Find Academic Supervisors

• Nonlinear Analysis
• Dynamical Systems
• Applied Mathematics
• +7 more

Hermen Jan Hupkes is a Professor of Non-linear Analysis at the Mathematical Institute of Leiden University . His research bridges mathematical analysis with dynamical systems , focusing on discrete media and reaction-diffusion equations . Key Research Areas: Nonlinear stability of travelling waves in discrete and stochastic systems Functional differential equations of mixed type (MFDEs) in economics and biology Curvature-driven wave propagation on lattices Numerical algorithms for discrete reaction-diffusion models Recent Publication Trends emphasize nonlinear dynamics across discrete spatial structures , including stochastic stability , economic agent-based models , and biological pattern formation . His work on lattice differential equations and MFDEs has advanced tools like exponential dichotomies and Lin's method in discrete settings. Awards: Vici Grant (2025) for innovative research in non-linear analysis Advising and Collaborations include mentoring PhD candidates such as Christian Hamster and Mia Jukic, as well as interdisciplinary collaborations with computational imaging experts and biophysicists.

Johannes TauschView profile

Professor

• Numerical approximation
• Boundary integral equations
• Computational electromagnetics
• +4 more

Johannes Tausch is a Professor at Southern Methodist University specializing in numerical approximation and fast methods for boundary integral equations. His research spans computational electromagnetics, optics, fluid mechanics, shape optimization, and high-dimensional quadrature. Applications include heat transfer, anomalous diffusion, wave propagation, and electromagnetic analysis. Research focuses on developing efficient computational techniques for boundary integral reformulations of PDEs. Key methodologies include fast multipole methods, adaptive quadrature, Galerkin formulations, and mesh-free approaches for complex geometries. Current work emphasizes parabolic problems, moving boundaries, and high-dimensional integration. Publications demonstrate consistent focus on accelerating integral equation solvers through hybrid algorithms, matrix compression techniques, and specialized quadrature. Recent advancements target time-dependent domains and multiphysics coupling. No awards or student advising information is provided in the source materials.

Yunkai ZhouView profile

Associate Professor

• Numerical Linear Algebra
• Scientific Computing
• Eigenvalue Problems
• +6 more

Yunkai Zhou is an Associate Professor at the Department of Mathematics , Southern Methodist University. His research spans Numerical Linear Algebra , Scientific Computing , and Electronic Structure Calculations with applications in Materials Science and Electrical Engineering . Education : Ph.D. in Computational Mathematics (2002) from Rice University , B.S./M.S. from Xi'an Jiaotong University . Research Focus : Developing polynomial filtered subspace methods for generalized eigenvalue problems, improving mixing schemes in self-consistent field calculations, and extending subspace techniques to time-dependent DFT. His work addresses challenges in dimensionality reduction , machine learning , and high-performance computing . Key Contributions include algorithms for solving large-scale eigenvalue problems in Density Functional Theory and Density Functional Tight Binding . His students include Zheng Wang (recipient of multiple SMU awards) and Iranga Nagasinghe. Contact : Office in Clements Hall 133, Southern Methodist University, Dallas, TX. Email: yzhou@smu.edu . Website: http://faculty.smu.edu/yzhou .

• Control Systems
• Electrical Engineering
• Robotics
• +3 more

Srinivasa M Salapaka is a Professor at the University of Illinois, holding joint appointments in the Department of Mechanical Science and Engineering and the Department of Industrial and Enterprise Systems Engineering. He is also affiliated with the Coordinated Science Lab. His research focuses on control systems, power electronics, optimization algorithms, and robotics applications such as UAV routing and atomic force microscopy. Salapaka has received prestigious awards including the ASME Fellow (2015), IEEE Senior Member (2020), and NSF CAREER Award (2005). His work integrates theoretical advancements in control theory with practical applications in energy systems, including inverters for microgrids and grid stability solutions. Recent research emphasizes scalable UAV path planning, maximum entropy principle frameworks for resource allocation, and robust control designs for dynamic systems. Collaborations span aerospace, mechanical, and electrical engineering disciplines, addressing challenges in renewable energy, autonomous systems, and manufacturing processes. Salapaka's publications highlight contributions to multi-agent systems, optimization under constraints, and real-time control architectures. His lab's activities involve developing novel control frameworks for high-speed microscopy and investigating fouling prediction in industrial heat exchangers using machine learning. Research trends reflect a balance between foundational control methodologies and their deployment in emerging technologies like advanced air mobility and smart grids.

• Nonconvex Optimization
• Semidefinite Programming
• Power Engineering
• +4 more

Richard Y Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois, affiliated with the Coordinated Science Lab. His research focuses on optimization theory, nonconvex optimization, semidefinite programming, and their applications in power systems, machine learning, and control systems. Key topics include low-rank matrix recovery, spurious local minima analysis, and robust optimization methods. He has received the NSF CAREER Award in 2021 for early career achievements. Research Interests: Nonconvex Optimization Semidefinite Programming Power System Analysis Low-Rank Matrix Recovery Machine Learning Algorithms Control Systems Design Recent work emphasizes certified optimization methods with global guarantees, adversarial machine learning robustness, and efficient algorithms for large-scale systems. His publications explore topics like phase synchronization in power systems, preconditioned gradient descent techniques, and sparse semidefinite program optimizations. Scientific Awards: NSF CAREER Award (2021) Advising and Grants: While specific student names aren’t listed, his research group focuses on cutting-edge optimization theory with applications to energy systems and data science. Collaborations include work on power grid stability, neural network certification, and large-scale convex/nonconvex problem solving. Labs/Teams: Active in the Coordinated Science Lab’s optimization and control research clusters.>

• Power Systems
• Electricity Markets
• Distributed Energy Resources
• +5 more

Subhonmesh Bose is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Lab and Health Care Engineering Systems Center . He holds the M. Stanley Helm Fellowship . His research focuses on power systems, optimization, machine learning, and their applications in energy markets and healthcare. Key areas include risk-sensitive market design, distributed optimization algorithms, and transient stability analysis using operator-theoretic methods. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in electrical engineering and applied mathematics. His work bridges theoretical foundations (e.g., game theory, control systems) with practical challenges like pandemic management in schools and grid resilience. Research Trends : Recent articles emphasize adaptive control under uncertainty, incentive mechanisms in multi-agent systems, and data-driven approaches for power system stability. His work on test-to-stay programs for K-12 schools during the pandemic highlights interdisciplinary impact. Awards : NSF CAREER Award (2021) for Risk-Sensitive Market Design for Power Systems . Grants : Leads projects on AI for mitigating COVID-19, energy storage placement, and market design for distributed energy resources. Labs/Teams : Active in Coordinated Science Lab (CSL) and Health Care Engineering Systems Center (HCESC), collaborating on cross-disciplinary challenges.

Michael HertyView profile

Professor

• Hyperbolic Conservation Laws
• Balance Laws
• Kinetic Theory
• +5 more

Professor Michael Herty is a faculty member at RWTH Aachen University, where he works in the Department of Geometry and Practical Mathematics. His research spans multiple international collaborations, including guest professorships at Southeast University in Nanjing, China and as an Extraordinary Professor at the University of Pretoria, South Africa. His research focuses on Hyperbolic Conservation and Balance Laws , Kinetic and Meanfield Theory , Inverse Problems and Nonlinear Optimization , and Control and Stabilization . His work bridges theoretical mathematics with practical applications in traffic flow, gas networks, and optimization problems. Professor Herty has developed numerical methods for coupled systems, multi-scale problems, and uncertainty quantification in fluid dynamics. His recent publications (2023-2025) demonstrate a strong trend toward consensus-based optimization methods, mean-field limits, and numerical schemes for hyperbolic systems. These works often involve interdisciplinary collaborations across mathematics, engineering, and computational science. His research shows increasing focus on machine learning integration with traditional numerical methods and applications to real-world network problems. Professor Herty serves on multiple editorial boards including: Communication in Mathematical Sciences Networks and Heterogenous Media Communications in Mathematical Research Communications in Applied and Industrial Mathematics Annali dell'Universita' di Ferrara -- Sezione VII - Scienze Matematiche He has supervised numerous PhD and Master's students, with recent theses focusing on multiresolution grid adaptation, turnpike properties in optimal control, and gradient-free optimization methods. His research is supported by multiple DFG projects, EU Marie Curie Doctoral Networks (DATAHYKING), and SPP projects on multi-scale modeling and control. Professor Herty leads significant collaborative efforts in the fields of hyperbolic balance laws, compressible Euler equations, and traffic flow modeling.

Alexander OlshevskyView profile

Professor

• Reinforcement Learning
• Distributed Optimization for Machine Learning
• Control Systems
• +3 more

Alexander Olshevsky is a Professor in the Department of Electrical and Computer Engineering and Systems Engineering at Boston University. He holds a PhD from MIT (2010) and is affiliated with the Hariri Institute and the Intelligent, Autonomous & Secure Systems group. His research focuses on reinforcement learning, distributed optimization for machine learning, control systems, and network science, with applications to epidemic control and federated learning. Key achievements include the NSF CAREER Award (2014), AFOSR Young Investigator Award (2015), and the SIAM SIGEST Selection (2011). His work bridges theoretical foundations in control systems and machine learning, with recent emphasis on distributed algorithms and their convergence properties. Publications span topics like MDP analysis, sample complexity in LQR systems, and network-based epidemic control. He leads projects funded by NSF and AFOSR, including federated learning for energy consumption prediction and sparse control algorithms. His interdisciplinary collaborations involve systems engineering, computer science, and public health domains.

• High-performance Computing
• Parallel Algorithms
• Scientific Computing

Professor Tobias Weinzierl heads the Scientific Computing research group at Durham University's Department of Computer Science. He is Co-director of the Institute for Data Science (IDAS) with responsibility for Large-Scale Computing initiatives and serves as inaugural director of the Master in Scientific Computing and Data Analysis (MISCADA). Professor Weinzierl is a reviewer for the EuroHPC JU and principal investigator on multiple ExCALIBUR projects. His research focuses on parallel algorithms, high-performance computing, and scientific computing methodologies. Key areas include adaptive mesh refinement, task-based parallelism, and exascale computing frameworks. Professor Weinzierl develops the Peano software framework for parallel grid traversals and contributes to the ExaHyPE engine for hyperbolic PDEs. His publications span parallel computing paradigms, performance optimization, and applications in computational geophysics. Research trends include GPU offloading, fault tolerance in HPC, and SYCL programming models. Professor Weinzierl has authored books including Principles of Parallel Scientific Computing and edited volumes such as Advanced Computing . He regularly presents at major HPC conferences and workshops worldwide. He advises postgraduate students working on parallel algorithms, GPU acceleration, and computational science applications. Research groups include the Scientific Computing and Data Analysis initiatives at Durham.

Christoph PflaumView profile

Professor

• simulation of airships
• computational optics
• high performance computing
• +2 more

Prof. Christoph Pflaum holds the position of Professor of Computational Engineering at the University of Erlangen-Nürnberg's Department of Computer Science. His academic journey includes a PhD (1996) and Habilitation (1999) from Technical University Munich and University of Würzburg, respectively. His research focuses on computational optics, high-performance computing, numerical analysis, and multigrid methods, with applications in laser simulation and energy-efficient systems. Education : 1992: Diploma in Mathematics, Technical University Munich 1996: Dr. rer. nat., Technical University Munich 1999: Habilitation, University of Würzburg Research Interests : PDE solvers on sparse grids, laser dynamics (thermal lensing, solid-state amplifiers), computational fluid dynamics for airships, and optical system optimization. His work bridges theoretical numerical methods with industrial applications in photonics and renewable energy. Key Contributions : Developed simulation tools for high-power lasers and solar cells, including LASCAD and FDFD methods. Advances in multigrid algorithms and finite element analysis for complex optical systems. Teaching : Courses in theoretical computer science, scientific computing, and computational optics, emphasizing practical implementation using advanced numerical libraries and HPC. Grants & Projects : Led research on laser systems optimization (Innonet SOL), solar cell simulations (Photonics Europe), and high-performance computing initiatives (Par-EXPDE project).

Stefan ZimmerView profile

Senior Lecturer

• Numerical Simulation
• Computational Science
• Sparse Grids
• +4 more

Stefan Zimmer is a Senior Lecturer and Academic Councillor at the University of Stuttgart, affiliated with the Simulation of Large Systems department under the IPVS institute. His work focuses on numerical simulation, computational science, and algorithm development with applications in sparse grids, multigrid methods, and high-performance computing. Research interests include adaptive algorithms for large-scale systems, model reduction techniques, and interdisciplinary challenges in technical education. He has contributed to foundational works in sparse grid techniques and parallel computing architectures. Publications span from 1993 to 2015, emphasizing advancements in numerical methods, data mining, and computational fluid dynamics. Notable works include the 2015 multigrid method for adaptive sparse grids and a 2009 textbook on modeling and simulation methodologies. No awards or student advisees are explicitly listed in the provided text.

Xiu YangView profile

Associate Professor

• Uncertainty Quantification
• Quantum Computing
• Scientific Machine Learning
• +4 more

Xiu Yang is an Associate Professor in the Department of Industrial and Systems Engineering at Lehigh University's College of Engineering. He previously worked at Pacific Northwest National Laboratory (PNNL) as a scientist and holds a Ph.D. in Applied Mathematics from Brown University, along with degrees from Peking University. His research focuses on modern scientific computing, including uncertainty quantification, quantum computing, physics-informed machine learning, and multi-scale modeling. Yang has applied these methods to fluid dynamics, hydrology, biochemistry, and energy storage systems, with recent emphasis on quantum computing algorithms and their applications in scientific computing. He has received notable awards, including the NSF CAREER Award (2022) and PNNL's Outstanding Performance Awards (2015 and 2016). His work bridges computational mathematics and real-world challenges, such as error modeling in NISQ devices and developing quantum algorithms for linear systems. Yang also contributed to the DOE applied mathematics visioning committee in 2019, reflecting his leadership in advancing computational science. His research outputs span interdisciplinary areas, combining quantum computing with machine learning (e.g., Quantum DeepONet) and enhancing Gaussian process regression techniques with constraints. This work underscores his commitment to advancing computational tools for complex scientific problems, from seismic wave equations to power grid systems.

Laurent CharlinView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +3 more

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

Alexander FlueckView profile

Associate Professor

• Power System Stability
• Power System Control
• Computational Tools for Power Engineering
• +4 more

Alexander Flueck is an Associate Professor of Electrical and Computer Engineering at Illinois Institute of Technology (IIT), part of the Armour College of Engineering. He earned his B.S., M.Eng., and Ph.D. in Electrical Engineering from Cornell University. His research focuses on power system stability and control, developing computational tools for large-scale power systems. He has been at IIT since 1996. Education: Ph.D., Electrical Engineering, Cornell University, 1996 M.Eng., Electrical Engineering, Cornell University, 1992 B.S., Electrical Engineering, Cornell University, 1991 Research interests include power system stability, numerical methods for power system analysis, and computational tools for system planning. His work emphasizes security and competitiveness in power systems. He has contributed to tools like CPFLOW for tracing power system steady-state behavior under varying conditions. Teaching emphasizes hands-on problem-solving and analytical skills, with a focus on reinforcing fundamentals through lab experiments and challenging assignments. Publications span topics like power flow equations, contingency ranking, and voltage stability assessments. His NSF CAREER Award (1999) supported research on transfer capability in deregulated power systems. Service activities include directing the IIT-sponsored American Power Conference program and leading the Chicago Chapter of the IEEE Power Engineering Society. He also contributes to smart grid initiatives, including distributed power flow solvers and agent-based control systems. Contact: flueck@iit.edu, Siegel Hall 224, Chicago, IL.

• High-Performance Computing
• Computational Fluid Dynamics
• Climate Modeling
• +5 more

Henry M. Tufo is a Researcher affiliated with the University of Colorado, USA . His work spans High-Performance Computing (HPC) , Cloud Computing , and Computational Fluid Dynamics , with a focus on climate modeling, grid systems, and scalable algorithms. Tufo has collaborated extensively with institutions like IBM, Argonne National Laboratory, and researchers such as Paul Fischer, Kate Keahey, and Paul Marshall. His research interests include: Developing scalable HPC systems for climate and astrophysical simulations Integrating cloud computing with scientific workflows Optimizing spectral element methods for atmospheric models Trends in his publications highlight expertise in parallel computing , secure execution environments , and numerical methods for fluid dynamics. Tufo has contributed to frameworks like the FLASH code and GraphBLAS for large-scale simulations.