Find Academic Supervisors

Luc van SummerenView profile

Assistant Professor

• Local Energy Transitions
• Sustainability Transitions
• Energy Communities
• +5 more

Luc van Summeren serves as Assistant Professor in the Technology, Innovation & Society research group within the Industrial Engineering and Innovation Sciences department at Eindhoven University of Technology (TU/e). His academic work bridges theoretical frameworks with practical applications in sustainability transitions, with particular emphasis on community-driven energy innovation and socio-technical system integration. Dr. van Summeren's research program focuses on local energy transitions, examining the critical roles of end-users, energy communities, and municipalities in shaping sustainable energy futures. His action-oriented approach involves direct collaboration with practitioners to identify promising socio-technical innovations across diverse contexts, analyzing implementation barriers, scaling opportunities, and strategies for effective local embedding. His work uniquely integrates digital technology applications for local energy management with considerations of heat-electricity system integration, always maintaining a focus on justice and equity in transition processes. Analysis of his publication record reveals consistent thematic development across European energy systems research. His work demonstrates increasing sophistication in analyzing energy communities as alternative organizational models, with growing attention to digitalization challenges, community empowerment mechanisms, and the socio-technical dimensions of virtual power plants. A distinctive thread throughout his scholarship emphasizes 'people first' approaches that prioritize social considerations alongside technological solutions, with particular attention to democratic governance and just outcomes in energy transitions. Dr. van Summeren leads multiple significant research initiatives including: Community-based Virtual Power Plant (cVPP) (Interreg NWE; 2017-2022) Organising Knowledge and Learning for the Regional Energy Transition (ORAKLE) (NWO-MARET; 2020-2025) Enabling Positive Energy Districts through Citizen-Centered Socio-Technical Models for Upscaling of the Heat Transition (EmPowerED) (NWA-ORC; 2025-2030) ACcelerate Energy Communities via Local Solar Energy Storage and Heat (ACCU) (Interreg NWE; 2025-2028) Smart Community-Owned Renewable Energy Management (SmartCORE) (Interreg NWE; 2025-2028) In teaching, Dr. van Summeren contributes to the Sustainable Innovation (Bachelor), Sustainability Transitions Track of Innovation Management (Master), and Sustainable Energy Technology (Master) programs. He developed the innovative course 'Managing Sustainable Technology Challenges in Society,' where students address real-world sustainability challenges presented by alumni using transition theories to investigate problems and propose solutions. His pedagogical philosophy emphasizes cultivating 'transition makers' equipped with systems thinking, understanding of transition dynamics, and strong ethical frameworks. A distinctive feature of Dr. van Summeren's academic profile is his integration of cartoon-based visual communication into research dissemination. Building on experience from his time at DuneWorks, he creates accessible illustrations that translate complex sustainability concepts into engaging formats. These cartoons appear in scientific reports, webinars, community communications, and his dedicated website, where they're shared under CC BY-ND licensing to promote wider adoption in sustainability practice. This multimodal communication approach exemplifies his commitment to making academic research accessible and actionable for diverse stakeholders engaged in energy transitions.

Katarzyna Szymańska-DębowskaView profile

Professor

• Nonlinear Analysis
• Differential Equations
• Boundary Value Problems
• +3 more

Katarzyna Szymańska-Dębowska is a full professor at the Department of Mathematical Modeling within the Faculty of Technical Physics and Applied Mathematics at Lodz University of Technology. She holds multiple leadership roles including Director of the Institute of Mathematics, Head of the Department of Mathematical Modeling, Deputy Chairman of the Mathematics Discipline Council, and Chairman of the Mathematical Methods in Data Analysis Faculty Council. Current academic rank: Professor Research focus: Nonlinear differential equations, nonlocal boundary conditions, and topological methods Notable collaborations: Jean Mawhin, Mirosława Zima, Bogdan Przeradzki Her work emphasizes existence theorems for boundary value problems, particularly those involving generalized p-Laplacian operators and resonance phenomena on bounded/unbounded domains. Publications span topological degree approaches, convex set theory, and time-scale analysis. Recent article trends show increasing specialization in nonlocal conditions, homeomorphism operators, and multidomain applications. She supervises PhD students Ewa Skrzypek and Igor Kossowski, co-edits special issues like the 2023 Mathematical Methods in the Applied Sciences celebrating Jean Mawhin's 80th birthday, and contributes to mathematical modeling in therapy dynamics.

Eran TreisterView profile

Assistant Professor

• Computational Science
• Scientific Computing
• Numerical Methods
• +8 more

Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +7 more

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.

Xavier MoreauView profile

Professor

• Control Systems Engineering
• Fractional-Order Calculus
• Vehicle Dynamics and Control
• +3 more

Xavier Moreau is a Professor at University of Bordeaux, affiliated with the IMS (Integration, Material to System) Laboratory, a joint research unit involving University of Bordeaux, Institut Polytechnique de Bordeaux, and CNRS. He works within the Automatic Control research group and is a key member of the CRONE team, specializing in fractional-order control systems and their applications. His research interests focus on Control Systems Engineering , particularly Fractional-Order Calculus applications, Vehicle Dynamics and Control , and Automatic Control Systems . Dr. Moreau's work bridges theoretical control theory with practical applications in automotive systems, acoustics, and electromechanical devices. His research demonstrates how fractional calculus can enhance traditional control approaches, particularly through the CRONE methodology which provides superior robustness and performance in complex systems. Dr. Moreau's publications reveal a consistent focus on applying control theory to real-world problems, with particular emphasis on vehicle guidance systems, headlight leveling mechanisms, suspension control, and even musical instrument acoustics. His work on the CRONE controller represents a significant contribution to control engineering, offering alternatives to traditional PID controllers with improved performance characteristics. His scientific contributions span multiple domains including Automotive Engineering, Control Theory, Fractional Calculus, and Acoustics. Dr. Moreau has collaborated extensively with industry partners including Groupe PSA (now Stellantis), demonstrating the practical relevance of his research to automotive applications. While specific advising information isn't provided in the available materials, Dr. Moreau's extensive publication record suggests active mentorship of graduate students and researchers within the IMS laboratory. His work has contributed to advancements in vehicle safety systems, driver assistance technologies, and precision control mechanisms. Dr. Moreau's research laboratory environment includes the CRONE team within the Automatic Control group at IMS, which appears to maintain strong connections with both academic and industrial partners in the automotive sector. His work demonstrates the integration of theoretical control concepts with practical engineering solutions for real-world applications.

Baptiste LafabregueView profile

Associate Professor

• Machine Learning
• Time Series Analysis
• Clustering
• +3 more

Baptiste Lafabregue is an Associate Professor of Computer Science at the University of Strasbourg, affiliated with Télécom Physique Strasbourg and the ICube Laboratory. His research focuses on artificial intelligence with specialization in clustering algorithms and time series analysis, particularly applied to remote sensing data. He teaches undergraduate and graduate courses in computer science while maintaining an active research program in machine learning. His educational background includes a PhD in Computer Science from Université de Haute Alsace (2021), an MSc in Computer Science Engineering from École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (2014), and a BSc in Computer Science from University of Strasbourg (2012). Lafabregue's research interests center around time series analysis and collaborative clustering methodologies. His work bridges theoretical machine learning with practical applications in satellite imagery and remote sensing, developing novel constrained clustering approaches that incorporate domain-specific knowledge while reducing reliance on labeled data. His research has significant implications for agricultural mapping and environmental monitoring. His publication record demonstrates a clear progression in deep learning applications for time series clustering, with increasing focus on satellite image time series analysis. The research shows evolution from traditional constrained clustering methods to deep learning integration, with particular attention to practical implementation challenges in remote sensing applications. Lafabregue has managed multiple research projects including two MSc research projects and supervised interns working on clustering and segmentation libraries for remote-sensing images. His professional experience includes research engineering positions focused on re-engineering and maintaining clustering libraries, demonstrating both theoretical and practical expertise in his field. He is a member of the Science of Data and Knowledge (SDC) team at the ICube research laboratory, where he contributes to advancing methodologies in time series analysis and clustering algorithms with applications to real-world remote sensing problems.

Jaroslaw NabrzyskiView profile

Professor

• Cyberinfrastructure
• Distributed Ledger Technologies
• Decentralized Identifiers
• +4 more

Jaroslaw Nabrzyski serves as the founding Director of the Center for Research Computing (CRC) at the University of Notre Dame and holds a Concurrent Professorship in the Department of Computer Science and Engineering. Under his leadership, the CRC has grown into a 55-member team of experts in cyberinfrastructure development, software engineering, data lifecycle management, and embedded systems, providing critical support to researchers across astrophysics, polar and environmental sciences, physics, biomedical sciences, engineering, social sciences, and the humanities. His research program centers on cutting-edge technologies including distributed ledger systems (blockchain), decentralized identifiers, verifiable credentials, and multimodal artificial intelligence, with applications spanning healthcare and societal challenges. As co-director of the CRC's Blockchain Research Lab, he actively bridges theoretical innovation with real-world implementation, fostering interdisciplinary collaborations that address complex societal problems through technological solutions. With an extensive track record, Professor Nabrzyski has served as Principal Investigator for over 75 national and international research projects, including the NSF EarthCube Polar-Cyberinfrastructure Research Coordination Network. His leadership extends to building high-performance teams and managing large-scale collaborations between academic institutions and industry partners to advance cyberinfrastructure capabilities for scientific discovery and enterprise applications.

• Cybersecurity
• Cyber Warfare
• Machine Learning
• +7 more

Professor Shin Dong-gyu is a faculty member in the Department of Computer Engineering at Sejong University, where he leads research in cybersecurity, cyber warfare, and machine learning applications. He is affiliated with both the Cyber Warfare Research Institute and the Smart Convergence Technology Research Institute, directing the Multimedia & Internet Lab which has produced over 100 graduates since its founding in 1998. His students have gone on to work at government research institutes, corporate research centers, and mid-sized technology companies. Professor Shin's research focuses on cybersecurity with particular emphasis on cyber resilience, intrusion detection systems, malware analysis, cyber range development, and the application of machine learning to security challenges. His work bridges theoretical security frameworks with practical defense mechanisms, often incorporating military and national security applications. The research spans both defensive cybersecurity measures and offensive cyber operations analysis. His recent publications (2023-2025) demonstrate a consistent research trajectory in cyber resilience metrics, data sanitization against poisoning attacks, DNS security, layered defense approaches for critical infrastructure, and the integration of Zero Trust architecture with threat modeling frameworks like MITRE ATT&CK. His work shows increasing sophistication in applying deep learning techniques to security problems while maintaining focus on practical, implementable security solutions. Professor Shin actively mentors graduate students through doctoral dissertation research, master's thesis research, and specialized courses including Algorithms and Practice, Computer Vision and Deep Learning for Drones, and Special Topics in Smart Computing Technology. His teaching schedule indicates regular engagement with both master's and doctoral students.

Jackson van DykeView profile

Research Fellow

• Topological Quantum Field Theory
• Higher Algebraic Geometry
• Quantum Topology
• +3 more

Dr. Jackson van Dyke is a Research Fellow at the Technical University of Munich under Prof. Dr. Claudia Scheimbauer in the Department of Mathematics (M2) at the TUM School of Computation, Information and Technology. His research explores anomalous properties of functorial quantum field theories. Broad applications include classification of topological orders Higher algebraic geometry Relative Langlands program Publications span 2023-2024 articles on projective TQFT symmetries, 2020 work on knot Floer homology, and 2017-2020 studies in radiation material testing and fission modeling. His presentation history includes talks at University of Lisbon, University of Hamburg, and Simons Collaboration events.

• Metamaterials
• Vibro-acoustics
• Biotremology
• +5 more

Dr. Can Nerse is an early-career researcher and Adjunct Fellow at the School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), where he works as a Postdoctoral Research Fellow at the Centre for Audio, Acoustics and Vibration (CAAV). He joined UTS in February 2021 and has progressed from Postdoctoral Research Associate to Postdoctoral Research Fellow. His academic journey includes a Postdoctoral Research Associate position at Gwangju Institute of Science and Technology in South Korea. Dr. Nerse received his PhD in Mechanical Engineering from Gwangju Institute of Science and Technology in August 2020, following an MSc from the same institution in 2015 and a BSc from Middle East Technical University, Ankara, Turkey in 2013. Dr. Nerse's research focuses on the intersection of vibro-acoustics, metamaterials, and bioinspired engineering. He investigates how eusocial insects like honey bees build structures with specific vibro-acoustic properties and applies these insights to develop novel materials and vibration control solutions. His work spans theoretical modeling, experimental validation, and practical industrial applications in noise and vibration mitigation. Current research interests include vibroacoustics, metamaterials, biotremology, self-organization, digital twins, and numerical methods. Dr. Nerse's publication record demonstrates a progression from fundamental vibration analysis to bioinspired metamaterial applications. His most recent work focuses on topological mechanical states in hyperuniform materials, wild bee signal classification, and bio-inspired acoustic metasurfaces manufactured through knitting technology. This research trajectory shows increasing integration of biological principles with engineering solutions for noise and vibration problems. International Institute of Acoustics and Vibration (IIAV) - Member since 2023 Society for Experimental Mechanics - Member since 2015 Korean Society for Noise and Vibration Engineering (KSNVE) - Member since 2013 Dr. Nerse has secured multiple research grants including the UTS FEIT Blue Sky Research Scheme Grant for bio-informed sensing and the UTS Cross-Faculty Collaboration Grant for acoustic well-being through metasurfaces. He has taught courses including Embedded Mechatronics System, Dynamics and Control, FEM and Optimization, and Sound and Vibration. He is available for Master's research supervision and PhD student supervision, particularly in areas related to his expertise in vibro-acoustics and metamaterials. Dr. Nerse leads research in the Centre for Audio, Acoustics and Vibration (CAAV) at UTS, working with interdisciplinary teams to develop innovative solutions for vibration and noise problems. His current projects involve creating acoustic metasurfaces using knitting technology to address noise issues in communal spaces, aligning with UN Sustainable Development Goals for Sustainable Cities and Communities, Industry Innovation and Infrastructure, and Climate Action.

• Differential Geometry
• Riemann-Finsler Geometry
• Geometric Analysis
• +2 more

Bankteshwar Tiwari is Professor in Mathematics at Banaras Hindu University, Varanasi, India, with additional affiliation at Slovakia's Lepage Research Institute. His academic profile centers on advanced geometric structures, particularly within Finsler geometry frameworks. Research focuses on Differential and Riemann-Finsler Geometry, specializing in Randers, Kropina, and Funk metric spaces. Key investigations include curvature properties, isoperimetric problems, geodesic behavior, and stability analysis. Recent work bridges pure geometry with biological modeling through predator-prey systems and cancer dynamics. Analysis of 2023-2025 publications reveals intensifying interdisciplinary applications: geometric methods now address ecological stability (Jacobi vs linear stability in predator-prey models) while maintaining core contributions to Finsler curvature theory, metric measure spaces, and homogeneous group inequalities. Collaborations span Czech, Slovak, Hungarian, Italian, Romanian, and Polish institutions through the International Visegrad Fund.

• Scientific Computing
• Applied mathematics
• Numerical methods
• +2 more

Marie E. Rognes is a Chief Research Scientist at Simula Research Laboratory's Numerical Analysis and Scientific Computing department. She specializes in computational mathematics and biomedical modeling, particularly focusing on cerebral fluid dynamics, electrodiffusion, and poroelasticity. Her work bridges advanced numerical methods with clinical applications in neuroscience. Research Pillars: Brain waterscape modeling, finite element methods, and biophysical simulations Software Leadership: Key contributor to FEniCS and Dolfin-adjoint projects Application Domains: Neurodegenerative diseases, cardiac electrophysiology, and personalized medicine Recent publications reveal methodological innovations in perivascular flow modeling , ionic transport simulations , and multi-scale brain mechanics . Her work on glymphatic system dynamics and cardiac tissue modeling demonstrates cross-disciplinary impact. While no explicit awards are listed, her extensive publication record in top-tier computational journals (SIAM, PLOS, Nature Computational Science) and invited talks at premier conferences (SIAM, ECCOMAS, FEniCS workshops) establish her as a leading figure in biomedical computing.

• Software Engineering
• Parallel Algorithms
• Computer Networks
• +7 more

Prof. Hasan BULUT is a full-time faculty member at Ege University's Faculty of Computer and Information Sciences, Department of Computer Engineering. His primary research focuses on software engineering, parallel algorithms, computer networks, artificial intelligence, and algorithm design. He has contributed to fields like distributed systems, data structures, and bioinformatics through innovative algorithmic solutions. His academic work spans over two decades, with notable contributions to machine learning applications in energy forecasting, DNA sequence analysis, and cloud computing optimization. Key areas of expertise include hybrid machine translation models, real-time data clustering, and optimization techniques for computational problems. Prof. BULUT's recent publications emphasize interdisciplinary approaches, combining deep learning with traditional methods to solve challenges in healthcare informatics, financial prediction, and bioengineering. His work on network slicing techniques for 5G and beyond networks highlights cutting-edge contributions to modern communication systems. Despite an extensive publication record and collaborations within Ege University, no formal scientific awards or grant information is explicitly mentioned in the provided texts. His academic career includes supervising numerous research projects but specific student advisee details are not documented here.

Ken M. L. YiuView profile

Professor

• Databases
• Query Processing
• Spatiotemporal Data
• +7 more

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.

• Cooperative game theory
• Social choice theory
• Game theory
• +1 more

Philippe Solal is a Professor of Economics at the University of Saint-Etienne, affiliated with GATE Lyon Saint-Etienne (UMR CNRS 5824), a research center focusing on economics and management science. His academic career spans several decades of research and publication in game theory and economic theory. Professor Solal's research focuses on: Cooperative game theory, particularly solution concepts like the Shapley value and its variants Social choice theory, including voting systems and ranking methods Coalition formation and stability in cooperative games Applications of game theory to economic problems and network structures His recent publications (2021-2025) demonstrate continued theoretical innovation in cooperative game theory, with emphasis on axiomatic characterizations, coalition structures, cost allocation problems, and social choice mechanisms. His work often involves collaborations with researchers across France, particularly with E. Rémila and S. Béal. Professor Solal has supervised five PhD students who have secured academic positions at French universities: Techer K. (2023) - Maître de Conférences at University of Franche-Comté Lowing D. (2022) - Maître de Conférences at ENS Rennes Lardon A. (2011) - Maître de Conférences at University of Saint-Etienne Ghintran A. (2009) - Maîtresse de Conférences at University of Lille Béal S. (2005) - Professor at University of Franche-Comté His research has practical applications in transportation networks, river sharing problems, and cultural policy. His work maintains strong connections between theoretical game theory and real-world economic problems.