Find Academic Supervisors

Zachary KincaidView profile

Associate Professor

• Program Analysis
• Logic
• Programming Languages
• +7 more

Zachary Kincaid is an Associate Professor in the Department of Computer Science at Princeton University. His research focuses on program analysis , logic , and programming languages , with emphasis on making analysis compositional and robust . PhD, University of Toronto (2016) BSc, Western University Research Interests Dr. Kincaid develops algebraic program analysis frameworks combining symbolic methods with abstract interpretation. His work addresses challenges in: Compositional analysis of concurrent and recursive programs Termination analysis for loops with complex control flow Non-linear numerical invariant generation Strategy synthesis for logical games Parameterized program verification Publication Trends His research output spans program analysis (2024-2010), formal verification (2018-2010), concurrency (2016-2010), and automated synthesis (2013-2012). Recent work (2024) explores polynomial ideals and nonlinear ranking functions , while foundational contributions include vector addition systems and recurrence-based invariants . Scientific Engagement Dr. Kincaid contributes to the academic community through: Program Committee service (PLDI, POPL, CAV, IJCAI, LICS, FMCAD, ESOP, etc.) Co-developing the Duet analyzer for unbounded concurrency Collaborative work with leading researchers (Tom Reps, Azadeh Farzan, Jason Breck) Advising & Grants He advises PhD students and leads research funded by the ONR grant N00014-19-1-2318 . Current advisees include Jake Silverman and Shaowei Zhu, while former student Charlie Murphy (PhD 2023) now holds a postdoctoral position at University of Wisconsin-Madison. Labs & Teams Dr. Kincaid co-developed the Duet program analyzer and contributes to tools like Srk and SimSat . His work integrates SMT solvers (MathSAT, Z3) and mathematical frameworks (rational vector addition systems, recurrence relations) for robust program analysis.

• Linear Algebra
• Graph Theory
• Combinatorial Matrix Analysis
• +4 more

Kevin Vander Meulen is a Professor of Mathematics at Redeemer University, where he has taught since 1994. He also holds an Adjunct Professor appointment in the Department of Mathematics and Statistics at McMaster University. His academic background includes a Ph.D. and M.Sc. in Mathematics and Statistics from Queen’s University (1995 and 1991) and a B.Sc. Hon. in Mathematics from Calvin College (1989). His research focuses on Linear Algebra and Graph Theory , including combinatorial matrix analysis, sign patterns, inertia, eigenvalue problems, and vertex decomposability. He has secured research funding through an NSERC Discovery Grant (2016–2021). Key trends in his publications (1994–2025) include spectral properties of matrices, graph theory applications, and interdisciplinary work linking mathematics with philosophy. He frequently collaborates with students on research projects. Professional memberships include the Association for Christians in the Mathematical Sciences , Canadian Mathematical Society , International Linear Algebra Society , and Society for Industrial and Applied Mathematics . Kevin resides in Hamilton with his family and enjoys outdoor activities like canoeing, hiking, and cycling.

Lambert TheisenView profile

Researcher

• Partial Differential Equations (PDEs)
• Numerical Analysis
• Computational Fluid Dynamics
• +5 more

Lambert Theisen is a PhD student and Researcher at RWTH Aachen University's Applied and Computational Mathematics (ACoM) department. He holds a BSc (2014–2018) and MSc (2018–2019) in Computational Engineering Science from RWTH Aachen. His research focuses on PDE eigenvalue problems, asymptotic analysis, directional homogenization, and numerical methods for rarefied gas dynamics. He has held research roles at RWTH Aachen, the University of Stuttgart's NMH/IANS, and ABB Corporate Research. His research spans computational fluid dynamics, domain decomposition methods, and preconditioning techniques for eigenvalue algorithms. Notable contributions include finite element solvers for electrolyte models and FEniCS-based simulations of rarefied gas flows. His work often integrates advanced mathematical frameworks with high-performance computing. Teaching roles include assistantships for courses like 'Mathematical Aspects in Computational Chemistry' and 'Higher Mathematics for Engineers.' His publications are tracked via ORCid and emphasize scalable eigensolvers, mesh generation, and tensorial methods. He maintains a personal website at thsn.dev for research updates.

Uğurcan EroğluView profile

Assistant Professor

• Structural Engineering
• Vibration Analysis
• Damage Identification
• +3 more

Uğurcan Eroğlu serves as an Assistant Professor in the Department of Mechanical Engineering at Istanbul Technical University (İTÜ Ayazağa Kampüsü, 34000 Istanbul). His research focuses on structural mechanics with emphasis on vibration analysis, damage identification techniques, and nano-structure mechanics. He maintains active collaborations with international researchers including G. Ruta, E. Tüfekci, and A. Pau. His research interests encompass Structural Engineering, Vibration Analysis, Damage Identification, Nano-mechanics, Functionally Graded Materials, and Curved Beam Theory. Eroğlu develops advanced mathematical models using perturbation methods and non-local elasticity theories to solve complex structural problems. His work bridges theoretical mechanics with practical applications in structural health monitoring, particularly through modal curvature analysis and acoustic beamforming techniques for damage detection in arch structures and nano-beams. Analysis of his recent publications (2022-2025) reveals a consistent trend toward developing closed-form solutions for vibration problems in non-local structures. His research demonstrates increasing focus on nano-mechanics applications and damage detection methodologies, with notable contributions in parabolic arch analysis and beamforming techniques. The publications show interdisciplinary connections between structural mechanics, materials science, and signal processing. Dr. Eroğlu has secured two research projects as Principal Investigator: Hüzme Oluşturma Yöntemlerinin Yapısal Mekanikte Hasar Tespiti Problemlerinde Kullanılması (Beamforming Methods for Damage Detection in Structural Mechanics, 2023-2024, completed) and Reçine Baskı Yöntemi ile Parça Analizi (Resin Printing Method for Part Analysis, 2025, active). His scholarly impact is evidenced by an h-index of 10 and 287 citations across 25 research outputs from 2015-2025.

Lars KarlssonView profile

Associate Professor

• High-Performance Computing
• Numerical Linear Algebra
• Parallel Algorithms
• +2 more

Lars Karlsson is an Associate Professor at the Department of Computing Science, Umeå University, where he serves as Assistant Head of Department with responsibilities for undergraduate education. His research focuses on developing efficient algorithms for matrix and tensor computations within high-performance computing environments. He is affiliated with the Parallel and Scientific Computing research group at Umeå. Karlsson's primary research areas include: Numerical Linear Algebra : Specializing in matrix factorizations and eigenvalue computations Parallel Algorithms : Designing scalable solutions for distributed and shared-memory systems Tensor Computations : Developing decomposition methods and completion algorithms Performance Optimization : Auto-tuning techniques for modern computing architectures His publications (2010-2025) demonstrate consistent focus on parallel algorithms for numerical problems, particularly matrix reductions, eigenvalue computations, and tensor decompositions. Recent work emphasizes auto-tuning, robustness in numerical methods, and efficient scheduling for high-performance systems. The majority of publications involve collaborative research within European computing consortia like NLAFET. Karlsson contributes to educational research, having explored mastery learning approaches in university settings. His administrative responsibilities include oversight of undergraduate programs at the Department of Computing Science.

Marie-Anne GuerryView profile

Professor

• Mathematical Modeling
• Markov Chain Models
• Manpower Planning
• +3 more

Marie-Anne Guerry is a Professor at the Vrije Universiteit Brussel (VUB) in Belgium, affiliated with the Faculty of Social Sciences & Solvay Business School. She teaches mathematics and earned her PhD in Sciences in 1992. Her research lies at the intersection of Manpower Planning, Markov Chain Models, and HR Analytics, focusing on quantitative Human Resources Planning and empirical studies of career progression. Mathematical Modeling Markov Chain Models Manpower Planning Quantitative Human Resources Planning Career Studies HR-Analytics Her recent publications emphasize military manpower planning using flow network modeling, predictive analytics for employee absenteeism and turnover, optimization of roster quality in staffing systems, and theoretical advancements in Markov chain perturbations and embedding conditions. These works highlight her expertise in applying stochastic processes to practical HR challenges.

Nicholas McCleereyView profile

Assistant Professor

• Complex Geometry
• Pluripotential Theory
• Monge-Ampère Equations
• +5 more

Nicholas McCleerey is an Assistant Professor in the Department of Mathematics at Purdue University, College of Science. He specializes in complex geometry, pluripotential theory, and Monge-Ampère equations. His research addresses singularities, geometric analysis, and non-Archimedean geometry. Education: Ph.D. in Mathematics (2020) from Northwestern University, supervised by Valentino Tosatti; B.Sc. in Mathematics (2015) from Rice University. His postdoctoral work was at the University of Michigan, Ann Arbor, under Mattias Jonsson. Research focuses on Kähler geometry, m-subharmonic functions, and the interplay between tropical and non-Archimedean geometry. Key topics include Monge-Ampère equations on Calabi-Yau manifolds, regularity theory, and geometric singularities. Recent work explores eigenvalue problems for complex Hessian operators and boundary regularity in real Monge-Ampère equations. Publications span advanced topics like plurisupported currents, Lelong numbers, and geometric envelopes, appearing in journals such as Advances in Mathematics , Journal of the Institute of Mathematics of Jussieu , and Indiana University Math Journal . His 2024 IMRN paper on simplex boundary regularity exemplifies his focus on geometric PDEs. Awards: 2020 Best Thesis Award from Northwestern University. He co-organizes Purdue’s Geometry and Geometric Analysis Seminar and mentors the Math Alliance. Teaching includes differential equations, topology, and calculus courses at Purdue and the University of Michigan. Labs/Teams: Leads the Purdue Experimental Math Lab (undergraduate research in Monge-Ampère eigenvalues) and mentored an REU program on planar Monge-Ampère singularities. Active in seminars and conferences globally, including at Columbia, Stony Brook, and Tsinghua University.

Gert LubeView profile

Professor

• Numerical Methods for Partial Differential Equations
• Computational Fluid Dynamics
• Turbulence Modeling
• +2 more

Prof. Dr. Gert Lube is a faculty member at the Institute for Numerical and Applied Mathematics (NAM) within the Faculty of Mathematics and Computer Science at Georg-August-University Göttingen. His research focuses on numerical methods for partial differential equations , with emphasis on stabilized finite element methods , turbulence modeling , and magnetohydrodynamics (MHD) . Workshops Organized : Calibration of Viscosity Models for Turbulent Flows (2010), Variational Multiscale Methods (2008), Local Projection Stabilization (2008), BAIL Conferences. His academic contributions include 15+ publications since 2010 on topics like Navier-Stokes simulations , LES/VMS methods , stabilized FEM , and FEM-BEM coupling . Collaborations span institutions like TU Graz, Saarbruecken University, and DLR Göttingen. Key Research Areas : Finite Element Methods, Turbulence Modeling, MHD, Incompressible Flows, Singularly Perturbed Problems, Parallel Computing. Advisees include PhD candidates working on topics such as non-isothermal flows , mass conservation , hybrid RANS/LES , and domain decomposition .

Scot AdamsView profile

Professor

• Dynamical systems
• foliations
• ergodic theory
• +5 more

Scot Adams is a Professor in the School of Mathematics at the University of Minnesota, with research spanning pure mathematics and machine learning applications. His primary academic affiliation is with the Mathematics Department at the University of Minnesota, where he maintains an active research program while managing phased retirement paperwork. His research interests include dynamical systems, foliations, ergodic theory, hyperbolic groups, trees, Riemannian geometry, and more recently neural networks and thermodynamics. In his diary entries through August 2025, he documents ongoing work developing neural network slides, focusing on visual chain rule explanations, diagram of functions, and coordinate vector spaces. His recent publications reflect a transition toward machine learning applications while maintaining his foundational mathematics work. The 15 most recent articles show increasing focus on neural network theory (2025), with earlier works covering financial mathematics, climate policy presentations, and pure mathematics topics like Banach-Tarski paradox and central limit theorem. As an advisor, he works with graduate student Aarya Garimella on neural network research, meeting regularly to refine slides and explore mathematical foundations of neural networks. His teaching activities include courses in linear algebra and financial mathematics. His diary reveals active engagement with climate policy through Citizens' Climate Lobby (CCL), including Capitol Hill lobbying efforts and local climate forum organization. He maintains detailed records of academic and civic activities while managing household responsibilities.

Antoine LevittView profile

Assistant Professor

• Quantum Chemistry
• Materials Science
• Numerical Analysis
• +3 more

Antoine Levitt is an Assistant Professor (CPJ) at the Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay. Previously, he was a researcher at Inria Paris in the MATHERIALS project-team, affiliated with CERMICS at École des Ponts. His research focuses on numerical methods for quantum chemistry and materials science, including density functional theory (DFT), Wannier functions, and electronic structure calculations. He has contributed to theoretical advances in numerical analysis, such as error bounds for DFT and preconditioning techniques for eigenvalue problems. Levitt’s work spans computational physics and applied mathematics, with applications to quantum dynamics, response properties, and defect systems. He has developed open-source software tools like DFTK.jl and Wannierization algorithms . His teaching includes courses on scientific computing, functional analysis, and quantum mechanics at both undergraduate and graduate levels. Key research themes include numerical stability in DFT, analysis of linear response theory, and efficient implementations of conjugate gradient methods. His recent work explores machine learning applications in canonical sampling and mathematical foundations of Density Matrix Embedding Theory.

Ming ZhouView profile

Senior Lecturer

• Numerical Linear Algebra
• Eigenvalue Problems
• Adaptive Finite Elements

Ming Zhou is a Senior Lecturer at the Institute of Mathematics, University of Rostock, Germany. His research focuses on numerical linear algebra, eigenvalue problems, and adaptive finite elements. He is known for contributions to convergence theory of preconditioned eigensolvers and iterative methods. Zhou co-developed the AMP Eigensolver, a software tool for solving elliptic partial differential operator eigenvalue problems in 2D domains. His work emphasizes robust bounds for Ritz values, block preconditioned gradient methods, and restarted Krylov subspace iterations. Collaborations with researchers like K. Neymeyr and A.V. Knyazev have produced influential studies in numerical analysis. Zhou’s recent publications (2023–2024) address cluster-robust estimates and angle-free Ritz value bounds, advancing eigensolver algorithms. He is based at the Institute of Mathematics in Rostock, with office 332. His email is ming.zhou@uni-rostock.de . Office hours are by appointment.

Giuseppe SavaréView profile

Professor

• Mathematical Analysis
• Optimal Transport
• Gradient Flows
• +4 more

Giuseppe Savaré is a Full Professor of Mathematical Analysis at the Department of Mathematics, University of Pavia, and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2019. His research focuses on variational methods for evolution problems, including gradient flows, rate-independent models, nonlinear PDEs, and optimal transport theory in metric spaces. Education: PhD in Mathematics (1988) from University of Pavia. His work bridges optimal transport, metric measure spaces, and analysis in non-Euclidean geometries, with applications to PDEs, curvature bounds, and generalized gradient flows. Key contributions include foundational studies on Wasserstein gradient flows, entropy-transport problems, and abstract action spaces in dynamical systems. Scientific awards include the prestigious Ennio De Giorgi Prize (2011) and the Gioachino Japichino Prize (1994), alongside an invitation as a speaker at the 7th European Congress of Mathematics (2016). Collaborations with researchers like Massimo Fornasier and Alexander Mielke have advanced consensus-based optimization, multi-agent control, and quantum drift-diffusion models.

• Algebraic Complexity Theory
• Computational Algebra
• Geometric Complexity Theory
• +4 more

Prof. Dr. Peter Bürgisser is a Professor at the Technical University of Berlin, affiliated with the Institute of Mathematics and the Algorithmic Algebra research group within Faculty II - Mathematics and Natural Sciences. His research focuses on algebraic complexity theory, computational algebra, and geometric methods in computer science. Bürgisser has made significant contributions to topics including invariant theory, numerical analysis of algorithms, and the computational complexity of algebraic problems. He has authored influential books such as *Algebraic Complexity Theory* and has published extensively in top-tier journals like the Journal of the ACM and SIAM Journal on Computing. His work includes developing polynomial-time algorithms for problems in invariant theory, analyzing the condition numbers of algebraic varieties, and studying the computational aspects of semialgebraic sets. Bürgisser's research also intersects with probability theory, particularly in understanding the statistical properties of zeros of random polynomials and the geometry of random algebraic varieties. Recent trends in his publications emphasize geometric complexity theory, non-commutative optimization, and the application of numerical methods to algebraic problems. He has collaborated with researchers such as Felipe Cucker, Michael Walter, and Avi Wigderson on foundational topics in computational mathematics and theoretical computer science. Bürgisser's office is located in room EB 116, and his contact information includes the email pbuerg@math.tu-berlin.de. His research has been supported through grants and collaborations, though specific grant details are not explicitly mentioned in the provided texts.

Noah WilliamsView profile

Associate Professor

• Probability Theory
• Random Polynomials
• Random Matrices
• +5 more

Noah Williams is a tenured Associate Professor in the Department of Mathematical Sciences at Appalachian State University, part of the College of Arts and Sciences. He has completed seven years of service as of fall 2025, actively contributing to both teaching and research in probability and its applications. Research Interests: Dr. Williams specializes in probability theory , particularly focusing on random polynomials and random matrices . His work investigates the asymptotic behavior of roots and critical points, pairing phenomena, and eigenvalue statistics. He also explores combinatorial problems arising in genome rearrangement, applying probabilistic and discrete methods to model evolutionary distance. Recent Research Trends: His recent publications reveal a strong focus on the interplay between complex analysis and probability, especially in the context of random polynomials. He frequently collaborates with Sean O'Rourke, producing rigorous results on the Gauss-Lucas theorem and local root-critical point pairing. A secondary thread involves discrete-to-continuous transitions in arithmetic progressions and genome evolution models under operations like DCJ and deletion-insertion. No scientific awards or honors are mentioned in the available text. Teaching & Academic Service: Dr. Williams teaches a wide range of courses including Calculus I, Techniques of Proof, Linear Algebra, and graduate-level Computational Probability. He previously served in multiple instructional roles at the University of Colorado Boulder, including Instructor of Record, Assistant Coordinator for large calculus sections, and Lead Graduate Teacher for pedagogical training. His teaching philosophy incorporates Inquiry-Based Learning (IBL), supported by resources from established IBL initiatives. Laboratories & Research Teams: While no formal lab is mentioned, Dr. Williams is part of a collaborative research network, primarily with Sean O'Rourke (University of Colorado Boulder), and has co-authored work with other mathematicians such as T. Greenwood, J. Kariv, A. Brisbin, M. Riehl, and J. Christy. These collaborations suggest an active research group or team in probabilistic combinatorics and random matrix theory.

Hoi NguyenView profile

Professor

• Combinatorics
• Probability
• Random Matrices
• +3 more

Hoi Nguyen is a Professor of Mathematics at The Ohio State University (OSU), specializing in Combinatorics, Probability, and Random Matrices. His research explores areas such as inverse Littlewood-Offord problems, random matrix universality, and stochastic processes. He holds a PhD from Rutgers University (2010) and has held postdoctoral positions at the University of Pennsylvania, Yale University, and the Institute for Advanced Study. His work is supported by NSF grants and a Simons Foundation Fellowship. Recent projects include studies on random matrix cokernels, eigenvalue gaps, and trigonometric polynomials' real roots. He teaches graduate courses in Probability and Combinatorics at OSU. Education: PhD in Mathematics, Rutgers University, 2010. Research interests focus on probabilistic combinatorics, random matrix theory, and stochastic models. Key themes include universality phenomena in random structures, anti-concentration inequalities, and applications to algebraic problems. His work bridges combinatorial methods with probabilistic analysis, addressing foundational questions in matrix theory and polynomial root distribution. Grants include NSF CAREER Award (2018-2024) and Simons Fellowship (2023-2024). His recent articles investigate universality in random matrix cokernels, rank fluctuations in matrix products, and exponential concentration in root counts. He organizes workshops on random polynomials and matrices, reflecting his leadership in these fields.