جستجوی استاد راهنمای دانشگاهی

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Pascal Syrenمشاهده پروفایل

پژوهشگر

• Cardiac Electrophysiology
• Histone Deacetylases
• Ventricular Arrhythmias
• +۳ مورد دیگر

Dr. Pascal Syren is a **Researcher** and **Resident** at Heidelberg University Hospital (UKHD), affiliated with the **Department of Cardiology, Angiology, and Pulmonology** and the **Heidelberg Center for Cardiac Arrhythmias**. His academic role includes contributing to the **Group of Molecular and Translational Cardiac Electrophysiology**, focusing on epigenetic and molecular mechanisms underlying cardiac arrhythmias and heart failure. He holds a PhD from Heidelberg University (2021) on histone deacetylases' influence in cardiac electrophysiology. **Research Interests**: Cardiac electrophysiology, epigenetic regulation (HDACs), ventricular and atrial arrhythmias, echocardiography, and translational cardiology. His work spans basic science to clinical applications, emphasizing ion channel remodeling and therapeutic targets for heart rhythm disorders. **Awards**: Recipient of the **Ernst & Berta Grimmke Foundation Research Grant (2023–2024)** and **Otto-Hess Scholarship (2017–2018)** from the German Cardiac Society. His research bridges molecular mechanisms with clinical cardiology, with contributions to understanding HDAC2's role in action potential dynamics. **Publications**: Over 8 peer-reviewed articles (2016–2025) in journals like *Basic Research in Cardiology* and *Cells*, focusing on histone deacetylase pathways, atrial fibrillation, and ventricular arrhythmia models. Recent work explores neonatal cardiomyocyte electrophysiology and TREK-1 potassium channels in heart failure. **Affiliations**: Active member of the **German Cardiac Society (DGK)** and **European Society of Cardiology (ESC)**, contributing to interdisciplinary cardiac arrhythmia research at Heidelberg's medical faculty.

Huining Liمشاهده پروفایل

استادیار

• Mobile Computing
• Internet of Things
• Cybersecurity
• +۵ مورد دیگر

Huining Li is an Assistant Professor in the Department of Computer Science at North Carolina State University . Her research focuses on Internet of Things (IoT) , cybersecurity , and mobile computing , with a specialized emphasis on mobile health (mHealth) technologies. Education: Ph.D. in Computer Science and Engineering from University at Buffalo (2024). Her work addresses privacy-preserving sensing mechanisms , biomarker measurement , and fairness in dynamic mobile environments , developing systems for chronic wound care, Parkinson’s disease management, and mental health therapy. Recent publications highlight innovations in mmWave biometrics , machine learning for health diagnostics , and non-contact monitoring . Scientific Awards: Best Paper Awards (SenSys 2019, BodyNet 2021, ICHI 2022) Best Paper Candidate (SenSys 2022) Harold O. Wolf Achievement Award (2024) EECS Rising Star (2023) NIH mHealth Training Institute Scholar (2025) She teaches courses in Mobile Health Systems and Applications and Computer Networks , and actively serves on NSF panels , TPC committees (ACM MobiSys, IEEE-EMBS BSN), and as Associate Editor for journals like Elsevier Smart Health.

• Cardiology
• Electrophysiology
• Cardiac Arrhythmias
• +۴ مورد دیگر

Professor Carlo Pappone is a Full Professor of Cardiology at Vita-Salute San Raffaele University (since 2019) and Director of the Arrhythmology Department at IRCCS Policlinico San Donato Hospital (since 2015). He has held previous academic/clinical leadership roles at IRCCS San Raffaele Hospital (2000-2010), Villa Maria Cecilia Hospital (2010-2015), and University of Naples Federico II (1990-2000). With 212 publications in top journals like NEJM, JAMA, and Circulation, he has made significant contributions to cardiac arrhythmia research. Current Positions Vita-Salute San Raffaele University (2019-present): Full Professor of Cardiology IRCCS Policlinico San Donato Hospital (2015-present): Director of Arrhythmology Department Previous Roles University of Naples Federico II (1990-2000) University of Michigan Ann Harbor (1990-2000) IRCCS San Raffaele Hospital (2000-2010) Villa Maria Cecilia Hospital (2010-2015) Research Focus: Specializing in cardiovascular diseases, his work spans atrial fibrillation ablation techniques, Brugada syndrome pathogenesis, heart failure device therapy, and ion channel disorders. His H-index of 53 and 18,407 citations reflect his substantial academic impact. Notable Scientific Contributions Author of 44 patents Principal Investigator in 14 clinical trials (clinicaltrials.gov) Developed circumferential pulmonary vein ablation technique Innovator in biventricular pacing systems for heart failure Pioneered research on non-excitatory current for cardiac contractility Scientific Recognition Awarded as Elite Reviewer of JACC (2005) Editorial Board Member of 6 leading journals Reviewer for NEJM, JAMA, Lancet, and Nature Medicine Education Medical Doctorate: University of Naples Federico II

• Artificial Intelligence
• Medical Imaging
• Cardiac MRI Analysis
• +۳ مورد دیگر

Qian Tao is an Assistant Professor at the Department of Imaging Physics , Faculty of Applied Sciences , Delft University of Technology . She previously worked at the Division of Image Processing, Department of Radiology, Leiden University Medical Center from 2009 to 2020. Academic Background: BSc in Electrical Engineering (Fudan University), MSc in Biomedical Engineering (Fudan University), PhD in Biometric Authentication (University of Twente) Research Interests: Focus on trustworthy AI methodologies for critical healthcare applications, including medical imaging for patient diagnosis and clinical intervention. Specializes in cardiac MRI analysis, image-guided interventions for cardiac arrhythmias, and AI in Radiology. Publication Trends: Recent work emphasizes motion correction in cardiac MRI, deep learning for image registration, and novel techniques like TRAFF2 mapping. Keywords include Medical Imaging , Machine Learning , Cardiac MRI , and Quantitative Analysis . Contact: Email: Q.Tao@tudelft.nl

Bill Sheelمشاهده پروفایل

استاد

• Exercise Physiology
• Respiratory Physiology
• Neuromechanics
• +۳ مورد دیگر

Dr. Bill Sheel is a Professor and Distinguished University Scholar at the School of Kinesiology, University of British Columbia. His research focuses on the integrative physiology of exercise, particularly respiratory and cardiovascular interactions. Email: bill.sheel@ubc.ca Office: Chan Gunn Pavilion, Room 221B, Vancouver, BC Lab: Health and Integrative Physiology Laboratory Research Interests: Dr. Sheel investigates respiratory physiology during exercise, cardiovascular modulation by breathing patterns, and human performance optimization. His work spans elite athletes, aging populations, and clinical applications. Publication Trends: Recent studies include pulmonary mechanics in exercise physiology, respiratory muscle training applications, and socio-cultural factors in sports science. Collaborative works address aging interventions and environmental influences on athletic performance. Scientific Recognition: Distinguished University Scholar, UBC Laboratory & Collaborations: Leads the Health and Integrative Physiology Laboratory at UBC, focusing on respiratory-cardiovascular integration and exercise tolerance mechanisms.

Rahul Mangharamمشاهده پروفایل

استاد

• Formal Methods
• Machine Learning
• Control Systems
• +۳ مورد دیگر

Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.

Pranav Rajpurkarمشاهده پروفایل

دانشیار

• Medical AI
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Pranav Rajpurkar is an Associate Professor at Harvard University, co-founder of a2z Radiology AI, and lead of the Rajpurkar Lab. His work pioneers AI systems that emulate physician-level expertise in medical tasks, with a focus on multi-modal medical AI and radiology. He joined Harvard faculty at age 25 and became Associate Professor at 30 after completing his Stanford PhD at 19. Education: Bachelor of Science (CS), Stanford University, 2015 Master of Science (CS), Stanford University, 2018 PhD (CS), Stanford University, 2021 Rajpurkar's research centers on building AI that thinks and communicates like doctors, with breakthrough work in ECG arrhythmia detection and chest X-ray interpretation. His lab develops foundational datasets (ReXGradient-160K, RadRevise) and benchmarks for medical AI, spanning computer vision, NLP, and multimodal systems. Current work focuses on generative AI for clinical workflows, voice-guided emergency assessment, and rigorous evaluation frameworks for medical LLMs. His 2025 publications reveal three dominant trends: (1) Generative medical AI for radiology report generation and editing, (2) Voice-enabled AI agents for prehospital care, and (3) Multimodal clinical monitoring systems. Key advancements include universal biomedical foundation models (UniBiomed), 3D CT segmentation from text reports, and frameworks for AI-human role separation in radiology. Scientific Awards: Forbes 30 Under 30 in Science (2022) MIT Tech Review Innovator Under 35 (2023) Nature Medicine Early-career Researcher To Watch (2022) Rajpurkar has mentored over 84,000 students through Harvard courses and Coursera's AI for Medicine program. His lab secures major NIH and NSF grants for medical AI development, with recent funding focused on multimodal emergency care systems (MC-MED) and radiologist-AI collaboration. He directs the Harvard-Stanford Medical AI Bootcamp and co-hosts The AI Health Podcast. The Rajpurkar Lab operates as a cross-institutional hub with collaborators at Stanford, MIT, and major hospitals. It drives initiatives like the MAIDA framework for global medical imaging data sharing and develops open-source tools including RadGraph for radiology report analysis. Current projects focus on voice AI for stroke assessment and generative models for non-invasive cancer management.

Stefan Lutherمشاهده پروفایل

استاد

• Biomedical Engineering
• Cardiac Physiology
• Nonlinear Dynamics
• +۳ مورد دیگر

Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.

Laura Munozمشاهده پروفایل

دانشیار

• Mathematical Biology
• Dynamical Systems
• Applied Control Theory
• +۳ مورد دیگر

Laura Munoz is an Associate Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), within the College of Science. She holds a BS from the California Institute of Technology and a Ph.D. from the University of California at Berkeley. Her research focuses on mathematical biology, dynamical systems, applied control theory, and cardiac electrophysiology, with a particular emphasis on understanding mechanisms underlying cardiac arrhythmias through mathematical modeling and computational methods. Her work explores topics such as ephaptic coupling in cardiac tissue, controllability of cardiac alternans, and the role of calcium dynamics in arrhythmogenesis. Recent studies include analyzing discordant alternans mechanisms and their link to ventricular fibrillation, as well as developing state estimation techniques for cardiac ionic models using Kalman filters. Munoz has published extensively in journals like Physical Review Letters , Chaos , and Computers in Biology and Medicine , and has presented at conferences such as the SIAM Conference on the Life Sciences. Munoz currently teaches courses such as Linear Algebra, Complex Variables, Mathematical Modeling I, and Mathematical Biology, emphasizing the application of mathematical tools to real-world biological systems. Her research integrates principles from applied mathematics, control theory, and computational biology to advance understanding of cardiac physiology and disease.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Behnaam Aazhangمشاهده پروفایل

استاد

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +۴ مورد دیگر

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

Joshua D. Rabinowitzمشاهده پروفایل

استاد

• Cellular Metabolism
• Metabolomics
• Systems Biology
• +۵ مورد دیگر

Joshua D. Rabinowitz is a Professor of Chemistry and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where he also serves as Director of the Ludwig Princeton Branch. His research focuses on achieving a quantitative, comprehensive understanding of cellular metabolism, with applications in both basic science and medical research. Dr. Rabinowitz's research interests span multiple areas of metabolism and systems biology: Quantitative analysis of metabolic networks and regulation Metabolomics and measurement of metabolite concentrations and fluxes Cancer cell metabolism and therapeutic targeting Metabolic regulation in microbes (E. coli, Saccharomyces cerevisiae) Biofuel production (focusing on Clostridium acetobutylicum) Metabolic impact of pathogen infection (viral infection of human cells) His laboratory has developed innovative methods for measuring cellular metabolites using state-of-the-art mass spectrometry technology and approaches for quantitating metabolic fluxes through isotope-labeling data interpretation. Analysis of recent publications reveals a strong focus on NAD+ metabolism, cancer metabolism, metabolic adaptations in disease states, and the intersection of metabolism with immunology and neuroscience, particularly in areas like T cell metabolism, Alzheimer's disease, and cardiac function. Dr. Rabinowitz has received recognition as a Highly Cited Researcher by Web of Science, indicating significant impact in his field. He advises several graduate students and has mentored numerous alumni, including Michel I. Nofal, Edmundo Leiva III, and Sean Hackett. His research is supported by multiple programs including NIH NHGRI Training Program and QCB Graduate Program. The Rabinowitz Lab operates at the intersection of chemistry, biology, and computational science, with all projects involving a mix of biological experiments, metabolomics, and computation to achieve their goal of a holistic understanding of cellular metabolism.

Maia Angelovaمشاهده پروفایل

استاد

• Artificial Intelligence in Healthcare
• Data-Driven Models
• Machine Learning
• +۷ مورد دیگر

Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.

Xin Zhouمشاهده پروفایل

پژوهشگر ارشد

• Computational Biology
• Health Informatics
• Cardiovascular Disease Modeling
• +۳ مورد دیگر

Dr. Xin Zhou is an Oxford-Bristol Myers Squibb Fellow at the Department of Computer Science, University of Oxford. Her research integrates computational modeling, clinical data, and experimental findings to investigate cardiac disease mechanisms and develop human-based simulations for drug evaluation. BSc and MSc in Life Sciences, Beijing Normal University DPhil in Computational Biology, University of Oxford Her work focuses on multi-scale cardiac modeling , particularly in ischemic heart disease and heart failure, exploring ionic currents, tissue conduction, and organ-level dynamics. She develops electromechanical simulations to study cardiac alternans and arrhythmic risks, translating these into clinical applications for patient stratification and pharmaceutical testing. Recent publications emphasize in silico clinical trials , sex-specific cardiometabolic analysis, and Purkinje network modeling. Collaborative efforts with clinicians and pharmaceutical partners highlight her translational approach to regulatory science. Model of the Year 2024, BioModels EPSRC Impact Acceleration Account Microsoft Research Project Award Recognition Award, University of Oxford She supervises PhD and MSc students in computational cardiology, while serving on the editorial board of Frontiers in Physiology . Her current projects involve digital twinning and predictive cardiac safety models to reduce animal testing reliance.