Find Academic Supervisors

Anna SoósView profile

Associate Professor

• Fractal Mathematics
• Stochastic Processes
• Brownian Motion
• +6 more

Anna Soós is an Associate Professor at the Hungarian Mathematics and Informatics Institute, Faculty of Mathematics and Informatics, Babes-Bolyai University (Cluj-Napoca, Romania). She holds a PhD in Mathematics (2002) and has been active in stochastic analysis, fractal theory, and approximation methods. Her academic career includes leadership roles such as Vice-Rector for Curriculum and Hungarian Studies (2012–present) and Vice-Dean of the Faculty of Mathematics and Informatics (2008–2012). Education: MSc in Mathematics (Babes-Bolyai, 1982), MSc in Computer Science (Eötvös Loránd University, 1996), PhD in Mathematics (Babes-Bolyai, 2002) Her research focuses on deterministic and random fractals, stochastic differential equations, and homogenization on fractal structures. She has contributed to stochastic analysis through: Developing contraction methods in probabilistic metric spaces Wavelet approximations for SDE solutions Fractal interpolation techniques Scientific awards include: Bolyai János Research Fellowship Erasmus and CEEPUS Fellowships Domus Hungarica Scientiarum et Artium Grants World Bank Project Participation Anna Soós has coordinated international research projects like ERASMUS IP DSL2013 and FP7/MASCIL, while organizing conferences including the International Symposium on Numerical Analysis and Approximation Theory and Central European Functional Programming Summer Schools.

• Geodesy
• GPS Technology
• Atmospheric Science
• +3 more

Anna Jensen is a Professor of Geodesy at KTH Royal Institute of Technology, specializing in atmospheric effects on Global Navigation Satellite Systems (GNSS). Her research focuses on modeling ionospheric and tropospheric influences on GPS signals, particularly how weather parameters and electric currents impact signal transmission. Applications of her work include precision surveying, aviation navigation, and construction site monitoring. She has contributed to projects such as the Fehmarnbelt Fixed Link positioning system and Arctic navigation challenges. Her research interests span geodesy, satellite navigation, and remote sensing, with a strong emphasis on improving GNSS accuracy in challenging environments like urban areas and polar regions. Jensen’s studies address issues like ionospheric scintillation, RFI geolocation with smartphones, and robust positioning in jamming conditions. Jensen has published extensively on topics including network RTK data screening, GNSS network optimization, and ionospheric modeling. Her work often integrates statistical methods and geostatistical techniques to enhance positioning reliability. No scientific awards or grants are explicitly mentioned in the provided texts. She has advised no formal students, though her involvement in the Fehmarnbelt project and other large-scale infrastructure initiatives highlights her role in applied geodetic engineering. No specific labs or teams are detailed, but her collaborations likely involve KTH’s geomatics and geophysics groups.

Ivan TyukinView profile

Professor

• Machine Learning
• Artificial Intelligence
• Dynamical Systems Theory
• +5 more

Professor Ivan Tyukin is a faculty member at King's College London's Department of Mathematics, holding the position of Professor of Mathematical Data Science and Modelling since 2022. Previously, he served as a Professor of Applied Mathematics at the University of Leicester (2018–2022) and held roles from Lecturer to Reader there (2012–2018). He also served as an Adjunct Professor at the Norwegian University of Science and Technology (NTNU) from 2019 to 2021. Education: PhD (2001), DSc (Habilitation, 2006), University of Leicester academic roles (2007–present). Research Interests: Focuses on high-dimensional data analysis, neural networks, machine learning, dynamical systems, control theory, and mathematical modelling. His work bridges theoretical foundations of AI with practical applications in healthcare, cybersecurity, and urban analytics. Publications: Over 123 peer-reviewed articles, including recent works on adversarial AI attacks, stealth edits to language models, and applications of ML in cardiology. His research often emphasizes robustness and reliability in AI systems. Awards: Best Paper Award (2023). Grants: Leads EPSRC-funded projects like the Turing AI Acceleration Fellowship and initiatives in clinical AI and material science. Labs/Teams: Involved in interdisciplinary teams at King's College, collaborating on projects like self-learning AI for clinical care and automated archaeological analysis.

Naomi NishimuraView profile

Associate Professor

• Combinatorial Reconfiguration
• Parameterized Complexity
• Graph Algorithms

Naomi Nishimura is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. She holds a Ph.D. from University of Toronto (1991) and researches combinatorial reconfiguration, parameterized complexity, and graph algorithms. Her work develops efficient algorithms for reconfiguration problems, including recent advances in parallel implementations for atom reconfiguration using GPUs. Nishimura's research establishes theoretical foundations for transforming combinatorial structures through step-by-step modifications while maintaining solution properties. Recent publications survey parameterized complexity landscapes for reconfiguration problems and introduce novel algorithms like the assignment-rerouting-ordering technique. Her group analyzes computational boundaries for problems involving independent sets, dominating sets, and graph minors.

Elman HazarView profile

Professor

• Applied Mathematics
• Ordinary Differential Equations
• Integral Equations
• +4 more

Elman Hazar is a Professor in the Department of Mathematics at Igdir University's Faculty of Arts and Sciences. His research spans applied mathematics , differential equations , and soil thermodynamics , with recent work integrating machine learning and mathematical modeling of sentiment dynamics in literature. He has published extensively on fixed point theorems , integral equations , and stability analysis of physical systems. Education: Doctorate in Mathematics, Baku State University (1982-1986) Master’s Degree in Mathematics, Baku State University (1978-1983) Licence in Mathematics, Baku State University (1971-1976) Research Trends: Recent publications focus on soil thermophysical properties (2025), vector optimization in topological spaces (2024), and fixed point theorems (2024). His 2023 work includes variational inequality analysis and sentiment modeling of literary texts using GPT-4 and differential equations. Earlier studies (2016-2017) addressed Volterra-Stieltjes equations and Riccati's equation solutions . Administrative Roles: Department Head, Igdir University (2017) Chair of Department, Sakarya University (2011-2014)

• Artificial Intelligence
• Theoretical Computer Science
• Automated Reasoning
• +3 more

Martin Kronegger serves as a Senior Lecturer in the Department of Automation Systems at Vienna University of Technology (TU Wien), where he teaches courses including Fundamentals of Digital Systems, Computer Engineering Projects, and Scientific Projects in Computer Science for the 2025W and 2026S semesters. His academic profile demonstrates active engagement in both teaching and research within the Faculty of Electrical Engineering and Information Technology. Dr. Kronegger's research centers on theoretical foundations of artificial intelligence with emphasis on parameterized complexity, automated planning, and answer set programming. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning, as evidenced by publications in premier venues like Artificial Intelligence journal and AAAI conferences. Key research themes include: Backdoor techniques for planning problems Parameterized complexity analysis of AI problems QBF solving for conformant planning SAT-based verification methods for software models His publication landscape reveals consistent contributions to parameterized complexity theory applied to planning and logic programming from 2011-2019, with significant work on multiparametric analysis of answer set programming and SAT-based approaches to planning problems. The research trajectory shows deepening theoretical contributions while maintaining connections to practical AI applications. Dr. Kronegger has supervised at least one diploma thesis on planning solvers and has participated in major research projects including FAIR (2013–2018), START (2014–2022), and X-TRACT (2014–2018). His project work demonstrates sustained collaboration with research groups at TU Wien focused on computational logic and automated reasoning. Based in the Automation Systems research group (E191-03) at TU Wien's Treitlstraße campus, he maintains active research collaborations through projects like the START program and contributes to the international answer set programming community as evidenced by involvement in the Fourth Answer Set Programming Competition.

Eduard EibenView profile

Researcher

• Theoretical Computer Science
• Algorithms
• Parameterized Complexity
• +3 more

Eduard Eiben is associated with the Algorithms and Complexity group at the Institut für Logic and Computation, Technische Universität Wien. He completed his PhD under advisors Stefan Szeider, Robert Ganian, and Georg Gottlob. His research focused on exploiting structural properties for fixed-parameter tractability, supported by the Austrian Science Fund (FWF) through projects X-Tract (FWF P26696) and the LogiCS Doctoral College (FWF W1255). In 2018, he received the Award of Excellence for his outstanding PhD thesis from the Austrian Ministry of Education, Science and Research (BMBWF).

David EckmanView profile

Assistant Professor

• Stochastic Simulation
• Operations Research
• Decision-Making Under Uncertainty
• +3 more

Dr. David Eckman is an Assistant Professor in the Wm Michael Barnes ’64 Department of Industrial and Systems Engineering at Texas A&M University. He previously served as a postdoctoral fellow in the Department of Industrial Engineering & Management Sciences at Northwestern University, under Professors Matthew Plumlee and Barry Nelson. He holds a Ph.D. in Operations Research from Cornell University (2019) and a B.S. in Industrial Engineering from the University of Pittsburgh (2014). His research focuses on stochastic simulation for decision-making under uncertainty, including ranking-and-selection procedures, simulation-optimization algorithms, and simulation output analysis. His work emphasizes developing methods to enhance efficiency and accuracy in simulation-based decision processes. Recent publications highlight advancements in simulation-optimization, such as data farming techniques, budget allocation strategies, and clustering methods for simulation outputs. His contributions span theoretical frameworks and practical tools, including the SimOpt testbed for algorithmic experimentation. No scientific awards or grants are explicitly listed in the provided texts. Dr. Eckman’s professional activities include maintaining a research website and an active Google Scholar profile, reflecting his commitment to disseminating knowledge in computational and operations research domains.

Fabio ViciniView profile

Assistant Professor

• Numerical Analysis
• Adaptive Finite Element Method
• Virtual Element Methods
• +9 more

Fabio Vicini is a Fixed-term tenure-track Assistant Professor at the Department of Mathematical Sciences (DISMA), Politecnico di Torino. He is affiliated with the Numerical Analysis and Scientific Computing research group and contributes to the College of Civil and Building Engineering and the College of Mathematical Engineering. His work bridges theoretical numerical methods and high-performance computing applications. Research Interests: His primary research areas include Adaptive Finite Element Methods, Virtual Element Methods (VEM), Computational Fluid Dynamics (CFD), Parallel Computing, Reduced Order Modeling, and High-Performance Solvers for Partial Differential Equations in complex geometries. His work emphasizes algorithmic robustness, efficiency, and scalability in scientific simulations. Recent Publication Trends: His latest articles (2024–2025) focus on advancing the Virtual Element Method—particularly in 3D adaptive settings, stabilization-free error bounds, mesh optimization, and performance on poorly shaped elements. Additional work involves nonlinear optimization for material simulation and extended finite elements for coupled 3D–1D problems, reflecting a strong trend in numerical analysis and computational mathematics. Scientific Awards: Communicating Research to the Citizens (Politecnico di Torino, 01-07-2025) Effective Communication with Businesses (Politecnico di Torino, 01-07-2025) Advising and Teaching: He supervises two PhD students—Lorenzo Neva and Karol Lizeth Cascavita Mellado—in the Mathematical Sciences program. He teaches core courses such as High-Performance Scientific Computing I & II, Model Order Reduction and Machine Learning, and Programming and Scientific Computing across multiple engineering programs including Aerospace, Automotive, and Mathematical Engineering. Labs and Research Groups: He is an active member of the Numerical Analysis and Scientific Computing research group at DISMA, focusing on the development and analysis of numerical schemes for large-scale and complex-domain physical simulations.

Frank StephanView profile

Professor

• Recursion Theory
• Kolmogorov Complexity
• Learning Theory
• +5 more

Frank Stephan is a Professor at the National University of Singapore , jointly affiliated with the Department of Mathematics and the School of Computing . His primary office is located in Block S17 (Mathematics), and he maintains a secondary office in Block COM2 (Computing). He teaches advanced courses including Computational Complexity , Logic and Foundations of Mathematics , and Advanced Automata Theory . Stephan co-organizes the departmental Logic Seminar and maintains an extensive publication record in theoretical computer science. His research spans: Recursion Theory & Kolmogorov Complexity : Foundational computability and information theory. Learning Theory : Inductive inference and algorithmic learning models. Computational Complexity : Hardness, parameterization, and structural graph algorithms. Automata & Formal Languages : Automatic structures and language recognition complexity. Recent publications focus on combinatorial optimization in graphs, including dominating sets, geodetic monitoring, identifying codes, and parameterized complexity. His work frequently appears in top venues (e.g., STACS, ICALP, ISAAC) and journals (e.g., Discrete Mathematics , Theoretical Computer Science ). Stephan holds memberships in the European Association for Theoretical Computer Science , Deutsche Mathematiker-Vereinigung , and other academic societies. No awards or student advisees are documented.

Haiko MullerView profile

Senior Lecturer

• Algorithms
• Complexity
• Graph Theory
• +6 more

Dr. Haiko Müller is a Senior Lecturer at the School of Computer Science, University of Leeds, within the Faculty of Engineering and Physical Sciences. He serves as the Study Abroad Coordinator and is an active member of the Algorithms and Complexity research group led by Professor Kristina Vušković. His research explores theoretical and applied aspects of algorithms, with emphasis on: Graph theory and specialized graph classes Approximation and randomized algorithms Fixed-parameter tractability Computational complexity of discrete structures Algorithms for partially ordered sets No specific awards, student advisements, or funded projects are detailed in the available sources.

Max BannachView profile

Research Fellow

• Parameterized Algorithms
• Descriptive Complexity
• Logic-Based Optimization
• +8 more

Dr. Max Bannach is a Research Fellow in Computer Science and Applied Mathematics at the European Space Agency's Advanced Concepts Team in Noordwijk, Netherlands. Prior to this role, he completed his Ph.D. at Universität zu Lübeck under Prof. Dr. Till Tantau. His work bridges theoretical insights in structural graph theory with practical applications in highly parallel optimization, including space mission planning and quantum computing. Education: Ph.D. in Computer Science (Universität zu Lübeck, Germany) Max Bannach's research focuses on parameterized algorithms, descriptive complexity, and logic-based optimization. He explores problems with tree-like structures via treewidth, leveraging Courcelle's theorem to develop efficient algorithms. His work extends to space applications, neuromorphic hardware, and quantum computing, aiming to integrate theoretical logic with real-world challenges. His recent publications span conferences like STACS, NFM, IAC, GECCO, and IPEC. Key topics include automated reasoning, MaxSAT variants, structural decomposition, and structural parameterization. He has organized computational challenges like PACE and SpOC, contributing to the advancement of exact and parallel algorithms. Dr. Bannach's collaborative efforts include projects with the European Space Agency, technical teams at conferences, and academic institutions like Universität zu Lübeck. He maintains active participation in program and steering committees for PACE and SpOC.

Jean-Jacques StrodiotView profile

Professor

• Optimization
• Equilibrium Problems
• Variational Inequalities
• +5 more

Jean-Jacques Strodiot is a Professor in the Department of Mathematics at the University of Liège's Faculty of Science. With a research career spanning over 45 years from 1977 to 2022, he has established himself as a leading expert in optimization theory and numerical methods. His research focuses on equilibrium problems, variational inequalities, and numerical optimization techniques. Key areas include algorithms development, convergence analysis, Hilbert space methods, and regularization techniques. His work bridges theoretical mathematics with practical computational approaches, making significant contributions to both pure and applied mathematics. Analysis of his 76 research outputs reveals a consistent focus on developing and analyzing algorithms for equilibrium problems and variational inequalities. His recent work (2017-2022) emphasizes strongly convergent algorithms, adaptive regularization parameters, and innovative stepsize rules that improve numerical performance. The research shows strong interdisciplinary connections between optimization theory, functional analysis, and computational mathematics. As Principal Investigator on 11 research projects, Professor Strodiot has secured substantial research funding. His projects include 'Duality and characterization of optimality for equilibrium problems,' 'Approximate solutions and Lagrangian duality for semi-infinite mathematical programming problems,' and 'Extension of proximal methods to equilibrium problems.' Professor Strodiot has maintained strong international collaborations, particularly with Vietnamese institutions. His teaching missions to Vietnam National University in Ho Chi Minh City demonstrate his commitment to academic exchange. He has supervised 90 students throughout his career, contributing significantly to mathematical education and research training.

Filip TurobośView profile

Assistant Professor

• Mathematical Analysis
• Automotive Safety Engineering
• Transportation Systems
• +3 more

Filip Turoboś is an Assistant Professor at the Division of Insurance and Capital Markets , Lodz University of Technology . His academic profile indicates active research engagement without mention of part-time status, retirement, or former institutional affiliations. Research Focus: Dr. Turoboś demonstrates interdisciplinary expertise spanning: Mathematical Analysis : Including semimetric spaces, fixed-point theorems, and topological extensions Automotive Safety Engineering : Specializing in precrash velocity modeling using machine learning and optimization techniques Urban Mobility & Transport : Analyzing behavioral patterns during crises (e.g., pandemics) and regulatory impacts Emerging Technologies : Blockchain applications in security and ontological ecosystems Publication Trends (2020-2024): Recent works cluster into three dominant themes: Mathematical foundations of generalized metric spaces and functional analysis (30% of publications) Data-driven transportation studies focusing on pandemic-related behavioral shifts and urban policy effects (40%) Automotive crash reconstruction using AI/statistical methods and blockchain cryptography (30%)

Andras SobesterView profile

Professor

• Aerospace Engineering
• Computational Design
• Parametric Geometry Modelling
• +3 more

Professor Andras Sobester is a Professor in the Department of Aeronautics and Astronautics at the University of Southampton, part of the Faculty of Engineering and Physical Sciences. His research focuses on design technology tools for aerospace engineering, including parametric geometry modeling and design space exploration. He leads open-source projects like ADRpy (aircraft design tools) and AirCONICS (parametric geometry toolkit). He collaborates closely with Airbus UK and has authored/co-authored books on aircraft design, statistical modeling, and UAV systems. His research interests span computational engineering, parametric design, and aerospace systems. Notable projects include EPSRC-funded initiatives on digital design processes, risk mapping for UAVs, and teleoperated systems. Sobester has been awarded a Royal Academy of Engineering Research Fellowship (2007) and has supervised multiple PhD students in aerospace and environmental engineering. Recent publications emphasize UAV performance validation, risk modeling for autonomous systems, and topology optimization. His work bridges academic research with industrial applications, particularly in aircraft design and computational methods.