Find Academic Supervisors

Henrietta TománView profile

Assistant Professor

• Medical image processing
• Fusion systems
• Quasigroups
• +3 more

Dr. Henrietta Tomán serves as an Assistant Professor in the Department of Data Science and Visualization at the University of Debrecen's Faculty of Informatics, where she bridges advanced mathematical theory with practical medical imaging applications. Her work integrates abstract algebraic structures with cutting-edge AI systems to solve critical healthcare challenges. Her research portfolio spans three interconnected domains: Medical image processing (particularly ensemble-based segmentation and quality assessment for ophthalmic diagnostics) Geometric structures (quasigroups, loops, and differentiable manifolds) Stochastic optimization for resource-constrained AI systems Analysis of her publication trajectory reveals evolving expertise: early work (2010-2014) established foundations in geometric loop theory applied to image processing, while recent research (2020-2024) pioneers stochastic fusion techniques for medical image ensembles under computational constraints. Her most significant contributions involve translating mathematical abstractions into robust clinical decision-support tools, particularly in diabetic retinopathy detection and epidemic modeling. Current work demonstrates increasing focus on real-time AI systems that maintain accuracy under hardware limitations, reflecting urgent needs in telemedicine and mobile health applications. Dr. Tomán maintains active collaboration within the Doctoral School of Informatics and contributes to Hungary's national research initiatives in medical AI, with consistent publication output in top-tier venues spanning computer vision, medical imaging, and mathematical computing.

Peggy Cenac-GuesdonView profile

Lecturer

• Statistics
• Probability
• Random Walks
• +3 more

Peggy Cenac-Guesdon is a Lecturer-Researcher at the University of Burgundy, affiliated with the Institute of Mathematics of Burgundy (IMB) and the UFR Sciences and Technology. Her research focuses on probability theory, stochastic processes, and their applications to biological sequence analysis. Education: PhD in Applied Mathematics (2006, Université Paul Sabatier Toulouse III), HDR (Habilitation à Diriger des Recherches). Research Interests: Cenac's work spans variable-length Markov chains, persistent random walks, and stochastic algorithms for analyzing multidimensional data. She applies these methods to biological sequences via Chaos Game Representation (CGR), enabling novel pattern detection and taxonomic classification. Her theoretical contributions include central limit theorems for martingales and optimization techniques for risk indicators. Key Publications: Recent papers address multidimensional persistent random walks, variable-length memory chains, and stochastic algorithms for geometric medians in Hilbert spaces. These works intersect probability theory, dynamical systems, and computational biology. Projects: She developed the MyCGR library in Objective-Caml for DNA sequence analysis using CGR, linking algorithmic design with statistical modeling. Her collaborations span mathematics, computer science, and actuarial risk modeling.

Ulrich MeyerView profile

Professor

• Algorithm Engineering
• Big Data
• High Performance Computing
• +5 more

Prof. Dr. Ulrich Meyer is a full Professor of Computer Science at Goethe University Frankfurt, where he joined as a full professor in 2007 at the age of 35. He is also a Fellow at the Frankfurt Institute for Advanced Studies (FIAS) since 2020 and serves as the spokesperson for the DFG priority program "Algorithms for Big Data" (SPP 1736), which spans over 15 sites across Germany, Austria, and Switzerland. Professor Meyer received his Ph.D. in computer science from Saarland University in 2002. Following his doctorate, he worked as a postdoc and later as a senior researcher (W2) at the Max-Planck Institute for Computer Science in Saarbrücken before joining Goethe University Frankfurt. His research focuses on the design and engineering of algorithms and data structures for very large data sets, with particular emphasis on parallelism (Shared-Memory, clusters, GPUs), memory hierarchies (caches, SSDs, hard disks), and energy efficiency. His work spans the spectrum from theoretical worst-case results and average-case analyses to experimental evaluation of heuristics. A significant portion of his research involves graph algorithms, where his group has achieved more than a thousand-fold speed-up over state-of-the-art methods through algorithms with better access patterns. His recent work has focused on memory-efficient graph exploration in dynamically changing data, approximate solutions, and efficient generation of randomized graph test data. Professor Meyer has received numerous prestigious awards including the 2019 ESA Test of Time Award for the Delta-Stepping Algorithm, Best Paper Awards at ESA 2019 for research on Fragile Computing and Graph Generation, the 2011 Award Germany Land of Ideas for Ecosort, records in the JouleSort Competition in 2009/10, and the 2003 Best Dissertation Award from the University of Saarland. As spokesperson for the DFG priority program SPP 1736 "Algorithms for Big Data," Professor Meyer coordinates research across multiple institutions. His Algorithm Engineering group is also part of the DFG Research Unit FOR 2971 "Algorithms, Dynamics, and Information Flow in Networks" and the LOEWE Priority Programme CMMS. His research has been supported by various grants that have enabled significant contributions to parallel and external-memory graph algorithms, large-scale network generation, and energy-efficient computing. Professor Meyer's research group forms part of the Computer Sciences and AI Systems division at FIAS. They collaborate extensively with researchers across Europe through the DFG priority program and other initiatives. The group is particularly known for their work on memory-efficient graph algorithms, large-scale network generation, and contributions to understanding the algorithmic foundations of big data processing.

Andrew FioriView profile

Associate Professor

• Algebraic Number Theory
• Algebraic Geometry
• Shimura Varieties
• +5 more

Andrew Fiori is an Associate Professor and Co-Chair of Mathematics at the University of Lethbridge, Faculty of Arts and Science, Department of Mathematics & Computer Science. He has been with the university since Fall 2017, becoming Associate Professor in July 2021 after serving as Assistant Professor. Prior to this, he held postdoctoral positions at the University of Calgary (2015-2017) and Queen's University (2013-2015). His academic background includes a Ph.D. (2013) and M.Sc. (2009) in Mathematics from McGill University, and a B.Math Honours in Computer Science and Pure Mathematics from the University of Waterloo (2007). He has approximately 24 months of work experience as a software developer. Fiori's research spans Algebraic Number Theory and Algebraic Geometry, with particular focus on Algebraic Groups, Shimura Varieties, Lattices, and their connections to each other and to broader mathematical fields. His most active research program concerns geometric and categorical approaches to the p-adic local Langlands correspondence, as part of the Automorphic Representations Research Group. He also investigates geometric interpretations of the local Langlands correspondence for p-adic groups, pseudo-primes, subgroup classifications of algebraic groups, and computational number theory problems. His publications from 2012-2017 demonstrate a consistent focus on advanced topics in number theory and algebraic geometry, particularly exploring structures related to p-adic rings, CM-elliptic curves, and symmetric spaces. The research shows progression from foundational work on orthogonal symmetric spaces to more specialized investigations of Hermitian lattices and Euler characteristics in algebraic contexts. PIMS Postdoctoral Fellow Professor Fiori actively supervises graduate students and postdoctoral researchers, with current and former students working on topics including zero-density bounds for zeta functions, Frobenius primality tests, and geometric approaches to the Langlands program. He has also supervised numerous undergraduate research projects, particularly through NSERC-USRA funding, focusing on prime counting functions and computational number theory. He is involved with the Automorphic Representations Research Group and maintains an active role in both teaching and research at the University of Lethbridge, where his office is located in C546.

• Theoretical Computer Science
• Circuit Design
• Computational Complexity
• +3 more

Günter Hotz is a full Professor at the Department of Computer Science (Fachrichtung Informatik), Universität des Saarlandes , Germany. His academic career spans over five decades, including roles as Director of the Computer Center (1972-1974) Spokesperson for SFB 100 (1982-1984) and SFB 124 (1991-1992) . Research Interests include Theoretical Computer Science Circuit Design Computational Complexity Information Theory Geometric Motion Planning Formal Verification . His work focuses on analytic machines, hardware verification, and motion planning algorithms, with applications in linguistics and VLSI design. Publications emphasize formal methods for hardware, computational models over real numbers, and efficient parsing algorithms. Key trends involve integrating mathematical theory with practical circuit design and motion planning solutions. Scientific Awards include Leibniz Prize (1986) Konrad Zuse Medal (1999) Grand Cross of Merit (1998) . Students : Supervised 40 dissertations, with over a third of his advisees becoming professors in mathematics and computer science. Labs & Projects : Led major research initiatives like SFB 100 (COMSKEE system) and SFB 124 (VLSI design methods). Collaborative works with international institutions in France, the U.S., and Georgia.

• Granular Media
• Solid mechanics
• Constitutive and multiscale modeling
• +6 more

Émilien Azéma is a Professor at the University of Montpellier since 2023, affiliated with the Department of Mechanics in the Faculty of Sciences and the Mechanics and Civil Engineering Laboratory (LMGC). Since 2024, he also holds an Associate Professor position at Polytechnique Montréal in the Department of Civil, Geological and Mining Engineering. Primary Institution: University of Montpellier, France Secondary Appointment: Polytechnique Montréal, Canada Research Recognition: French University Institute (IUF) Research Chair (2020-2025) Dr. Azéma received his complete education at the University of Montpellier, earning degrees in Mathematics, Solid Mechanics, and completing his Doctorate and Habilitation (HDR) in Mechanics. His doctoral research focused on railway ballast behavior using Discrete Element Method (DEM) simulations, supported by a joint CNRS and SNCF grant. Licence/Maîtrise (Master 1) in Mathematics DEA (Master 2) in Solid Mechanics Doctorate in Mechanics Habilitation (HDR) in Mechanics His research program centers on the micromechanics of granular systems with realistic microstructure. Using innovative Discrete Element Method (DEM) approaches, he systematically investigates the nonlinear effects of particle shape characteristics (angularity, non-convexity, elongation) and size polydispersity on granular rheology across flow regimes from quasi-static to rapid. His work extends to evolving granular systems where particles can fragment or undergo large deformations, with applications to geomechanical materials, industrial powders, and extraterrestrial granular systems. An analysis of his recent publications (2023-2025) reveals a strong emphasis on particle shape effects, cohesive strength mechanisms, and polydisperse system behavior. His research bridges fundamental granular physics with practical applications in railway engineering, planetary science (particularly asteroid regolith modeling), and geomechanics, appearing in high-impact journals including Physical Review E, Granular Matter, and Icarus. Dr. Azéma's significant research contributions have been recognized through: A prestigious Research Chair from the French University Institute (IUF) as Junior Member (2020-2025) An extensive publication record with 81 publications across multiple high-impact journals He teaches core courses in solid mechanics, numerical computation, finite element method, discrete approaches, and rheology of granular media. His administrative responsibilities include: International affairs coordinator at the Faculty of Sciences (since 2018) Responsible for the first year curriculum of the Mechanical Engineering Master's program (since 2021) His primary research laboratory is the Mechanics and Civil Engineering Laboratory (LMGC) at the University of Montpellier, where he conducts his investigations into granular media and solid mechanics phenomena, with significant collaborations extending to Polytechnique Montréal and international institutions.

Cyril AllignolView profile

Lecturer

• Combinatorial optimization
• Metaheuristics
• Semantics of languages
• +4 more

Cyril Allignol is a Lecturer and Researcher in the OPTIM team at the National School of Civil Aviation (ENAC) research laboratory. His work focuses on two primary themes: (1) solving combinatorial optimization problems related to air traffic and airport operations using constraint programming , and (2) formalizing reactive languages to ensure guaranteed properties for air traffic control and piloting assistance tools. PhD in Computer Science and Telecommunications (2011) from the University of Toulouse ENAC Engineer (2006) Master's in Computer Science and Telecommunications (2006) from the University of Toulouse His research spans air traffic conflict resolution , detect-and-avoid algorithms for UAVs/UAS , and formal methods in reactive language compilation . He has contributed to constraint programming frameworks, robust gate allocation models, and 3D trajectory deconfliction systems. His work integrates metaheuristics , geometrical algorithms , and formal verification techniques. Publications reveal expertise in mathematical optimization , UAS integration , and bigraph-based modeling for avionics systems. He collaborates with institutions across France, Italy, Georgia, and the United States through conferences like ICRAT , ATM Seminar , and ROADEF . His team affiliation ( OPTIM ) and technical focus on conflict resolution , self-separation , and navigation accuracy highlight his contributions to air traffic safety and efficiency . Current projects include 4D-trajectory deconfliction and formal methods for avionics software .

Michel L. LapidusView profile

Professor

• Fractal Geometry
• Mathematical Physics
• Spectral Analysis
• +5 more

Michel L. Lapidus is a Distinguished Professor of Mathematics and holder of the Burton Jones Endowed Chair in Pure Mathematics at the University of California, Riverside. He holds cross-appointments in Physics/Astronomy and Computer Science. He earned his Ph.D. and Doctorat d'État ès Sciences from Université Pierre et Marie Curie (Paris VI). His research bridges fractal geometry , mathematical physics , and number theory , focusing on spectral analysis of fractal drums, p-adic fractal strings, Feynman path integrals, and connections between fractal zeta functions and the Riemann hypothesis. Recent work explores complex dimensions, multifractal zeta functions, and noncommutative fractal structures. Publication trends reveal deep interplays between geometric measure theory, dynamical systems, and spectral theory, with applications to quantum physics and arithmetic geometry. His articles frequently employ zeta functions, tube formulas, and operator algebras to characterize fractal spaces. Scientific Awards: Fellow of AAAS and AMS; Anassilaos International Prize; Dissertation Mentor Award; Creative Research Medal He directs the Fractal and Mathematical Physics Research Group , mentoring over 20 PhD students and postdocs. Funded by NSF grants including DMS-1107750, his work supports investigations into fractal spectral theory and noncommutative flows.

Marek IzydorekView profile

Professor

• Hamiltonian Systems
• Lagrangian Systems
• Bifurcation Theory
• +7 more

Marek Izydorek is a Professor at the Institute of Applied Mathematics within the Faculty of Applied Physics and Mathematics at Gdańsk University of Technology. With a distinguished career spanning over two decades, he has established himself as a leading researcher in dynamical systems and differential equations, contributing significantly to both theoretical foundations and practical applications in mathematical physics. Professor Izydorek's primary research interests encompass Hamiltonian and Lagrangian systems, bifurcation theory, Conley index theory, and variational methods in differential equations. His work frequently addresses complex problems involving singular potentials and strong force conditions, with particular focus on homoclinic and heteroclinic solutions. His research bridges pure mathematics with applications in mechanics and physics, demonstrating the profound connections between abstract mathematical structures and physical phenomena. Analysis of his publication record from 2012-2024 reveals a consistent trajectory of sophisticated mathematical investigation, with increasing sophistication in the application of topological methods to dynamical systems. His recent work shows a notable expansion into equivariant methods and deeper integration of Conley index theory with spectral flow analysis. The 2023-2024 publications demonstrate continued innovation in handling challenging problems in nonlinear analysis, particularly in the convergence properties of perturbed systems and alternative approaches to fundamental conjectures in symplectic geometry. With 26 publications documented in the Bridge of Knowledge repository and teaching involvement in 67 courses, Professor Izydorek maintains an active research and educational profile. His work has established him as a significant contributor to the mathematical community, particularly in the specialized areas of dynamical systems with singularities and topological methods in differential equations.

• Visualization
• Computer-Aided Geometric Design
• Computer-Aided Design
• +2 more

Georges-Pierre Bonneau is a Professor at University of Grenoble conducting research at Laboratoire Jean Kuntzmann (LJK) and INRIA in the MAVERICK research group. His academic career spans over three decades, beginning with his PhD from Universität Kaiserslautern in 1993, followed by positions as CNRS researcher (1994-2001), and Professor at University of Grenoble since 2001. Dr. Bonneau's research focuses on scientific visualization, computer-aided geometric design (CAGD), computer-aided design (CAD), and computational fabrication techniques. His work bridges theoretical geometric modeling with practical applications in structural monitoring, energy consumption visualization, and digital fabrication. He has developed innovative methods for surface reconstruction from sparse data, bending-active structures, and visualization of uncertain scalar data fields. His recent publications demonstrate a strong focus on immersive 3D modeling techniques, computational fabrication methods, and advanced visualization systems. Bonneau's research shows consistent evolution from foundational geometric modeling work in the 1990s-2000s toward contemporary applications in VR/AR interfaces, computational design of physical structures, and energy consumption visualization systems. Throughout his career, Bonneau has actively contributed to the academic community as conference organizer and program committee member for major venues including SPM, SMI, Eurographics, and IEEE Visualization conferences. He has supervised numerous PhD students whose work spans diverse applications of geometric modeling and visualization, with recent students focusing on surface creation from sparse strokes, bending-active structures, and structural monitoring systems. His former students have gone on to successful careers at institutions including AAC Clyde Space, Zurich Insurance, Pixminds, ISIR, and CEA.

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

Hongwei LongView profile

Professor

• Stochastic Analysis
• Mathematical Finance
• Parameter Estimation
• +2 more

Hongwei Long is a Professor and Graduate Director in the Department of Mathematics and Statistics at Florida Atlantic University. He holds a Ph.D. in Mathematics from the University of Warwick (1998) and specializes in stochastic systems, mathematical finance, and nonlinear filtering theory. His research bridges theoretical mathematics with applied domains like financial modeling and statistical inference for complex systems. Research Focus: Develops computational methods for stochastic differential equations, parameter estimation techniques for financial Lévy processes, and filtering applications in economics and engineering. His work frequently integrates heavy-tailed distributions and jump processes to model real-world volatility. Publications: Recent articles explore geometric Lévy processes, α-stable Ornstein-Uhlenbeck motions, and optimal control in forex markets, reflecting a consistent emphasis on quantitative finance and statistical methodology.

Robert LubarskyView profile

Lecturer

• Constructive Mathematics
• Higher Computability
• Set Theory

Robert Lubarsky is a faculty member in the Department of Mathematics and Statistics at Florida Atlantic University. His research explores constructive mathematics, higher computability theory, and set theory, focusing on models where classical mathematical theorems fail without the Principle of Excluded Middle. Education includes a Ph.D. in Mathematical Logic from MIT (1984). Research develops model-building techniques for constructive set theories and investigates computational hierarchies through feedback Turing machines and hyperjump operators. Recent publications examine topological necessities in constructive frameworks and inner/outer models for set theories. Scholarly service includes organizing the Logical Foundations of Computer Science conference series.

Alperen ErgürView profile

Assistant Professor

• Real algebraic geometry
• Convex geometry
• Randomized numerical algorithms
• +5 more

Alperen Ergür is an Assistant Professor at the University of Texas at San Antonio , affiliated with the College of Sciences and Department of Mathematics. His teaching integrates modern mathematical and computational skills, including courses on probability theory, optimization, algorithmic foundations of data science, reinforcement learning, and computational abstract algebra. Ph.D. in Mathematics from Texas A&M University M.S. in Mathematics from TOBB University of Science and Technology B.S. in Mathematics from Bilkent University Research spans real algebraic geometry , convex geometry , randomized numerical algorithms , and reinforcement learning . Key trends in his publications include: Polynomial system analysis Tropical geometry applications Tensor rank approximation Algorithm complexity in computational geometry Probabilistic methods in optimization Homotopy algorithms for real zeros He organizes the UTSA Algorithms Seminar and co-founded a student-friendly research seminar on Geometry, Probability, and Computing with Grigoris Paouris and Petros Valettas. With Suleyman Tek and Rodrigo Velez, he established a creative/competitive math circle for middle schoolers.

• Stochastic Analysis
• Partial Differential Equations
• Numerical Analysis
• +3 more

Dr. Petru Cioica-Licht is an Assistant Professor (Akademischer Rat) at the Institut für Mathematik, Universität Kassel, specializing in Stochastic Analysis and Applied Mathematics. His research focuses on stochastic partial differential equations (SPDEs), regularity theory in functional spaces (Sobolev/Besov), and numerical methods for high-dimensional problems. He holds a PhD from Philipps-Universität Marburg (2010-2013) and has held positions at the University of Otago (New Zealand) and the University of Duisburg-Essen. His work bridges theoretical analysis and computational approaches, addressing challenges in stochastic integration, domain geometry effects, and adaptive numerical schemes. Professional History: Since 2021 – Academic Council Member (Akademischer Rat), Universität Kassel. 2019–2021 – Researcher in Stochastic Analysis at Universität Duisburg-Essen. 2017–2018 – Lecturer (Assistant Professor) at University of Otago. 2016–2017 – Postdoctoral Fellow funded by the Marsden Fund (New Zealand). 2014–2015 – Postdoc in DFG-funded project on SPDE regularity in quasi-Banach spaces. Research Interests: SPDEs on non-smooth domains, regularity theory in weighted spaces, numerical approximation of high-dimensional PDEs, and adaptive wavelet methods. His work emphasizes the interplay between stochastic processes and functional analytic techniques, particularly in analyzing solution regularity and developing efficient computational frameworks. Publications Trends: Recent articles address Sobolev/Besov regularity for SPDEs on angular domains, stochastic integration in quasi-Banach spaces, and overcoming the curse of dimensionality via deep neural networks. Earlier work includes Besov regularity for Navier-Stokes equations and convergence analysis of adaptive Rothe methods. Grants & Funding: Recipient of DFG and Marsden Fund support for projects on SPDE regularity and stochastic fluctuations. Active contributor to collaborative research in numerical analysis and stochastic processes. Labs/Teams: Part of the Analysis and Applied Mathematics group in Stochastics at Universität Kassel, focusing on interdisciplinary applications of stochastic and functional analytic methods.