Find Academic Supervisors

Jenni RinkerView profile

Associate Professor

• Wind Turbine Design
• Aeroelasticity
• Control Systems
• +4 more

Jenni Rinker is an Associate Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). Her research focuses on wind turbine dynamics, aeroelasticity, and control systems, with particular emphasis on improving turbine reliability and optimizing energy output through advanced measurement techniques like lidar technology. She leads initiatives such as the DigiWind project and actively collaborates on international efforts like the IEA Wind 15 MW Reference Turbine. Dr. Rinker teaches courses including LAC (46320) and PiWE (46120), emphasizing hands-on project management and student supervision. PhD Supervision: Esteban Soto Sagredo (Flow-field estimation), Seyed Mozafari (Fatigue Reliability), Mikkel G. Pedersen (Structural Degradation) Key Projects: DigiWind (2024–2027), PyConTurb (Python-based turbulence tools), Turbulence-Driven Design Optimization Her work contributes to UN Sustainable Development Goals, notably SDG7 (Affordable and Clean Energy). Recent research highlights include wind field reconstruction using lidar data, fatigue reliability analysis, and aeroelastic stability in floating turbines. She has supervised over 15 student projects, emphasizing structured weekly reporting and milestone tracking through DTU’s CampusNet system.

Martin Otto Laver HansenView profile

Associate Professor

• Wind Turbine Engineering
• Airfoil Design
• Rotors
• +3 more

Martin Otto Laver Hansen is an Associate Professor at the Department of Wind and Energy Systems in the Technical University of Denmark . His expertise spans wind turbine design, aerodynamics, and computational fluid dynamics. Research highlights include vortex generators, high Reynolds number flows, and vertical/horizontal axis wind turbines. Current projects: TIP and Root Vortex Generators , FAST LES model for floating wind farms , and NEXTgenT offshore rotor design . Research focuses on boundary layer control, dynamic stall mitigation, and advanced numerical modeling (e.g., Lattice-Boltzmann methods, actuator line models). Recent work emphasizes vibration control, unsteady flow conditions, and efficiency optimization for 25+ MW turbines. Publications highlight trends in Wind Energy and Fluid Mechanics , particularly computational methods for turbine design and high Reynolds number challenges. Collaborations span offshore wind engineering and sustainable energy systems. Scientific award: Best paper recognition at 2009 International Symposium on Energy, Informatics, and Cybernetics. Active supervising PhD students and participating in EU-funded projects like DigiWind and Horizon Europe . Collaborations involve Denmark, Germany, and international institutions.

Taeseong KimView profile

Associate Professor

• Wind Turbine Design
• Aeroelasticity
• Control Systems
• +5 more

Taeseong Kim is an Associate Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). His research focuses on wind turbine design, aeroelasticity, hydrodynamics, and control systems, with a particular emphasis on floating offshore wind turbines and icing effects. He leads major projects such as the Horizon Europe-funded NEXTgenT, aiming to develop over 25 MW offshore wind turbine rotors. His work contributes to UN Sustainable Development Goals related to affordable and clean energy. Kim’s expertise includes rotor dynamics, blade structural analysis, and advanced simulation tools. He has pioneered innovations like segmented blade concepts and partial pitch control systems for large turbines. His research integrates computational fluid dynamics (CFD), finite element analysis (FEA), and machine learning for structural health monitoring and damage detection. Key projects include DTWO (federated digital twins for offshore wind) and DigiWind (digital masters training in wind energy systems). He collaborates internationally through initiatives like IEA Wind Task 54 on cold-climate wind energy. His publications span aeroelastic stability, icing simulation, and hydrodynamic control strategies, with over 96 peer-reviewed articles and 19 active/funded projects. Kim’s work addresses challenges in turbine stability, load reduction, and environmental resilience, positioning him as a leader in advancing next-generation wind energy systems.

Alan Wai Hou LioView profile

Associate Professor

• Wind Turbine Control
• Predictive Modeling
• Renewable Energy Systems
• +2 more

Alan Wai Hou Lio is an Associate Professor at the Technical University of Denmark's Department of Wind and Energy Systems, specializing in wind turbine control and energy system dynamics. His research develops advanced control strategies to optimize energy capture and reduce structural loads in wind farms. Research Focus: Predictive wind turbine control algorithms LiDAR-assisted flow measurement techniques Offshore floating turbine dynamics Real-time estimation methods for turbulent flows His publications demonstrate consistent focus on experimental validation of control theories, with recent work emphasizing field implementation challenges. Current projects include DigiWind (digital wind energy systems) and PowerKey (wind plant optimization). Education: PhD in Automatic Control (Sheffield, 2017), M.Eng in Electrical Engineering (Imperial College London, 2012).

Georg KastlungerView profile

Associate Professor

• Electrocatalysis
• CO2 Reduction
• Density Functional Theory
• +3 more

Georg Kastlunger is an Associate Professor at the Department of Physics, Technical University of Denmark (DTU), affiliated with the Surface Physics and Catalysis group and the Center for Catalysis Theory. His research focuses on electrocatalysis, CO2 reduction, and theoretical modeling of surface reactions. He has contributed to understanding reaction mechanisms in CO2 electrolysis, electrochemical barriers, and catalyst design. Education: PhD in Physics (inferred from role) Affiliations: DTU's Center for Catalysis Theory, Surface Physics Group Research interests include computational electrochemistry, density functional theory (DFT) applications, and sustainable energy materials. Recent work explores tandem catalytic effects, non-Nernstian electrochemical phenomena, and biomass valorization. He supervises multiple PhD students investigating electrochemical reaction pathways and catalyst design. Publications emphasize mechanistic insights into CO2 reduction, water adsorption on metals, and molecular dynamics simulations in alkaline environments. Projects include theoretical investigations of electrochemical oxidation reactions and computational discovery of clean energy materials. Advising: Supervising 5 PhD students (Giese, Jensen, Xu, Liu, Tsang) Grants/Projects: 6 active and completed projects on catalysis theory and electrochemical systems Labs/Teams: Active in DTU's catalysis research groups, focusing on atomistic modeling and experimental-computational collaborations.

Thomas BligaardView profile

Professor

• Heterogeneous Catalysis
• Density Functional Theory
• Machine Learning in Materials Science
• +5 more

Thomas Bligaard is a Professor at the Technical University of Denmark (DTU) in the Department of Energy Conversion and Storage. His research focuses on atomic-scale materials modeling, heterogeneous catalysis, and computational methods such as density functional theory (DFT), machine learning, and micro-kinetic modeling. He leads the Atomic Scale Materials Modelling group and contributes to the Center for Catalysis Theory and the VISION Center for Visualizing Catalytic Processes. Research interests include designing functional materials for energy applications, optimizing catalytic processes, and advancing computational tools like the GPAW software package. His work addresses challenges in CO2 reduction, electrocatalysis, and catalyst discovery. Collaborations span theoretical and experimental studies, with a focus on sustainable energy solutions aligned with UN Sustainable Development Goals. Recent projects include benchmarking water adsorption on metal surfaces, developing machine learning approaches for catalyst optimization, and imaging atomic processes in catalysts using advanced microscopy techniques. He supervises multiple PhD students in catalysis and computational materials science. Bligaard has published extensively on topics ranging from theoretical catalysis to computational methods, with a strong emphasis on bridging theory and experimental validation. His research aims to accelerate the discovery of clean energy materials through interdisciplinary approaches.

Jakob SchiøtzView profile

Professor

• Nanoparticles
• Molecular Dynamics
• Density Functional Theory
• +3 more

Jakob Schiøtz is a Professor at the Department of Physics, Technical University of Denmark (DTU), leading the theory group in the Center for Individual Nanoparticle Functionality (CINF). His research focuses on nanoparticle shape-functional relationships, hot-electron catalysis, and nanocrystalline materials' mechanical properties. He coordinates the Master's program in Physics and Nanotechnology and has advised numerous PhD students. Education includes a PhD in Physics (DTU, 1992–1995), M.Sc. in Electronics Engineering/Applied Physics (DTU, 1986–1991), and postdoctoral experience at Washington University (1995–1996). He holds teaching qualifications in research management and didactics. Research interests span nanomaterials, catalytic processes, and computational methods like molecular dynamics and density functional theory. His work contributes to sustainable energy solutions through advanced material characterization and nanotechnology. Key grants include a Danish National Research Foundation Center (2005–2010) and collaborations with industry partners. Awards include the P. Gorm-Petersen Memorial Award (1997). Schiøtz maintains the open-source ASAP molecular dynamics software and actively reviews for top journals. His lab focuses on in situ electron microscopy, machine learning for image analysis, and nanoscale material dynamics. Projects involve catalytic nanoparticles, defect formation in 2D materials, and thermal transport phenomena.

Sergio Escalera GuerreroView profile

Professor

• Computer Vision
• Machine Learning
• Human Pose Recovery
• +3 more

Sergio Escalera is Full Professor at Aalborg University's Department of Architecture, Design and Media Technology. He is Distinguished Professor at Universitat de Barcelona, adjunct professor at Universitat Oberta de Catalunya and Dalhousie University, and ELLIS Fellow. He leads the Human Pose Recovery and Behavior Analysis Group and serves as vice-president of ChaLearn. Research focuses on human-centric computer vision including pose estimation, behavior analysis, and ethical AI. Current projects include REPAI (Responsible AI for Value Creation) and trustworthy computer vision systems. Scientific Awards: Amazon Research Award 2020 Fellow of European Laboratory for Learning and Intelligent Systems (2019) Recent publications explore machine unlearning, power system stability, object tracking, and 3D cloth simulation, reflecting interdisciplinary applications of deep learning.

Pantelis Pipergias AnalytisView profile

Associate Professor

• Decision Making
• Computational Modeling
• Social Influence
• +5 more

Pantelis Pipergias Analytis serves as an Associate Professor at the Department of Business and Management within the Faculty of Business and Social Sciences at the University of Southern Denmark (SDU), while also holding a senior fellowship at the Danish Institute of Advanced Study. His academic journey spans multiple institutions and disciplines, having studied economics at the University of Athens, cognitive science at EHESS in Paris, and earned his PhD in psychology from Humboldt University in Berlin. Before joining SDU, Analytis conducted research as a predoctoral researcher at the Max Planck Institute for Human Development and as a postdoctoral researcher in the Computer and Information Science department at Cornell University. His scholarly fingerprint reveals significant contributions to decision making (100%), search engine psychology (80%), social learning (80%), individual-level analysis (40%), human-computer interfaces (40%), and strategic choice (40%). Analytis's research program centers on developing mathematical and computational models of individual and collective behavior, employing experimental methods, big-data analysis, and large-scale simulations. His work investigates how algorithms shape human decision-making and how human behavior influences algorithmic outcomes, with particular attention to bias, diversity, and efficiency in digital systems. Current research examines social influence in recommender networks, strategic sophistication in interactive decision-making, and the dynamics of collective intelligence. His publication record demonstrates consistent scholarly output across computer science, psychology, and management disciplines. Recent work includes examinations of social influence in online rating systems, algorithmic bias in recommender systems, and the mathematical properties of ranking-based cumulative advantage processes. His research has practical implications for designing fairer and more efficient digital platforms and decision support tools. Analytis leads the active research project 'Ranking-based cumulative advantage' (2023-2026), investigating how ranking mechanisms influence attention allocation in competitive environments. He regularly participates in research seminars globally, with upcoming presentations scheduled through May 2025, demonstrating his active scholarly engagement. As an educator, he previously taught 'DS817: Algorithms we live by' (2020-2022), exploring how algorithms shape human experience and decision-making processes.

Owen JonesView profile

Associate Professor

• Comparative Demography
• Evolution of Aging
• Life History Databases
• +4 more

Owen Jones is an Associate Professor at the University of Southern Denmark (SDU), affiliated with the Department of Biology and the Interdisciplinary Centre on Population Dynamics (CPop). His research focuses on understanding demographic diversity across species, particularly the evolution of aging and population responses to climate change. He co-leads the COMPADRE and COMADRE database projects, which compile global matrix population models for plants and animals. Education: PhD in Biology, Imperial College London (2004) MSc in Ecology, University of Aberdeen (1998) BSc in Biology, University of York (1997) Research Interests: Comparative demography and life history strategies Senescence and aging mechanisms Climate change impacts on populations Matrix population modeling Recent work emphasizes high-resolution demographic data and the development of tools like the mpmsim R package for simulating population models. His projects include analyzing demographic responses to environmental changes and promoting open-access data for scientific collaboration. Awards: Cozzarelli Prize (2016) for outstanding scientific achievement. Teaching and Grants: Teaches courses on population dynamics and ecological data analysis. Involved in grants addressing climate-driven phenological shifts and invasive species management. Labs/Teams: Core member of COMPADRE/COMADRE initiatives and SDU Climate Cluster, collaborating globally on biodiversity and demographic research.

Christos TserkezisView profile

Associate Professor

• Theoretical Condensed Matter Physics
• Light-Matter Interactions at Nanoscale
• Quantum Plasmonics
• +10 more

Christos Tserkezis is an Associate Professor at the University of Southern Denmark (SDU) in the Faculty of Engineering since 2023. His academic journey includes a PhD (2012) and Dr.Techn. (2021) in Theoretical Condensed Matter Physics from the National and Kapodistrian University of Athens and SDU, respectively. He held postdoctoral positions at the Donostia International Physics Center (2012-2015) and as an H.C. Ørsted Fellow at the Technical University of Denmark (2015-2017) before joining SDU in 2017. Education: PhD (2012) and Dr.Techn. (2021) in Physics Research Focus: Light-matter interactions at the nanoscale, bridging classical electromagnetism and quantum physics His research spans quantum and classical plasmonics, polaritons, metamaterials, and non-Hermitian photonics, with a particular emphasis on nanophotonic systems and strong coupling regimes. Recent publications (2024-2025) highlight nonlocal effects, quantum-informed plasmonic models, and tunable polaritonic devices. He contributes to advancing theoretical frameworks for nanoscale optical phenomena, including chiroptical effects and mesoscopic ensemble averaging. Christos is affiliated with SDU's Center for Nano Optics, where he develops analytical and numerical methods to study plasmonic and photonic structures. While no specific awards or students are mentioned in the provided text, his work integrates experimental validation with theoretical rigor, as seen in studies involving electron spectroscopies and cathodoluminescence.

Alexandra BoltassevaView profile

Professor

• Nanophotonics
• Plasmonics
• Optical Metamaterials
• +3 more

Alexandra Boltasseva is a Professor of Electrical and Computer Engineering and Materials Engineering at Purdue University, affiliated with the Birck Nanotechnology Center and the Purdue Quantum Science and Engineering Institute. A leading expert in nanophotonics and plasmonics, her research focuses on optical metamaterials, nanofabrication, and quantum information technologies. She has held editorial roles at Optical Materials Express and is recognized for advancing machine-learning-assisted photonic design. Moscow Institute of Physics and Technology (B.Sc., M.Sc.) Technical University of Denmark (Ph.D. in nanophotonics) Her research explores the discovery and optimization of nanophotonic structures, including plasmonic materials and metamaterials, for applications in quantum information, on-chip circuitry, and sustainable technologies. Key themes span low-loss photonic devices, tunable optical platforms, and nonlinear optical phenomena. Recent publications emphasize quantum photonics, machine learning for optical design, 2D materials, and time-varying photonic systems. Her work bridges fundamental physics and practical implementation across quantum computing, sensing, and energy conversion. Awarded by multiple societies, including: Fellow of the American Physical Society (2023) Fellow of the Materials Research Society (2021) Fellow of the National Academy of Inventors (2020) Blavatnik National Award Finalist (2018) IEEE Photonics Society Young Investigator (2013) Boltasseva leads the development of platforms for nanophotonic applications, with significant contributions to silicon nitride quantum emitters, plasmonic color printing, and photonic time crystals. Her lab’s work integrates experimental and theoretical approaches for next-generation photonic technologies.

• Quantum Field Theory
• Elementary Particle Physics
• Scientific Computing
• +3 more

Claudio Pica is a Professor of Computational Science and Head of Section at the University of Southern Denmark (SDU), affiliated with the Department of Mathematics and Computer Science (IMADA) and CP3-Origins. He holds leadership roles as CEO of DeiC National HPC Center and Head of the SDU eScience Center. With a PhD in Physics from the University of Pisa (2005) and a Laurea cum Laude in Theoretical Physics from the Scuola Normale Superiore (2001), his expertise spans quantum field theory, lattice gauge simulations, and high-performance computing. His research focuses on fundamental particle physics, including composite Higgs models, walking technicolor theories, and lattice QCD. He leads large-scale computational projects, such as scaling SU(2) simulations to 1000 GPUs and optimizing algorithms for supercomputers. His outreach initiatives include award-winning educational projects like 'KvanteBanditter' (Lovie Awards 2018) and the SDU Supercomputing Challenge. He has secured € millions in grants, including the €4M European ITN ETN 'EuroPLEx' (2018-2022) and Danish Lundbeckfonden funding (2013-2018). Pica has organized major conferences like the 'Origin of Mass' series and the 'Odense Winter School on Geometry and Theoretical Physics.' His work bridges theoretical physics with computational innovation, addressing challenges in conformal dynamics, dark matter, and supercomputing infrastructure. He is a frequent speaker at international forums and a reviewer for prestigious journals like Physical Review D and European Physical Journal C.

Jonas Alexander FinklerView profile

Research Fellow

• Atomistic Simulation
• Computational Materials Science
• Glass Structure Mechanics
• +4 more

Jonas Alexander Finkler is a Research Fellow in the Department of Chemistry and Bioscience within the Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on computational methods for disordered materials, particularly glass structure mechanics and atomistic simulations. His primary research interests include Atomistic Simulation , Electronic Structure Calculation , and Machine Learning Potentials for materials science. He develops advanced computational techniques for modeling disordered systems, with emphasis on charge distribution, energy surfaces, and mechanical properties of glasses. His work bridges quantum chemistry and materials engineering through high-dimensional neural network potentials and density functional theory innovations. Recent publications demonstrate expertise in Noise-Tolerant Force Calculations , Fourth-Generation Machine Learning Potentials , and Boson Peak Analysis in metal-organic frameworks. His research output shows consistent annual productivity with 15 publications between 2020-2025, including high-impact journals like Journal of Chemical Physics and Journal of Physical Chemistry A. He operates within the Section of Disordered Materials Glass Structure and Mechanics, utilizing advanced computational infrastructure for large-scale simulations. His collaborative network spans international institutions with significant connections to Swiss research groups through Swiss National Science Foundation projects.

Irfan Ahmed HalepotoView profile

Researcher

• Electric Vehicle Engineering
• Autonomous Vehicles
• Optimization
• +3 more

Irfan Ahmed Halepoto serves as a Guest Researcher at Aalborg University's Faculty of Engineering and Science within the Department of Power Electronics System Integration and Materials, Denmark. His work focuses on advancing electric vehicle (EV) technology through energy consumption modeling and drive range optimization. Research interests center on Electric Vehicle Engineering , Autonomous Systems , and Eco-driving Optimization . He employs black-box modeling techniques and AI-driven predictive frameworks to integrate real-time parameters like traffic patterns and weather data, developing novel strategies for energy-efficient route planning in EVs. His fingerprint analysis reveals dominant expertise in Key Parameters Engineering (100%), Black Box methodologies (100%), and Driving Route optimization (100%). Recent publications demonstrate a clear trend toward multi-objective optimization of EV performance using NSGA-II algorithms and real-time data integration. His research bridges theoretical modeling with practical applications in sustainable transportation, specifically targeting traffic avoidance systems and dynamic range extension for electric vehicles through eco-driving strategies. Based at Pontoppidanstraede 111, Aalborg East, Halepoto operates within AAU's Power Electronics research ecosystem, contributing to cutting-edge system integration projects focused on sustainable energy solutions for next-generation electric mobility.