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Wei-Tang ChangView profile

Assistant Professor

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +4 more

Dr. Wei-Tang Chang is an Assistant Professor in the Department of Radiology at the University of North Carolina School of Medicine. His research focuses on advancing ultrahigh-resolution functional and diffusion MRI techniques, with emphasis on improving spatial and temporal resolution while reducing scan times. Key projects include submillimeter isotropic-resolution fMRI for hippocampal subfield analysis (funded by NIH R21), novel dMRI approaches to overcome resolution limits, and clinical translation of robust imaging methods resistant to motion/noise artifacts. Dr. Chang holds a PhD in Biomedical Engineering from National Taiwan University and completed postdoctoral training at the Martinos Center for Biomedical Imaging (MGH), Massachusetts General Hospital, and Singapore BioImaging Consortium (SBIC). His research innovations include SORDINO fMRI for awake rodent imaging, pPRISM diffusion MRI for submillimeter resolution, and ZTE pulse sequences for ultra-fast acquisitions. Awards include the 2011 OHBM Trainee Award for work on MEG source localization and fMRI temporal resolution breakthroughs. Current work bridges basic neuroimaging science with clinical applications, particularly in neurodegenerative disease biomarker development and rodent disease model studies. Education: PhD in Biomedical Engineering, National Taiwan University Postdoctoral Fellowships: Martinos Center for Biomedical Imaging (MGH) Singapore BioImaging Consortium (SBIC) Key Technologies Developed: ZTE pulse sequences (25 ms temporal resolution fMRI) pPRISM diffusion MRI (navigator-free submillimeter imaging) Draining-vein suppression layer-dependent fMRI Awards & Funding: NIH R21 Grant (2019) for hippocampal subfield fMRI OHBM Trainee Award (2011) Dr. Chang's translational focus involves adapting laboratory innovations for clinical use, with particular interest in Alzheimer's disease biomarkers through hippocampal network analysis and Huntington's disease models using rodent functional connectivity studies. His lab actively develops open-source MRI reconstruction algorithms and collaborates internationally on multi-center neuroimaging projects.

• Algorithms
• Complexity Theory
• Probability
• +5 more

James R. Lee is a Professor in the Department of Computer Science at the University of Washington, with a focus on algorithms, complexity, and the theory of computation. He is affiliated with the UW Theory Group and currently on leave at Microsoft Research, which may delay responses to UW emails. Research Interests: Algorithms, complexity, geometry/discrete-continuous interfaces, probability, stochastic processes, metric embeddings, spectral graph theory, convex optimization. Recent Work Trends: Sparsification of generalized linear models and norms, spectral hypergraph methods, entropic regularization for metrical task systems, and analysis of scaling exponents in random graphs. His papers address sparsifier existence, lower bounds for SDP/LP relaxations, and geometric random walk properties. Scientific Awards: Best Paper Award at STOC 2015. Students: Farzam Ebrahimnejad, Ewin Tang, Yichuan Deng (co-advised with Shayan Oveis-Gharan, Shirshendu Ganguly, and others). Email: jrl@cs.washington.edu

Michael HintermüllerView profile

Professor

• Applied Mathematics
• Optimization and Control
• Partial Differential Equations
• +4 more

Prof. Michael Hintermüller is the Director of the Weierstrass Institute (WIAS) and holds a professorship in Applied Mathematics at Humboldt-Universität zu Berlin. He serves as Founding Coordinator of BR50, Spokesperson of the Mathematical Research Data Initiative (MaRDI), and Board Member of the MATH+ Cluster of Excellence. His research focuses on nonsmooth optimization, PDE-constrained control, mathematical image processing, and quasi-variational inequalities. Leadership Roles: Director of WIAS; Founding Coordinator of BR50; Spokesperson of MaRDI; Board Member of MATH+ Key Research Areas: Mathematical image processing, optimization under uncertainty, PDE-constrained optimization, shape/topology optimization, learning-informed constraints Recent Applications: Image deblurring/denoising/demodulation, energy network modeling, gas dynamics on pipeline networks, thermoforming simulations, strained photonic device design. His work combines analytical rigor with numerical methods for inverse problems, including adaptive regularization and physics-informed neural networks. Scientific Leadership: Active in mathematical modeling for biomedical imaging (e.g., quantitative MRI) and industrial applications (e.g., semiconductor design, gas flow optimization). Develops novel algorithms for nonsmooth PDE systems and contributes to the theoretical foundations of quasi-variational inequalities and generalized Nash equilibrium problems.

Guillaume PuelView profile

Professor

• Mechanical Engineering
• Materials Science
• Inverse Problems
• +6 more

Guillaume Puel is a Professor at Universite Paris-Saclay and a researcher at the Laboratory Paris-Saclay Mechanics (LMPS). His work focuses on inverse problems, parameter identification, and homogenization techniques in structural dynamics and mechanical engineering. His research bridges computational modeling with experimental validation, particularly in railway noise, vibro-acoustic coupling, and fatigue simulation of materials. He employs multi-scale methods, adaptive meshing, and regularization to solve transient nonlinear models with contact phenomena. Recent publications highlight trends in medium-frequency computing platforms (e.g., pyTVRC), time homogenization for fatigue analysis, and Trefftz methods for railway noise prediction. His collaborations include Denis Aubry, Andrea Barbarulo, and Nhat Quang Ta. Labs and teams: LMPS laboratory at Universite Paris-Saclay, where he contributes to advanced mechanical modeling and railway engineering applications.

B. V. Rathish KumarView profile

Professor

• Scientific Computing
• Computational Fluid Dynamics
• Numerical Analysis
• +8 more

B. V. Rathish Kumar is a Professor at the Department of Mathematics and Statistics , Indian Institute of Technology Kanpur, with a PhD from SSSIHL, Prasanthinilayam. His research spans Numerical Analysis , Computational Fluid Dynamics , Finite Element Methods , and Biomedical Image Processing . Education: PhD in Applied Mathematics (SSSIHL, Prasanthinilayam) His research interests include Wavelet Methods for PDEs , Cardiac Electrophysiology Modeling , Convection in Porous Media , and AI/ML for Differential Equations . He has pioneered courses like Finite Element Error Estimation and AI/ML Methods for PDEs . His recent publications focus on convection dynamics , image processing , and singularly perturbed equations , contributing to fields like Biomedical Engineering and Thermal Systems . Scientific Awards: Fellow of Indian Association of Mathematical Modelling and Simulation (2018) Fellow of National Academy of Sciences (2009) Erasmus Mundus Fellowship (2004) University Gold Medal (1987)

• Civil Engineering
• Computational Fluid Dynamics
• Porous Media Flow
• +3 more

Dr. Luka Malenica is a dedicated researcher at ETH Zurich's Professorship for Durability of Engineering Materials, focusing on computational modeling of material degradation processes in civil infrastructure. His work bridges theoretical numerical methods with practical engineering applications through direct involvement in the department's research operations. His research program centers on multiphase flow in porous media , corrosion mechanisms in reinforced concrete , and advanced numerical techniques . He employs pore-scale direct numerical simulations to investigate steel-concrete interface phenomena, developing predictive models for infrastructure durability. His methodological innovations include control volume isogeometric analysis, adaptive multiresolution modeling, and deep learning applications for adaptive meshing—addressing challenges in heterogeneous media flow and transport phenomena. Analysis of his 15 most recent publications reveals a consistent research trajectory since 2015, with accelerating output in corrosion science (5 papers in 2024-2025) and computational methods (7 papers 2019-2022). Key interdisciplinary connections span civil engineering (corrosion in reinforced concrete), chemical engineering (bubble column reactors), and hydrology (karst aquifer modeling), unified by his expertise in numerical simulation of transport phenomena. Recent work demonstrates increasing sophistication in modeling macrovoid formation at steel-concrete interfaces and capillary-driven multiphase systems.

• Probability Theory
• Stochastic Analysis
• Numerical Analysis
• +7 more

Mateusz Majka is an Assistant Professor in Stochastics at the School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh. His research focuses on probability theory, stochastic analysis, numerical analysis, optimization, optimal transport, computational statistics, and machine learning. He holds a PhD from the University of Bonn (2017) and has held postdoctoral positions at the University of Warwick and King's College London. Education: PhD in Applied Mathematics, University of Bonn (2017) Research Fellow, University of Warwick (2018–2020) Research Associate, King's College London (2017–2018) Research Interests: Stochastic differential equations Mathematical foundations of machine learning (mean-field optimization, optimal transport, stochastic gradient algorithms) Lévy processes Ergodicity of Markov processes Monte Carlo methods (MCMC, Multi-Level Monte Carlo) Recent Research Trends: His work emphasizes algorithmic development for stochastic systems, including mean-field games, Wasserstein geometry, and convergence analysis of numerical schemes. Notable contributions span Lévy processes, Euler schemes, and coupling techniques for Markov chains. Awards: None explicitly listed. Advising & Grants: Supervises PhD students Linshan Liu (since 2021) and Razvan-Andrei Lascu (since 2022). Active in securing research funding through collaborative projects. Labs/Teams: Engaged in cross-disciplinary collaborations in computational statistics and stochastic analysis at Heriot-Watt University.

Nicholas PerezView profile

Associate Professor

• Tectonics
• Basin Dynamics
• Fold-thrust Belts
• +1 more

Nicholas Perez is an Associate Professor at Texas A&M University, affiliated with the Basin Tectonic Group. His research focuses on tectonic processes such as basin dynamics, fold-thrust belts, and continental rifting. He investigates these phenomena through field and lab-based techniques, working in regions like the central Andes, Cascade Range, and Atlas Mountains. Perez holds a Ph.D. in Geological Sciences from the University of Texas at Austin (2015) and a B.S. in Geological Sciences (Special Honors) from the same university (2009). He has received notable awards including the NSF CAREER Award (2019) and the Dean's Distinguished Achievement Faculty Teaching Award (2020). His research emphasizes integrating sedimentology, structural geology, and thermochronology to understand mountain-building processes and crustal deformation. Key projects include studying the impact of flat-slab subduction on Andean tectonics and the role of structural inheritance in guiding deformation. Perez has advised multiple graduate students, including Pesek M. E., Gao Z., and Findlay C. P., and has led NSF-funded research initiatives. His work has been presented at major conferences like AGU and GSA, and he contributes to educational initiatives through the Center for Teaching Excellence. Perez is actively involved in the Basin Tectonic Group, which explores tectonic problems through multi-disciplinary approaches. His publications span over 30 peer-reviewed articles, focusing on topics like exhumation mechanisms, basin evolution, and Andean geodynamic processes. He has also contributed to invited presentations on cordilleran margin development and structural inheritance effects on deformation patterns.

Shao Jian-FuView profile

Professor

• Mechanics of geomaterials and structures
• Sequestration of CO2
• Thermo-hydromechanical and chemical coupling processes
• +5 more

Shao Jian-Fu is an Exceptional class Professor of Civil Engineering at the University of Lille, France, a position he has held since 2010. He also serves as a Distinguished Professor of Geomechanics at Hohai University, China on a part-time basis. His academic career at the University of Lille has been progressive, having previously held positions as First class Professor (2001-2010), Second class Professor (1994-2001), and Associate Professor (1989-1994) of civil engineering. Professor Shao's educational background includes: 1992: Habilitation to supervise research (HDR) in Civil and Mechanical Engineering, University of Lille, France 1987: PhD in Civil Engineering, University of Lille, France 1984: Master in Solid Mechanics, University of Lille, France 1982: Bachelor in Hydraulic Engineering, China Agricultural University, China Professor Shao's research focuses on the mechanics of geomaterials and structures, with particular expertise in sequestration of CO2, thermo-hydromechanical and chemical coupling processes, and geological disposal of radioactive waste. His work spans multiple scales, from atomistic studies to field applications, and employs both experimental and computational approaches. He has made significant contributions to multi-scale modeling and the development of continuous and discontinuous numerical methods for geomechanical problems. His recent publications demonstrate a strong focus on phase-field modeling of cracking processes, constitutive modeling of heterogeneous and anisotropic materials, and the application of advanced computational techniques to geomechanical problems. His research has important applications in areas such as rainfall-induced landslides, radioactive waste disposal systems, and understanding the mechanical behavior of clayey rocks under various environmental conditions. Professor Shao has received numerous prestigious awards, including: 2023: Senior member of the Institut Universitaire de France (IUF) 2022: Chandrakant S. Desai Medal, IACMAG 2022: Maurice A. Biot Medal, ASCE 2021: Member of the European Academy of Sciences and Arts 2020: Member of the European Academy of Sciences 2019: Prize of Natural Science (2nd rank), Chinese Ministry of Education 2013: Prize of Science and Technology (2nd rank), Jiangsu Province, China 2012: Prize of Science and Technology (1st rank), The Chinese Association of Hydraulic Power 2011: Excellent contributions Award, IACMAG As a principal investigator, Professor Shao has led numerous significant research projects, including EU projects MAGIC and CEBAMA related to radioactive waste disposal, China State Key R&D projects on hydro-landslides, and various ANR and NSFC-funded projects. He has also served as an associate editor for several prestigious journals including the International Journal of Rock Mechanics and Mining Sciences and as a member of editorial boards for journals such as Computers and Geotechnics and International Journal of Plasticity. His extensive service includes reviewing for major funding agencies including the European Research Council, Swiss National Science Foundation, and Czech Science Foundation.

• Condensed Matter Physics
• Theoretical Physics
• Quantum Materials
• +7 more

Rafael Fernandes is a Professor in the Department of Physics at the University of Illinois Urbana-Champaign, where he joined in 2024 after serving as a Distinguished McKnight University Professor at the University of Minnesota (2023-2024). His academic journey includes positions as Professor (2021-2024), Associate Professor (2017-2021), and Assistant Professor (2012-2017) at the University of Minnesota, following postdoctoral appointments at Columbia University/Los Alamos National Lab (2011-2012) and Ames Laboratory/Iowa State University (2008-2011). His educational background includes: B.S. in Physics from the University of Campinas, Brazil (2003) M.Sc. in Physics from the University of Campinas, Brazil (2005) Ph.D. in Physics from the University of Campinas, Brazil (2008) Professor Fernandes is a leading condensed matter physics theorist renowned for his work on electronic nematicity and vestigial electronic order in quantum materials. His research focuses on establishing clear relationships between microscopic electron behavior and macroscopic material properties through theoretical modeling of quantum many-body systems. He investigates model quantum materials to develop frameworks applicable to broader problems in interacting quantum matter, with notable contributions to understanding nematicity in iron pnictides and its extension to vestigial phases in diverse systems. His current research interests span electronic nematicity, altermagnetism, intertwined electronic phases, unconventional charge-order in kagome materials, electron-phason coupling in moiré systems, and multi-component superconductivity. His publication record reveals a strategic evolution from foundational work on iron-based superconductors toward cutting-edge research on altermagnetism, moiré systems, and kagome metals. The articles demonstrate a consistent methodological approach combining analytical field-theory techniques with numerical quantum Monte Carlo simulations to address phase transitions, electronic order parameters, and competing electronic orders in quantum materials. His scientific achievements have been recognized with prestigious awards: Mercator Fellow, DFG, Germany (2020-present) Fellow, American Physical Society (2017) Cottrell Scholar Award (2016-2019) DOE Early Career Award (2014-2019) Professor Fernandes teaches courses including PHYS 211 - University Physics: Mechanics and maintains extensive collaborations with experimental groups utilizing scanning tunneling microscopy, angle-resolved photoemission spectroscopy, nuclear magnetic resonance, and various scattering techniques. His theoretical work bridges fundamental quantum mechanics with practical material properties, incorporating realistic crystal elastic properties into models of quantum materials. His research group at UIUC investigates the fundamental physics of quantum materials through theoretical frameworks that connect microscopic quantum behavior to observable macroscopic phenomena, with particular emphasis on developing predictive models for novel electronic phases and phase transitions.

Zhou FanView profile

Associate Professor

• Mathematical Statistics
• Probability Theory
• Computational Algorithms
• +6 more

Zhou Fan is an Associate Professor in the Department of Statistics and Data Science at Yale University, specializing in mathematical statistics, probability theory, and computational algorithms with applications in statistical genetics and computational biology. Education: Ph.D. in Statistics, Stanford University, 2018 His research spans Random matrices and free probability , Statistical physics and inference , High-dimensional statistics and machine learning , and Applications in genetics and computational biology . He develops theoretical frameworks for complex data analysis, focusing on inferential problems in scientific contexts through advanced computational methods. Recent publications demonstrate leadership in Approximate Message Passing algorithms, empirical Bayes methods, and group orbit estimation, with significant contributions to high-dimensional statistics and biological applications. His work bridges statistical theory with practical computational solutions for modern data challenges. As Co-Director of Graduate Studies, Professor Fan provides academic leadership for the department's graduate program while teaching advanced courses in high-dimensional probability, statistical theory, and random matrix applications.

Yu SaikiView profile

Associate Professor

• Thermal Engineering
• Fluid Engineering
• Combustion Science
• +4 more

Yu Saiki serves as Associate Professor in the Department of Mechanical Engineering at Nagoya Institute of Technology's Graduate School of Engineering. His research focuses on thermal/fluid engineering with specialization in combustion science, plasma diagnostics, and decarbonization technologies for industrial applications. Dr. Saiki earned his Master of Engineering (2006) and Doctor of Engineering (2009) from The University of Tokyo, following undergraduate studies in Mechanical Engineering at Nagoya University (graduated 2004). His academic journey reflects deep specialization in thermal fluid systems. His research interests span Thermal Engineering , Fluid Engineering , and Combustion Science with emphasis on radical surface reactions, microscale combustion, and decarbonization of industrial thermal processes. Current projects include ammonia/hydrogen combustion for metal processing and plasma-based surface modification techniques. Analysis of his recent publications reveals strong focus on radical-surface interactions (70% of works), decarbonization technologies (20%), and advanced diagnostics (10%), with increasing industry collaboration for practical applications. Combustion Society of Japan Encouragement Award (2019) Nagoya Institute of Technology Excellent Faculty Evaluation Award (2017) Combustion Society of Japan Paper Award (2016) Japan Society of Mechanical Engineers Encouragement Award (2014) Dr. Saiki leads significant research funding including NEDO's Green Innovation Fund Project (2023-2032) on thermal process decarbonization and multiple JSPS grants. He actively serves on committees for the Combustion Society of Japan (Colloquium Chair), Japan Society of Mechanical Engineers, and Japan Heat Transfer Society. His laboratory collaborates with DENSO Corporation on particle velocity measurements and jet flow control.

Hugues BersiniView profile

Professor

• Artificial Intelligence
• Complex Systems
• Neural Networks
• +13 more

Hugues Bersini is a Professor at Université Libre de Bruxelles (ULB) and Co-Director of the IRIDIA laboratory, the Artificial Intelligence research laboratory of ULB. His academic career spans over three decades, with significant contributions to the fields of artificial intelligence, complex systems, and biological networks. Bersini earned his MS degree in 1983 and his Ph.D. in engineering in 1989, both from Université Libre de Bruxelles. After working as a researcher with an EEC grant from the JRC-CEE in Ispra (1984-1987), he joined the IRIDIA laboratory at ULB, where he has remained throughout his career, eventually becoming a full professor. His research spans a diverse range of topics within artificial intelligence and complex systems. Bersini is particularly known for his work on modeling and control of complex systems, neural networks, fuzzy control, data mining, autonomous agents, and biological networks. He pioneered the exploitation of biological metaphors, especially from the immune system, for engineering and cognitive sciences applications. His research has evolved to include computational chemistry, immune engineering, cognitive sciences, bioinformatics, and object-oriented technology. In recent years, he has focused on business intelligence applications and public goods through the Brussels Institute FARI. Throughout his career, Bersini has published approximately 300 papers, demonstrating consistent productivity and evolving research interests. His early work focused on optimization algorithms and immune-inspired computing, which gradually expanded to include fuzzy and neuro control systems, biological networks, and more recently, applications to real-world problems through spin-off companies and the FARI institute. His publications show a clear trajectory from theoretical foundations to practical applications, with growing emphasis on interdisciplinary approaches that bridge computer science with biology, chemistry, and cognitive sciences. Bersini has been actively involved in the academic community, having co-organized major conferences including the Parallel Problem Solving from Nature (PPSN), European Conference on Artificial Life (ECAL), European Workshops on Reinforcement Learning (EWRL), and International Competitions on Evolutionary Optimization (ICEO). He also organized tributes to Francisco Varela and the International Conference on Artificial Immune Systems (ICARIS). As an educator, Bersini teaches artificial intelligence, object-oriented programming (C++, Java, .Net, Kotlin, UML, Django/Python), and design patterns to both university students at Solvay and Polytechnic Schools and for industry professionals. He has authored fourteen French books covering computer science fundamentals, complex systems, and the intersection of computer science with other fields. His books range from technical manuals to philosophical explorations of complex systems and emergence. Bersini has coordinated significant research projects including the FAMIMO LTR European Project on fuzzy control for multi-input multi-output processes and participated in ESPIRIT projects NEMORETS and METHODS. His work has led to practical applications through spin-off companies such as Cluepoints, Tevizz, and In Silico DB, and more recently through the Brussels Institute FARI which addresses public goods like mobility, epidemics, access to jobs and schools, and energy transition.

Maria Teresa ChiriView profile

Assistant Professor

• Optimal Control
• Hyperbolic Conservation Laws
• Calculus of Variations
• +4 more

Maria Teresa Chiri is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University. She holds a Ph.D. from the University of Padova (2019), an M.Sc. from the University of Pisa (2016), and a B.Sc. from the University of Salento (2013). Her research focuses on optimal control, hyperbolic conservation laws, and calculus of variations, applied to traffic flow modeling, biological systems, and bioinspired robotics. She has collaborated on projects such as the CIRCLES initiative, addressing congestion reduction through connected autonomous vehicles. Key research areas include traffic dynamics with autonomous vehicles, control of invasive species, fire confinement strategies, and numerical methods for hyperbolic systems. She has held postdoctoral positions at Penn State University and contributed to interdisciplinary collaborations with institutions like UC Berkeley and Vanderbilt University. Maria is actively recruiting M.Sc. and Ph.D. students interested in pure and applied mathematics. Her work bridges theoretical analysis and real-world applications, emphasizing numerical simulations and control theory. She has published extensively on topics ranging from traffic control algorithms to bioinspired PDE models.

Scott KovaleskiView profile

Professor

• Electromagnetics
• Pulsed Power
• Nuclear Engineering
• +2 more

Scott Kovaleski is Professor of Electrical Engineering and Computer Science at the University of Missouri. He holds a Ph.D. and M.S.E. from the University of Michigan and a B.S. from Purdue University. His research develops charged particle sources, electromagnetic systems, and nanofabrication methods using pulsed power and computational techniques. Current projects focus on piezoelectric-driven particle accelerators, metamaterial design optimization, and carbon nanotube electron sources. His laboratory advances compact radiation sources and computational methods for electromagnetic simulations. Recent publications demonstrate growing integration of deep learning in optical metasurface design and electromagnetic modeling. Key themes include physics-informed neural networks for inverse design, nanofabrication techniques, and vacuum electronics applications. His federally funded projects include research in charged particle generation, electromagnetics simulation, and pulsed power systems. Laboratory capabilities include computational modeling and experimental validation of particle acceleration systems.