جست‌وجوی استاد و پژوهشگر

Ofodike Ezekoyeمشاهده پروفایل

استاد

• Combustion
• Heat Transfer
• Thermal Fluids Systems
• +۴ مورد دیگر

Ofodike Ezekoye is a Professor and holds multiple endowed chairs at the University of Texas at Austin's Cockrell School of Engineering, Department of Mechanical Engineering. His research focuses on combustion, heat transfer, and fire safety engineering, particularly in high-temperature systems like combustion engines, furnaces, and structure fires. He has led over 200 peer-reviewed publications and secured funding from agencies including the Department of Commerce, Air Force Office of Scientific Research (AFOSR), and Department of Energy (DOE). His current work addresses thermal runaway in lithium-ion batteries, fire risk modeling, and energy-efficient furnace optimization. Research Interests: Ezekoye’s expertise spans combustion fundamentals, fire dynamics, and energy systems. He leads the UT Fire Research Group, investigating fire safety in residential and industrial contexts, including firebrand ignition mechanisms, battery failure analysis, and smoke transport in high-rise buildings. His interdisciplinary approach combines experimental methods with computational modeling (e.g., CFD, Bayesian inference) to address practical challenges in fire forensics and sustainable engineering. Awards: He holds prestigious chairs such as the Cockrell Family Dean's Chair in Engineering Excellence and the John T. MacGuire Professorship. His funded projects include smart manufacturing for steam-methane reformers and multi-objective optimization of insulation materials for sustainable designs. Key Contributions: Developed methodologies for fire origin determination via Bayesian inference, ultrasonic inspection of lithium-ion batteries, and reduced-order models like Fire-STORM for high-rise fire simulations. Collaborates with industry partners on real-world applications like furnace heat integration and battery safety standards. Labs/Groups: UT Fire Research Group, with active projects in battery thermal management, fire plume modeling, and acoustic-based fire detection systems.

Omar Ghattasمشاهده پروفایل

استاد

• Computational Geosciences
• Inverse Problems
• Uncertainty Quantification
• +۹ مورد دیگر

Dr. Omar Ghattas is a Professor and the Ernest & Virginia Cockrell Chair in Engineering at The University of Texas at Austin, leading the Center for Computational Geosciences within ICES. He holds courtesy appointments in Computer Science, Biomedical Engineering, the Institute for Geophysics, and the Texas Advanced Computing Center. His research focuses on computational geosciences, inverse problems, and large-scale simulation of geophysical and biological systems. Prior to UT Austin, he was a professor at Carnegie Mellon University for 16 years, earning degrees from Duke University (BS, MS, PhD, 1984–1988). Education: BS, MS, PhD in Engineering from Duke University (1984–1988). Affiliations: Director of the KAUST-UT Austin Alliance, member of multiple editorial boards, and recipient of prestigious awards including the Gordon Bell Prize. His research interests span computational geosciences, inverse problems, uncertainty quantification, and high-performance computing. Key areas include mantle convection modeling, seismic wave propagation, ice sheet dynamics, and subsurface flows. He has pioneered methods for large-scale inverse problems on supercomputers, addressing challenges in computational efficiency and statistical rigor. Recent work emphasizes real-time Bayesian inference for disaster prediction (e.g., tsunamis) and advanced neural operator techniques for high-dimensional Bayesian inverse problems. His publications explore topics like derivative-informed neural operators, multifidelity sampling, and scalable computational frameworks for geophysical applications. Awards: Allen Newell Medal, Gordon Bell Prize, SIAM Fellowship, and multiple HPC awards. Grants & Leadership: Directorships of ICES research centers and international alliances, funded by NSF and DOE. Labs/Teams: The Center for Computational Geosciences in ICES, collaborations with KAUST, and leadership in interdisciplinary computational science initiatives.

Ana Djurdjevacمشاهده پروفایل

استادیار

• Numerical Analysis
• Stochastic Processes
• Partial Differential Equations
• +۳ مورد دیگر

Ana Djurdjevac is an Assistant Professor in the Department of Numerical Analysis and Stochastics at the Freie Universität Berlin , within the Department of Mathematics and Computer Science. Her research focuses on numerical analysis, stochastic processes, and partial differential equations, with particular emphasis on uncertainty quantification and mathematical modeling in evolving domains. She teaches advanced courses such as Numerical Methods for Stochastic Differential Equations and Stochastik I , reflecting her expertise in computational methods and probabilistic frameworks. Her work integrates theoretical analysis with practical numerical techniques, addressing challenges in domains such as fluid dynamics, quantum systems, and biological surface fluctuations. Recent contributions include studies on hybrid algorithms for particle systems, rough homogenization in stochastic dynamics, and synchronization mechanisms in conservation laws. Djurdjevac actively participates in academic events, including the 2025 SIAM Conference on Computational Science and Engineering, and collaborates on projects involving quasi-Monte Carlo methods for Bayesian inversion and domain decomposition techniques. Professional activities highlight her role in shaping emerging fields like stochastic PDEs on evolving domains and feedback loops in agent-based models. While no specific awards are listed, her prolific publication record and teaching roles underscore her contributions to computational science and applied mathematics.

• Applied Probability
• Computational Statistics
• Machine Learning
• +۷ مورد دیگر

Andrew B. Duncan is a Senior Lecturer in Statistics and Data-Centric Engineering at Imperial College London's Department of Mathematics, part of the Faculty of Natural Sciences. He also leads the Data Centric Engineering Programme at the Alan Turing Institute. His research integrates applied probability, computational statistics, and machine learning to solve industrial challenges in areas like cellular biology, aerospace, and energy systems. He holds a PhD from the University of Warwick and previously lectured at the University of Sussex. Education: PhD in Mathematics, University of Warwick (2013), supervised by Andrew Stuart and Charlie Elliott Postdoctoral research at Imperial College London and the University of Oxford Research Interests: Focuses on developing statistical methodologies for complex engineering systems, including predictive health monitoring, computational statistics, and interdisciplinary applications. Key areas include uncertainty quantification, Bayesian inference, and energy-based models. Advising & Grants: Supervised multiple PhD students and postdocs, many now in academic and industry roles. Active in securing funding for projects on sensor optimization, digital twins, and data-centric engineering. Labs/Teams: Leads the Data Centric Engineering group at the Alan Turing Institute and collaborates with the Statistics section at Imperial College. His work bridges academia and industry, emphasizing practical applications of statistical methods.

Razvan Marinescuمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۱۰ مورد دیگر

Razvan Marinescu is an Assistant Professor in the Department of Computer Science and Engineering at the University of California Santa Cruz, where he leads a research lab focused on machine learning for healthcare, biomolecular systems, and neuroimaging. He is also the co-founder and CTO of GiwoTech Inc., a drug screening startup. Education: PostDoc, MIT CSAIL, Polina Golland’s Lab PhD in Computer Science, 2019, Center for Medical Image Computing, University College London MEng in Computer Science, 2014, Imperial College London BSc in Computer Science, 2013, Imperial College London Razvan's research spans machine learning, Bayesian inference, generative models, and their applications in medicine and molecular biology. His work includes modeling neurodegenerative diseases like Alzheimer’s and Posterior Cortical Atrophy, developing differentiable simulators for MRI, and advancing generative AI for medical image reconstruction and drug discovery. He has a strong interest in the mathematical foundations of statistical inference in machine learning. His recent publications demonstrate a strong trend toward using deep generative models, diffusion models, and Bayesian frameworks for solving inverse problems in medical imaging and simulating biological systems. Key themes include super-resolution MRI, microstructure reconstruction, and applying Malliavin calculus to diffusion models. Scientific Awards: Best Paper Award, NeurIPS Deep Generative Models and Downstream Applications Workshop (2021) Razvan advises a large group of PhD and master’s students and has secured significant research visibility through community challenges like TADPOLE. He has also contributed to open-source tools such as BrainPainter and actively collaborates with institutions worldwide. His lab receives support through academic grants and entrepreneurial funding via his startup. Labs and Projects: He leads a research group at UC Santa Cruz focusing on projects including AI for Material Science, Simulating a Virtual Cell, ML Compositionality, Image Reconstruction, Generative Modeling, ML Benchmarks, Disease Progression Modeling, Differentiable Simulators, ML for Molecular Dynamics, and Medical Visualization.

Mathias Trabsمشاهده پروفایل

استاد

• Nonparametric Statistics
• High-dimensional Statistics
• Statistics for Stochastic Processes
• +۳ مورد دیگر

Professor Mathias Trabs is a faculty member at the Karlsruhe Institute of Technology (KIT), where he has been serving as a Professor since 2021. He is affiliated with the Department of Mathematics, specifically within the Institute of Stochastics. Previously, he held positions as Heisenberg professor at Universität Hamburg (2021) and Assistant professor at Universität Hamburg (2016-2021). His research spans several key areas in modern statistics and probability theory. Professor Trabs specializes in Nonparametric and high-dimensional Statistics, Statistics for stochastic processes, Statistical inverse problems, Statistical Learning, and Stochastic (partial) differential equations. His work bridges theoretical statistics with practical applications in physics, machine learning, and high-energy experiments. Analysis of his recent publications reveals a strong focus on the intersection of statistical theory and machine learning, particularly in generative models, neural networks, and high-dimensional data analysis. His work also demonstrates significant contributions to the mathematical foundations of stochastic processes and their applications in physical sciences. Professor Trabs has supervised numerous doctoral students including Lea Kunkel, Thea Engler, Jan Rabe, Sebastian Bieringer, Maximilian F. Steffen, and Florian Hildebrandt. His research has been supported by various projects including the Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (DASHH), DFG project TR 1349/3-1 on high-dimensional statistics, and the LD-SODA research project. He is actively involved in academic leadership, serving as Deputy speaker of the KIT Center MathSEE (Mathematics in Sciences, Engineering, and Economics), and as a member of the steering boards of both the DMV-Fachgruppe Stochastik (Probability and Statistics Group of the German Mathematical Society) and the KIT Graduate School Computational and Data Science (KCDS).

Bartosz Protasمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Optimization and Control Theory
• +۹ مورد دیگر

Bartosz Protas is a Professor in the Department of Mathematics and Statistics at McMaster University , where he served as Director of the School of Computational Science & Engineering (2009-2019) and currently holds the Chair of the Department. His research bridges computational fluid dynamics, optimization theory, and applied mathematics, focusing on extreme behavior in fluid models, vortex dynamics, and electrochemical systems. Research Themes: Finite-time singularity formation in Navier-Stokes/Euler equations, vortex stability analysis via shape calculus, optimal turbulence closures, electrochemical inverse problems with binder migration and dendrite growth, and Riemann-Hilbert applications in fluid mechanics. Collaborations: Kyoto University (Takashi Sakajo), University of Rouen (Ionut Danaila), University of Michigan (Charles Doering), and industrial partners like General Motors. Awards & Invitations: JSPS Visiting Fellow (2017), SHARCNET Chair (2003), and multiple visiting professorships at top European institutions. Advising: Supervised 15+ Ph.D./M.Sc. students including Diego Ayala (recipient of Canadian Applied Mathematics Society's Cecil Graham Award) and Vladislav Bukshtynov. His recent publications analyze lithium plating in batteries, singularities in 3D Euler flows, and Sobolev gradient methods for PDE optimization.

Nelson Trujillo-Barretoمشاهده پروفایل

مدرس

• Brain Dynamics
• Computational Neuroscience
• Neuroimaging Analysis
• +۷ مورد دیگر

Nelson Trujillo-Barreto is a Lecturer in Computational Neuroscience at the School of Health Sciences, University of Manchester since January 2023. With over 25 years of experience in brain dynamics analysis, he specializes in developing probabilistic and biophysical generative models of neuroimaging data. His academic journey includes a PhD in Physical Sciences from Havana University (2006) and prior work as Head of the Department for Brain Dynamics at the Cuban Neuroscience Centre (1995-2014). His research focuses on bridging the gap between recorded neuroimaging data and underlying neuronal activity through solving forward and inverse problems. Key areas include Brain Electromagnetic Tomography (BET), Bayesian models for estimating time-varying dynamical connectivity, and multimodal integration of EEG/MEG and fMRI data. His work has significant clinical applications in neurofeedback, brain-computer interfaces, and non-invasive brain stimulation for conditions like Neurofibromatosis Type 1 and Multiple Sclerosis. His recent publications demonstrate strong trends in applying computational neuroscience to neurological disorders, particularly focusing on working memory, brain connectivity, and therapeutic interventions. His research shows increasing emphasis on multimodal integration and clinical translation of advanced neuroimaging techniques. UoM PCHN Best Poster Award (2025) Dr. Trujillo-Barreto maintains active research collaborations with leading institutions including University College London, Cambridge University, McGill University, and various European neuroscience centers. His editorial roles include Associate Editor for Frontiers in Brain Imaging Methods and Frontiers in Computational Neuroimaging. He serves on prestigious review panels including the EPSRC Peer Review College and European Commission College of Experts.

• Laser-Plasma Physics
• Relativistic Plasma Physics
• Laser-Driven Ion Acceleration
• +۷ مورد دیگر

Martin King is a Research Fellow in the Department of Physics at the University of Strathclyde, Faculty of Science. He is actively engaged in cutting-edge research in laser-plasma physics, with a focus on ion acceleration, relativistic plasmas, and computational modeling. His work is supported by major grants from EPSRC, and he collaborates extensively with leading researchers in the field. His research interests lie at the intersection of high-intensity laser physics and plasma dynamics. He investigates laser-driven ion acceleration , relativistic transparency , and nonlinear optical effects in plasmas . He also pioneers the use of machine learning for optimizing electron temperature and developing synthetic diagnostics for proton spectra. His work enables advances in compact particle accelerators and fusion research. The recent publications show a strong trend toward integrating artificial intelligence with plasma simulations and experimental diagnostics . His work spans from fundamental plasma instabilities to applied beam engineering, with emphasis on proton beam stability , structured light generation , and energy-selective focusing . He has contributed to multiple EPSRC-funded projects, including Relativistic plasma apertures and Nonlinear Optics in Relativistically Transparent Plasmas . While no formal students are listed, he has collaborated with numerous early-career researchers and contributed to doctoral training initiatives. His datasets and software tools are publicly archived, supporting open science. Dr. King is involved in key research labs and teams focused on laser-plasma interaction experiments, often collaborating with the wider Scottish Universities Physics Alliance (SUPA). His work is conducted within advanced laser laboratories, utilizing high-power laser systems for probing relativistic plasma phenomena.

Akil Narayanمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Approximation Algorithms
• +۳ مورد دیگر

Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.

• Mechanical Engineering
• Structural Engineering
• Materials Science
• +۷ مورد دیگر

Prof Zhen (Jeff) Luo is a Professor at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS). Since 2012, he has led the Advanced Metamaterials & Metastructures (AMM) and Engineering Computation and Optimisation (ECO) research groups, focusing on multi-disciplinary engineering innovations. Expertise : Advanced materials design, topology optimization algorithms, additive manufacturing, and computational mechanics. Education : PhD in Mechanical Engineering from Huazhong University of Science and Technology (2005). His research bridges Mechanical, Structural, Aerospace, and Biomechanical Engineering , developing cutting-edge metamaterials and computational methods. Recent work includes two-scale lattice optimization , stochastic bandgap analysis , and machine learning-aided virtual modeling for structural reliability. Key contributions span 3D-printed heat sinks , frequency-selective surfaces , and hydrogen storage systems . As a World’s Top 2% Scientist (Stanford, 2019–present), he has secured over AUD $7 million in grants, including from the Australian Research Council (ARC) and National Intelligence Discovery Research Grants (NI220100074). Awards : IAAM Scientist Award, IAAM Fellow. Leadership : Editorial roles in Structural and Multidisciplinary Optimization , Frontiers in Bioengineering and Biotechnology , and organizing roles in 21 international conferences.

Elizabeth Qianمشاهده پروفایل

استادیار

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +۵ مورد دیگر

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.

Eduard Campillo-Funolletمشاهده پروفایل

استاد مهمان

• Mathematical Biology
• Epidemiological Modeling
• Bayesian Inference
• +۷ مورد دیگر

Dr Eduard Campillo-Funollet is a Visiting Professor in the Department of Mathematics at the University of Sussex, affiliated with the School of Mathematical and Physical Sciences. His work bridges mathematical biology, computational modeling, and public health, with a focus on pandemic response systems and cellular dynamics. Specializes in epidemiological modeling and Bayesian inference for biological systems Active in computational oncology and cell contraction dynamics Developed frameworks for predicting healthcare demand during disease outbreaks Collaborates across disciplines including genetics, physics, and social sciences His research spans mathematical formulations of biological processes and data-driven modeling approaches. Recent work has focused on the mathematical underpinnings of disease transmission dynamics and cellular mechanics, with applications in both public health and molecular biology. Current affiliations include the University of Sussex's Centre for Computational Neuroscience and Mathematical Biology. He maintains active collaborations with institutions across the UK and international research networks.

Müjdat Çetinمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Data Science
• +۱۲ مورد دیگر

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

• Probability Theory
• Mathematical Statistics
• Characterizations of Distributions
• +۷ مورد دیگر

Prof. Dr. hab. Jacek Wesołowski is a faculty member at Warsaw University of Technology, affiliated with the Faculty of Mathematics and Information Science, where he serves as Head of the Department of Probability and Mathematical Statistics. He is actively involved in research and teaching, with a focus on probability theory and mathematical statistics. His research interests include probability theory, mathematical statistics, characterizations of distributions, stochastic processes, survey sampling, multivariate analysis, free probability, and quadratic harnesses . These areas are evident from his extensive publication record in top-tier journals such as the Annals of Probability, Journal of Multivariate Analysis, and Stochastic Processes and their Applications. The trends in his recent publications reveal a strong focus on distributional characterizations (e.g., gamma, Kummer, Wishart), regression properties of order and record statistics , quadratic harnesses and martingale structures , and methodological contributions to survey sampling and imputation . His work often bridges theoretical probability with statistical applications. No scientific awards were mentioned in the provided text. He advises graduate students, as indicated by his organization of probabilistic workshops for PhD and master’s students. He has collaborated extensively with researchers such as W. Bryc, G. Letac, H. Massam, and G. Rempała. While no specific grants are listed, his sustained research output suggests active funding. He teaches core courses such as Probability Calculus I/II and Survey Sampling. There is no mention of specific labs or research teams, but his departmental leadership and collaborative publications suggest an active research group environment.