جست‌وجوی استاد و پژوهشگر

Jie Chenمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Design Optimization
• Machine Learning
• +۴ مورد دیگر

Jie Chen is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. Their research bridges machine learning with engineering analysis and design under uncertainty, focusing on process-structure-property-performance relationships. PhD, Mechanical Engineering (2022) – Arizona State University MS, Civil Engineering (2018) – Beihang University BS, Civil Engineering (2015) – Beihang University Research interests include: physics-informed machine learning, uncertainty quantification, predictive maintenance, materials design, and advanced manufacturing. The SEAD Lab develops methods to integrate engineering analysis into stochastic machine learning algorithms and uses AI for knowledge discovery in uncertain environments. Recent publications emphasize: Digital twin frameworks combining machine learning and Bayesian optimization Graph neural networks for high-entropy alloy and molecular mixture property prediction Physics-guided neural networks for fatigue life analysis of additively manufactured alloys Uncertainty quantification in imbalanced regression tasks and multi-fidelity data fusion Real-time imaging of polymer deformation mechanisms The lab actively mentors students, including PhD candidate Yisheng Lu, and manages projects in predictive maintenance, fatigue modeling, and materials design.

Tobias Neckelمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +۴ مورد دیگر

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

Jingbo Liuمشاهده پروفایل

استادیار

• Statistical Inference
• Information Theory
• Signal Processing
• +۴ مورد دیگر

Jingbo Liu is an Assistant Professor in the Department of Statistics at the University of Illinois, Urbana-Champaign, with an affiliate appointment in Electrical and Computer Engineering. He received his B.E. (2012) from Tsinghua University, M.A. (2014) and Ph.D. (2018) from Princeton University, all in Electrical Engineering, followed by a postdoc at MIT IDSS. Education Ph.D. in Electrical Engineering, Princeton University (2018) M.A. in Electrical Engineering, Princeton University (2014) B.E. in Electronic Engineering, Tsinghua University (2012) His research focuses on statistical inference under systems constraints, information-theoretic inequalities, graphical models, and applications of high-dimensional probability to information sciences. Key areas include mutual covering bounds, hypercontractivity, Brascamp-Lieb inequalities, and their connections to machine learning and communication systems. Recent work applies information theory to generative AI, analyzing diffusion models' utility, privacy enhancements, and computational efficiency. He also investigates statistical physics techniques for high-dimensional problems like Lasso distributional limits and tensor model free energy, with applications in variable selection and PCA. Scientific awards include the Thomas M. Cover Dissertation Award (2018) and Princeton's Wallace Memorial Fellowship (2016). Courses taught include STAT 578 (High-Dimensional Statistics), STAT 430 (Nonparametric Statistics), and STAT 542 (Statistical Learning).

Alex Kamenevمشاهده پروفایل

استاد

• Theoretical Condensed Matter Physics
• Disordered Systems
• Many-Body Physics
• +۵ مورد دیگر

Alex Kamenev is a Professor in the School of Physics and Astronomy at the University of Minnesota and serves as Director of the William I. Fine Theoretical Physics Institute. His academic career spans multiple decades with continuous research output since 1991, demonstrating sustained contributions to theoretical physics. His research focuses on theoretical condensed matter physics, with particular emphasis on disordered systems and glasses, field-theoretical treatment of many-body systems, mesoscopic systems, and out-of-equilibrium phenomena. His fingerprint analysis reveals strong expertise in Instanton Physics (100%), Fermion Physics (90%), Conductance (69%), Quantum Dot Physics (64%), and Superconductor physics (62%). Analysis of his recent publications shows a clear trend toward quantum computing applications, non-equilibrium quantum dynamics, and advanced field-theoretical approaches to many-body problems. His work bridges fundamental theoretical physics with practical applications in quantum information science, particularly in understanding quantum dissipation, localization phenomena, and quantum annealing processes. As Principal Investigator, Kamenev has led numerous significant research projects, primarily funded by the National Science Foundation. His current active projects include the REU Site: Physics and Astronomy at the University of Minnesota (2024-2027) and NSF-BSF: Many Body Physics of Quantum Computation (2024-2027), demonstrating his leadership in training the next generation of physicists and advancing quantum computing research. He actively mentors graduate students, as indicated by his statement that he is "Accepting new graduate research students." His research group contributes to the Condensed Matter Theory research area within the School of Physics and Astronomy, focusing on theoretical approaches to quantum systems.

James Martinمشاهده پروفایل

مدرس

• Probability Theory
• Statistical Physics
• Random Graphs
• +۹ مورد دیگر

James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.

Jochen Garckeمشاهده پروفایل

استاد

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +۵ مورد دیگر

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +۴ مورد دیگر

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Alvaro Köhn-Luqueمشاهده پروفایل

دانشیار

• Mathematical Oncology
• Computational Biology
• Biostatistics
• +۷ مورد دیگر

Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.

• Digital Communications
• Signal Processing
• Networking
• +۳ مورد دیگر

Mahsa Derakhshani is a Senior Lecturer (equivalent to Associate Professor) in Digital Communications at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. She leads research in the Signal Processing and Networks Research Group (SPNRG) and serves as an Associate Editor for the IET Signal Processing Journal. Her academic journey includes a PhD from McGill University (2013), followed by postdoctoral roles at the University of Toronto and Imperial College London. Key awards include the Royal Academy of Engineering/The Leverhulme Trust Research Fellowship (2020-21) and NSERC Postdoctoral Fellowships (2015-2017). Research interests focus on digital communications, machine learning for signal processing, wireless networks, and reinforcement learning applications. Recent work addresses challenges in satellite communications, OTFS modulation, and opto-physiological monitoring. She has authored over 70 publications spanning topics like NOMA systems, MIMO optimization, and edge-assisted live streaming. Education: PhD (McGill, 2013), MSc (Sharif University, 2008), BSc (Sharif University, 2006) Affiliations: IEEE Senior Member, IET Member, Fellow of the Higher Education Academy Grants & Funding: Leverhulme Trust, Royal Academy of Engineering, NSERC Labs/Teams: Active in SPNRG and collaborates on projects involving 5G/6G networks, satellite systems, and biomedical signal processing. Current research emphasizes AI-driven solutions for communication networks and wearable health monitoring technologies.

Michael L. Madiganمشاهده پروفایل

استاد

• Human Factors Engineering
• Ergonomics
• Occupational Biomechanics
• +۶ مورد دیگر

Michael L. Madigan is a Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. His research focuses on human factors engineering, biomechanics, and ergonomics, particularly addressing issues related to slips, trips, falls, and musculoskeletal disorders. He leads the Madigan Biomechanics Group, which investigates the biomechanics of human motion and neuromuscular control to enhance occupational safety and elderly health. Education: Ph.D., Biomedical Engineering, Virginia Commonwealth University, 2001 M.S., Bioengineering, Texas A&M University, 1996 B.S., Bioengineering, Texas A&M University, 1994 Research Interests: Obesity’s impact on mobility, aging-related biomechanics, workplace ergonomics, and biomechanical interventions for fall prevention. His work combines experimental and computational methods to develop preventive measures like exercise interventions and ergonomic designs. Professional Roles: Editor-in-Chief, Journal of Applied Biomechanics (2017–present) Formerly Editor, Medicine and Science in Sports and Exercise (2013–2017) Secretary, American Society of Biomechanics (2009–2012) Awards: Kevin P. Granata Faculty Fellow (2013–2014) Virginia Tech College of Engineering Certificate of Teaching Excellence (2014) Finalist for International Society of Biomechanics Clinical Award (2005) Advising & Grants: Advised over 20 doctoral and undergraduate students since 2007. His grants support studies on obesity-related falls, exoskeleton design, and biomechanical modeling. Collaborates with the Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences. Labs/Teams: Director of the Madigan Biomechanics Group and core member of the Occupational Ergonomics and Biomechanics Laboratories. Active in developing smart prosthetics and wearable technologies for injury prevention.

• Nonlinear Dynamics
• Planetary Dynamics
• Solar Physics
• +۶ مورد دیگر

Norm Murray is a Professor at the Canadian Institute for Theoretical Astrophysics (CITA) within the University of Toronto . With a Ph.D. from UC Berkeley (1986), his research spans nonlinear dynamics , planetary formation , solar system evolution , and active galactic nuclei . His work combines theoretical physics with observational data from radio telescopes, X-ray satellites, and cosmological simulations. Recent research focuses on galaxy formation (via FIRE simulations), dark matter interactions in dwarf galaxies, and AGN disk dynamics . He employs machine learning for planetary collision modeling and investigates the interplay of magnetohydrodynamics and radiative transfer in quasar environments. Publications highlight his expertise in computational astrophysics, spanning topics from cosmic molecular gas mapping to the stability of exoplanetary systems.

Ashley Willisمشاهده پروفایل

مدرس ارشد

• Fluid Dynamics
• Turbulence
• Astrophysical Fluid Dynamics
• +۳ مورد دیگر

Dr. Ashley Willis is a Senior Lecturer in Fluid Dynamics at the University of Sheffield's School of Mathematical and Physical Sciences. He holds roles including Admissions Head and Programme Leader for Study Abroad. His research focuses on fluid dynamics, turbulence, astrophysical flows, and magnetohydrodynamics, with applications in clean energy and planetary systems. Willis completed his Ph.D. in Applied Mathematics at Newcastle University (2002) and has held postdoctoral positions at the University of Bristol, Leeds, and a Marie Curie Fellowship at Ecole Polytechnique, Paris. He leads the Fluid Dynamics Group and is part of the cross-faculty Sheffield Fluid Mechanics Group. His research interests include transition to turbulence, nonlinear dynamics, and magnetic field generation in planetary interiors. He supervises PhD students like Shijun Chu and collaborates with industry on projects like turbulence suppression in pipe flows. Key contributions include the open-source Openpipeflow simulation code and studies on dynamo action in geophysical flows. Publications highlight work on turbulent transition mechanisms, optimal flow configurations, and dynamo theory. His teaching includes courses on mechanics, fluid dynamics, and mathematical modeling of natural systems.

Toby Daviesمشاهده پروفایل

دانشیار

• Crime Science
• Urban Analytics
• Spatial Analysis
• +۶ مورد دیگر

Toby Davies is an Associate Professor in Criminal Justice Data Analytics at the University of Leeds, School of Law. His work focuses on quantitative criminology, spatial analysis, and computational methods to inform crime prevention. He holds a Mathematics degree from the University of Oxford (2008) and a PhD from University College London (UCL), followed by postdoctoral research on the EPSRC-funded Crime, Policing and Citizenship project. Before joining Leeds in 2023, he was at UCL’s Department of Security & Crime Science. His research spans interdisciplinary topics including urban form and crime, crime modeling, social networks, and cybercrime. He has collaborated with police agencies (West Yorkshire, Thames Valley, West Midlands Police) and governmental bodies (UK Home Office, London Mayor’s Office). A strong advocate for Open Science, he co-founded JDI Open to promote open practices in crime science. His recent work emphasizes policy interventions like phasing out pointed kitchen knives to reduce knife crime, leveraging data-driven approaches. He publishes widely in criminology, physics, network science, and general science journals, and has guest-edited special issues. His applied research aims to bridge theory and practice, developing tools deployed operationally in policing. Current interests include financial crime dynamics, social contagion of crime, and street network configurations’ impact on crime patterns.

Monika Nedaمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulence
• Numerical Methods for Partial Differential Equations
• +۴ مورد دیگر

Monika Neda is a Professor in the Department of Mathematical Sciences and Associate Dean for Research at the University of Nevada, Las Vegas (UNLV). She holds a Ph.D. in Mathematics from the University of Pittsburgh (2007) and a B.S. in Mechanical Engineering from the Technical Faculty Mihajlo Pupin in Serbia (2001). Her research focuses on theoretical and computational fluid dynamics, turbulence modeling, and numerical methods for partial differential equations, with particular emphasis on Navier-Stokes equations and sensitivity analysis. Her work explores model derivation, numerical stability, finite element error estimates, and computational simulations of fluid flow problems. She also investigates educational strategies to improve STEM students' foundational math skills through game-based and activity-based instruction. Neda is affiliated with UNLV's Center for Applied Math & Statistics and has contributed to interdisciplinary projects involving engineering education and photovoltaic panel efficiency. Recent research trends highlight advancements in stabilization techniques for fluid models (e.g., time relaxation, deconvolution operators) and innovative pedagogical approaches leveraging technology platforms like Canvas. Her publications span computational mathematics, turbulence closure models, and education-focused interventions. Neda's academic leadership includes roles in curriculum development and institutional research, reflecting her dual commitment to scholarship and academic administration.

Alexander Rodríguezمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Time Series Analysis
• +۷ مورد دیگر

Alexander Rodríguez is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan. His research focuses on advancing AI methods for modeling complex spatiotemporal dynamics, particularly in applications related to population health and community resilience. He specializes in machine learning, time series analysis, uncertainty quantification, and multi-agent systems, with an emphasis on scientific modeling and data-driven decision-making. Recent contributions include keynote talks at AAMAS 2025 (Autonomous Agents for Social Good workshop), presentations at the US National Academies Symposium, and invited talks at AAAI 2025 on topics like knowledge-guided machine learning and public health prediction. He co-organizes AAMAS 2025 as sponsorship co-chair and leads initiatives in AI for science and epidemic forecasting. His publications emphasize neural networks for time series forecasting, biomedical foundation models, and epidemic surveillance systems. Notable work includes 'Neural Conformal Control for Time Series Forecasting' (AAAI 2025) and 'Deepcovid: An operational deep learning-driven framework for explainable real-time forecasting' (2021). No scientific awards explicitly listed in available texts. His research group actively collaborates on grants related to AI applications in public health and infrastructure resilience, with a focus on data-centric methodologies and multi-agent systems.