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Jürgen HesserView profile

Professor

• Medical Imaging
• Computational Oncology
• Inverse Problems in Science
• +7 more

Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.

Tayfun AkgülView profile

Professor

• Image Processing
• Signal Processing
• Audio and Speech Processing
• +5 more

Tayfun Akgül is a Professor at Istanbul Technical University's Faculty of Electrical and Electronics Engineering, Department of Electronics and Communication Engineering. With academic affiliations spanning decades, he combines engineering rigor with innovative research in signal processing and underwater acoustics. His research focuses on advanced signal processing techniques, including Compressive sensing and cyclostationary analysis Underwater acoustic monitoring and sensor arrays Thermal imaging and infrared reflection modeling Biometric identification through facial attributes Seismic signal processing Recent publications demonstrate expertise in Propeller noise analysis in maritime environments Micro-Doppler helicopter signature detection Seismic activity precursor identification Novel fisheye camera human detection systems Awarded Most Successful Doctoral Thesis Award from TESID (2024) IEEE Top 10 Award (2013) he maintains active IEEE membership since 1992 and has led multiple high-impact projects including casualty detection systems and electric vehicle warning systems.

Ali TavallaeiView profile

Assistant Professor

• Image Guided Therapy
• Cardiovascular Interventions
• Medical Imaging Instrumentation
• +3 more

Ali Tavallaei serves as an Assistant Professor at Toronto Metropolitan University since 2019 and concurrently holds a Visiting Scientist position at Sunnybrook Research Institute. He is also the President and Co-founder of Magellan Biomedical Inc. (2018-present) and Vital Biomedical Technologies Inc. (2012-present), demonstrating strong industry engagement in medical device commercialization. His academic foundation includes: Ph.D. in Biomedical Engineering from Western University (2010-2015) Medical Innovation Fellowship at University of Minnesota/Western University (2015-2016) Postdoctoral Fellowship at Sunnybrook Research Institute, University of Toronto (2016-2019) Dr. Tavallaei's research centers on image guided therapy with emphasis on solving unmet clinical needs in minimally invasive interventions. His core focus areas include: Cardiovascular device innovation and evaluation Medical imaging instrumentation for real-time guidance Robotic systems for catheter navigation Mechatronic solutions for therapeutic delivery His work bridges engineering design with clinical translation through preclinical and clinical validation. Recent publications (2023-2026) reveal a concentrated effort in developing next-generation catheter technologies, including steering mechanisms (CathPilot), imaging tools (CathEye, CathCam), and specialized devices for vascular interventions. The research consistently emphasizes performance validation, mechanical characterization, and clinical feasibility across peripheral artery disease, aneurysm repair, and cardiac ablation applications. As director of the Medical Devices and Systems Lab, Dr. Tavallaei leads a translational research program focused on fundamental advances in cardiovascular disease management. The lab's workflow integrates solution design, system verification, preclinical testing, and technology transfer to address global healthcare challenges posed by cardiovascular diseases—the leading cause of death worldwide.

• High-Performance Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +9 more

Niclas Jansson is a researcher at the PDC Center for High Performance Computing at KTH Royal Institute of Technology. He holds an M.S. in Computer Science (2008) and a Ph.D. in Numerical Analysis (2013) from KTH. His career spans roles such as postdoctoral researcher at RIKEN Advanced Institute for Computational Science (2013-2016) and visiting scientist at RIKEN (2018-2021), where he contributed to the Japanese exascale program Flagship 2020. A core focus of his research involves extreme-scale computing and numerical method development. He is a key developer of RIKEN's multiphysics framework CUBE , the HPC branch of FEniCS , and the spectral element flow solver Neko . His work is currently supported by a Swedish Research Council Starting Grant aimed at enhancing high-order spectral element methods for exascale fluid simulations. Niclas has published extensively on topics such as GPU acceleration , adaptive finite element methods , in situ visualization , and extreme-scale turbulence modeling . He also teaches Computational Fluid Dynamics (SG2212) at KTH.

Alice HaynesView profile

Researcher

• Human-Computer Interaction
• Soft Robotics
• E-Textiles
• +6 more

Alice Haynes is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, working on the Felt Connections project under the Division of Media Technology and Interaction Design . She collaborates with Prof. Kristina Höök and Associate Prof. Iolanda Leite to create shape-changing textile interfaces that foster meaningful bodily interactions for children and adults. Education PhD in Engineering Mathematics, University of Bristol (2022) Specialization in Soft Robotics and Haptic Interfaces Her research blends soft robotics, e-textiles, and soma design to develop tactile technologies that prioritize bodily engagement over traditional visual/auditory interfaces. Current work explores: first-person design for scoliosis, symmetry-asymmetry dynamics in bodily interactions, and soma-driven methods that emphasize felt experiences. Key article trends include shape-changing textiles (SMA-actuated smocking, machine embroidery), emotional/therapeutic applications (anxiety relief, social touch), and multisensory integration (audio-tactile mappings, biosignal interaction). Scientific Contributions Recipient of Digital Futures Postdoctoral Fellowship Co-design methodologies for child-centered technology Material-driven evaluation frameworks for e-textiles Embodied interaction paradigms through haptic cushions Alice teaches Human-Computer Interaction Research Seminars (DH2632) and Media Technology and Interaction Design (DM2601) , while actively seeking Master's students for collaborative thesis work.

Jignesh M. PatelView profile

Professor

• Database Systems
• Query Optimization
• Data Analytics
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

Jon HeiselmanView profile

Research Professor

• Biomedical Engineering
• Image-Guided Surgery
• Surgical Navigation
• +3 more

Jon Heiselman is a Research Assistant Professor in the Department of Biomedical Engineering at Vanderbilt University School of Engineering. He serves as Associate Director of the Master of Engineering in Surgery and Intervention Program and leads research in image-guided surgical technologies. His work focuses on soft tissue deformation modeling, augmented reality applications, and computational frameworks for precision surgery. Education: PhD in Biomedical Engineering (Vanderbilt University, 2020) Advisor: Michael Miga, Harvie Branscomb Professor Research interests span image-guided surgical navigation, deformable registration algorithms, and digital twin modeling for therapeutic forecasting. Articles highlight advancements in soft tissue deformation correction, augmented reality integration, and machine learning approaches for real-time surgical guidance. Current affiliations include Vanderbilt's Biomedical Modeling Laboratory (BML) and the VISE Steering Committee. He contributes to NIH-funded training programs and has received recognition for his work in surgical data science and computational oncology.

Meredith VoylesView profile

Associate Clinical Professor

• Veterinary Ophthalmology
• Ophthalmic Surgery
• Ophthalmic Neoplasia
• +2 more

Meredith Voyles, DVM, MS, DACVO, serves as an Associate Clinical Professor in the Department of Clinical Sciences at Auburn University's College of Veterinary Medicine, where she teaches veterinary students and delivers medical/surgical ophthalmology specialty care at Auburn’s Gulf Shores Specialty Hospital. Her academic credentials include: BS in Zoology from Auburn University (1996) DVM from Auburn University (2001) MS in Biomedical Sciences from Auburn University (2011) Comparative Ophthalmology Residency at Auburn University (2011) Dr. Voyles' research centers on veterinary ophthalmology, with specialized expertise in ophthalmic neoplasia, surgical interventions for ocular diseases, and age-related vision changes in geriatric animals. Her work uniquely integrates clinical practice with biomedical engineering, particularly through collaborations developing drug-impregnated contact lenses for sustained therapeutic delivery. Her publication history reveals a trajectory from foundational clinical research to authoritative textbook contributions, emphasizing translational applications in veterinary ophthalmology. Early engineering collaborations focused on contact lens drug kinetics, while recent work addresses specialized clinical domains like ocular tumors and geriatric vision impairment, demonstrating evolving expertise from bench to bedside. Professional recognition includes: Diplomate of the American College of Veterinary Ophthalmologists (DACVO) Dr. Voyles actively mentors veterinary students in clinical rotations at the Gulf Shores Specialty Hospital, though formal graduate student advisement isn't documented. Her research collaborations with the College of Engineering imply participation in grant-funded projects, though specific awards aren't detailed in available materials. She operates within Auburn University's integrated veterinary specialty network, contributing to the ophthalmology team at the Gulf Shores Specialty Hospital that serves coastal Alabama veterinarians and referral cases, combining academic instruction with frontline specialty care.

Tyler Van BurenView profile

Assistant Professor

• Fluid Mechanics
• Turbulence
• Flow Control
• +6 more

Tyler Van Buren is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware, part of the College of Engineering. He holds a Ph.D., M.S., and B.S. in Aerospace and Mechanical Engineering from Rensselaer Polytechnic Institute (2008–2013). Previously, he served as a Research Scientist at Princeton University (2014–2019), focusing on bio-inspired propulsion, turbulence, and flow control. His research emphasizes unsteady flows and coherent structures, with a focus on fluid-structure interaction, bioinspired systems, and practical applications in energy efficiency and robotics. Key areas include vortex dynamics, synthetic jet actuation, and turbulent boundary layer control. Recent work explores optimal parameters for oscillating fins, vortex generator emulation, and eddy self-similarity in pipe flows. His studies bridge fundamental fluid mechanics with real-world impacts, such as improving vehicle design and energy-saving technologies. Lab activities concentrate on experimental methods for analyzing unsteady flows, including flapping propulsion systems and turbulence-induced phenomena. Collaborative projects address challenges in wind energy harvesting and biomedical fluid dynamics.

Monica FabianiView profile

Professor

• Cognitive Neuroscience
• Human Memory
• Aging
• +5 more

Monica Fabiani is a Professor at the University of Illinois Urbana-Champaign, affiliated with the Neuroscience Program and the Beckman Institute for Advanced Science and Technology. She holds academic roles in Psychology and Neuroscience. Her research focuses on cognitive neuroscience of human memory and aging, neuroimaging tool development (e.g., EROS), and cerebrovascular contributions to brain aging. Prof. Fabiani’s work is funded by NIH (NIA) and MindPortal, emphasizing interdisciplinary integration of behavioral, electrophysiological, and neuroimaging data. Education: Ph.D. in Biological Psychology/Cognitive Neuroscience from the University of Illinois Urbana-Champaign. She serves as Editor of Psychophysiology and has received prestigious awards, including the Association for Psychological Science Fellowship (2006). Research Interests: Cerebrovascular health, neurophysiological bases of cognitive control, and multimodal imaging techniques. Key projects include studying arterial elasticity (pulse-DOT), cognitive aging mechanisms, and combining EEG, MRI, and optical methods. Notable Achievements: 181+ publications, including high-impact studies on white matter microstructure and the effects of physical activity on brain health. Her lab develops innovative tools like trimodal imaging and optical signal analysis for clinical and cognitive applications. Grants & Collaborations: Active projects funded by NIA and MindPortal, with international collaborations in brain aging and neuroimaging. She leads efforts to bridge basic science and translational research in cognitive health.

Kristin O'GradyView profile

Assistant Professor

• Quantitative MRI Methodologies
• Neurological Diseases (Multiple Sclerosis)
• Spinal Cord Imaging
• +3 more

Dr. Kristin O'Grady is an Assistant Professor in the Department of Biomedical Engineering and Department of Radiology & Radiological Sciences at Vanderbilt University's School of Engineering. Her research focuses on developing quantitative MRI methodologies for the brain and spinal cord, particularly improving spinal cord MRI for neurological diseases like multiple sclerosis. She specializes in diffusion tensor imaging, functional connectivity analysis, and high-field MRI applications. Her work spans advanced imaging techniques including MP2RAGE, susceptibility-weighted MRI, and phase imaging, with a focus on clinical feasibility and disease markers. She has contributed to studies on spinal cord morphometry, paramagnetic rim lesions, and biological interactions affecting CNS structure. No scientific awards or grants are explicitly listed in the provided materials. Dr. O'Grady collaborates across interdisciplinary teams within the School of Engineering, focusing on translational research in neuroimaging technologies.

Marjan FirouzniaView profile

Researcher

• Medical Imaging
• Machine Learning
• Deep Learning
• +3 more

Marjan Firouznia is a Principal Research Engineer at Linköping University , affiliated with the Division of Diagnostics and Specialist Medicine (DISP) under the Faculty of Medicine and Health Sciences . With a PhD in Electrical Engineering from Amirkabir University of Technology and postdoctoral experience at institutions like Case Western Reserve University, she specializes in advancing machine learning models for precise segmentation of cardiac structures including the left atrium , epicardial fat , and fibrosis using CT and MRI scans. Her work aims to improve diagnostic accuracy and treatment planning in cardiovascular care. Marjan's research focuses on medical imaging , deep learning , and computational anatomy , with recent publications on FractalRG , FK-means , and Poincare-guided UNet for cardiac structure segmentation. Her academic contributions span 15 recent publications , emphasizing fractal geometry , chaos theory , and optimization algorithms in biomedical applications. She actively develops open-source datasets and tools, such as the FK-means codebase , to support reproducibility in medical AI research.

Kechen ZhangView profile

Associate Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Neural Computation
• +3 more

Kechen Zhang is an Associate Professor of Biomedical Engineering and Neuroscience at Johns Hopkins University School of Medicine. He holds affiliations with the Center for Hearing and Balance and the Kavli Neuroscience Discovery Institute. His research focuses on theoretical and computational neuroscience, particularly neural computation and spatial navigation models. Zhang earned a B.S. and M.S. from Peking University, a Ph.D. in Cognitive Science from UC San Diego, and completed a postdoc at the Salk Institute. Research Interests Zhang’s lab studies nervous system dynamics using mathematical and computational models, collaborating with experimental labs. Key areas include grid cells, place cells, path integration, oscillatory interference models, and neural coding mechanisms. His work bridges biophysical models and network-level computations. Publications Over 30 peer-reviewed articles in Neural Computation , PNAS , Journal of Neuroscience , and others, addressing topics like attractor networks, theta rhythms, and spatial representation. Recent work explores cognitive swarming and neuro-inspired robotics. Collaborations & Teaching Collaborates with Jim Knierim, Xiaoqin Wang, and others. Teaches Biomedical Systems II , Theoretical Neuroscience , and co-leads the Johns Hopkins Systems Neuroscience Journal Club.

Moo ChungView profile

Professor

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +5 more

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.