Professor

• Medical Imaging
• Machine Learning
• Computer Vision
• +3 more

Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.

Thomas GrenierView profile

Associate Professor

• Medical Image Segmentation
• Clustering
• Filtering
• +3 more

Thomas Grenier is an Associate Professor in the Department of Electrical Engineering at INSA Lyon and a member of the CREATIS laboratory (CNRS UMR 5220, INSERM U1294). He obtained his HDR (Habilitation à Diriger des Recherches) in 2023 and his Ph.D. in Image Processing from INSA Lyon in 2005. His research focuses on medical image segmentation, clustering, and filtering using feature space, scale-space, and deep learning approaches. Doctoral School: EEA (Electronics, Energy, and Automatics) Research Affiliation: CREATIS Lab (CNRS/INSERM/INSA Lyon/Université Lyon 1/Université Jean Monnet Saint-Etienne) He has contributed to 20 papers and co-supervised 5 PhD students, including Léo Dumortier and Florent Guépin. Grenier leads the annual Deep Learning for Medical Imaging (DLMI) school, which he co-founded, and has organized five editions across Lyon and Montreal since 2019. The school emphasizes practical deep learning applications in medical imaging for participants of all expertise levels. His work spans interdisciplinary domains such as medical imaging , deep learning , and image processing , with recent publications on generative AI for MRI synthesis, explainable networks, and segmentation of neurological pathologies in preclinical models. He manages pedagogical platforms, coordinates LabEx PRIMES project activities, and oversees lab room infrastructure for 200 hours/year across 10 training programs. Grenier also leads the MUSIC transversal project on Multiple Sclerosis since 2019.

Anthony A GattiView profile

Research Fellow

• Biomechanics
• Medical Imaging
• Machine Learning
• +7 more

Anthony A Gatti is a Postdoctoral Scholar at Stanford University's Wu Tsai Human Performance Alliance and School of Medicine. His research integrates biomechanics , medical imaging , and machine learning to advance musculoskeletal health diagnostics, particularly focusing on knee osteoarthritis and exercise physiology. Education : Ph.D. in Rehabilitation Science (McMaster University, 2021), M.Sc. in Rehabilitation Science (McMaster University, 2015), B.Sc. in Kinesiology (McMaster University, 2013) His research develops automated tools for quantifying knee anatomy and integrating anatomical data with biomechanical models . These methods analyze acute responses to exercise and long-term joint degeneration, leveraging MRI , deep learning , and statistical shape modeling . Recent publications emphasize AI-driven segmentation , exercise-induced cartilage changes , and biomechanical simulations , spanning journals like Magnetic Resonance in Medicine and Arthritis & Rheumatology . Trends include machine learning validation for clinical predictions and open-source tool development for musculoskeletal analysis. Scientific Awards : CIHR Postdoctoral Fellowship (top 1%), Mitacs Accelerate Entrepreneur, Forge Student Start-Up Competition Winner, multiple scholarships from McMaster University He founded NeuralSeg , a company commercializing deep learning-based MRI segmentation technology. Collaborations include Stanford's Digital Athlete Moonshot Project with advisors like Scott Delp and Garry Gold.

Mahmoud El-SakkaView profile

Associate Professor

• Image Compression
• Medical Imaging
• Image Segmentation
• +5 more

Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.

Hee Kwon SongView profile

Associate Professor

• MRI Physics
• Biomedical Engineering
• Medical Imaging
• +2 more

Hee Kwon Song, Ph.D. is an Associate Professor of Radiology at the University of Pennsylvania , affiliated with the Institute for Translational Medicine and Therapeutics and the Penn Center for Musculoskeletal Disorders . His expertise lies in MRI physics and MRI engineering , focusing on novel dynamic imaging strategies. Education: BS in Electrical Engineering, Northwestern University (1991) MS in Bioengineering, University of Pennsylvania (1994) PhD in Bioengineering, University of Pennsylvania (1999) Master of Law, University of Pennsylvania Carey Law School (2021) Dr. Song’s research specializes in dynamic contrast-enhanced MRI (DCE-MRI) using radial data acquisition and KWIC reconstruction for ultra-fast imaging. His work enables retrospective respiratory motion compensation and flexible resolution adjustments in tumor treatment monitoring ( lung, liver, breast, kidney, ovarian ). Applications extend to non-contrast MRA and tissue T1 mapping . Selected Publications demonstrate his focus on accelerated MRI , bone-selective imaging , and deep learning reconstruction , with collaborations spanning Medicine, Obstetrics and Gynecology, Neurology , and Radiation Oncology . Contact: heekwon.song@pennmedicine.upenn.edu

• Medical Imaging
• Deep Learning Applications
• Ultrasound Imaging
• +3 more

Mehrdad Salehi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich (TUM) . His work focuses on the intersection of computer science and medical imaging, with expertise in ultrasound technology, deep learning, and surgical navigation systems. Key research areas include sonification of medical data, 3D ultrasound reconstruction, and machine learning-based segmentation. He has contributed to innovative projects like PRO-TIP calibration phantoms and ColibriDoc autonomous docking systems. His publications highlight trends in acoustic feedback mechanisms, neural radiance fields for medical imaging, and real-time image analysis. He can be reached at mehrdad.salehi@tum.de .

• Biomedical Engineering
• Biofabrication
• Tissue Engineering
• +7 more

Professor Mia Woodruff is a leading academic at Queensland University of Technology (QUT), holding the position of Professor in the School of Mechanical, Medical and Process Engineering within the Faculty of Engineering. She is the Group Leader of the Biofabrication and Tissue Morphology Group and Acting Director of the Herston Biofabrication Institute. Her research focuses on biofabrication, tissue engineering, and 3D printing applications in medicine, particularly in bone regeneration, vascular surgery, and personalized prosthetics. Woodruff holds a PhD in Materials Engineering from the University of Nottingham, UK. She has led over $40 million in external grants, including ARC and Advance Queensland funding. Her work emphasizes translating biofabrication technologies into clinical practice, such as developing patient-specific implants and surgical tools. She has published over 120 peer-reviewed articles and co-founded initiatives like the Herston Biofabrication Institute to bridge clinical, research, and industry collaboration. Key research areas include composite biomaterials, 3D scanning/modelling, electrospinning, and large-animal models for bone regeneration. She has been awarded the QUT Vice Chancellor’s Research Fellowship and ARC Fellowship. Her outreach efforts include promoting STEM through media appearances and school programs, such as the 3D Printing in All High Schools initiative. Grants & Awards $40M Innovation Manufacturing CRC (Co-Investigator) $3.72M ARC Industrial Transformation Training Center (Additive Biomanufacturing) Advance Queensland Fellowships ($1.2M Cluster Grant for Biofabrication) Media & Engagement Featured in SCOPE TV, SBS The Feed , and ABC News Keynote speaker at global conferences (e.g., TERMIS, ORS) Research Infrastructure Herston Biofabrication Institute: Integrates 3D printing, clinical trials, and patient collaboration World-first Biofabrication Master’s program launched in 2014

• Inverse Problems
• Nonlinear Acoustics
• Medical Imaging
• +5 more

Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.

• Machine Learning
• Computer Vision
• Signal Processing
• +6 more

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

Jianping FuView profile

Professor

• Stem Cell Bioengineering
• Developmental Bioengineering
• Mechanobiology
• +4 more

Jianping Fu is a Professor in the Department of Mechanical Engineering at the University of Michigan , with joint appointments in Biomedical Engineering and Cell and Developmental Biology. His research integrates micro/nanoengineering , mechanobiology , and stem cell biology to model human development and disease. Education: PhD (MIT, 2007), BE (University of Science and Technology of China, 2000) His research interests focus on stem cell bioengineering , developmental bioengineering , and mechanobiology , particularly in modeling early post-implantation human development, neural tube formation, and pluripotent stem cell mechanoregulation. His work combines biomimetic culture systems with microfluidic gradients to study embryogenesis and organogenesis. Recent publications highlight advances in human embryo modeling (2024 Cell, Nature, Cell Stem Cell), neural tube patterning (2024 Nature), and mechanobiology of stem cells (2024 Nature Reviews Physics). These studies emphasize computational methods , single-cell analysis , and standardization of embryo models . Scientific honors include: Friedrich Wilhelm Bessel Research Award (2022) ISSCR Merit Award (2024) Fellow, American Institute for Medical and Biological Engineering (2019) NSF CAREER Award (2012) Life Member, World Association of Chinese Biomedical Engineers (2024) Dr. Fu mentors extensively, with 20+ alumni including PhD students and postdocs now in academic and industry positions. His lab has received $3M NIH funding for immunological diagnostics and MTRAC grants for translational research. Collaborations with institutions like Cincinnati Children's Hospital and Rice University enhance his interdisciplinary approach to regenerative medicine.

Michelle CameronView profile

Associate Professor

• Evolutionary Anthropology
• Bioarchaeology
• Human Evolution
• +2 more

Michelle Cameron is an Associate Professor in the Department of Anthropology at the University of Toronto's Faculty of Arts & Science. She joined the department in 2018 and specializes in skeletal biology and bioarchaeology, integrating skeletal analysis with archaeological and ecological evidence to study human adaptability across diverse environments. Her research explores how environmental and cultural contexts shape human biology through time, focusing on biocultural stressors and skeletal responses. She employs advanced methodologies including 3D modeling, biomechanical analyses, and spatial analysis frameworks to examine human adaptation to environmental challenges. Primary research regions: Africa (South Africa, Namibia, Lake Turkana), North America, Europe Key methodologies: Cross-sectional geometry, skeletal biomechanics, paleodiet reconstruction Dr. Cameron's publications demonstrate expertise in subsistence transitions (herding practices), human evolutionary morphology, and contemporary skeletal biology applications. She maintains active fieldwork programs in southern Africa while engaging in comparative studies across continents. Scientific Awards: SSHRC Insight Development Grant Connaught New Researcher Award Education: PhD, University of Cambridge (2017) As co-host of the YouTube series 'Humans in 5', she actively participates in science communication, distilling complex anthropological concepts into accessible five-minute segments.

• Cadmium Toxicology
• Environmental Pollutants
• Pharmacology
• +5 more

Professor Walter C. Prozialeck is a distinguished academic at Midwestern University, serving in the Department of Pharmacology across multiple schools including the Chicago College of Osteopathic Medicine (CCOM), College of Pharmacy, College of Dental Medicine-Illinois, and College of Graduate Studies - Illinois. He holds a Ph.D. in Pharmacology from Thomas Jefferson University (1978) and a B.S. from Juniata College (1974). Dr. Prozialeck's research spans 35 years of cadmium toxicology, focusing on mechanisms of nephrotoxicity, microRNA expression, and biomarker development. He also investigates vancomycin pharmacokinetics and herbal medications like kratom and marijuana, contributing to drug policy discussions. His work has secured over $3.0 million in NIH funding. Recent publications highlight his expertise in cadmium-induced kidney injury biomarkers (e.g., Kim-1, cystatin C), microRNA alterations, and vascular toxicity. He has authored over 100 papers and book chapters. Career Achievement Award, Metals Specialty Section of Society of Toxicology (2021) Dr. Prozialeck teaches Pharmacology to medical, pharmacy, optometry, dental, and allied health students, and lectures in CME programs. He has held leadership roles including President of the Great Lakes Chapter of ASPET and Midwest Regional Chapter of the Society of Toxicology.

• Microfluidics
• Cell Biology
• Biophysics
• +4 more

Dr. Sung Sik Lee serves as a Lecturer in the Department of Materials at ETH Zurich, Switzerland. Affiliated with ScopeM (Scientific Center for Optical and Electron Microscopy), he develops microfluidic platforms for real-time cellular analysis at the HPM C 52.2 facility (Otto-Stern-Weg 3, Zürich). His research bridges engineering and biology to investigate cellular responses to mechanical and chemical stimuli. His primary research domains include: Microfluidics : Design of microfabricated devices for cell stretching, particle separation, and dynamic stimulation Cellular Aging : Mechanisms of chromosome loss and nuclear pore complex reorganization in yeast models Nanotoxicology : Impact of nanoplastics on macrophage inflammation and intestinal barrier integrity Advanced Imaging : Application of holotomography and Raman spectroscopy for label-free cellular analysis His work consistently targets translational applications in disease modeling and diagnostics. Analysis of his 50+ publications reveals strong interdisciplinary integration, particularly the convergence of machine learning with microscopy (e.g., automated vacuole quantification in yeast) and the development of open-access resources like MicrobioRaman. Recent trends emphasize nanoparticle-cell interactions and microfluidic solutions for inflammatory conditions including IBD and acute kidney injury. Dr. Lee actively contributes to ScopeM's mission of advancing microscopy techniques, maintaining collaborations across ETH Zurich's research ecosystem. His laboratory focuses on microfluidic device fabrication, cellular mechanotransduction studies, and biophysical characterization of particles and cells, with ongoing projects extending through 2025.