جست‌وجوی استاد و پژوهشگر

Aly A Faragمشاهده پروفایل

استاد

• Computer Vision
• Biomedical Imaging
• Image Processing
• +۵ مورد دیگر

Aly A. Farag is a Professor of Electrical and Computer Engineering at the University of Louisville, where he founded the Computer Vision and Image Processing (CVIP) Laboratory. His research focuses on imaging science, computer vision, and biomedical imaging, with applications in cancer detection and medical visualization. He has authored over 350 technical papers and two upcoming textbooks. Dr. Farag holds patents in imaging technologies and has led projects funded by NSF, DoD, NIH, and industry. Educations: B.S. in Electrical Engineering, Cairo University, 1976 M.S. in Bioengineering, University of Michigan, 1984 M.S. in Biomedical Engineering, Ohio State University, 1981 Ph.D. in Electrical Engineering, Purdue University, 1990 Research Interests: Scene analysis, multimodal imaging reconstruction, statistical segmentation, and biomedical visualization. His work has advanced tubular topology visualization, colon segmentation, and lung nodule analysis. Collaborations span medical institutions and federal agencies. Awards: 2002 University Scholar designation for technical achievements. Served as associate editor for IEEE Transactions on Image Processing and general co-chair of IEEE ICIP-09. Grants & Advising: Principal investigator on NSF/NIH-funded projects. Advised 15 PhD and 26 MS students, trained 10 postdocs, and introduced new ECE curriculum topics. Labs/Teams: CVIP Lab pioneers innovations in medical imaging and AI-driven diagnostics. Active in interdisciplinary teams for STEM education engagement metrics.

• Biomedical Engineering
• Semiconductor Devices
• Digital Signal Processing
• +۴ مورد دیگر

Dr. Alok Kushwaha is a Researcher at the University of Adelaide's School of Electrical and Mechanical Engineering, affiliated with the Biomedical Engineering department. He has 24 years of experience in academia, research, and administration, with expertise in semiconductor devices, digital signal processing, and antenna design. Currently, he collaborates with Dr. Jiawen Li and Dr. Robert McLaughlin at the Institute for Photonics and Advanced Sensing (IPAS) on Optical Coherence Tomography (OCT) and Fluorescence systems. His research interests include biomedical engineering applications, advanced semiconductor technologies, and nanoelectronics. Notable projects involve developing handheld OCT probes for oral tissue imaging and analyzing terahertz communication systems. He has contributed to studies on molecular communication protocols and sensor technologies for livestock health monitoring. Research Trends : Recent work focuses on biomedical imaging innovations (e.g., 3D-printed OCT devices), semiconductor device optimization (e.g., subthreshold MOSFET performance), and high-speed antenna designs. His publications span nanotechnology, image processing, and materials science. Grants & Advising : No student advisees or grant details listed. Collaborates extensively within interdisciplinary teams at IPAS. Labs/Teams : Active member of the Institute for Photonics and Advanced Sensing (IPAS), contributing to advanced sensing and photonics research.

• Artificial Intelligence in Radiology
• Machine Learning
• Health Informatics
• +۲ مورد دیگر

Dr. Axel W. E. Wismüller is a Professor of Radiology and Biomedical Engineering at the University of Rochester Medical Center . As Director of the AI Radiology Laboratory , he uniquely combines clinical practice in cardiothoracic/body imaging with pioneering research in Artificial Intelligence (AI) for medical imaging . Holding MD, MSc (Physics), and PhD (Electrical Engineering) degrees from German institutions, he maintains dual US and German medical licenses and board certifications. Ludwig-Maximilians-Universitaet München (1990-1996) Technical University of Munich (MD 1992, PhD 2006) Ludwig Maximilians University Munich (Residency 1997-2005) His research focuses on machine learning and network connectivity analysis in neuroimaging, particularly for HIV-associated neurocognitive disorders and multiple sclerosis . He invented the XOM algorithm (patented in US/EU) and developed AI systems for chest radiograph analysis , breast cancer imaging , and osteoporosis diagnostics . His work on large-scale Granger causality has transformed brain network analysis methodology. Scientific awards include SPIE Senior Membership (2016), NIH/NIDA R01 funding (2012-2015), and NYSTAR public-private partnerships . Supervising over 30 PhD/MSc students across engineering, computer science, and medicine , he bridges clinical and computational disciplines through active roles in ACR, RSNA, and SPIE .

• Machine Learning
• Data Mining
• Big Data
• +۴ مورد دیگر

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

Patrick Le Tallecمشاهده پروفایل

استاد

• Computational Mechanics
• Nonlinear Mechanics
• Domain Decomposition Methods
• +۵ مورد دیگر

Patrick Le Tallec is a Professor of Mechanics at École Polytechnique in France, where he currently serves as Dean of the Bachelor Program and is a member of the M3DISIM project. His distinguished academic career spans multiple institutions including Université Paris Dauphine, INRIA (French National Institute for Research in Digital Science and Technology), and international universities such as Stanford University, University of Wisconsin, and Shanghai Jiao Tong University. He has held leadership positions including Vice President for Education and Head of the Laboratory of Solid Mechanics at École Polytechnique. His educational background includes: Graduate from École Polytechnique Ph.D. in Engineering Mechanics from The University of Texas at Austin (1980) Thèse d'Etat in Applied Mathematics from Université Pierre et Marie Curie in Paris (1981) Professor Le Tallec's research focuses on computational mechanics and applied mathematics with expertise in nonlinear mechanics, domain decomposition methods, and multiscale modeling. His work bridges theoretical mathematics with practical engineering applications, particularly in material science and fluid-structure interactions. He has developed advanced numerical methods for elasticity, viscoelasticity, and fluid dynamics with applications in industrial manufacturing and biomedical engineering. His recent publications demonstrate progression from foundational numerical methods to sophisticated multiscale approaches addressing complex engineering challenges in material science. The research shows particular emphasis on rubber mechanics, fatigue analysis, and computational methods for nonlinear structures, reflecting his ongoing commitment to solving real-world engineering problems through mathematical innovation. His scientific honors include: CISI award in Scientific Computing Prize Blaise Pascal of the French Academy of Sciences Chevalier des Palmes Académiques Chevalier de la Légion d'Honneur Officier de l'Ordre National du Mérite Professor Le Tallec has directed over 40 Ph.D. students from 10 different nationalities, demonstrating significant impact in academic mentoring. His research has been supported through extensive collaborations with industrial partners including Michelin, PSA Group, and Dassault Aviation, as well as scientific advisory roles at the French Alternative Energies and Atomic Energy Commission. He has served as president of the French Society of Applied and Industrial Mathematics and held editorial positions with leading journals in his field. His laboratory work centers around computational mechanics research, particularly through the M3DISIM project at École Polytechnique. His research team brings together mathematicians, engineers, and computer scientists to develop innovative solutions for complex problems in material science and structural mechanics, with applications ranging from industrial tire manufacturing to biomedical engineering.

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.

Reihaneh Safavisohiمشاهده پروفایل

استادیار

• Extracellular Vesicles
• Nano-Bio-Science
• Proteomics & Mass Spectrometry
• +۳ مورد دیگر

Dr. Reihaneh Safavisohi is an Assistant Professor in the Department of Chemistry and Biochemistry at Seton Hall University, leading the SAFAVI Lab. Her research focuses on extracellular vesicles (EVs) for cancer biomarker detection, integrating analytical chemistry, nanotechnology, and biochemistry. Prior to this, she held postdoctoral positions at the University of Notre Dame and Michigan State University. Affiliations: Seton Hall University (2023–Present), SAFAVI Lab Director Education: Postdoctoral training at Harper Cancer Research Institute (Notre Dame), Michigan State University Research interests include EV-based diagnostics, nanoparticle protein corona dynamics, proteomic analysis via mass spectrometry, and statistical modeling for biomarker discovery. Recent work addresses sex-based differences in nanoparticle interactions and lipid nanomedicine for vaccines. She teaches graduate and undergraduate biochemistry courses and advises students in bioanalytical techniques. Grants: Recipient of Undergraduate STEM Research Grants (2024–2025) and University Research Council funding (2024). Service roles include the Seton Hall Faculty Search Committee and the Middle Atlantic Regional Meeting of the ACS.

• 6G Networks
• Telemedicine
• Medical Robotics
• +۳ مورد دیگر

Nicolai Kröger is a researcher at the Chair of Communication Networks (Prof. Kellerer) at the Technical University of Munich (TUM). He holds an M.Sc. in Electrical and Computer Engineering from TUM, where his thesis focused on P4 switch performance modeling using queuing theory. His current research centers on 6G networks for critical telemedicine applications, particularly within the 6G-Life project, emphasizing end-to-end communication for medical robotics and surgical systems. He contributes to projects like the 6G Future Lab Bavaria and collaborates with the MITI group at Rechts der Isar Hospital to develop medical testbeds requiring high availability and low latency. Kröger also supervises student theses on 5G/6G security, network optimization, and in-network computing. His technical expertise spans programmable networks (P4), SDN, and performance analysis of network devices. He serves as a supervisor for student projects and internships, including implementations of medical testbeds and security analyses of cellular broadcast messages. His work bridges academic research with practical applications, aiming to advance communication networks for healthcare and future 6G systems.

Anton Westveldمشاهده پروفایل

مدرس ارشد

• Bayesian Methodology
• Network Analysis
• Game Theoretic Data
• +۳ مورد دیگر

Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.

Alberto L. Sangiovanni-Vincentelliمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Design Automation
• Embedded Systems
• +۷ مورد دیگر

Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.

Wenrui Liمشاهده پروفایل

استادیار

• Statistics for network data
• Causal inference
• High-dimensional data analysis
• +۳ مورد دیگر

Wenrui Li serves as an Assistant Professor in the Department of Statistics at the University of Connecticut, where she develops advanced statistical methodologies for complex biomedical data structures. Her work bridges theoretical statistics and practical healthcare applications, with particular emphasis on network-based modeling and high-dimensional data analysis in oncology and epidemiology. Dr. Li's research program centers on statistics for network data, causal inference under interference, and Bayesian modeling of structured high-dimensional datasets. She pioneers techniques for handling network noise in graph-guided models, with direct applications to cancer genomics, multi-omics integration, and infectious disease surveillance. Her methodological innovations address critical challenges in noisy data environments where traditional statistical approaches fail. Analysis of her 2021-2025 publications reveals three dominant research threads: (1) Digital health interventions to reduce time burden in cancer care, exemplified by her TIME text-messaging system; (2) Graph-guided Bayesian methods for multi-omics data integration with noisy network priors; and (3) Epidemiological modeling of time-varying parameters in noisy surveillance systems. These threads demonstrate consistent focus on methodological rigor applied to pressing biomedical problems. No scientific awards, student advisement records, grant funding details, or laboratory affiliations were documented in the source materials. Her collaborative work spans biostatistics, oncology, and network science, with frequent co-authorship on interdisciplinary projects addressing cancer care optimization and disease modeling.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +۷ مورد دیگر

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

Hametner, Bernhardمشاهده پروفایل

مدرس

• Biomedical Engineering
• Arterial Pressure Modeling
• Cardiovascular Physiology
• +۳ مورد دیگر

Prof. Dr.techn. Bernhard Hametner is a Lecturer at the Institute of Biomedical Engineering (E363) within Technische Universität Wien (TU Wien) . His research focuses on arterial pressure dynamics, cardiovascular physiology, and biomedical signal analysis with applications in clinical diagnostics and space medicine. He leads projects involving wearable medical devices, multisensor monitoring systems, and computational models of vascular systems. Key affiliations: TU Wien's Network Lab and collaborative EU projects like VascAgeNet Expertise in pulse wave analysis, vascular aging, and translational biomedical engineering Research Interests: His work spans arterial stiffness quantification, non-invasive cardiac output estimation, and developing methods to detect early cardiovascular dysfunction through waveform analysis. He has pioneered approaches using difference equations for modeling arterial wave reflections and contributed to understanding microgravity's impact on cardiovascular systems. Publications Highlight Trends: Recent work emphasizes clinical applications of wearable devices (2023), AI-driven medical recommendation systems (2022), and space physiology studies (2020-2022). Earlier research focused on computational modeling of vascular dynamics and validation of novel diagnostic methods. Advising & Education: Supervised 8 thesis students (2016-2021) in topics like biomedical signal analysis, machine learning in healthcare, and cardiovascular modeling. Active in training the next generation of biomedical engineers through TU Wien's graduate programs. Labs/Teams: Part of TU Wien's Network Lab, collaborating with international teams on projects like the EU's VascAgeNet initiative. Engaged in multidisciplinary teams combining engineering, medicine, and data science.

Carlos Vázquezمشاهده پروفایل

استاد

• Computer vision
• Medical imaging
• 3D reconstruction
• +۴ مورد دیگر

Carlos Vázquez is a Professor at the Department of Software Engineering and IT, École de technologie supérieure (ÉTS). He holds a B.Eng. and M.Sc. from ISPJAE, Cuba, and a Ph.D. from INRS, Montreal. His research focuses on computer vision, medical imaging, 3D reconstruction, and immersive video technologies. He co-leads the Summit Tech Research Chair in Immersive and Interactive Video and is affiliated with the Multimedia Research Laboratory (LABMULTIMEDIA) and the Open Innovation Laboratory in Health Technologies (LIO-ÉTS). His expertise includes stereoscopic imaging, multi-view video coding, and GPGPU programming. Notable research axes are software systems, multimedia, cybersecurity, and health technologies. He has supervised numerous doctoral and master’s theses, including works on 3D spine reconstruction, personalized femur modeling, and immersive video compression. Key contributions include advancements in medical imaging analysis, 3D reconstruction from biplanar radiographs, and efficient video coding for virtual reality. His work bridges clinical applications and engineering, with implications in surgical planning, patient rehabilitation, and multimedia systems optimization.

Javier Cabreraمشاهده پروفایل

استاد

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +۵ مورد دیگر

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.