جست‌وجوی استاد و پژوهشگر

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +۴ مورد دیگر

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.

Anouar Soufianiمشاهده پروفایل

پژوهشگر

• Radiative Heat Transfer
• Computational Fluid Dynamics
• Plasma Physics
• +۳ مورد دیگر

Anouar Soufiani is an active researcher specializing in radiative heat transfer, computational fluid dynamics, and plasma physics. His work focuses on complex thermal systems involving coupled radiation-convection phenomena, molecular gas radiation, and high-temperature spectroscopy. Research Interests: Soufiani's investigations span: Fundamental studies of Rayleigh–Bénard convection under radiation effects High-temperature gas radiative properties (CO 2 , argon plasmas) Development of numerical models for turbulent participating media Experimental spectroscopy at extreme temperatures (up to 6000K) Applications in aerospace thermal protection and geological systems Publication Trends: His recent articles (2024-2025) predominantly explore coupled heat transfer mechanisms in confined geometries, with frequent examination of: Radiation-convection interactions in cubical cavities Validation of reduced-order models against experimental data Molecular radiation effects in plasma environments Advanced measurement techniques for radiative properties This consistent focus demonstrates expertise in multiscale thermal transport modeling.

Sjoerd van Ophemمشاهده پروفایل

مدرس

• Vibro-acoustic analysis
• Model order reduction
• Digital twins
• +۳ مورد دیگر

Dr. Sjoerd van Ophem is a Lecturer in Structural Dynamics at the Institute of Sound and Vibration Research (ISVR), University of Southampton. He holds a PhD in Mechanical Engineering from KU Leuven, Belgium, and previously studied at the University of Twente, Netherlands. Mechanical Engineering (BSc, MSc) Noise and Vibration (PhD, KU Leuven) His research focuses on vibro-acoustic digital twins for machine monitoring and noise reduction. Key methodologies include model order reduction, finite element simulations, and digital twin technologies applied to complex acoustic systems. Recent publications emphasize: Time-domain vibro-acoustic simulations Digital twin development Model reduction techniques Acoustic material characterization Moving sound source modeling Experimental modal analysis Scientific awards include: FWO postdoctoral fellowship (junior and senior) MSCA FP7 project ANTARES funding He supervises two PhD students and teaches: ISVR3059: Acoustical Engineering Design FEEG1201: An Introduction to Engineering Design

Serkan Gugercinمشاهده پروفایل

استاد

• Model Reduction
• Dynamical Systems
• Numerical Analysis
• +۶ مورد دیگر

Serkan Gugercin is the Class of 1950 Professor of Mathematics and Deputy Director of the Division of Computational Modeling and Data Analytics (CMDA) at Virginia Tech's College of Science. He is also affiliated with the Department of Mechanical Engineering. His research focuses on model reduction, dynamical systems, numerical analysis, and scientific computing, with applications in engineering and data-driven methods. Gugercin has held prestigious titles such as the A.V. Morris Professorship (2016–2019) and received awards like the NSF Early CAREER Award (2007) and Alexander von Humboldt Fellowship (2016). He earned his Ph.D. in Electrical Engineering from Rice University (2003) and has secured over $5.5M in research funding. His work bridges numerical methods, control theory, and optimization, emphasizing high-fidelity reduced models for complex systems. Education: B.S., Middle East Technical University (1997); M.S. and Ph.D., Rice University (1999, 2003). Key contributions include co-authoring the textbook *Interpolatory Methods for Model Reduction* (SIAM) and advancing structure-preserving interpolation techniques for nonlinear systems. Research areas include data-driven modeling, parametric systems, and energy-based approximation methods. Labs/Teams: CMDA Program, part of Virginia Tech’s Academy of Integrated Science. Collaborates on interdisciplinary projects involving power networks, fluid dynamics, and mechanical systems. Active in editorial roles for SIAM Journal on Scientific Computing and Systems & Control Letters.

Erick Delageمشاهده پروفایل

استاد

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +۷ مورد دیگر

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

• Applied Mathematics
• Computational Methods
• Control Theory
• +۵ مورد دیگر

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

Felix Schindlerمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis , part of the Department of Mathematics and Computer Science at the University of Münster . His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency. Research Interests include: Numerical analysis of parametric and multiscale PDEs Localized reduced basis methods (LRBM) and adaptive enrichment Integration of model order reduction (MOR) with machine learning (ML) Conservative flux reconstruction techniques Development of software libraries like dune-xt and pyMOR Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks. Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.

Herke van Hoofمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +۵ مورد دیگر

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

Irene Koronakiمشاهده پروفایل

استاد

• Thermodynamics
• Cooling Technology
• Heat Pumps
• +۷ مورد دیگر

Irene Koronaki is a Professor at the National Technical University of Athens , affiliated with the School of Mechanical Engineering and the Thermal Engineering Section . She serves as Director of the Laboratory of Applied Thermodynamics, Cooling Technology & Refrigerated Vehicles since 2022 and has held academic roles at NTUA since 1999. Her research focuses include thermodynamics, heat pumps, energy efficiency, and renewable energy systems. Diploma in Mechanical Engineering, NTUA (1996) PhD in Thermal Engineering, NTUA (2000) Postdoctoral Researcher, NTUA (2002) Her research spans thermodynamics of cooling cycles, heat pumps, power cycles, energy saving in buildings, and thermal energy storage. She has pioneered work in nanofluids, solar cooling, and CO2 absorption systems. Her publications and projects reflect expertise in Stirling engines, hybrid solar collectors, and building energy optimization. Her recent articles highlight advancements in superfluid thermodynamics, solar PV/T systems, and medical robotics. Awards include the Edward F. Obert Award (2022) and leadership of the 2021 ASHRAE Student Design Competition winning team. She serves on ASME and ASHRAE committees and co-authored educational materials for refrigeration and energy inspection standards.

Linus Wunderlichمشاهده پروفایل

مدرس

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Dr. Linus Wunderlich is a Lecturer in Financial Mathematics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. He holds a PhD in Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics from Technical University Munich. His research focuses on integrating machine learning with numerical techniques in mathematical finance, including solving high-dimensional PDEs via neural networks and accelerating risk computations through Chebyshev interpolation. He also explores the theoretical foundations of AI and its applications in finance, engineering, and education. Linus teaches courses such as Mathematical Tools for Asset Management and Masterclass in Business Analytics , emphasizing practical machine learning skills for business contexts. His work spans computational finance, numerical analysis, and interdisciplinary projects including Olympic medal prediction models and violin bridge modeling from CT scans. Research grants include a £10,800 project with Numerical Algorithms Group Ltd on efficient volatility interpolation (2018–2019). He collaborates with institutions like LSE, Birkbeck, and University College Dublin, presenting at conferences such as SIAM Financial Mathematics and the German Probability & Statistics Days. His activities include developing interactive art projects and teaching machine learning courses during the pandemic. No scientific awards are explicitly mentioned in the provided texts. His research interests and publications reflect a blend of mathematical rigor and applied innovation, bridging finance, engineering, and education.

Tim Mitchellمشاهده پروفایل

استادیار

• Numerical Computing
• Numerical Analysis
• Numerical Linear Algebra
• +۴ مورد دیگر

Tim Mitchell is an Assistant Professor in the Department of Computer Science at Queens College / CUNY and the CUNY Graduate Center. His research focuses on designing fast and reliable algorithms for robust control and stability analysis of dynamical systems, with applications in nonsmooth constrained optimization and benchmarking of numerical algorithms. Queens College / CUNY, Assistant Professor, Computer Science CUNY Graduate Center, Faculty Affiliation, Data Science His work spans numerical linear algebra, optimization, and scientific computing, addressing both small-scale and large-dimensional problems. He develops open-source software packages like GRANSO (non-smooth optimization), ROSTAPACK (stability measures), and betaRMP (benchmarking visualization). The 15 most recent articles reflect his expertise in nonsmooth optimization, stability measures for dynamical systems, and computational methods in control theory, with recurring themes of algorithm design, benchmarking, and applications in machine learning and numerical analysis. Keywords include numerical radius, pseudospectral abscissa, hybrid expansion-contraction algorithms, and Kreiss constants. He actively collaborates with institutions such as the Max Planck Institute for Dynamics of Complex Technical Systems and the Courant Institute of Mathematical Sciences.

• Nonlinear Dynamics
• Mechanical Systems
• MEMS/NEMS
• +۴ مورد دیگر

Anil Bajaj is the Alpha P. Jamison Professor of Mechanical Engineering at Purdue University's College of Engineering. He specializes in nonlinear dynamics, mechanical systems, and advanced material behavior, with a focus on applications in automotive engineering, MEMS/NEMS, and vibrational analysis. His research often involves numerical modeling, experimental validation, and bifurcation analysis of complex systems. Key research interests include bistable systems, viscoelasticity, resonance phenomena, and the dynamics of nonlinear structural components such as beams, plates, and trusses. He has contributed significantly to understanding parametric resonances, chaos in mechanical systems, and mitigation strategies for noise and vibrations in vehicles. Prof. Bajaj has collaborated on projects involving seat-occupant systems, fluid-structure interactions, and MEMS switch dynamics. His work frequently integrates computational methods like incremental harmonic balance and reduced-order modeling to predict and analyze nonlinear responses. He is affiliated with Purdue's ProEd Academic Advisory Committee and holds an office in ME 3180. His recent articles highlight advancements in reservoir computing with bistable systems, torsional vibration mitigation in vehicle drivelines, and stability analysis of viscoelastic structures. Despite his extensive publications, no specific awards or student advisories are listed in the provided materials.

Asokan Variyathمشاهده پروفایل

استاد

• Statistical Process Control
• Experimental Design
• Machine Learning
• +۲ مورد دیگر

Asokan Variyath is a Professor of Statistics at Memorial University of Newfoundland, specializing in experimental design, statistical process control, and educational statistics. Education: Ph.D. in Statistics, University of Waterloo (2006) S.D.P Fellow (SQC & OR), Indian Statistical Institute (1991) Post Graduate Diploma in SQC & OR, Indian Statistical Institute (1988) M.Sc. Agricultural Statistics, Kerala Agricultural University (1987) Research Interests: Develops novel statistical methods in experimental design, quality control, and multivariate analysis. Applied research spans fisheries management, reliability engineering, and computational statistics. Created educational statistical applets to enhance student learning. Publication Trends: Recent publications focus on Bayesian methods, statistical education tools, and applications in fluid dynamics. Methodological work emphasizes variable selection, longitudinal data analysis, and nonparametric techniques. Leadership: Director of Statistical Consultancy Centre since 2010. Former Chair of Master of Data Science program and Deputy Head for Graduate Studies. Received Dean's Service Awards in 2023 and 2024.

Alexander Kemperمشاهده پروفایل

دانشیار

• Quantum Materials
• Non-Equilibrium Physics
• Condensed Matter Physics
• +۳ مورد دیگر

Dr. Alexander (Lex) Kemper is an Associate Dean for Research and Associate Professor in the Department of Physics at North Carolina State University (NC State), within the College of Sciences. He holds a PhD in Physics from the University of Florida (2010), followed by postdoctoral research at Stanford University/SLAC National Lab and Lawrence Berkeley National Lab as an Alvarez Fellow. He joined NC State in 2015 as an Assistant Professor, advancing to his current roles. His research focuses on quantum materials in and out of equilibrium, combining ultrafast laser experiments with computational modeling. Key areas include non-equilibrium dynamics in correlated materials, pump-probe spectroscopy, and quantum computing algorithms for simulating condensed matter systems. He received an NSF CAREER Award in 2018 for studying light-induced phases in 2D materials. Research interests span superconductivity (cuprates and Fe-based systems), electron-phonon coupling, and quantum machine learning applications. His lab develops software tools like eigenvector continuation and Hankel projections to study many-body systems. Recent work addresses quantum state preparation resilience, fast scrambling in hyperbolic models, and geometric quantum algorithms. Current lab members include graduate researchers (Anjali Agrawal, Heba Labib) and undergraduates (Liam Doak, Arvin Kushwaha). Alumni include notable contributors like Akhil Francis and Avinash Rustagi. Collaborations involve experimentalists in ultrafast spectroscopy and quantum hardware teams. Publications (2025) highlight advances in quantum algorithms (e.g., error mitigation, horizontal gates) and experimental techniques (correlation-ARPES, RIXS). His work bridges theoretical predictions with quantum hardware implementation, aiming to solve complex materials physics challenges on near-term quantum devices.