جست‌وجوی استاد و پژوهشگر

Svitlana Mayborodaمشاهده پروفایل

استاد

• Analysis and partial differential equations
• Wave localization
• Harmonic analysis
• +۱ مورد دیگر

Svitlana Mayboroda is a Professor of Mathematics at ETH Zurich and the McKnight Presidential Professor at the University of Minnesota. Her research focuses on partial differential equations, harmonic analysis, and wave localization phenomena. University of Minnesota: School of Mathematics, 127 Vincent Hall, Minneapolis, MN ETH Zurich: Department of Mathematics, Ramistrasse 101, Zurich Research Interests: Analysis and partial differential equations Wave localization and Anderson localization Elliptic theory on non-smooth and lower-dimensional domains Harmonic measure and geometric measure theory Applications to quantum mechanics and semiconductor physics Recent Publications: Her 2023-2022 works investigate Anderson mobility edges, landscape functions in spectral theory, regularity problems for elliptic operators, and Green function estimates. Key themes include localization landscape theory, uniformly rectifiable domains, and spectral analysis of disordered systems. Grants & Collaborations: Simons Collaboration on Localization of Waves (Director, 2018–2025, $14M) NSF RAISE–TAQS grant ($1M) Academic Leadership: She has organized numerous conferences and workshops, including annual meetings of the Simons Collaboration on Wave Localization (2020–2024) and programs at MSRI and PCMI. Her mentorship includes postdocs and PhD students working on elliptic theory, spectral problems, and applied mathematical physics.

Justin Wanمشاهده پروفایل

استاد

• Scientific Computing
• Computer Graphics Simulation
• Medical Image Processing
• +۴ مورد دیگر

Justin Wan is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on scientific computing, medical image processing, computational finance, and machine learning. He holds a Ph.D. from UCLA (1998), an M.A. from UCLA (1995), and a B.Sc. from the Chinese University of Hong Kong (1992). Wan’s work bridges numerical methods, optimization, and deep learning, with applications in financial modeling, medical imaging, and fluid dynamics. His research interests include advanced techniques in scientific computing (e.g., multigrid methods), computer graphics simulation, and medical image enhancement (e.g., CT scan artifact reduction). He has pioneered applications of machine learning to computational finance, including option pricing and hedging using deep neural networks and GANs. His recent work explores denoising diffusion models and multi-agent systems for optimal execution in finance. Publications span topics like volatility surface computation, optimal mass transport for image registration, and parallel solvers for fluid dynamics. His methods address challenges in high-dimensional problems, robust numerical valuation, and scalable algorithms for large datasets. Wan collaborates across disciplines, integrating mathematical rigor with practical engineering solutions.

Jeffrey Guastoمشاهده پروفایل

دانشیار

• Biophysics
• Soft Matter Physics
• Microscale Fluid Mechanics
• +۵ مورد دیگر

Jeffrey Guasto , Associate Professor at Tufts University, holds joint appointments in the School of Engineering (Mechanical Engineering) and School of Arts and Sciences (Physics & Astronomy). His work bridges engineering, physics, and biology to study transport properties in complex systems. Ph.D., Engineering (2009), Brown University Sc.M., Engineering (2004), Brown University Dual B.S. in Physics and Mechanical Engineering (2003), Lehigh University Research Interests focus on: Biophysics : Flagellar mechanics, chemotaxis, cell-fluid interactions Soft Matter : Active suspensions, colloids, viscoelastic materials Microfluidics : Device design for cell motility studies and gradient generation Environmental Transport : Microbial ecology in porous systems Scientific Trends from his 77+ publications show emphasis on microscale fluid dynamics, bacterial transport mechanisms, and viscoelastic flow instabilities. His 2024 Nature Microbiology work reveals phage-infected bacteria driving marine chemotaxis, while 2023 PNAS research explores stress topology in viscoelastic flows. Scientific Awards : NSF CAREER Award (2016) for cell dispersal mechanisms Collaborative NSF grants (2015-2023) Advising includes mentoring 15+ students and postdocs. His grants portfolio features 9+ awards, notably NSF grants for viral-microbe interactions (2018) and flagellar mechanics (2020). Labs & Teams : Leads the Guasto Laboratory at Tufts, integrating microfluidics and high-speed imaging for studying microbial transport, while collaborating with MIT, Harvard, and international institutions.

• Quantum Computing
• Superconducting Circuits
• Quantum Noise Reduction
• +۳ مورد دیگر

Jonas Bylander is a Professor at Chalmers University of Technology in the Department of Microtechnology and Nanoscience, specifically within the Quantum Technology division. He leads a research group focused on developing quantum computers using superconducting circuits.

Noel T. Clemensمشاهده پروفایل

استاد

• Aerothermodynamics
• Fluid Mechanics
• Hypersonics
• +۷ مورد دیگر

Noel T. Clemens serves as a Professor and holds the prestigious Clare Cockrell Williams Centennial Chair in Engineering within the Aerospace Engineering and Engineering Mechanics Department at the University of Texas at Austin's Cockrell School of Engineering. He has been a faculty member since 1993 and served as department chair from 2012 to 2020. His research laboratory is part of the Center for Aeromechanics Research (CAR) where he directs the Flowfield Imaging Laboratory. Dr. Clemens' research focuses on experimental investigations of hypersonic flows, turbulent combustion, and advanced optical diagnostic techniques. His current work emphasizes 3D shock wave/boundary layer interactions, inlet unstart control, flashback in high-pressure combustors, turbulent combustion with non-equilibrium effects, and high-temperature ablation phenomena. He has pioneered laser-based measurement techniques for extreme environments, particularly for hypersonic flight applications where conventional measurement approaches fail. His recent publication record through 2025 demonstrates continued leadership in experimental fluid dynamics, with particular emphasis on plasma diagnostics for ablation studies, shock/boundary layer interaction physics, and advanced optical measurement techniques for extreme environments. The research spans fundamental fluid mechanics investigations to applied aerospace engineering problems relevant to hypersonic vehicle development. Elected to National Academy of Engineering (2024) AIAA Aerodynamic Measurement Technology Award (2022) Elected AIAA Fellow (2019) National Science Foundation Presidential Faculty Fellow (1996) Editor-in-Chief of Experiments in Fluids (2009-2013) Fellow of the American Physical Society Dr. Clemens has secured substantial research funding for his experimental investigations in hypersonics and combustion, leading multiple major research projects with government and industry partners. His laboratory facilities include advanced wind tunnels and state-of-the-art optical diagnostic systems for high-speed flow visualization. The Flowfield Imaging Laboratory at UT Austin serves as a national resource for advanced flow measurement techniques development. As an educator, he teaches core courses in compressible flow, viscous flow, combustion, experimental methods, and laser diagnostic techniques, training the next generation of aerospace engineers in both fundamental principles and cutting-edge measurement technologies.

Jeff Linderothمشاهده پروفایل

استاد

• Large-Scale Numerical Optimization
• Mixed-Integer Nonlinear Programming
• Operations Research
• +۵ مورد دیگر

Jeff Linderoth is the Harvey D. Spangler Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on large-scale numerical optimization, mixed-integer nonlinear programming, and stochastic programming, with applications in energy systems, global routing, and industrial processes. Education: BS in General Engineering (highest honors) from University of Illinois at Urbana-Champaign, MS in Operations Research from Georgia Institute of Technology, PhD in Industrial Engineering from Georgia Institute of Technology. Linderoth's work addresses theoretical and applied challenges in optimization, including developing algorithms for mixed-integer programming, analyzing knapsack polytopes, and creating tools like the Minotaur optimization toolkit. His recent publications explore integer programming techniques for subspace clustering, complementarity constraints, and customized coverage instrumentation. Selected trends in his research include advancements in stochastic programming, orbital branching for symmetric integer programs, and congestion analysis in power systems. His group contributes to optimization software and data-driven libraries like MIPLIB. Scientific Award: Harvey D. Spangler Professor.

Mark Crowleyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Deep Learning
• Ensemble Methods
• +۶ مورد دیگر

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.

Yukun Liمشاهده پروفایل

دانشیار

• Finite Element Methods
• Numerical Solutions of Stochastic Differential Equations
• Adaptive Methods
• +۳ مورد دیگر

Yukun Li is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research focuses on numerical analysis, stochastic partial differential equations, and computational finance. He holds a Ph.D. in Mathematics from the University of Tennessee, Knoxville (2010-2015), followed by postdoctoral roles at Penn State (2015-2016) and The Ohio State University (2016-2019). He has secured grants including NSF REU funding (2023-2026) and led an NSF-funded project on stochastic phase field models (2021-2025). Research interests include: Continuous/Discontinuous Finite Element Methods Numerical Solutions of Stochastic ODEs/PDEs Adaptive Algorithms and Fast Solvers Computational Finance Models Recent publications emphasize stochastic wave equations, phase field models, and financial mathematics. His work spans theoretical analysis and numerical methods for complex systems. Notable recognition includes the 2015 Achievement Award from the University of Tennessee's Mathematics Department. Teaching highlights include advanced graduate courses like Computational Methods for Financial Mathematics and Numerical Linear Algebra, alongside contributions to undergraduate mathematics education. He is proficient in computational tools including MATLAB, Python, FEniCS, and MPI.

Li Caiمشاهده پروفایل

استاد

• Psychometrics
• Latent Variable Models
• Item Response Theory
• +۳ مورد دیگر

Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.

Yang Kaidiمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +۷ مورد دیگر

Yang Kaidi is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA) within NUS’s Smart Nation Research Cluster. Their research focuses on intelligent transportation systems, traffic control, shared mobility, and machine learning applications in mobility. Key interests include connected and automated vehicles, privacy-preserving data sharing, and reinforcement learning for traffic optimization. Research highlights include developing parameter privacy-preserving strategies for mixed-autonomy platoons, enhancing safety in autonomous driving via transformer-based trajectory prediction, and optimizing traffic signal timing using connected vehicle data. Their work bridges theoretical control systems with practical urban mobility challenges, addressing issues like ridesourcing-public transit integration, modular transit service operations, and weaving section management in mixed traffic environments. Recent publications emphasize real-time control frameworks, cooperative safety mechanisms, and data-driven solutions for urban and highway systems. Yang’s interdisciplinary approach integrates robotics, optimization, and cybersecurity to advance smart transportation infrastructure. Their contributions are particularly notable in privacy-preserving techniques for traffic state estimation and federated learning applications. While no specific awards or grants are listed, their research aligns with Singapore’s Smart Nation initiatives through IORA’s strategic focus areas. Yang’s work has implications for future traffic management systems, autonomous vehicle coordination, and sustainable urban mobility solutions.

Jim Luedtkeمشاهده پروفایل

استاد

• Operations Research
• Stochastic Optimization
• Integer Programming
• +۲ مورد دیگر

Jim Luedtke is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison. His research focuses on operations research, integer programming, and stochastic optimization methods for solving discrete and uncertain decision problems. Educational Background: BS in Industrial Engineering from University of Wisconsin-Madison MS in Operations Research from Georgia Institute of Technology PhD in Industrial and Systems Engineering from Georgia Institute of Technology Postdoctoral Research at IBM T.J. Watson Research Center His work spans applications in power systems optimization, healthcare analytics, and network design, with particular emphasis on developing cutting-edge algorithms for chance-constrained and multistage stochastic programming problems. Recent publications demonstrate strong focus on Benders decomposition techniques, Lagrangian dual methods, and distributionally robust optimization frameworks. Scientific Awards: NSF CAREER Award (2010) for "Risk Management via Stochastic Programming: Models, Computation, and Applications"

Keith A. Brownمشاهده پروفایل

دانشیار

• Hierarchical Soft Matter
• Autonomous Experimentation
• Scanning Probe Techniques
• +۴ مورد دیگر

Keith A. Brown is an Associate Professor in Mechanical Engineering at Boston University's College of Engineering with additional appointments in Materials Science & Engineering and Physics. He serves as Associate Chair for Graduate Programs in ME and leads the interdisciplinary KABLab research group. Education: PhD, Harvard University Dr. Brown's research centers on hierarchical soft matter systems including polymers and smart fluids. His group develops innovative approaches to accelerate materials research through nanocombinatorics , autonomous experimentation , and scanning probe lithography . Key focus areas include connecting nanoparticle properties to bulk smart fluid behavior, designing 3D-printed structures with programmed mechanics, and creating self-driving laboratories for materials discovery. His recent publications (2024-2025) demonstrate a strong emphasis on autonomous experimentation systems integrating machine learning with physical research. This work spans energy-absorbing foam design, nanoscale fluid manipulation, and physics-informed modeling for mechanical systems, establishing new paradigms in accelerated materials development. Scientific Awards: The Early Career Research Excellence Award, College of Engineering, 2021 Professor of the Year, Mechanical Engineering, 2020 Frontiers of Materials Award, The Minerals Metals and Materials Society (TMS), 2020 Dean’s Catalyst Award (2018) Dean’s Catalyst Award (2020) Moorman-Simon Interdisciplinary Career Development Professor, 2016 Dr. Brown teaches undergraduate courses including Fluid Mechanics (ME 303), Introduction to Materials (ME 306), and Nanomanufacturing (ME/MS 576). His research is supported by: Federal Grants : AFOSR MURI, NSF Nanomanufacturing, ACS Petroleum Research Fund Foundations : Gordon and Betty Moore Foundation Industry : Google Faculty Research Award University : BU Dean's Catalyst Award, Nanotechnology Innovation Center The KABLab employs interdisciplinary teams to develop novel instrumentation for hierarchical soft matter research, with particular expertise in autonomous experimentation platforms that combine scanning probe techniques with machine learning for accelerated materials discovery.

Pietro de Annaمشاهده پروفایل

دانشیار

• Environmental Fluid Mechanics
• Porous Media Dynamics
• Biogeochemical Processes
• +۳ مورد دیگر

Pietro de Anna is an Associate Professor at the Institute of Earth Sciences (ISTE), University of Lausanne, since August 2021. He holds an Italian nationality and completed a Master's in Theoretical Physics (2009) at the University of Florence, followed by a PhD in Earth Sciences at the University of Rennes 1 (2012). His research focuses on reactive transport in porous media, filtration, and interactions between bacteriological activity and flow dynamics. He directs the Environmental Fluid Mechanics Laboratory since 2015, employing microfluidics, numerical simulations, and theoretical models to study coupled physical, biological, and chemical mechanisms in confined systems. He has published 21 peer-reviewed articles, teaches environmental science courses at the Bachelor's and Master's levels, and supervised two PhD theses and four postdoctoral researchers. His work includes investigations into microbial biomass accumulation in porous media, diffusion-limited mixing, and biocementation processes. Key research themes are spatial heterogeneity effects, chemotaxis, and quorum sensing in microbial systems. He has pioneered methods combining microfluidics with microscopy to analyze transport at pore scales.

YING-TSONG LINمشاهده پروفایل

استاد

• Applied Ocean Sciences
• Autonomous Ocean Platforms
• Internal Waves and Ocean Mixing
• +۳ مورد دیگر

YING-TSONG LIN is an Acting Professor at the Scripps Institution of Oceanography (SIO), UC San Diego. His research focuses on applied ocean sciences, autonomous ocean platforms, internal waves, ocean acoustics, and instrumentation. He leads projects like the New England Shelf Break Acoustics (NESBA) experiment, emphasizing real-time acoustic modeling and environmental interactions. Research interests include 3D acoustic propagation modeling, ocean mixing dynamics, and seabed characterization. His work integrates high-performance computing and distributed sensor networks for oceanographic studies. Recent studies address underwater explosions, renewable energy impacts, and vessel localization using acoustic coherence. Publications emphasize advancements in hydroacoustic modeling, seabed inversion techniques, and environmental asymmetry effects. His contributions span interdisciplinary areas like bioacoustics and seismic-to-acoustic wave conversions. Labs/Teams: Involved with SIO's Acoustics and Oceanography research groups, focusing on autonomous platforms and global observing systems.

Gian-Luca Oppoمشاهده پروفایل

استاد

• Nonlinear Photonics
• Quantum Cavity Solitons
• Bose-Einstein Condensates
• +۲ مورد دیگر

Gian-Luca Oppo is Professor of Computational and Nonlinear Physics at the University of Strathclyde and Director of the Institute of Complex Systems. His research spans nonlinear photonics, quantum cavity solitons, Bose-Einstein condensates, and optical pattern formation. Oppo develops theoretical models for laser dynamics, quantum correlations in light sources, and soliton formation in microresonators. Recent work (2024-2025) explores topological photonics applications in frequency combs, polarization symmetry breaking for optical Ising machines, and optomechanical quantum droplet dynamics. He has made fundamental contributions to understanding spontaneous symmetry breaking in Kerr resonators and control of extreme optical events. Oppo's group collaborates internationally on experimental implementations of photonic computing architectures and quantum sensing technologies. Honors include the Occhialini Medal (2011), Royal Society-Leverhulme Senior Research Fellowship (2003), and fellowships from the Royal Society of Edinburgh, OSA, and Institute of Physics.