Find Academic Supervisors

• Bayesian Modeling
• Longitudinal Data Analysis
• Experimental Designs
• +5 more

Elizabeth Slate is a Professor in the Department of Statistics at Florida State University. Her expertise includes longitudinal data analysis, experimental designs, and Bayesian modeling. She focuses on interdisciplinary applications in biomedical research, particularly in musculoskeletal biomechanics, clinical trial design, and public health disparities. Her work bridges statistical methodology with real-world clinical and epidemiological challenges. Research Interests: Her research spans Bayesian statistical methods, causal inference, and the analysis of complex biomedical datasets. Key areas include biomechanical modeling of joint disorders, risk factor analysis for chronic diseases, and the development of innovative statistical frameworks for clinical trials and observational studies. She has contributed to understanding the structural-functional relationships in musculoskeletal tissues and the sociocultural determinants of health behaviors in underserved populations. Publications Trends: Recent work emphasizes Bayesian borrowing of historical data in basket trials, explainable AI in medical imaging, and the biomechanical correlates of TMJ disorders. She also investigates longitudinal health outcomes in diabetes and HPV vaccination uptake among marginalized groups. Methodologically, her articles address causal mediation, sensitivity analysis, and mixed-scale data integration. Grants and Advising: While specific grant details are not listed, her publication record indicates sustained funding in interdisciplinary health research. No advising information is explicitly provided in the source text. Labs/Teams: Collaboration networks likely span biomedical engineering, public health, and clinical departments, though formal affiliations are not detailed here.

• Structural Biology
• Ion Channels
• Cryo-Electron Microscopy (Cryo-EM)
• +3 more

Frederick Sigworth is a Professor of Cellular and Molecular Physiology, Biomedical Engineering, and Molecular Biophysics and Biochemistry at Yale University. He holds primary faculty appointments in Cellular & Molecular Physiology and secondary appointments in Molecular Biophysics and Biochemistry. His research focuses on the structural and functional analysis of ion-channel proteins using cryo-electron microscopy (Cryo-EM) and electrophysiological techniques. Sigworth’s work has significantly advanced understanding of ion channel gating mechanisms, voltage sensor function, and membrane protein structure. Education: PhD in Physiology from Yale University (1979). Postdoctoral training at the Max Planck Institute for Biophysical Chemistry (Göttingen, Germany) under Erwin Neher, co-developer of the patch-clamp technique. Key contributions include pioneering cryo-EM methods for membrane protein analysis and structural studies of potassium channels. Research interests span structural biology, ion channel biophysics, and cryo-EM methodology. Notable projects include determining the structure of the BK potassium channel and developing computational tools for single-particle image analysis. Collaborations include work with Henry J. Binder and Joseph Santos-Sacchi on ion channel physiology and engineering. Awards include AAAS Fellowship (2023), National Academy of Sciences membership (2016), and the K. C. Cole Award (1997). His lab, the Sigworth Lab, integrates experimental and computational approaches to study ion channel mechanisms. Phased retirement status indicates reduced but ongoing academic engagement.

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +5 more

Gerhard Holzapfel is a Professor at TU Graz's Department of Biomechanics. His research focuses on biomechanics of soft biological tissues, cardiovascular systems, and advanced material modeling. He leads the Institute of Biomechanics, conducting experimental and computational studies on tissue mechanics, vascular diseases, and medical device design. Education : Details not explicitly provided in source text. Research Interests : Dr. Holzapfel's work spans constitutive modeling of soft tissues, computational fluid dynamics (CFD) in vascular systems, mechanobiology of cells and tissues, and biomaterials. He emphasizes translating biomechanical insights into clinical applications such as endovascular devices and surgical simulations. His studies often integrate microstructural analysis with macroscopic mechanical behavior to understand pathologies like atherosclerosis and aortic dissection. Publications : Recent work highlights advancements in fiber dispersion models for skin mechanics, Bayesian frameworks for material calibration, and CFD-driven evaluations of TEVAR (thoracic endovascular aortic repair). His articles emphasize predictive modeling of tissue behavior under various loading conditions, with applications in cardiology and regenerative medicine. Awards : No specific honors or fellowships mentioned in the provided text. Advising & Grants : No student/advisor relationships or grant details explicitly listed. The Institute of Biomechanics likely supports collaborative projects in biomechanical engineering and medical research. Labs/Teams : Director of TU Graz's Institute of Biomechanics, leading interdisciplinary teams in biomechanics, material science, and computational modeling. Collaborates with clinical partners on vascular biomechanics and surgical device development.

Ceren Vardar AcarView profile

Assistant Professor

• Stochastic Processes
• Extreme Values and Risk Management
• Financial Mathematics/Statistics
• +3 more

Ceren Vardar Acar is an Assistant Professor in the Department of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds a PhD in Mathematics and Statistics from Bowling Green State University, USA (2002-2008), and prior degrees from METU, including a BSc and MSc in Statistics. Her professional experience includes roles as Assistant Professor at TOBB ETU (2011-2015) and instructorships at various institutions in Turkey and the USA. Her research focuses on Stochastic Processes , particularly their path properties, extreme value analysis, and applications in financial mathematics, risk management, and population dynamics. Key areas include fractional Brownian motion modeling, Lévy processes, and the analysis of maximum loss/drawdown phenomena. She has also contributed to methodological advancements in parameter estimation and Monte-Carlo simulation techniques. Academic leadership roles include serving as Co-Head of the Department of Mathematics at TOBB ETU (2013-2015) and coordinating service courses for non-mathematics departments (2010-2015). She secured a TÜBİTAK grant (2011-2014) exploring maximum loss distributions in fractional Brownian motion and Lévy processes. Her work bridges theoretical probability with applied domains like finance and healthcare, evidenced by publications in journals such as Extremes , Journal of Computational and Applied Mathematics , and Statistics and Probability Letters . She maintains active collaborations in interdisciplinary research, including dental education simulation and population modeling with delays.

Stéphane BORDASView profile

Professor

• Computational Mechanics
• Biomechanics
• Finite Element Methods
• +6 more

Stéphane BORDAS is a Full Professor in Computational Mechanics at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), leading the Computational Mechanics (Legato) research group. His work focuses on free boundary problems, method development for complex geometries, and applications in fracture mechanics, biomechanics, and computational engineering. He previously held roles at Cardiff University and the Swiss Federal Institute of Technology in Lausanne (EPFL). His research integrates computational methods like XFEM, isogeometric analysis, and meshfree techniques to address challenges in engineering and medicine. Education: Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2003) M.Sc. in Civil Engineering, École Spéciale des Travaux Publics and Northwestern University (1999) Research Interests: Computational Mechanics, Biomechanics, Finite Element Methods, Fracture Mechanics, High-Performance Computing, Isogeometric Analysis. Grants & Projects: ERC Starting Grant (RealTCut) for surgical simulation and material cutting FP7 ITN INSIST for meshless methods Labs/Teams: Computational Mechanics (Legato) Group at the University of Luxembourg. His work bridges academia and industry, with applications in aerospace, biomedical engineering, and materials science. He is active in open-source software development, including codes for XFEM, isogeometric analysis, and meshfree methods.

Aad van der VaartView profile

Professor

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +5 more

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.

Tayo Obafemi AjayiView profile

Associate Professor

• Explainable Artificial Intelligence
• Bioinformatics
• Data Mining
• +4 more

Dr. Tayo Obafemi Ajayi is an Associate Professor in the Department of Electrical and Computer Engineering at Missouri State University, where she holds the Mace/Turblex Professor of Engineering title. She also serves as an Adjunct Professor at Missouri University of Science and Technology and directs the Computational Learning Systems (CLS) Lab. As site coordinator for the Missouri Louis Stokes Alliance for Minority Participation (MoLSAMP), she champions diversity in STEM while leading committees in IEEE organizations like the Bioinformatics and Bioengineering Technical Committee and the Engineering in Medicine and Biology Society. Primary Affiliation: Missouri State University Secondary Affiliation: Missouri University of Science and Technology Labs: Computational Learning Systems Lab Leadership: IEEE Technical Committees, MoLSAMP Her research focuses on Explainable Artificial Intelligence (XAI) for biomedical applications, integrating Data Mining , Machine Learning , and Bioinformatics to address complex health problems. Key projects include: Anemia prediction models for spinal surgery outcomes Alzheimer’s disease severity analysis Autism genotype-phenotype mapping Traumatic brain injury subgroup identification Recent publications span IEEE Access , Frontiers in Human Neuroscience , and the Pacific Symposium on Biocomputing . Her work emphasizes model transparency, statistical rigor, and practical validation through biomedical case studies. Scientific Awards Bear Bridge Outstanding Mentoring Award (2023) IEEE CIBCB Best Student Paper Award (2021) College of Natural and Applied Sciences Faculty Research Award (2019) Dr. Ajayi's research combines Computational Learning with Clinical Applications , securing grants through collaborative projects with institutions like Missouri S&T and participation in national conferences. Her work has been cited over 560 times with an h-index of 14, reflecting sustained impact in AI-driven healthcare solutions.

Aly A FaragView profile

Professor

• Computer Vision
• Biomedical Imaging
• Image Processing
• +5 more

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
• +4 more

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
• +2 more

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
• +4 more

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 TallecView profile

Professor

• Computational Mechanics
• Nonlinear Mechanics
• Domain Decomposition Methods
• +5 more

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
• +4 more

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 SafavisohiView profile

Assistant Professor

• Extracellular Vesicles
• Nano-Bio-Science
• Proteomics & Mass Spectrometry
• +3 more

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
• +3 more

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.