Find Academic Supervisors

Sjoerd van OphemView profile

Lecturer

• Vibro-acoustic analysis
• Model order reduction
• Digital twins
• +3 more

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 GugercinView profile

Professor

• Model Reduction
• Dynamical Systems
• Numerical Analysis
• +6 more

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 DelageView profile

Professor

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +7 more

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
• +5 more

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 HaasdonkView profile

Professor

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +7 more

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 SchindlerView profile

Researcher

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

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.

Salar FattahiView profile

Assistant Professor

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +4 more

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

Herke van HoofView profile

Associate Professor

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +5 more

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.

Linus WunderlichView profile

Lecturer

• Mathematical Finance
• Machine Learning
• Deep Neural Networks
• +6 more

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.

• Nonlinear Dynamics
• Mechanical Systems
• MEMS/NEMS
• +4 more

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 VariyathView profile

Professor

• Statistical Process Control
• Experimental Design
• Machine Learning
• +2 more

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 KemperView profile

Associate Professor

• Quantum Materials
• Non-Equilibrium Physics
• Condensed Matter Physics
• +3 more

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.

Selin AslanView profile

Assistant Professor

• Applied Mathematics
• Computational Mathematics
• Imaging Science
• +2 more

Selin Aslan serves as an Assistant Professor in the Department of Mathematics at Koç University, Istanbul, Turkey, where she conducts research at the intersection of computational mathematics and imaging science. Her academic appointments and research activities are centered within the university's mathematics department, contributing to both undergraduate and graduate education in mathematical sciences. Her educational qualifications include: PhD in Mathematics from Virginia Polytechnic Institute and State University (2018) Master's in Mathematics from Rochester Institute of Technology (2013) B.A. in Mathematics from Ege University (2010) Dr. Aslan's research program focuses on developing advanced computational methods for solving inverse problems in imaging, with particular expertise in phase retrieval, tomographic reconstruction, and ptychography. Her work bridges theoretical mathematics with practical applications in medical imaging, microscopy, and materials science, emphasizing algorithmic innovation and computational efficiency. She integrates techniques from deep learning, optimization theory, and high-performance computing to address challenges in image reconstruction under physical constraints. Analysis of her publication record reveals a consistent trajectory toward solving complex imaging problems through hybrid approaches that combine physics-based models with data-driven techniques. Her recent work demonstrates increasing emphasis on scalability for large datasets, robustness in photon-limited scenarios, and real-time processing capabilities, with applications spanning biomedical imaging to advanced microscopy. No scientific awards were documented in the available sources. Information regarding student advising and research grant activities was not specified in the provided materials, though her publication record suggests active research collaboration. Her computational focus implies engagement with high-performance computing resources for large-scale image reconstruction tasks. While specific laboratory infrastructure details were unavailable, her research on multi-GPU implementations and distributed computing indicates utilization of advanced computational facilities for handling large-scale imaging datasets.

Irene KoronakiView profile

Professor

• Thermodynamics
• Cooling Technology
• Heat Pumps
• +7 more

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.

Gilles SavardView profile

Professor

• Operations research
• Revenue management
• Mathematical optimization
• +7 more

Gilles Savard is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director General of the Institute for Data Valorization (IVADO), a position he held until July 2021 when Luc Vinet succeeded him. He is also a member of the Research Group in Decision Analysis (GERAD) and the Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT). In October 2021, he was appointed Acting President of Polytechnique Montréal. His research spans operations research, revenue management, and mathematical optimization with applications in transportation networks, energy systems, and logistics. He has made significant contributions to bilevel programming, network pricing, demand forecasting, and combinatorial optimization. His work bridges theoretical advances in mathematical programming with practical applications in revenue management systems across transportation and energy sectors. Analysis of his recent publications reveals a strong focus on integrating machine learning techniques with traditional optimization methods for revenue management problems. His work increasingly addresses smart grid optimization and energy market applications while maintaining core expertise in transportation revenue systems. The publications demonstrate sophisticated use of bilevel programming frameworks to model complex decision hierarchies in pricing problems. Order of Academic Palms from the French Ministry of National Education (2018) Innovation Personality Award from ADRIQ (2017) Professor Savard has supervised 23 PhD students and 28 Master's students throughout his career, with research spanning revenue management, transportation optimization, bilevel programming, and energy systems. His students have addressed problems in airline revenue management, railway demand forecasting, media placement optimization, and inventory management. He has participated in numerous research projects related to transportation networks, smart grid systems, and revenue optimization frameworks. Professor Savard has been instrumental in establishing Montreal as a hub for artificial intelligence research through his leadership at IVADO.