جست‌وجوی استاد و پژوهشگر

• Acoustics
• Sensors for water industry
• Acoustic materials
• +۵ مورد دیگر

Professor Kirill V Horoshenkov (FREng) is a leading academic at the University of Sheffield , holding a Personal Chair in Acoustics within the School of Mechanical, Aerospace and Civil Engineering . With a MEng in Electro-Acoustics and Ultrasonic Engineering from Moscow University and a PhD in Computational and Experimental Acoustics from the University of Bradford, he transitioned to Sheffield in 2013 after a distinguished career at Bradford. Acoustic sensors for water infrastructure Physical acoustics and wave propagation Acoustic material characterization Pipe condition monitoring systems Research Focus : Horoshenkov's work bridges acoustic engineering with water industry applications , developing innovative solutions for pipeline diagnostics and monitoring. His team has pioneered acoustic vector receivers , MEMS hydrophones , and Bayesian acoustic models for material analysis. Notable projects include the Pipebots Programme Grant and EPSRC Acoustics Network . Scientific Leadership : A Fellow of the Royal Academy of Engineering, he serves as Editor-in-Chief of the Nature Portfolio Journal npj Acoustics . His research has yielded 12 patents and over 200 publications , including commercialization through spin-offs like Acoutechs Limited (licensed to Armacell) and Acoustic Sensing Technology Limited .

Andreas Kyprianouمشاهده پروفایل

استاد

• Pure and Applied Probability
• Branching Markov Processes
• Superprocesses
• +۷ مورد دیگر

Professor Andreas Kyprianou is a leading probabilist at the University of Warwick's Department of Statistics, specializing in pure and applied probability. His research spans Branching Markov processes , Superprocesses , Lévy processes , and stochastic radiation transport . He directs the Centre for Mathematical and Computing Sciences (CAMaCS) and leads the £7.3M EPSRC MaThRad programme grant, focusing on nuclear technology applications. Academic Affiliations : University of Warwick (2023-present), University of Bath (2006-2023), Utrecht University (2001-2006), Heriot-Watt University, London School of Economics, University of Edinburgh Research Themes : Stochastic Modelling, Self-Similar Processes, Fragmentation-Coalescence, Neutron Transport, Monte-Carlo Simulation His recent work includes α-stable Lévy processes , jump SDEs in proton therapy , and non-local branching process stability . Scientific awards include the Royal Society Wolfson Merit Award and the Dutch Mathematics in Focus fellowship. He has supervised over 20 PhD students and co-led international research platforms like Prob-L@B and CIMAT-UNAM-Warwick-Bath . Grants section highlights major EPSRC and Royal Society funding for his interdisciplinary projects.

• Bayesian Statistics
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Yanan Fan is a Senior Principal Research Scientist at CSIRO's Data61 and an Adjunct Professor of Statistics at the University of New South Wales (UNSW). His research focuses on Bayesian models, computational methods for real-world problems, and interdisciplinary applications in fields like medical imaging, cosmology, and climate science. He holds a PhD in Statistics from the University of Bristol and has over 20 years of academic experience at UNSW's School of Mathematics and Statistics. Education: PhD in Statistics, University of Bristol, UK Undergraduate Degree in Mathematics, University of Melbourne Research Interests: Fan develops Bayesian semiparametric models, approximate Bayesian computation (ABC), and scalable computational methods for medical imaging (e.g., PET), cosmology, and climate modeling. He also investigates gender bias in educational evaluations and leads initiatives like the Data4Good stream of UDASH. His work emphasizes practical problem-solving through advanced statistical techniques. Leadership & Contributions: As Team Leader of Bayesian Computational Methods and Applications at Data61, he drives innovation in statistical methodologies. He has served on the Scientific Committee of MATRIX research institute and as an Associate Editor for major statistical journals. His projects include probabilistic climate projections and bias analysis in student evaluations. Labs & Groups: Active member of the StatML Group and leader of the Bayesian Computational Methods team, focusing on integrating machine learning and statistical computing.

Mohsen Badieyمشاهده پروفایل

استاد

• Ocean Acoustics
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Mohsen Badiey , Professor in the Department of Electrical and Computer Engineering at the University of Delaware's College of Engineering, leads the Ocean Acoustics & Engineering Laboratory (OAELab) with facilities at Evans Hall and STAR campus. His interdisciplinary work spans applied physics, mechanical systems, ocean sensing, and computational signal analysis. Research Focus: Geoacoustic inversion, waveguide physics, machine learning for seabed classification, and underwater communication challenges. Key Projects: Shallow Water 2006 (SW06) and Shallow Water Acoustic in Random Media (SWARM95) experiments analyzing nonlinear internal wave dynamics. Scientific Contributions include developing dictionary learning techniques for sound speed profile analysis, advancing graph neural networks for underwater signal processing, and studying acoustic propagation through intense internal waves. His work emphasizes both fundamental and applied research with field data collection and computational modeling. Recent Publications demonstrate expertise in physics-based machine learning for source localization, seabed classification, and time-varying signal reconstruction. His team's 2024 studies on transiting ocean observers and Sobolev graph networks highlight cutting-edge methodologies. Laboratory: OAELab employs specialized instrumentation for broadband acoustic signal analysis, combining experimental data with computational approaches to solve real-world oceanographic problems.

Amirreza Khodadadianمشاهده پروفایل

مدرس

• Uncertainty Quantification
• Numerical Analysis
• Computational Mechanics
• +۷ مورد دیگر

Dr. Amirreza Khodadadian is a Lecturer in Mathematics at the School of Computer Science and Mathematics, Keele University, since August 2023. He holds a Ph.D. from the University of Vienna (2017), followed by postdoctoral positions at the Technical University of Vienna and Leibniz University Hannover. His research focuses on uncertainty quantification, numerical methods for stochastic PDEs, finite element methods, computational mechanics, and machine learning applications in nanoelectronics and biological systems. Key research interests include Bayesian inversion, multiscale modeling, reduced-order methods, and the design of nanoscale sensors. He has collaborated with institutions like the University of Oxford and secured an Austrian Science Fund (FWF) grant (476k€) for nanozyme sensor research. Dr. Khodadadian mentors postdoctoral researchers, including Dr. Samaneh Mirsian, and actively publishes in top-tier journals such as Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . His work bridges applied mathematics with engineering challenges, emphasizing efficient numerical algorithms for real-world problems like battery degradation, groundwater contamination, and biomedical sensor optimization. Recent projects involve machine learning integration for enhanced predictive modeling. Education: Ph.D. in Mathematics, University of Vienna, Austria (2017) Postdoctoral Fellowships: TU Vienna (2018), Leibniz University Hannover (2018–2022) Grants/Awards: Austrian Science Fund (FWF) Grant: Single Atom Catalysts as Nanozymes in FET Sensors (2023) Advising: Postdoctoral Mentor: Dr. Samaneh Mirsian (Keele University) Dr. Khodadadian’s publications span computational mechanics, stochastic modeling, and interdisciplinary applications, reflecting his expertise in translating mathematical theory into practical engineering solutions.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Sarah Perezمشاهده پروفایل

پژوهشگر

• Mathematical modelling
• Numerical simulation
• AI-driven approaches
• +۵ مورد دیگر

Sarah Perez is a Postdoctoral Research Associate in the GeoEnergy research group at the Lyell Centre, part of the School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University. Holding a PhD in Applied Mathematics from the University of Pau in France, she specializes in developing numerical and machine learning models for geological carbon storage applications. Her research sits at the intersection of mathematical modeling, numerical simulation, and AI-driven approaches for subsurface energy systems. She develops frameworks that integrate pore-scale imaging with AI-based uncertainty quantification to improve reliability of subsurface predictions, with particular focus on fractured and reactive media. Her work bridges multiple scales to address leakage risks and subsurface integrity in carbon storage systems. Analysis of her publications reveals consistent focus on computational methods for geological applications, with increasing integration of AI techniques across her work from 2022-2025. Her research shows strong interdisciplinary connections between computational physics, machine learning, and geoscience. Beyond research, Perez actively contributes to academic community through science communication and early-career initiatives. She co-chairs the InterPore Student Awards Sub-Committee and organizes YouTube webinars on Porous Media research and AI for Net Zero, demonstrating commitment to knowledge exchange and interdisciplinary collaboration. Her professional network shows significant international collaboration, with research activities spanning multiple countries and institutions. She maintains active engagement with the scientific community through conference presentations and peer-reviewed publications in high-impact journals.

Jun Liuمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Bayesian Methods
• +۳ مورد دیگر

Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.

• Biomechanics
• Soft Tissue Mechanics
• Cardiovascular Biomechanics
• +۵ مورد دیگر

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.

Aad van der Vaartمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +۵ مورد دیگر

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.

Lena Maier-Heinمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +۳ مورد دیگر

Prof. Lena Maier-Hein is a full professor at Heidelberg University and managing director of the National Center for Tumor Diseases (NCT) Heidelberg. She leads the division of Intelligent Medical Systems (IMSY) at the German Cancer Research Center (DKFZ) and oversees the cross-topic program 'Data Science and Digital Oncology'. Her research focuses on machine learning in biomedical imaging, particularly surgical data science and computational biophotonics. She chairs the Surgical Data Science initiative and serves on editorial boards for journals like Nature Scientific Data and IEEE TPAMI. Her awards include the 2024 German Cancer Award, 2013 Heinz Maier-Leibnitz Prize, and European Research Council grants. She advocates for trustworthy AI in healthcare, co-developing frameworks like Metrics Reloaded and TRIPOD+ AI. Her work bridges academic, clinical, and industrial sectors through initiatives like the FeTS challenge. Key contributions include advancing photoacoustic imaging, surgical AI systems, and validation methodologies. She emphasizes ethical AI deployment and interdisciplinary collaboration to address clinical challenges.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +۷ مورد دیگر

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

• Nanophotonics
• Quantum Technologies
• Computational Optics
• +۵ مورد دیگر

Dr. Sven Burger is a leading Researcher at the Zuse Institute Berlin (ZIB) within the Modeling and Simulation of Complex Processes department. His work focuses on Nanophotonics , Quantum Technologies , and Optical Resonance Computation , particularly in photonic crystals, plasmonic systems, and quantum light sources. Key projects: NanoLab GRIPS 2024 , MATH+ TES QT , MATH+ PaA-1 (perovskite solar cells), Colour Impression of Solar Cells Collaborations: MATH+ , BIFOLD , Research Campus MODAL His research spans Bayesian optimization for quantum systems, quasinormal mode expansions , chiral plasmonics , and terawatt-scale photovoltaics . Recent work emphasizes RPExpand software for resonance analysis and AAA algorithm applications in photonic design. He contributes to quantum key distribution via plug&play single-photon sources, hot carrier dynamics in plasmonic nanocrystals, and high-efficiency light extraction for deep-UV LEDs. His computational methods address non-Hermitian systems , exceptional points , and self-interference nanoparticle tracking .

Shirin Jalaliمشاهده پروفایل

دانشیار

• Computational Imaging
• Machine Learning
• Information Theory
• +۴ مورد دیگر

Shirin Jalali is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. She holds a Ph.D. and M.Sc. in Electrical Engineering and Statistics from Stanford University, with earlier degrees from Sharif University of Technology. Prior to Rutgers, she was a Research Scientist at Nokia Bell Labs and held roles at Princeton University and NYU Tandon School of Engineering. Her research focuses on computational imaging, machine learning, and information theory, addressing challenges like speckle noise mitigation and snapshot compressive imaging. She received the NSF CAREER Award in 2023 and serves as an Associate Editor for IEEE Transactions on Information Theory. Education: Ph.D. in Electrical Engineering, Stanford University, 2010 M.Sc. in Statistics, Stanford University, 2010 M.Sc. in Electrical Engineering, Sharif University of Technology, 2004 B.Sc. in Electrical Engineering, Sharif University of Technology, 2002 Research Interests: Her work spans high-dimensional inference, computational imaging, and machine learning applications in imaging systems. She develops algorithms for coherent imaging in speckle noise environments and explores structure learning through information-theoretic methods. Recent trends in her publications emphasize unrolled neural networks, Bayesian despeckling, and theoretical frameworks for compressive imaging systems. Awards and Roles: 2023 NSF CAREER Award Associate Editor, IEEE Transactions on Information Theory (since 2021) Leadership in research groups addressing imaging and machine learning challenges Grants and Labs: Her NSF CAREER grant funds theoretical work on snapshot compressive imaging systems. She collaborates across academia and industry, leading interdisciplinary projects in signal processing and AI-driven imaging solutions.

• Computational Physiology
• Biomechanics
• Cardiac Electromechanics
• +۷ مورد دیگر

Joakim Sundnes is a Chief Research Scientist and Research Professor at the Department of Scientific Computing, Simula Research Laboratory. He specializes in computational physiology, cardiac biomechanics, and mathematical modeling of cardiovascular systems. Key Research Areas: Cardiac electromechanics, computational fluid dynamics in cardiology, uncertainty quantification in cardiac models, and mechano-electric feedback mechanisms Recent Trends: Focus on patient-specific modeling, left atrial flow dynamics, right ventricular mechanics in pulmonary hypertension, and personalized treatment simulations Scientific Contributions: Active participant in international conferences and editorial work. Co-author of multiple benchmark studies and educational texts on physiological modeling.