Find Academic Supervisors

• Electric Power Distribution Networks
• Distributed Energy Resources (DER) Integration
• Smart Grids
• +3 more

Carlos Mateo Domingo is a Research Fellow at the Technological Research Institute (IIT) under the Comillas Pontifical University . He coordinates the Master's Degree in Smart Grids and leads the Sustainable Smart Grids area at IIT. With a PhD in Industrial and Computer Engineering (2007), he specializes in distribution network modeling and distributed energy resources integration. Key collaborations: MIT, NREL, World Bank, European Commission Accreditations: Three six-year research periods (2003-2020) with ANECA/ACAP certifications Research Focus: Electricity distribution network planning and optimization Distributed generation integration (PV, storage, EVs) Smart grid technologies and digitalization Energy system modeling for developing countries Meta-heuristic algorithms application in grid problems Publication Trends (2016-2025): Over 69 projects and 49 journal papers focusing on grid flexibility, synthetic network modeling, and renewable integration. Key journals include IEEE Access , Applied Energy , and IEEE Transactions on Smart Grid . Scientific Awards: 2024 - Directed award-winning Final Year Project on electric vehicle grid integration Multiple European Commission Horizon 2020 grants (ATTEST, ECEMF) Notable Projects: SMART-DS with NREL/MIT for U.S. Department of Energy, DSO Observatory for European Commission, and rural electrification initiatives with World Bank.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.

Bertrand GueninView profile

Professor

• Combinatorics
• Optimization
• Graph Theory
• +5 more

Bertrand Guenin is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo's Faculty of Mathematics. He holds academic appointments as Associate Dean for Graduate Studies (2021–2024) and previously served as Associate Chair for Undergraduate Studies (2010–2016). His research focuses on combinatorial optimization, matroid theory, and graph theory, with notable contributions to dyadic programming, ideal clutters, and even-cycle/even-cut matroids. Education: PhD in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University (supervised by Gérard Cornuéjols). Postdoctoral positions at Georgia Tech (School of Mathematics) and the Fields Institute. Master's from École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Guenin explores advanced topics like dyadic solutions in optimization, structural properties of matroids, and the Cycling Conjecture. His work bridges combinatorial optimization with foundational graph theory, addressing problems such as the Fulkerson Prize-winning contributions to weakly bipartite graphs and ideal clutters. Key areas include algorithm design for matroid lifts, parity conditions in signed graphs, and excluded minor characterizations. Publications: His work emphasizes theoretical advancements in optimization and matroid theory, with recent focus on dyadic linear programming and recognition algorithms for matroids. Earlier contributions include foundational results on even-cut and even-cycle matroids. Awards: Fulkerson Prize Tucker Prize SIAM Outstanding Paper Prize INFORMS Nicholson Prize Advising & Grants: Active in mentoring over 20 graduate and undergraduate students. Current openings for master's and PhD students. Leads research projects on optimization theory, matroid structure, and algorithmic graph theory. Labs/Teams: Collaborates widely, with co-authors including Gérard Cornuéjols, Levent Tunçel, and students like Ahmad Abdi and Cheolwon Heo. His work often intersects with computational and theoretical challenges in discrete mathematics.

Jochen KoenemannView profile

Professor

• Approximation Algorithms
• Algorithmic Game Theory
• Combinatorial Optimization

Jochen Koenemann is a Professor & Chair in the Department of Combinatorics & Optimization at the University of Waterloo. He holds a Ph.D. in Algorithms, Combinatorics & Optimization from Carnegie Mellon University (2003) and completed a postdoc at La Sapienza University of Rome (2003-2004). His research focuses on Approximation Algorithms , Algorithmic Game Theory , and Combinatorial Optimization . Notable contributions include work on stable matchings, nucleolus computation, and network design algorithms. He has organized major events like the Hausdorff Summer School and IPCO'17 in Waterloo. Academic leadership roles include chairing conferences and serving on program committees such as EC'18, APPROX'17, and SODA'14. Teaching highlights include courses on Combinatorial Optimization , Game Theory , and Algorithmic Game Theory . He co-authored an undergraduate textbook on optimization with B. Guenin and L. Tuncel. Current advisees include M. Rundstrom, H. Sun, W. Toth, and M. VanDyk. His work frequently addresses interdisciplinary challenges, such as collaborations with SickKids Hospital on algorithmic solutions for medical applications. Professional activities include editorial roles and international collaborations. Recent research trends emphasize algorithmic frameworks for game-theoretic problems and improving approximation bounds for classical combinatorial challenges. Awards and recognitions are not explicitly listed, but his extensive program committee participation underscores his academic influence.

Lee D. HanView profile

Professor

• Traffic Engineering
• Intelligent Transportation Systems (ITS)
• Transportation Data and Information Systems
• +14 more

Lee D. Han is a Professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, within the College of Engineering. He is also a Collaborating Scientist at Oak Ridge National Laboratory (ORNL), reflecting his strong ties to national research initiatives. His work bridges academia and applied transportation systems, with a focus on intelligent transportation, traffic operations, and emergency management. Dr. Han earned his PhD from the University of California, Berkeley, an MS from Virginia Tech, and a BS from National Taiwan University, all in Civil & Environmental Engineering. His educational background reflects a strong foundation in transportation systems and infrastructure. His research interests are centered on traffic engineering, intelligent transportation systems (ITS), traffic flow theory, emergency evacuation, crash analysis, and transportation data systems. He has pioneered work in evacuation modeling, license plate recognition, and adaptive signal control. His research integrates simulation, data analytics, and real-world applications to improve safety and efficiency in transportation networks. The recent articles highlight a consistent focus on intelligent transportation technologies, including driver behavior analysis, incident detection, evacuation modeling, and freight systems. His work often combines empirical data with advanced computational methods such as machine learning, fuzzy logic, and optimization. There is a clear trend toward real-time, adaptive systems that enhance traffic safety and operational efficiency. TCE Teaching Fellow, 2019 L.R. Hesler Award for Excellence, 2017 Chancellor's Citation for Research, 2014 TCE Research Achievement Award, 2014 TCE Teaching Fellow, 2013 Leon & Nancy Cole Superior Teaching Award, 2012 Chair of Faculty Senate Research Council, 2011-13 TRB University Representative, 2007- Charles E. Ferris Faculty Award, 2007 COE Teaching Fellow Award, 2013 Closest to Hole prize at the 15th ASCE Scholarship Golf Tournament, 2012 Dr. Han has secured over $10 million in externally sponsored research funding, significantly reducing institutional fiscal burden by recovering much of his salary. He advises PhD students, including Stephanie Hargrove, who has received the NSF EPASI and Dwight D. Eisenhower Fellowships. He leads the Transportation Systems Laboratory (TSL) and an eFacility for data transfer, supporting collaborative research with sponsors, visiting scholars, and students. He leads the Transportation Systems Laboratory (TSL) at Perkins Hall and has established an eFacility for secure data exchange. The lab supports research in traffic modeling, simulation, and ITS applications, serving as a hub for students, faculty, and collaborators. His team engages in both theoretical and applied research, often in partnership with federal and state agencies.

• Graph Theory
• Network Analysis
• Machine Learning
• +3 more

Manuel Curado is a researcher with a focus on graph theory, network analysis, and machine learning applications. His work spans spatio-temporal forecasting, traffic accident prediction, medical imaging, and urban mobility studies. He collaborates extensively with researchers like José-Francisco Vicent, Francisco Escolano, and Leandro Tortosa. 2025 : Self-explainable graph convolutional recurrent networks for traffic forecasting 2024 : GeoCC-ConvLSTM for air quality and CTextureFusion for lung CT super-resolution 2023 : Return Random Walk Gravity Centrality for node influence metrics 2022 : Human mobility analysis using Twitter data and directed graph centrality models His research emphasizes graph-based algorithms, deep learning, and data science for real-world problems in transportation and healthcare. He has contributed to journals like Information Sciences , Applied Soft Computing , and Neural Computing & Applications .

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +4 more

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching

• Mathematical modeling in biomedical contexts
• Tissue engineering
• Peripheral nerve regeneration
• +6 more

Rebecca J Shipley, MD, serves as an Assistant Professor at UMass Chan Medical School within the Department of Emergency Medicine and the T.H. Chan School of Medicine. Her academic foundation includes a BS in International Health from Georgetown University and an MD from Emory University. Dr. Shipley has established herself as an interdisciplinary researcher bridging mathematical modeling with biomedical applications. Her research interests focus on applying mathematical and computational approaches to biomedical problems, particularly in nerve tissue engineering, peripheral nerve regeneration, microvascular network modeling, and biomechanics. Dr. Shipley's work demonstrates a consistent trajectory from fundamental mathematical modeling to translational applications in tissue engineering and regenerative medicine. Her expertise spans mathematical modeling, computational biology, and biomedical engineering, with particular emphasis on quantitative approaches to understanding biological systems. Analysis of her recent publications reveals a strong focus on developing computational frameworks for nerve regeneration, vascular modeling, and drug delivery systems. Her work often integrates experimental data with sophisticated mathematical models to address challenges in tissue engineering and regenerative medicine. A notable trend in her research is the increasing use of machine learning techniques combined with physics-based modeling approaches. Dr. Shipley has collaborated extensively with researchers including Phillips JB, Sweeney PW, Walker-Samuel S, and others across multiple institutions. Her work demonstrates significant interdisciplinary reach, connecting mathematics, engineering, and clinical medicine. While formally affiliated with Emergency Medicine, her research portfolio aligns more closely with biomedical engineering and computational biology, highlighting her role as an interdisciplinary scientist bridging quantitative methods with biomedical applications.

• Mathematics Education
• Educational Didactics
• Diagnostic Tools in Education
• +4 more

Prof. Dr. Michael Kleine is a Professor of Mathematics Education at the Faculty of Mathematics, Bielefeld University since 2012. He specializes in mathematics didactics, focusing on developing diagnostic tools for identifying at-risk students, integrating origami into mathematical teaching, and exploring linguistic factors in mathematical task design. Research interests include enactive learning, blended learning environments, and mathematical modeling Actively involved in international collaborations (KMTUT Bangkok, Peking University) and ERASMUS+ projects Previously served as Professor at Weingarten University of Education (2008-2012) Recent publications emphasize experimental mathematics, geometric reasoning through origami, and linguistic complexity in mathematical tasks. He developed the DITOM diagnostic framework for early identification of mathematical learning challenges. Prof. Kleine combines theoretical research with practical teacher training, particularly for secondary education. His work bridges cognitive psychology, educational technology, and curriculum innovation.

IBRAHIM EKEView profile

Associate Professor

• Power Systems
• Optimization Algorithms
• Load Frequency Control
• +4 more

Ibrahim Eke serves as an Associate Professor in the Department of Electrical and Electronic Engineering within the Faculty of Engineering and Natural Sciences. His academic work centers on advancing power system stability and optimization through innovative computational methods, with primary affiliations spanning electrical engineering research and education. Professor Eke's research spans critical areas of electrical power engineering including economic load dispatch, load frequency control, voltage stability, and renewable energy integration. He specializes in developing heuristic optimization algorithms such as Electric Fish Optimization, Chaotic Particle Swarm, and Vortex Search to solve complex power system problems involving time delays, multi-area networks, and renewable integration challenges. His methodology consistently bridges theoretical algorithm development with practical power grid applications. Analysis of his 15 most recent publications (2020-2024) reveals three dominant research trajectories: (1) Mitigation of communication time delays in load frequency control using fractional-order PID controllers and neural networks, (2) Development of hybrid optimization techniques (Taguchi-Vortex, Chaotic PSO) for combined heat-power economic dispatch, and (3) Integration of electric vehicles and energy storage systems into frequency regulation frameworks. His work demonstrates increasing focus on renewable-heavy grid stability amid evolving power system architectures.

• Geophysical Engineering
• Applied Geophysics
• Archeo-geophysical
• +5 more

Prof. AYSEL ŞEREN is a distinguished Professor in the Department of Geophysical Engineering at the Faculty of Engineering, Karadeniz Technical University. With over 98 publications in Web of Science and an impressive H-index of 182, she has established herself as a leading expert in applied geophysics, particularly in ground-penetrating radar applications, karst studies, and archaeological geophysics. Her research spans diverse areas including engineering geophysics, hydrogeology, and mineral exploration. Her educational background includes a Doctorate from Istanbul University (1994-1998), Postgraduate studies at Karadeniz Technical University (1990-1993), and Undergraduate studies at the same institution (1985-1989). Her academic journey reflects a deep commitment to geophysical sciences and engineering applications. Prof. Şeren's research interests primarily focus on Geophysical Engineering , Applied Geophysics , Archeo-geophysical studies, and Gravity applications. Her work demonstrates exceptional integration of multiple geophysical methods for solving complex geological and engineering problems. She has made significant contributions to understanding karst systems, slope stability, archaeological site investigations, and infrastructure assessment. Her approach consistently combines theoretical foundations with practical field applications, advancing both scientific knowledge and engineering solutions. Analysis of her recent publications reveals a strong trend toward integrated geophysical approaches, particularly using Ground-Penetrating Radar (GPR) in combination with other methods. Her research has evolved from fundamental geophysical studies to practical applications addressing real-world challenges in engineering, archaeology, and environmental science. The interdisciplinary nature of her work connects geophysics with civil engineering, hydrology, archaeology, and geological hazards assessment, demonstrating her ability to bridge theoretical concepts with practical implementation. Prof. Şeren has supervised 14 theses and secured numerous research grants, demonstrating her leadership in the field. Her funded projects span archaeological investigations, landslide risk assessment, karst studies, and infrastructure evaluation. She has led significant projects including 'Deprem Tetikli Kütle Hareketlerinin Yer Bilimsel Jeoloji-Jeofizik-Jeomorfoloji Yaklaşımlarla Modellenmesi' (2023-Present) and 'Hidrojeofizik Çalışmalarla Dolinlerin, Karstik Boşlukların ve Yeraltı Su Akış Sisteminin Araştırılması' (2022-2024). These projects highlight her ongoing commitment to addressing contemporary challenges through innovative geophysical approaches. Her research group focuses on advanced geophysical techniques, particularly GPR applications, for solving engineering and archaeological challenges. The team conducts comprehensive field investigations, sophisticated data processing, and detailed interpretation for various geological and engineering problems, with strong connections to cultural heritage preservation and infrastructure safety. Their work consistently demonstrates methodological rigor while maintaining practical relevance to real-world applications.

Sunčica ČanićView profile

Professor

• Applied Mathematics
• Partial Differential Equations
• Fluid-Structure Interaction
• +5 more

Sunčica Čanić is a Full Professor in the Department of Mathematics at the University of California, Berkeley, with prior appointments at the University of Houston. She holds a Ph.D. in nonlinear hyperbolic conservation laws from Stony Brook University (1992). Education: Ph.D. in Nonlinear Hyperbolic Conservation Laws, Stony Brook University (1992) Research Interests: Applied and numerical analysis of partial differential equations Deterministic and stochastic fluid-structure interaction Optimal design of vascular prostheses (stents) Mathematical and computational modeling of bioartificial pancreas Computational modeling of collective states in magnetic colloidal suspensions Microswimmer dynamics Recent Publications: Her recent work focuses on nonlinearly coupled stochastic and deterministic fluid-structure interaction problems, poroelastic materials, and computational optimization of biomedical devices like vascular stents and bioartificial pancreas scaffolds. Articles address existence of solutions, numerical methods, and applications in hemodynamics. Scientific Awards: National Science Foundation Distinguished MPS Lecturer Award (2007) Esther Farfel Award (2018) SIAM Fellow (2014) AMS Fellow (2020) AWM-SIAM Sonia Kovalevsky Lecture Prize (2024) Advising and Grants: Advised Ph.D. student Jeffrey Kuan , who received an NSF Postdoctoral Fellowship (2023). Current research grants include NSF DMS-2408928 (2024-2027) for next-generation stent design and DMS-2247000 (2022-2025) for cell encapsulation modeling. Labs and Collaborations: Collaborates with medical specialists at the Texas Medical Center and UC San Francisco Biodesign Laboratory. Developed computational frameworks for stent design, bioartificial pancreas, and microswimmer dynamics with interdisciplinary teams.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +12 more

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Anne Gégout-PetitView profile

Professor

• Statistics
• Applied Statistics
• Biostatistics
• +3 more

Anne Gégout-Petit is a Professor at the University of Lorraine (UL) within the Faculty of Science and Technology, where she leads significant research initiatives in statistics and its applications. She serves as Director of the Elie Cartan Institute of Lorraine (UMR 7502) and is an active member of the INRIA BIGS team, demonstrating her leadership position in the French statistical research community. Her research expertise spans multiple domains of statistics with strong application components: Applied statistics and biostatistics Statistical modeling for biological and environmental systems High-dimensional data analysis Probability theory applications Epidemiological modeling Algorithm development for medical diagnostics Professor Gégout-Petit's publication record from 2022-2024 reveals a strong interdisciplinary focus, with research spanning forest pathology, cancer biology, pandemic surveillance, and genetic profiling. Her work consistently bridges theoretical statistical methodology with practical applications, particularly in environmental science, medicine, and public health. She has developed novel approaches including the FINE algorithm for medical data analysis and specialized modeling techniques for disease spread in ecosystems. She has demonstrated significant organizational leadership through directing major statistical events including the ENBIS meeting in Nancy (September 2-6, 2018) and the Statistics Days conference in Nancy (June 3-7, 2019), highlighting her standing within the European statistical community. Her professional activities include: Directing the Elie Cartan Institute of Lorraine Collaborating with researchers across biology, medicine, and environmental science Developing statistical methodologies for complex real-world problems Contributing to public health research during the COVID-19 pandemic

Hao LiView profile

Research Fellow

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +2 more

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.