جست‌وجوی استاد و پژوهشگر

Andrew Whittleمشاهده پروفایل

استاد

• Geotechnical Engineering
• Constitutive Models for Geomaterials
• Ground Improvement Techniques
• +۵ مورد دیگر

Andrew Whittle is the Edmund K. Turner Professor at MIT's Department of Civil and Environmental Engineering (CEE), part of the School of Engineering. His research focuses on geotechnical engineering, constitutive modeling of geomaterials, ground improvement techniques, and environmental sensing in water distribution systems. He has pioneered advancements in clay mechanics, bridge abutment analysis, and infrastructure resilience against climate change. Whittle teaches courses such as 1.031 Introduction to Geotechnical Engineering and 1.364 Advanced Geotechnical Engineering, emphasizing theoretical soil mechanics and practical applications. His awards include National Academy of Engineering membership (2010), multiple ASCE accolades (e.g., Middlebrooks Award), and early-career distinctions like the Skempton Prize. His research group (AJW-Group) develops innovative models for soil behavior, borehole heat exchangers, and biofilm dynamics in water networks. Recent work addresses climate adaptation for transit infrastructure, shale softening mechanisms, and computational frameworks linking nano- to macro-scale geomechanics. Education : B.Sc., Imperial College of Science and Technology (1981) Sc.D., MIT (1987) Labs/Teams : Whittle Group (AJW-Group) focusing on geotechnical and environmental systems Grants : J-WAFS Solutions Grants (2023) for water treatment innovations Publications span 40 years, with recent emphases on clay nano-aggregate mechanics, bridge seismic resilience, and microbial biofilms in water infrastructure. His work bridges fundamental geomechanics with real-world applications like tunneling-induced ground movements and climate-resilient urban systems.

Tim Valicentiمشاهده پروفایل

مدرس

• Generative AI
• Machine Learning
• Operations Research

Tim Valicenti is a Lecturer at MIT Sloan School of Management specializing in Generative AI, Machine Learning, and Operations Research. He develops courses focused on industrial applications of generative AI, including co-creating 'Generative AI for Managers' and 'Generative AI Lab'. His research integrates systems engineering and data science to address sustainability challenges in energy and manufacturing sectors. Prior to academia, he led data science teams at McKinsey & Company and QuantumBlack, developing machine learning solutions for operational optimization. Valicenti holds a BS in Mechanical Engineering from Brown University, an MBA, and a Master of Analytics from MIT. His educational background includes advanced mathematics research in stochastic processes and differential equations.

Sameer Bajikarمشاهده پروفایل

استادیار

• Bioinformatics and Genomics
• Cell and Developmental Biology
• Computational Biology
• +۳ مورد دیگر

Sameer Bajikar, Ph.D., is an Assistant Professor in the Departments of Cell Biology and Biomedical Engineering at the University of Virginia. His research focuses on understanding neurodevelopmental disorders such as autism spectrum disorders and Rett syndrome using multidisciplinary approaches combining human stem cells, genetically engineered mouse models, and computational systems biology. He aims to identify key biological pathways for therapeutic intervention. Education: B.S. and Ph.D. in Biomedical Engineering, University of Virginia Postdoctoral Fellowship in Molecular and Human Genetics at Baylor College of Medicine His research interests include bioinformatics, computational biology, stem cell biology, and translational science. Current projects investigate the GDF11-SMAD3 signaling pathway in Rett syndrome, supported by an Independence Award from the International Rett Syndrome Foundation. Key Achievements: Recipient of Zoghbi Scholar Award and NRSA F32 fellowship Developed novel models for MECP2 duplication syndrome and Rett syndrome He leads the Bajikar Lab, recruiting students to explore neurodevelopmental disorder mechanisms. Grants include studies on GDF11 regulation and therapeutic approaches using antisense oligonucleotides.

• Numerical Analysis
• Computational Fluid Dynamics
• Partial Differential Equations
• +۳ مورد دیگر

Aleksey Sikstel is a part-time faculty member at RWTH Aachen University , holding the title of Vertr.-Prof. (Part-Time Lecturer). His research focuses on advanced numerical methods for hyperbolic conservation laws, including Discontinuous Galerkin schemes, stochastic Galerkin formulations, and error estimation techniques. Key research areas: Numerical Analysis, Computational Fluid Dynamics, and Applied Mathematics Primary affiliation: Faculty 10 Institutions at RWTH Aachen University Contact: aleksey.sikstel@rwth-aachen.de His work emphasizes coupling hyperbolic systems, entropy-stable discretizations, and adaptive grid methods. Recent publications analyze Baer-Nunziato-type models, multiresolution strategies, and boundary control for hyperbolic equations.

• Reduced-Order Modelling
• Machine Learning for Scientific Applications
• Urban and Environmental Flows
• +۶ مورد دیگر

Claire Heaney is an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow affiliated with both Imperial-X and the Department of Earth Science & Engineering within the Faculty of Engineering at Imperial College London . Her research focuses on applying advanced machine learning techniques to complex scientific problems in environmental and computational physics. Research Interests: Reduced-Order Modelling Machine Learning for Scientific Applications Urban and Environmental Flows Scientific Contributions: Claire's work demonstrates expertise in: Neural network-based solutions for partial differential equations Hybrid physics-ML models for environmental systems Uncertainty quantification frameworks Mesh adaptation techniques for improved computational efficiency Her research spans multiple disciplines including applied mathematics, computational fluid dynamics, and nuclear engineering. Scientific Awards: Co-author of one of Wiley's top cited articles (Jan 2021 - Dec 2022) Recipient of the prestigious Schmidt AI in Science Postdoctoral Fellowship Collaborative Research: Claire works at the intersection of earth science and engineering, collaborating across disciplines to develop AI solutions for environmental challenges. Her current research investigates the use of green infrastructure to mitigate air pollution in urban environments.

Eric Darveمشاهده پروفایل

استاد

• Machine Learning
• Numerical Linear Algebra
• High-Performance Computing
• +۵ مورد دیگر

Eric Darve is a Professor of Mechanical Engineering and Director of the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. His research focuses on machine learning for science/engineering, numerical linear algebra, high-performance computing, and GPU-accelerated algorithms. He holds a PhD in Applied Mathematics from Pierre et Marie Curie University (2009), and completed postdoctoral work at Stanford/NASA Ames before joining Stanford’s faculty in 2001. Education: PhD, Applied Mathematics, Paris VI University (1999) MS, Applied Mathematics, Paris IX University (1994) BS, Mathematics & Physics, Paris VI University (1993) Research interests include: Machine learning for scientific applications Surrogate/Reduced Order Modeling Anomaly detection in engineering systems Parallel computing and numerical algorithms Physics-informed learning machines Awards include: H. Julian Allen Award (NASA, 2010) Habilitation à Diriger des Recherches (France, 2007) Leslie Fox Prize in Numerical Analysis (2001) James H. Clark Faculty Scholar (2001) Led development of scalable linear solvers and hybrid physics-data approaches. Active in computational engineering education and interdisciplinary research initiatives through ICME.

Lexing Yingمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Science
• Quantum Computing
• +۷ مورد دیگر

Lexing Ying is a Professor of Mathematics at Stanford University. His research focuses on applied mathematics, computational science, and interdisciplinary areas such as quantum computing, machine learning, and numerical analysis. He has made significant contributions to high-dimensional problems, inverse problems, and the development of efficient algorithms for complex systems. His work spans theoretical advancements and practical applications, including quantum signal processing, tensor-based numerical methods, and probabilistic modeling. Key themes in his research include the analysis of diffusion models, stochastic systems, and optimization algorithms. Recent publications highlight innovations in solving high-dimensional PDEs, improving sampling techniques, and advancing quantum algorithms. Notably, Ying has developed methods for operator learning, functional hierarchical tensors, and robust phase estimation. His research often bridges mathematical theory and computational practice, addressing challenges in areas like statistical inference and numerical simulation.

Qifei Niuمشاهده پروفایل

استادیار

• Near-surface geophysics
• Hydrogeophysics
• Soil and rock physics
• +۴ مورد دیگر

Qifei Niu is an Assistant Professor in the Department of Geosciences at Boise State University. He holds a Ph.D. in Civil Engineering from the Hong Kong University of Science and Technology (2014), an M.S. in Geotechnical Engineering from the China Institute of Water Resources and Hydropower Research (2010), and a B.S. in Irrigation and Drainage from Wuhan University (2007). His research focuses on near-surface geophysics, hydrogeophysics, and critical zone hydrology, integrating geophysical methods, field experiments, and computational modeling to study subsurface processes. He leads the Multiscale Hydrogeophysics Lab, training the next generation of geoscientists at the intersection of hydrology, geology, and geophysics. Teaching responsibilities include courses on Applied and Environmental Geophysics, Soil and Rock Physics, and Computational Geoscience. He actively seeks M.S. and Ph.D. students for Fall 2023 admission, emphasizing hydrology and geophysics. Contact via email (qifeiniu@boisestate.edu) for inquiries. Research highlights include studies on subsurface water storage dynamics, geophysical-hydrologic modeling integration, and pore-scale electrochemical processes in geomaterials. His work bridges fundamental science with practical applications in water resource management and environmental monitoring.

• Numerical Analysis
• Optimal Control
• Multi-Agent Systems
• +۵ مورد دیگر

Giacomo Albi is an Associate Professor in Numerical Analysis at the Department of Computer Science, University of Verona. He holds a PhD in Mathematics and Computer Science from the University of Ferrara (2014) and has conducted research at TU München under an ERC project on optimal control. His expertise spans numerical methods for kinetic equations, optimal control of high-dimensional systems, and mathematical modeling of multi-agent systems in socio-economic and biological contexts. Teaching includes modules like Numerical Analysis I, Logistic Optimization, and Foundation of Data Analysis. He leads research in contemporary applied mathematics, focusing on multi-scale particle systems and PDEs. Projects include data-driven control strategies, efficient numerical schemes for PDEs, and computational social dynamics. Active in the INdAM Research Unit, he also contributes to third mission activities and oversees internationalization efforts in Erasmus programs. Research groups involve advanced methods for transport phenomena, high-dimensional control, and computational social dynamics. Key interests include plasma confinement control, opinion-epidemic modeling, and evacuation strategies using multi-scale models.

• Reliability Engineering
• Optimization
• Machine Learning
• +۳ مورد دیگر

Jianchao Zeng is affiliated with North University of China's Department of Computer Science and Control Engineering, and also associated with Taiyuan University of Science and Technology's Complex System and Computational Intelligence Laboratory. His research focuses on reliability engineering, optimization, machine learning, and image processing. He has collaborated extensively on projects involving maintenance modeling, evolutionary algorithms, and medical imaging analysis. Affiliations: North University of China, Taiyuan University of Science and Technology Key research interests include reliability engineering (predictive maintenance, degradation modeling), optimization techniques (surrogate-assisted algorithms, swarm intelligence), and medical image processing (radiomics, segmentation). His recent work emphasizes interdisciplinary applications in reliability systems, remote sensing, and healthcare. Publications highlight contributions to maintenance optimization frameworks, deep learning models for image analysis, and hybrid algorithms combining evolutionary computation with machine learning. Notably, his work on remaining useful life prediction and radiomics-enhanced segmentation addresses critical challenges in industrial and medical domains.

Lihong Zhaoمشاهده پروفایل

استادیار

• Mathematical Biology
• Mathematical Epidemiology
• Dynamical Systems
• +۳ مورد دیگر

Lihong Zhao is an Assistant Professor in the Department of Mathematics at Kennesaw State University (2024-present). Previously held positions include Visiting Assistant Professor at Virginia Tech (2023-2024) and Postdoctoral Fellowships at UC Merced (2020-2023) and University of Idaho (2018-2020). Holds a Ph.D. in Mathematics from University of Louisiana at Lafayette (2012-2017) and B.S. in Mathematics from Beijing University of Posts and Telecommunications (2007-2011). Research focuses on mathematical modeling of biological systems with applications to epidemiology, immunology, and public health. Expertise includes dynamical systems analysis, stochastic modeling, and multi-scale modeling approaches. Notable projects involve modeling malaria vaccination strategies, Legionnaires' disease dynamics, and microbiome interactions with pathogens. Received awards including AAI Intersect Fellowship (2022), NSF-Simons Multiscale Cell Fate Research grant (2022), and MAA Project NExT Fellowship (2021). Active in teaching roles including College Algebra, Differential Equations, and Mathematical Modeling courses at multiple institutions. Involvement in collaborative initiatives includes the NSF-Simons Center for Multiscale Cell Fate Research and the Health Sciences Research Institute. Engages in interdisciplinary work combining mathematical approaches with experimental biology to address real-world health challenges.

Courtney L. Davisمشاهده پروفایل

استاد

• Mathematical Biology
• Immune System Modeling
• Mathematical Ecology
• +۳ مورد دیگر

Courtney L. Davis is a Professor of Mathematics at Pepperdine University's Natural Science Division within Seaver College. She specializes in mathematical biology, focusing on immune system modeling and ecological systems. Dr. Davis holds a PhD in Mathematics from the University of Utah (2010) with postdoctoral training at the University of Maryland. Her research bridges immunology and ecology, including modeling Shigella and COVID-19 immune responses and invasive species management in Santa Monica Mountains. She directs the Summer Undergraduate Research in Biology program and founded Pepperdine's Science Modeling Club for hands-on learning. Education highlights include a BA/BS in Mathematics & Biology (Trinity University, 2003), MS Mathematics (University of Utah, 2005), and PhD with postdoctoral work at the University of Maryland's Center for Scientific Computation (2010–2012). Her work emphasizes interdisciplinary collaboration, combining differential equations, computational models, and field biology. Research trends show strong focus on disease dynamics (infectious diseases and invasive species) using mathematical frameworks. Publications span immunology (Shigella vaccine design, cytokine storm modeling) and ecology (tick life cycles, newt population viability). She has pioneered computational approaches for predicting disease outcomes and ecological interventions. Active in education and outreach through undergraduate research programs, Dr. Davis' work integrates theoretical models with real-world biological problems. Her labs focus on developing predictive tools for public health and conservation challenges.

Jean-Michel DISCHLERمشاهده پروفایل

استاد

• Computer Graphics
• Texture Synthesis
• Procedural Modeling
• +۴ مورد دیگر

Jean-Michel Dischler is a full Professor of Computer Science at the University of Strasbourg, affiliated with the ICUBE Lab (UMR CNRS-UDS 7357). He leads rendering and visualization research activities within the Department of Computer Science at the Faculty for Mathematics and Computer Science. His academic roles include former leadership of the Computer Science department, vice-director of LSIIT Lab (until 2008), and joint-director of the 3D Computer Graphics Group in ICUBE (until 2018). He has organized major conferences like Eurographics 2014 and EGSR/HPG 2019, and chaired the Eurographics association since 2021. Research focuses on texture synthesis, procedural modeling, 3D acquisition, and high-performance graphics. Key projects include the ASTex C++ library for texture generation. His work bridges procedural and data-driven methods, addressing challenges like non-stationary textures and real-time rendering of complex materials. He has contributed to Eurographics committees, journals, and professional governance, including a decade on the EG professional board. Scientific Awards: Fellow of the Eurographics Association Grants & Projects: INRIA CALVI project (2010), ASTex library development Labs/Teams: ICUBE Lab (3D Graphics Group), former LSIIT Lab Future Work: Continued innovation in procedural texture synthesis and real-time graphics

Kathrin Hellmuthمشاهده پروفایل

پژوهشگر ارشد

• Inverse Problems
• Partial Differential Equations (PDEs)
• Mathematical Biology
• +۳ مورد دیگر

Kathrin Hellmuth is a PostDoc at the California Institute of Technology (Caltech), affiliated with the Computing and Mathematical Sciences (CMS) division under the supervision of Franca Hoffmann. She holds a PhD in Mathematics from Julius-Maximilians-Universität Würzburg, Germany, where she was supervised by Prof. Dr. Christian Klingenberg. Her research focuses on inverse problems in partial differential equations (PDEs), mathematical modeling of chemotaxis, and numerical methods integrating machine learning. She also explores applications in financial mathematics, such as solving the Black-Scholes equation with uncertain volatility. Education highlights include a PhD (April 2025) with a thesis on qualitative experimental design for PDE parameter identification, an M.Sc. in Mathematics (April 2020) from University of Würzburg (with an Erasmus+ semester at the University of Padua, Italy), and a B.Sc. in Mathematics (October 2017). Her work bridges theoretical analysis and applied mathematics, with contributions to kinetic chemotaxis models, Bayesian inverse problems, and multiscale convergence techniques. She has published in journals like SIAM Journal on Applied Mathematics and has participated in international conferences such as HYP 2022. Her teaching experience includes leading exercise classes in partial differential equations, linear algebra, and mathematics for machine learning at the University of Würzburg.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۷ مورد دیگر

Prof. Dr. Werner Frank holds the Chair of Mathematics IX (Inverse Problems) at the University of Würzburg. He is a leading researcher in statistical inverse problems, focusing on regularization theory, uncertainty quantification, and applications in biophysics, particularly fluorescence microscopy. His work bridges statistics and inverse problem methodologies with non-Gaussian noise analysis. Education: PhD in Mathematics from Georg-August-Universität Göttingen (2012) Postdoctoral Research (2012–2014) at the Institute for Numerical and Applied Mathematics Group Leader, Statistical Inverse Problems in Biophysics, at the Max Planck Institute for Biophysical Chemistry (2014–2020) Research Interests: Statistical inverse problem theory, multiscale scanning, minimax testing, super-resolution microscopy, and imaging techniques with statistical guarantees. His recent work emphasizes applications in biophysics and nanoscale imaging. Publications: Over 30 peer-reviewed articles in top journals like Inverse Problems , Journal of the Royal Statistical Society , and SIAM Journal on Scientific Computing . Key topics include Poisson data regularization, ill-posed problem analysis, and variational methods for image denoising. Affiliations: Active in professional societies such as the Society for Inverse Problems (GIP), IFIP Working Group 7.4, and the International Photonic Imaging Association (IPIA). Regular speaker at international conferences like Inverse Problems: Modeling and Simulation (IPMS) and Applied Inverse Problems (AIP). Future Work: Expanding research on adaptive regularization methods, spatial statistical resolution in microscopy, and interdisciplinary collaborations in imaging science.