Find Academic Supervisors

• Programming Languages
• Formal Methods
• Software Engineering
• +4 more

Alexander Summers is an Associate Professor at the Department of Computer Science , University of British Columbia . He joined UBC in March 2020 after serving as a Senior Researcher (Oberassistent) at ETH Zurich from 2014-2020. His research bridges Programming Languages , Formal Methods , and Software Engineering , with a focus on automated verification tools for heap-based and concurrent programs. MSc Joint Mathematics and Computer Science, Imperial College London (2004) PhD Computer Science, Imperial College London (2009) Postdoc, ETH Zurich (2009-2014) Summers leads the Prusti Project , developing deductive verification tools for Rust, and contributes to the Viper Project for intermediate verification languages. His work addresses challenges in: Memory safety and concurrency verification Ownership models and aliasing control Automated reasoning with SMT solvers Resource-oriented programming specifications Debugging verification condition quantifiers Formal validation of verification infrastructure His research has been recognized with a Amazon Research Award and ACM SIGPLAN Distinguished Paper Awards . He teaches courses like Advanced Software Engineering and Program Verifiers and Program Verification , and supervises graduate students in formal verification and Rust-related research.

Paula HilbertView profile

Researcher

• Numerics of PDEs
• Adaptive FEM
• Contractive Iterative Solvers
• +5 more

Paula Hilbert is a PhD student and University Assistant at TU Wien's Institute of Analysis and Scientific Computing, supervised by Prof. Dirk Praetorius. Her research focuses on Numerics of PDEs, adaptive FEM, and contractive iterative solvers, with current emphases on Finite Element Methods, Multigrid methods, and optimal preconditioners for Galerkin matrices. She teaches the course 'Introduction to Programming' (4 VU) in the summer term of 2025. Education: BSc (1998) and MSc (2022) in Technical Mathematics from TU Wien. Current affiliation includes the ASC-Nextcloud and TU-Webmail platforms. No awards or grants are explicitly listed in the provided materials.

Maximilian BrunnerView profile

Researcher

• Numerics of PDEs
• Finite Element Methods (FEM)
• Contractive Iterative Solvers
• +3 more

Maximilian Brunner is a Postdoc at TU Wien's Institute of Analysis and Scientific Computing (E 101), specializing in Numerics of Partial Differential Equations (PDEs). He holds a Dipl.-Ing. (MSc) and Dr.techn. (PhD) from TU Wien, with his doctoral thesis on On optimal adaptivity for semilinear PDEs earning the 2025 Study Prize from the Austrian Mathematical Society (ÖMG). His research focuses on adaptive finite element methods (FEM), iterative solvers for nonlinear PDEs, and cost-optimality analysis. He collaborates with Prof. Dirk Praetorius and contributes to projects funded by the Austrian Science Fund (FWF). Previously, he was a PhD student (2020–2024) and taught courses in numerical mathematics and scientific programming. His work bridges theoretical advancements in adaptive algorithms with practical computational efficiency, addressing challenges in nonsymmetric and semilinear PDEs. Education: PhD in Technical Mathematics, TU Wien (2024), supervised by Dirk Praetorius MSc in Technical Mathematics, TU Wien (2020), supervised by Winfried Auzinger Research Interests: His interdisciplinary work centers on developing adaptive FEM strategies for cost-optimal solutions to PDEs. Key areas include: Contractive iterative solvers for nonlinear systems Goal-oriented error estimation for targeted accuracy Quasi-optimal computational cost in adaptive algorithms His methods prioritize minimizing computational resources while maintaining solution fidelity, with applications to elliptic and semilinear PDEs. Awards: 2025 Study Prize (ÖMG) for PhD thesis Grants & Funding: FWF Projects P33216 and P28367 Vienna School of Mathematics membership (2020–present) Labs/Teams: Core member of the Numerics of PDEs research group, part of TU Wien's ASC unit. Active in collaborative projects with international conferences and workshops.

• Numerical Linear Algebra
• High Performance Computing
• Krylov Subspace Methods
• +4 more

Dr. Hussam Al Daas is a Postdoctoral Research Fellow at the Max Planck Institute for Dynamics of Complex Technical Systems since January 2019, specializing in numerical algorithms for large-scale scientific computing. His work bridges theoretical numerical analysis and high-performance implementation. Education: PhD in Applied Mathematics, Inria-Paris and Sorbonne University (2015-2018), funded by TOTAL Master in Fundamental and Applied Mathematics, Paris-Sud University (2012-2014) His research centers on developing communication-avoiding algorithms for sparse linear systems and tensor computations, with emphasis on robust preconditioners (algebraic two-level Schwarz, domain decomposition), Krylov subspace methods, and low-rank approximations. He addresses critical challenges in parallel scalability through rigorous complexity analysis and memory-efficient implementations, particularly for distributed-memory architectures. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Algebraic multilevel preconditioners achieving robustness for ill-conditioned sparse systems, (2) Theoretical communication lower bounds with optimal algorithm design for tensor/matrix operations, and (3) Novel extensions of Krylov methods for sequences of shifted systems and tensor train decompositions. These contributions target reservoir simulation, PDE-constrained optimization, and high-dimensional data problems. Funded by TOTAL during his PhD and currently by the Max Planck Society, his work shows no evidence of student advisement or external grant leadership. He contributes to the Computational Methods in Systems and Control Theory group, focusing on scalable solvers for complex dynamical systems through collaborative software development and algorithmic innovation.

Tim MitchellView profile

Assistant Professor

• Numerical Computing
• Numerical Analysis
• Numerical Linear Algebra
• +4 more

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.

Thomas WihlerView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Prof. Thomas Wihler is a Professor at the Mathematical Institute (MAI) of the University of Bern, within the Faculty of Science. His research focuses on numerical analysis, partial differential equations, and computational methods, particularly involving finite element methods and discontinuous Galerkin techniques. He holds a leadership role in the Institute Management and is contactable at thomas.wihler@unibe.ch. His academic background includes advanced studies in mathematics, though specific details of his education are not explicitly provided in the text. His research interests emphasize adaptive algorithms, numerical solutions to elliptic and parabolic PDEs, and the development of efficient discretization strategies for complex physical models. Recent work highlights contributions to energy-based adaptivity, error estimation, and the analysis of nonlinear systems such as the Gross-Pitaevskii equation. Publications from 2021–2025 reflect a focus on advancing numerical methods for PDEs, including studies on optimal finite element approximations, exponential convergence of discontinuous Galerkin schemes, and adaptive strategies for nonlinear problems. His work often bridges theoretical analysis with practical computational techniques, addressing challenges in both stability and efficiency. No specific scientific awards or grants are mentioned in the provided texts. His academic contributions are primarily through research publications and leadership in the Mathematical Institute.

• Formal Methods
• Model Checking
• Satisfiability (SAT) Solving
• +4 more

Tommi Junttila serves as a Senior University Lecturer in the Department of Computer Science at Aalto University, Finland, where he conducts cutting-edge research at the intersection of formal methods and computational logic. His academic profile demonstrates sustained contributions to theoretical computer science with practical applications in system verification and blockchain technology. His research program centers on advancing formal verification techniques, with core expertise in: Symmetry reduction algorithms for state space explosion SAT/SMT solving with specialized parity and XOR reasoning Bounded model checking of timed and asynchronous systems Blockchain protocol verification (notably DeFi lending pools) Canonical labeling tools for graph automorphism detection Recent work shows increasing focus on decentralized finance applications while maintaining foundational contributions to solver technology. Analysis of his publication trajectory (2011-2022) reveals consistent innovation in SAT solving methodologies, with symmetry reduction and parity reasoning forming persistent research threads. His 2022 work on DeFi lending pools represents a strategic expansion into blockchain verification, leveraging established formal methods expertise for emerging financial technologies. Tool development (bliss, PySMT) demonstrates commitment to practical research impact. No scientific awards were documented in the source materials. Similarly, no information regarding student supervision, grant funding, or laboratory affiliations was present in the provided text. His independent tool development (including bliss for graph canonical labeling and PySMT for SMT solver interfaces) indicates significant technical leadership within the formal methods community.

• Aerospace Engineering
• Computational Fluid Dynamics (CFD)
• Aerodynamic Sensitivity Analysis
• +4 more

Arthur Taylor is a Professor of Mechanical & Aerospace Engineering at Old Dominion University (ODU), part of the Batten College of Engineering and Technology. His expertise lies in computational fluid dynamics (CFD), aerodynamic sensitivity analysis, and multidisciplinary design optimization. Taylor holds a Ph.D. in Mechanical Engineering from Virginia Tech (1989), an M.S. from Virginia Tech (1986), and dual B.S. degrees in Mechanical Engineering and Natural Science from Old Dominion University (1985) and Washington and Lee University (1976). His research focuses on advancing CFD methodologies, particularly in sensitivity analysis for aerodynamic design optimization. He has led projects funded by NASA and other agencies, including studies on parallel computing, automatic differentiation, and fluid-structure interaction. Notable contributions include the development of the CFL3D.ADII code and methodologies for efficient sensitivity derivative calculations. Grants & Contracts: "Advanced Applications of ADIFOR 3.0" ($62,562), 2001–2002 "Aerodynamic Sensitivity Analysis on Parallel Computers" ($38,047), 1998 "Multidisciplinary Design Optimization for Fluid/Structure Interaction" ($66,000), 1994–1997 Taylor has received prestigious awards such as the Gordon C. Oates Air Breathing Propulsion Award and is a member of Phi Kappa Phi and Tau Beta Pi. His work bridges theoretical CFD advancements with practical engineering applications, emphasizing computational efficiency and robust design frameworks.

Robert KirbyView profile

Professor

• Finite elements
• Partial differential equations
• Preconditioners
• +2 more

Robert Kirby is a Professor of Mathematics and Undergraduate Advisor at Baylor University, affiliated with the College of Arts & Sciences and the Department of Mathematics. He holds a Ph.D. from the University of Texas at Austin (2000) and has held academic positions at Texas Tech University, the University of Chicago, and as a Dickson Instructor. His research focuses on automating numerical methods for partial differential equations (PDEs), including finite elements, preconditioners for multiphysics problems, and high-performance computing. Kirby's research interests bridge mathematics and computer science, emphasizing the development of efficient algorithms for PDE simulation using domain-specific languages, Bernstein polynomials, and multicore architectures. His work integrates theoretical analysis with practical software implementation in projects like FEniCS and Firedrake. His publications consistently address finite element methods, numerical stability, and computational efficiency. Recent articles emphasize automated time-stepping, domain truncation, and preconditioning techniques, reflecting a trend toward scalable and user-friendly tools for scientific computing. Grants and Advising: Kirby has secured significant funding, including NSF awards for automated algorithms and Sandia National Laboratory contracts. He advises doctoral students in mathematics and computing, with alumni at Amazon, NSA, and academia. Grants include: NSF CCF award 1117794: Metanumerical computing ($500k) NSF CCF award 0830655: Automated intrusive algorithms ($270k) DOE Early Career Award: Automatic parallel finite elements ($300k) He leads collaborations on open-source projects (FEniCS, Firedrake) and develops tools like FIAT and Irksome to streamline finite element workflows.

• Applied Mathematics
• Partial Differential Equations (PDE)
• Numerical Analysis
• +5 more

Nilima Nigam is a Professor of Mathematics at Simon Fraser University (SFU), part of the Department of Mathematics within the Faculty of Science. She holds a Ph.D. in Applied Mathematics from the University of Delaware (1999). Her research focuses on numerical analysis of partial differential equations (PDEs), computational electromagnetics, mathematical physiology, and biomechanics. She is also actively involved in interdisciplinary projects, such as modeling muscle-tendon complexes and spectral geometry problems. Nigam serves on editorial boards for journals like the SIAM Journal on Scientific Computing and Mathematics-in-Industry Case Studies. She supervises graduate and undergraduate students in applied mathematics, with a focus on structure-preserving discretizations, computational biology, and spectral problems. Her work includes collaborations with researchers in biomechanics, notably with James Wakeling in studying muscle mechanics and energy dynamics. She has mentored numerous students through programs like the SFU Department of Mathematics USRA competition. Her teaching spans advanced courses in numerical analysis, PDEs, and mathematical physics.

Alexander HeinleinView profile

Assistant Professor

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +6 more

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Mathematical Optimization
• Conic Optimization
• Smart Grids
• +7 more

Miguel Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh. He serves as Deputy Head of School and leads the Data and Decisions research theme. His professional roles extend to Chair of the Mathematical Optimization Society (2023–2025) and Vice-President (International Activities) of INFORMS. He holds Fellowships from the Humboldt Foundation, EUROPT, and the Canadian Academy of Engineering. Research Focus: Mathematical optimization for large-scale nonlinear problems in engineering applications, particularly conic optimization in facility layout and power grid optimization. Collaborations: National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, EDF, and Yeji Data Lab. Datasets: Maintains QAPLIB, FLPLIB, and Jones Benchmark for optimization research. His work addresses smart grid challenges: maximizing grid efficiency, integrating renewables, optimizing energy storage, and enabling customer participation. Former students have secured positions at universities (University of Tennessee, Western Ontario) and tech companies (Amazon, Shopify). Scientific Awards: Fellow of the Humboldt Foundation Fellow of EUROPT Fellow of the Canadian Academy of Engineering He has supervised PhD researchers including Elspeth Adams (2014 MOPTA runner-up), Christian Bingane (2019 CORS Student Paper Competition winner), and Mathieu Tanneau (2018–2021 FRQNT Merit Scholar). His postdocs have contributed to bilevel attacks in AI, energy storage scheduling, and electric vehicle infrastructure planning.