جست‌وجوی استاد و پژوهشگر

Alan Kuntzمشاهده پروفایل

استادیار

• Robot Motion Planning
• Autonomous Systems
• Robot Design Optimization
• +۴ مورد دیگر

Alan Kuntz is an Assistant Professor at the University of Utah's Kahlert School of Computing (KSoC) and a core member of the Robotics Center. He leads the interdisciplinary Kuntz Research Lab, focusing on robotics and computational methods with medical applications, particularly in healthcare and surgery. His work spans robot motion planning, autonomous systems, and robot design optimization. Education: Ph.D. in Computer Science from the University of North Carolina at Chapel Hill, with research in the Computational Robotics Research Group. Previously a postdoctoral scholar at Vanderbilt University's Medical Engineering and Discovery Lab. Research interests include surgical robotics, continuum robots, needle steering, and medical device design. Recent projects include autonomous needle navigation, continuum lung staplers, and metamaterial-based robots. His team has published extensively on topics like kinematic modeling, uncertainty quantification, and medical intervention systems. Notable awards include the 2022 IEEE Access Best Video Award for his group's work, and mentoring over 15 students through the University of Utah's Undergraduate Research Opportunities Program. The Kuntz Lab actively collaborates on clinical applications, presenting at top conferences like IROS, Hamlyn Symposium, and ISMR. Labs/Teams: Directs the Kuntz Research Lab, known for its innovative medical robotics projects. The lab's work has been featured in Forbes and other media outlets for breakthroughs like in vivo needle steering demonstrations.

Peter K. Allenمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Medical Robotics
• +۶ مورد دیگر

Peter K. Allen is a Professor of Computer Science at Columbia University's School of Engineering and Applied Science, with a career spanning over three decades in robotics research. His work focuses on robotic grasping , 3D vision and modeling , and medical robotics , where he has made significant contributions to autonomous manipulation and sensor integration. Current affiliation: Columbia University Robotics Lab Academic rank: Professor Key research areas: Robotics, Computer Vision, Artificial Intelligence Education A.B. in Mathematics-Economics from Brown University M.S. in Computer Science from University of Oregon Ph.D. in Computer Science from University of Pennsylvania (recipient of CBS Foundation Fellowship, Army Research Office Fellowship) Research Interests Allen's research bridges fundamental robotics challenges with applied domains. His work on robotic grasping explores low-dimensional subspaces and semantic task suitability, while 3D vision contributions include illumination coherence and texture registration methods. In medical robotics , he develops surgical imaging tools and BCI-enabled grasping systems. Recent publications show trends in: Deep learning for robotic manipulation (2017-2022) Human-robot interaction through BCI and augmented reality Deformable object manipulation (garments, thin shells) Multi-modal sensing (vision-tactile fusion) Scientific Recognition NSF Presidential Young Investigator Award Best Student Paper Award (2007) for collaborative work Over 30 years of continuous funding from NSF, Army Research Office, and medical grants Teaching and Mentorship He has taught graduate courses in robotics (COMS 4733/6731) since 2010, emphasizing hands-on projects with advanced platforms like Baxter, PR2, and Fetch robots. His lab provides immersive training in: 3D photography Humanoid robotics Autonomous navigation Grasp planning

Shervin Shirmohammadiمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Health Monitoring Systems
• +۴ مورد دیگر

Dr. Shervin Shirmohammadi is a Professor at the University of Ottawa's Faculty of Engineering, specifically within the School of Electrical Engineering and Computer Science. With an impressive h-index of 41 and over 6,800 citations across 473 publications, his research has made significant contributions to the fields of computer vision, biomedical instrumentation, and health monitoring systems. His academic journey spans over two decades, beginning with work on communication architectures for virtual environments in 2001 and evolving toward practical healthcare applications. Dr. Shirmohammadi's research interests center on Computer Vision , Image Processing , and Embedded Systems with a strong focus on healthcare applications including nutrition monitoring, mental health assessment, and driver safety systems. His most influential work examines computer vision applications for health monitoring, particularly food calorie measurement systems that use smartphone cameras to analyze nutritional content. His research has evolved to include EEG-based systems for ADHD detection and serious games for autism therapy, demonstrating a consistent trajectory toward practical healthcare solutions using advanced instrumentation techniques. Dr. Shirmohammadi maintains active collaborations with researchers including A. Yassine (118 joint publications), D. Ahmed, Ali Asghar Nazari Shirehjini, and B. Hariri. His publications appear primarily in IEEE Transactions on Instrumentation and Measurement, reflecting his strong connection to the instrumentation and measurement community.

• Biophotonics
• Nanotechnology
• Fluorescence Applications
• +۷ مورد دیگر

Professor Ewa Goldys is a distinguished academic at the University of New South Wales (UNSW), serving as a Professor in the School of Engineering with a focus on Biomedical Engineering. She holds the prestigious position of Deputy Director at the ARC Centre of Excellence for Nanoscale Biophotonics (CNBP), where she leads partnerships, knowledge transfer, and research commercialization efforts. Her work spans interdisciplinary research connecting engineering, medicine, and biology, with significant contributions to biophotonics and nanotechnology applications in healthcare. Professor Goldys' research interests center around advanced imaging techniques, particularly autofluorescence characterization, which provides a non-invasive metabolic 'fingerprint' for distinguishing healthy from diseased cells. Her work has significant applications in cancer, diabetes, and neurodegenerative diseases. She has pioneered research in fluorescent and luminescent nanomaterials for biological applications, developing innovative approaches for high-contrast, background-free imaging using time-gating techniques. Her research portfolio also includes significant contributions to CRISPR-based biosensing, stem cell characterization, and non-invasive diagnostic approaches using multispectral imaging. Her publication record demonstrates a clear trend toward increasingly sophisticated applications of autofluorescence imaging combined with machine learning and molecular techniques. Recent work integrates hyperspectral imaging with transcriptomics, develops CRISPR-based point-of-care diagnostics, and applies autofluorescence techniques to diverse medical challenges from kidney disease diagnosis to immune cell characterization. The interdisciplinary nature of her work spans oncology, immunology, nephrology, and regenerative medicine. Professor Goldys has received notable recognition for her work, including: Eureka award in 2016 for Innovative Use of Technology Her research leadership has secured substantial funding, including directing the $23 million ARC investment in the CNBP (matched by $17 million from partners), establishing the $2 million ARC/NHMRC Network 'Fluorescence Applications in Biotechnology and Life Sciences,' and leading research that leveraged an additional $155 million in external funding. She has founded the Optical Characterisation Facility at Macquarie University and has been instrumental in establishing international research networks in biophotonics. Professor Goldys has made significant contributions to the international biophotonics community through conference organization, having chaired 11 conferences including SPIE 'Biophotonics Australasia' and serving as Track Chair for Nanobiophotonics at BIOS, the world's largest biomedical optics meeting. Her work has established foundational methodologies in autofluorescence characterization that continue to drive innovation in label-free medical diagnostics.

Rodrigo Rico Biniمشاهده پروفایل

مدرس ارشد

• Biomechanics
• Exercise Science
• Sports Science
• +۴ مورد دیگر

Rodrigo Rico Bini is a Senior Lecturer at La Trobe University's Rural Health School, where he leads the Discipline of Exercise Science & Physiology and coordinates the Bachelor of Exercise Science program. His research focuses on quantitative analysis of human movement to improve athletic performance and reduce injury risks, specializing in cycling and running biomechanics using motion capture, force measurements, and AI modeling. He holds editorial positions at multiple sports science journals including Journal of Science and Cycling and Sports Biomechanics . His professional affiliations include the Holsworth Biomedical Research Centre and AUT-SPRINZ research institute. Dr. Bini's educational background includes a PhD in Biomechanics from Auckland University of Technology (2009-2011), an MSc from UFRGS Brazil (2006-2008), and a BPhEd from UFRGS (2000-2005). His research demonstrates consistent focus on sports biomechanics applications, with recent work exploring AI-driven motion analysis, equipment ergonomics, and active transportation health impacts. Key thematic trends include technological validation studies (wearables, neural networks), injury prevention strategies, and performance optimization across cycling, running, and occupational activities. Fellow of the International Society of Biomechanics in Sport Mid Career Research Award 2024 - La Trobe Rural Health School Certificate of Excellence in Reviewing: Physical Therapy in Sport AUT Laptop Scholarship First place, II Biofenac Brazilian Award for Exercise Science Dr. Bini supervises graduate students and leads industry collaborations, including an Australia Post-funded project on cycling biomechanics in postal delivery. He directs biomechanics research through the Holsworth Biomedical Research Centre and maintains international partnerships with institutions in New Zealand and Brazil.

Sueda SAYLANمشاهده پروفایل

استادیار

• Memristive Devices
• Photovoltaics
• Light-Matter Interactions
• +۳ مورد دیگر

Dr. Sueda Saylan is an Assistant Professor at the Faculty of Engineering, Özyeğin University, since 2024. Her academic journey includes a Ph.D. in Interdisciplinary Engineering (2016) from Masdar Institute (now Khalifa University), postdoctoral research at Khalifa University (2016-2022), and an MSCA Postdoctoral Fellowship at Bilkent University (2022-2024). She has also held visiting researcher positions at MIT (2014) and the University of Tokyo (2016). Education Doctorate: Interdisciplinary Engineering, Masdar Institute of Science and Technology (2016) Master's: Microelectronic Manufacturing Engineering, Rochester Institute of Technology (2004) Bachelor's: Mechanical Engineering, Middle East Technical University (2002) Dr. Saylan's research focuses on memristive devices , photovoltaics , and light-matter interactions at micro/nanoscale . Her work bridges materials science and electronic engineering, with recent publications on memristor-based sensors, spectral filtering in silicon, and machine learning integration for biomedical diagnostics. Key trends from her 15 most recent articles (2013-2025) include: Advancing memristor technology for radiation sensing and vacuum monitoring Optimizing photovoltaic efficiency through light management and antireflection coatings Developing compact, low-power diagnostic devices for pathogen detection Exploring nanoscale electrode materials and switching mechanisms Applying Fourier transforms and interferometry in optical systems Scientific Awards Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship (2022-2024) Dr. Saylan has received research support from prestigious programs and has contributed to interdisciplinary projects involving semiconductor physics, optical engineering, and biomedical diagnostics. Her collaborations span institutions like Khalifa University, MIT, and the University of Tokyo.

• Big data analytics
• Data analysis
• Data management
• +۸ مورد دیگر

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

Jian Kangمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +۱۱ مورد دیگر

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +۷ مورد دیگر

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

• Sleep
• Circadian rhythms
• Bioinformatics
• +۴ مورد دیگر

Ron C Anafi serves as Associate Professor of Medicine (Sleep Medicine) at the Hospital of the University of Pennsylvania and holds key affiliations as Faculty Scholar at the Penn Institute for Bioinformatics, Co-Investigator at the Center for Subcellular Genomics, and member of both the Chronobiology and Sleep Institute and ITMAT. His work bridges computational biology with clinical sleep medicine within the Perelman School of Medicine. His educational trajectory includes a B.S.E. in Biomedical Engineering and Philosophy from Tulane University (1995), followed by a Ph.D. in Applied Mechanics (2002) and M.D. (2004) from the University of Minnesota. This dual training underpins his unique approach to circadian research. Dr. Anafi's laboratory pioneers computational methods to decode sleep and circadian influences on human physiology, focusing on neurodegeneration and cancer. By applying machine learning and systems biology to large-scale omics data, his team reconstructs molecular rhythms from cross-sectional human tissue databases—overcoming the critical barrier of limited longitudinal sampling in patients. His research demonstrates how circadian disruptions in ribosomal function link to Alzheimer's pathology and how tumor clock strength predicts breast cancer outcomes. Analysis of his 15 most recent publications (2022-2025) reveals intensifying focus on cell-type-specific circadian disruptions in neurodegenerative diseases, with significant expansion into cancer chronobiology and contactless sleep monitoring technologies. His methodological innovations like CYCLOPS enable rhythmic signal extraction across diverse tissues—from brain to skin—while increasingly integrating multi-omics layers to identify therapeutic targets. As mentor in the Genomics and Computational Biology and Neuroscience graduate groups, he guides students through rotation projects at his Translational Research Laboratories facility (Suite 2100, 125 South 31st Street). His research is supported by NIH grants investigating circadian mechanisms in Alzheimer's disease and cancer progression, with recent work exploring sex-specific metabolic impacts of circadian genes and non-invasive sleep monitoring.

Stuart Gibsonمشاهده پروفایل

مدرس ارشد

• Digital Image Processing
• Machine Learning
• Forensic Science
• +۵ مورد دیگر

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

• Fuzzy Logic
• Soft-Computing
• Health Monitoring
• +۹ مورد دیگر

Nishchal K. Verma is a Professor at the Department of Electrical Engineering, Indian Institute of Technology Kanpur. He holds a PhD from IIT Delhi (2007), an M.Tech from IIT Roorkee (2003), and a B.Tech from DEI Agra (1996). His postdoctoral research includes work at the University of Tennessee (2009) and Louisiana Tech University (2008). Specialization: Fuzzy Logic, Health Monitoring, Intelligent Informatics Current Research Interests: Intelligent Data Mining, Computer Vision, Smart Grids, Biomedical Applications His research focuses on Fuzzy Systems , Machine Learning , and Health Monitoring with applications to power systems, biomedical data, and wireless sensor networks. He has developed technologies like the Transducers and Instrumentation Virtual Laboratory and Brain Computer Interface Laboratory , emphasizing predictive modeling and fault diagnosis. Key sponsored projects include DST-funded Fuzzy Rule-Based Image Prediction and DRDO-supported Visual Surveillance Systems . His work spans 15+ years of interdisciplinary publications in journals and conferences. Scientific Awards : Devendra Shukla Young Faculty Research Fellowship (2013-16) He has served as Associate Editor for journals and Chairman of IEEE chapters, with leadership roles in academic administration at IIT Kanpur.

Bo Huمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Neurosurgery
• +۷ مورد دیگر

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.

Sylvain Faisanمشاهده پروفایل

استادیار

• Image Processing
• Machine Learning
• Geometry
• +۶ مورد دیگر

Sylvain Faisan is a permanent Assistant Professor at ICube - MIV (University of Strasbourg, France). His research focuses on image processing, statistical modeling, and geometry, with applications in medical imaging and neuroscience. He works on advanced methodologies integrating machine learning and mathematical frameworks. Key Research Areas: Polarimetric image processing, retinal image registration, 3D statistical model comparison, topology-preserving image deformation, and fMRI brain mapping Technical Expertise: Bayesian inference, non-local means filtering, reversible jump MCMC algorithms, causal modeling, and constrained optimization His publications demonstrate interdisciplinary applications in optics, biomedical imaging, and computational anatomy. He contributes to developing algorithms that maintain physical admissibility and topological integrity in complex imaging problems.

David Smithمشاهده پروفایل

استاد

• Microscale biological fluid dynamics
• Sperm motility and fertility modeling
• Cilia and flagellar mechanics
• +۵ مورد دیگر

David Smith is a Professor of Applied Mathematics at the University of Birmingham and Deputy Director of Research and Knowledge Transfer at the Engineering and Physical Sciences Healthcare Technologies Institute. He is renowned for his interdisciplinary research applying mathematical modeling to medicine and biology, particularly in microscale fluid dynamics of fertility, sperm motility, cilia mechanics, and mathematical endocrinology. Research Interests: Microfluid dynamics of fertility and reproduction, especially sperm motility and embryonic nodal cilia Mathematical endocrinology, including pharmacokinetics of cortisol and thyroid disease Development and application of regularized Stokeslets methods for biological flows Bayesian modeling for spectroscopic biomedical diagnostics Multiscale modeling in reproductive health and cell motility His recent publications span computational tools for viscous flow, dinoflagellate swimming, kinetic modeling of biochemical reactions, and Bayesian diagnostics using Raman spectroscopy, reflecting a broad and impactful interdisciplinary portfolio. Projects & Grants: Principal Investigator, EPSRC project on rapid sperm capture using imaging and machine learning (2016–2022) Co-Investigator, US Army and UK Ministry of Defence projects on traumatic brain injury biomarkers (2021–2028) Alan Turing Institute Turing Fellowship (2019–2020) EPSRC and Proctor & Gamble supported parameter estimation projects Smith chairs the editorial board of Mathematics in Medical and Life Sciences , has organized major conferences on bioactive fluids, and delivered keynote lectures on regularized Stokeslets methods. He currently supervises four PhD students and a postdoctoral fellow, welcoming new doctoral applicants.