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Gianluca IaccarinoView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +6 more

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

• Electromagnetic Compatibility (EMC)
• Lightning Electromagnetics
• Electromagnetic Time Reversal
• +4 more

Farhad Rachidi-Haeri is a Titular Professor and Head of the Electromagnetic Compatibility (EMC) Group at EPFL. His expertise spans EMC research, lightning electromagnetics, time reversal techniques, and fault location in power systems. He has led the EMC Group since the 1980s, with funding from the Swiss National Science Foundation, European Union, and private sector collaborations. His work involves international partnerships with institutions like the University of Toronto and KTH. Education: PhD in Electrical Engineering from EPFL (1991), M.S. from EPFL (1986). Roles: President of Swiss National Committee of URSI (2012–2020), Editor-in-Chief of IEEE Transactions on EMC (2013–2015), and member of the Academy of Sciences of Bologna Institute (2019). Research Focus: Lightning interaction with infrastructure, electromagnetic field modeling, time reversal applications for fault detection, and high-frequency transient analysis. His work bridges theoretical physics and engineering, addressing challenges in power systems, lightning protection, and aerospace. Awards: IEEE EMC Technical Achievement Award (2005), Berger Award (2016), and Distinguished Honorary Professor at Tsinghua University (2024). Over 400 peer-reviewed papers and 500 conference contributions reflect his prolific research output. Labs/Teams: Leads the EMC Laboratory at EPFL, focusing on experimental and numerical studies of electromagnetic phenomena. Collaborates with global networks on projects like Laser Lightning Control and structural lightning protection for wind turbines.

• Numerical analysis
• Partial differential equations
• Level set methods
• +7 more

Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University, specializing in applied mathematics, numerical analysis, and computational methods. His research focuses on numerical methods for partial differential equations, fluid mechanics, interface problems, and computer graphics. He holds a PhD from UCSB (2004) and has held academic positions at MIT and McGill since 2005. Currently, he serves on committees such as the Steering Committee of the Institut des Sciences Mathematiques and the CRM Applied Mathematics Lab. His educational background includes a PhD under Professors Xu-Dong Liu and Sanjoy Banerjee. Key research areas include level set methods, fluid-structure interaction, and invariant numerical methods. Notable works include the Correction Function Method for interface problems and the Characteristic Mapping Method for advection problems. Nave’s publications span topics like Poisson equations with discontinuous coefficients, fluid dynamics simulations, and high-order numerical schemes. He has advised numerous graduate and undergraduate students, contributing to their research in applied mathematics and computational science. His work bridges theoretical rigor and practical applications in engineering and physics. He teaches advanced courses such as Numerical Analysis I/II and Computational Methods in Applied Mathematics. His research group collaborates on projects involving fluid dynamics, elasticity, and geometric algorithms, with a focus on developing robust numerical tools for complex systems.

• Set Theory
• Mathematical Logic
• Forcing
• +7 more

Vera Fischer is an Associate Professor and Privatdozent in the Department of Mathematics at the Faculty of Mathematics, University of Vienna. She has been an active researcher since 2008, with a strong publication record in mathematical logic and set theory. Her research centers on set-theoretic combinatorics , focusing on cardinal characteristics of the continuum, forcing, definability, and structures such as maximal almost disjoint families, cofinitary groups, and independent families. She investigates foundational questions in independence, spectra of combinatorial objects, and the interplay between definability and generic extensions. Her work often involves constructing models to separate cardinal invariants or to realize specific spectra under forcing. The recent publications show a consistent trend in combinatorial set theory , with a focus on tower spectra, tight families, mad families, and their behavior under various forcing notions like Cohen and Sacks forcing. Her research frequently explores the definability and destructibility of combinatorial families, often in collaboration with leading researchers such as Corey B. Switzer, Stefan Geschke, and Saharon Shelah. FWF START Prize, 2017 Förderungspreis der ÖMG, 2018 Förderungspreis, 2018 She leads and participates in research projects such as Comparing the Real Line to Combinatorics of the Uncountable and A Sacks-like model with large continuum , indicating active grant funding and supervision of research activities. She also organizes academic events like Colloquium Logicum and engages in public outreach on topics like infinity. Her research is conducted within the Set Theory Group at the University of Vienna, where she collaborates with other logicians and contributes to the academic community through publications, conferences, and mentorship.

• Computational Mathematics
• Numerical Analysis
• Tsunami Modelling
• +5 more

Stephen Roberts is a Professor at the Australian National University (ANU) in the College of Science, Department of Mathematics. He is the lead developer of the ANUGA open-source hydrodynamic modeling software, which simulates dam breaks, floods, and tsunamis for governments and engineers. Roberts has made significant contributions to computational mathematics, particularly in numerical methods for partial differential equations, sparse grid data fitting, and finite element approximations scaling to millions of data points. MSc, Flinders University (1980) PhD, University of California, Berkeley (1985) His research interests include: Computational methods for shallow water wave equations Development of Python-based scientific computing frameworks Global sensitivity analysis and uncertainty quantification Adaptive mesh algorithms for fluid dynamics Recent publications focus on energy-stable numerical schemes, multiscale flood simulation, and convergence analysis in sensitivity methods. Roberts actively collaborates with environmental agencies and has led major computational science education programs at ANU. Stephen serves as Treasurer of the Computational Mathematics Group (ANZIAM) and leads projects in: Parallelization of hydrodynamic models CO2 leak detection via atmospheric measurements Optimization of sparse grid combinations His work combines theoretical advancements with real-world applications in disaster risk reduction and climate policy.

Jee ChoiView profile

Assistant Professor

• High Performance Computing
• Parallel and Scalable Algorithm Design
• Performance Analysis Optimization
• +2 more

Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.

Hadi DaneshmandView profile

Assistant Professor

• Foundations of Machine Learning
• Understanding neural networks
• Optimization for machine learning
• +3 more

Hadi Daneshmand is an Assistant Professor of Computer Science at the University of Virginia, specializing in theoretical machine learning. Prior to joining UVA, he completed postdoctoral research at FODSI (jointly hosted by MIT and Boston University), Princeton University, and INRIA Paris following his 2020 PhD in Computer Science from ETH Zurich. Education Ph.D. in Computer Science, ETH Zurich, 2020 His research bridges computational perspectives and neural network theory, focusing on theoretical guarantees for deep learning systems. Key interests include understanding neural network mechanisms through optimization frameworks, foundations of machine learning, and stochastic processes in learning systems. His work reveals how neural networks implement computational primitives like gradient descent and optimal transport through architectural components. Recent publications demonstrate a cohesive trajectory analyzing transformers' computational capabilities, batch normalization's theoretical properties, and optimization dynamics in deep learning. His studies consistently establish formal connections between neural architectures and classical optimization methods, particularly in in-context learning scenarios. Scientific Awards Stanford CPAL Rising Star Award Spotlight award at ICML In-context Learning workshop (2024) Postdoc fellowship of the Foundation of Data Science Institute (FODSI) Early Postdoc Mobility grant from SNSF Best poster award at Max Planck ETH deep learning workshop (2016) Reviewer awards for ICML (2022, 2019) and NeurIPS (2020) Dr. Daneshmand actively mentors graduate students, with advisees including PhD candidates at ETH Zurich who have secured positions at Harvard, Yale, Meta, and NVIDIA. His research is supported by competitive grants including the SNSF Early Postdoc Mobility award and FODSI fellowship. He serves the community as Area Chair for NeurIPS 2023-2024 and ICML 2025, and regularly reviews for top machine learning conferences and journals. He teaches specialized courses including "Neural Networks: A Theory Lab" at UVA, emphasizing experimental-theoretical connections in neural computation through hands-on coding exercises.

Dan JiaoView profile

Professor

• Computational Electromagnetics
• Computational Multiphysics
• Electronic Design Automation (EDA)
• +8 more

Prof. Dan Jiao is the Synopsys Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School. She leads the Rapid-Heterogeneous Integration (Rapid-HI) Design Institute and serves as Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques. Her research focuses on computational electromagnetics, multiphysics modeling, and AI-driven design automation for advanced integrated circuits and quantum systems. She has held academic positions since 2005, progressing from Assistant to Full Professor, and has extensive industry experience at Intel Corporation (2001–2005). Education: PhD in Electrical Engineering, University of Illinois at Urbana-Champaign (2001) Senior Staff Engineer at Intel Corporation (2001–2005) Research Interests: Fast numerical methods for large-scale electromagnetic analysis AI/ML integration in design automation (EDA/MDA) Quantum circuits and spin qubit systems Heterogeneous integration and advanced packaging Multiphysics co-simulation for nano-scale devices Signal/power integrity in high-speed systems Key Projects: Leads the NSTC AIDRFIC program (first NSTC R&D Jump Start project), the DARPA NGMM Rapid-HI Design Institute, and the GENIE-RFIC generative design tool initiative. Also directs the Consortium for Electromagnetic Science and Technology. Awards & Honors: 2022 ACES Computational Electromagnetics Award IEEE Fellow (2016) Intel Outstanding Researcher Award (2019) MTT-S Distinguished Microwave Lecturer (2020–2023) 2013 Schelkunoff Prize Paper Award Advising & Grants: Advised over 30 PhD/master's students and led projects funded by NSF, DARPA, Intel, SRC, and industry partnerships. Key grants include NSF CAREER (2008), ONR Young Investigator (2006), and multiple industry-sponsored initiatives. Labs & Teams: Rapid-HI Design Institute (DARPA NGMM) Quantum device co-design group Multiphysics modeling team

Carlos Torres-VerdinView profile

Professor

• Borehole Geophysics
• Petrophysics
• Formation Evaluation
• +8 more

Carlos Torres-Verdín is a Professor and holds the Brian James Jennings Memorial Endowed Chair and Zarrow Centennial Professorship in Petroleum Engineering at The University of Texas at Austin's Hildebrand Department of Petroleum and Geosystems Engineering, within the Jackson School of Geosciences. He earned a B.S. in Geophysical Engineering from the National Polytechnic Institute of México (1983), an M.Sc. in Electrical Engineering from UT Austin (1985), and a Ph.D. in Engineering Geoscience from UC Berkeley (1991). His research focuses on petrophysical and geophysical characterization of subsurface regions using well logging, seismic, and multi-physics data. Key areas include borehole geophysics, rock physics, reservoir characterization, and hydraulic fracturing. He has received numerous accolades, including the 2020 Virgil Kauffman Gold Medal (SEG) and the 2017 Conrad Schlumberger Award (EAGE). His work integrates advanced numerical methods and machine learning to enhance reservoir evaluation and CO2 sequestration monitoring. Torres-Verdín teaches courses such as PGE 358 (Formation Evaluation) and directs the Formation Evaluation Joint Industry Research Consortium, fostering industry-academia collaboration. Awards & Honors 2020 Virgil Kauffman Gold Medal, SEG 2019 Anthony F. Lucas Gold Medal, SPE 2017 Conrad Schlumberger Award, EAGE 2017 Lockheed Martin Excellence in Engineering Teaching Award Research & Teaching His recent studies address challenges in unconventional reservoirs, fluid dynamics in nanoporous media, and real-time geosteering. He has published over 150 peer-reviewed articles, emphasizing innovation in inversion techniques, NMR applications, and reservoir simulation.

Bryon AragamView profile

Associate Professor

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +6 more

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

Dond Asha KisanView profile

Assistant Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +4 more

Dond Asha Kisan is an Assistant Professor at the School of Mathematics , Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). His research focuses on numerical analysis and computational mathematics , particularly in finite element methods for partial differential equations. He can be contacted at ashadond@iisertvm.ac.in or via phone at +91 (0)471-2778247. PhD : Mathematics, Indian Institute of Technology Bombay M.Sc. : Mathematics, K.T.H.M. College, Nashik Kisan's research spans adaptive finite element methods , stabilized formulations for convection-diffusion problems , and optimal control governed by Stokes equations . His work includes convergence analysis, nonconforming discretizations, and hybrid numerical schemes. Recent publications (2023-2025) address stochastic modeling in liquid crystal physics, advanced WENO schemes, and adaptive algorithms for control problems. Scientific Awards : No explicit awards mentioned in the data, though he held prestigious postdoctoral fellowships including National Post-Doctoral Fellowship and NBHM Post-Doctoral Fellowship. Kisan has extensive teaching experience , including MATLAB workshops and undergraduate course assistantships. He has presented at major international conferences like ICIAM and Hyperbolic Problems, demonstrating global engagement in computational mathematics.

Justin WanView profile

Professor

• Scientific Computing
• Computer Graphics Simulation
• Medical Image Processing
• +4 more

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.

Paul G DupuisView profile

Professor

• Probability Theory
• Stochastic Processes
• Control Theory
• +4 more

Paul G Dupuis is the IBM Professor of Applied Mathematics at Brown University. His research focuses on applications of probability theory, stochastic processes, control theory, and numerical methods. He holds affiliations with the American Mathematical Society, Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematical Statistics (IMS). His work emphasizes large deviation theory, Markov chain approximations, Monte Carlo simulation, and partial differential equations. Education: Ph.D. in Applied Mathematics from Brown University (1985), M.S. from Northwestern University (1982), and B.S. from Brown University (1981). Research Interests: Control of deterministic and stochastic processes, differential games, numerical methods, operations research, and stochastic processes. His contributions include foundational work on large deviation theory, risk-sensitive control, and queueing networks. Awards: Elected SIAM Fellow (2010), Fellow of the Institute for Mathematical Statistics (2011), IBM Professor of Applied Mathematics (2012), and AMS Fellow (2014). Previously held an NSF Postdoctoral Fellowship (1985-1988). Grants: Current funding from the Army Research Office and National Science Foundation. Key collaborations include work on stochastic approximation, constrained diffusions, and reflected Brownian motion. Teaching: Courses include Operations Research: Probabilistic Models, Information Theory, and Advanced topics in Probability and Stochastic Control.

Bruce E. HansenView profile

Professor

• Econometrics
• Time Series Analysis
• Model Selection and Averaging
• +4 more

Bruce E. Hansen is the Mary Claire Aschenbrener Phipps Distinguished Chair and Trygve Haavelmo Professor of Economics at the University of Wisconsin-Madison, Department of Economics. He maintains an active research program with publications extending through 2025, demonstrating his continued prominence in econometric methodology. His research interests include: Econometric theory and methodology Time series analysis and forecasting Model selection, averaging, and shrinkage techniques Threshold and structural change models Statistical inference for clustered and dependent data Hansen's recent work focuses on innovative approaches to model averaging, standard error estimation for complex data structures, and unit root testing. His publications demonstrate both theoretical rigor and practical applicability to economic data analysis, with particular attention to handling clustered data, serial correlation, and model uncertainty. His influential publications include 'Least Squares Model Averaging' in Econometrica (2007) which introduced Mallows Model Averaging, and 'A Modern Gauss-Markov Theorem' (2022), both representing significant theoretical contributions to econometrics. His two textbooks 'Probability and Statistics for Economists' and 'Econometrics' (Princeton University Press, 2022) reflect his commitment to teaching and disseminating econometric knowledge. Hansen's research has been supported by multiple National Science Foundation grants (SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111), highlighting the significance and quality of his contributions to the field.

• Physical Oceanography
• Marine Turbulence and Current Dynamics
• Tidal Energy and Renewable Resources
• +3 more

Professor Göran Broström works at the Department of Marine Sciences at the University of Gothenburg . His research focuses on physical oceanography, marine turbulence, tidal energy systems, and biophysical processes in marine ecosystems. Current research themes include methane emissions from ocean infrastructure, turbulence in tidal flows, and wave-current interactions He utilizes advanced numerical modeling (e.g., Large Eddy Simulation, Bayesian inversion) and field observations Recent publications emphasize climate impacts (methane plumes), tidal energy innovations, and marine ecological connectivity His work appears in high-impact journals like Nature , Molecular Ecology , and Frontiers in Marine Science . Collaborative projects span oceanographic modeling, environmental monitoring, and marine renewable energy. No specific student advising information appears in the provided text.