Find Academic Supervisors

• Stochastic programming
• Two-phase and multi-phase models
• Scenario generation and reduction
• +9 more

Trine Krogh Boomsma is a Professor in the Department of Insurance and Economics at the University of Copenhagen's Department of Mathematical Sciences. Her research focuses on optimization under uncertainty with significant applications in energy systems, particularly electricity markets, renewable energy investments, and power system planning. PhD in Mathematics-Economics, Aarhus University (2003-2007) Visiting PhD at University of Duisburg-Essen (2004) Academic career includes positions at Risø National Laboratory for Renewable Energy and Imperial College London Her work spans stochastic programming, real options analysis, and dynamic programming to address energy sector challenges. Key areas include support schemes for renewables, market risk modeling, and operational optimization of hybrid conventional-renewable systems. Recent research explores policy impacts on investment decisions and advanced scenario generation techniques. Major publications (2012-2020) cover renewable energy policy frameworks, power plant valuation models, and sequential market bidding strategies. These works emphasize electricity market dynamics, investment risk quantification, and robust planning under uncertainty. She teaches linear programming, integer programming, and stochastic programming applications in operational analysis, contributing to energy economics education at the department.

Allan LarsenView profile

Professor

• Operations Research
• Supply Chain Management
• Logistics
• +4 more

Allan Larsen is a Professor and Deputy Head of Division at the Department of Technology, Management and Economics (DTU Management) at the Technical University of Denmark. He heads the Operations and Supply Chain Management Section. His academic background includes an MSc in Applied Mathematics and a PhD in Operations Research, both from DTU. He teaches courses in Operations Management and Simulation at both undergraduate and graduate levels. Research Focus: His work applies operations research methodologies to complex planning problems in supply chain management, logistics, healthcare operations, and public transport. Key areas include urban freight transport, healthcare supply chains, and optimization of public transport resources like crew and fleets. Collaborations with industries, especially in freight transport, have been extensive. He previously co-directed the Transport DTU research center and co-chaired Denmark’s Transport Innovation Network. Education: MSc in Applied Mathematics (1989–1995), PhD in Operations Research (1997–1999), both from DTU. Recognition: Recipient of the Hedorf’s transportpris award in 2022 for contributions to transport research. Research Projects: Supervises multiple PhD students (e.g., on electric freight transport, healthcare resource optimization, and Industry 4.0 applications). Active in projects like 'Pioneering Electric Heavy-Duty Freight Transport' and 'Resource optimization in healthcare.' Labs/Teams: Leads the Operations and Supply Chain Management research group and collaborates in initiatives like the Transport Innovation Network, focusing on sustainable transport solutions.

Birgitte Bak-JensenView profile

Professor

• Smart Grids
• Electric Power Distribution Systems
• Multi-Energy Systems Integration
• +6 more

Birgitte Bak-Jensen is a Professor at the Department of Energy, Aalborg University, where she has worked since 1988. She specializes in intelligent control of power distribution systems, with research focusing on grid stability, power quality, and integration of dispersed generation and smart grid technologies. Her work also addresses multi-energy system interactions between electrical grids, heating, and transport sectors. Projects : Led EU H2020 projects (SERENE, SUSTENANCE) and Danish initiatives (EFFORT, SMARTCE2H) Publications : Over 250 papers on distribution grid control and smart energy systems Her research combines renewable energy integration , electric vehicle grid interaction , and energy storage optimization . Recent work includes explainable AI for wind forecasting and voltage control strategies for EV charging. 2025 Awards : Best Student Paper Award (2023), Best Paper Award (2021), CIGRE Technical Council Award (2018) Organizational Roles : Vice Head of AAU Energy Research, leadership positions in IEEE and CIGRE

• Applied Probability Theory
• Financial Mathematics
• Insurance Mathematics
• +3 more

Jesper Lund Pedersen is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen , specializing in applied probability theory with applications in financial mathematics and insurance mathematics . His research spans stochastic processes, optimal stopping time problems, and stochastic control. Education : PhD in Mathematics (2000, Aarhus University) His work addresses: (Nonlinear) optimal stopping time problems Stochastic control and filtering Multidimensional point processes Levy processes in finance Key publications reveal expertise in Bayesian changepoint detection , random drift identification , and mean-variance portfolio optimization , with interdisciplinary applications in neuroscience (V-ATPase dynamics) and epidemiology. Scientific awards : Villum Experiment Grant (2018-2020) Steno Research Fellowship (2002-2005) His research collaborations span Denmark, the UK, Germany, and the USA, focusing on probability theory, financial mathematics, and biomedical applications.

Francisco Camara PereiraView profile

Professor

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +3 more

Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

Kristian MiltersenView profile

Professor

• Capital Structure Modeling
• Real Options Valuation
• Commodity Derivatives
• +3 more

Kristian Miltersen is a Professor at the Department of Finance and Center for Financial Frictions (FRIC) at Copenhagen Business School (CBS). He holds expertise in capital structure optimization, real options valuation, commodity derivatives, and structured financial products. His research bridges theoretical finance with practical applications in corporate finance and asset pricing. After earning his PhD in 1992 on continuous-time interest rate models, Miltersen has expanded into dynamic capital structure modeling and real options. His landmark 1997 Journal of Finance paper on LIBOR Market models critiques the Black-76 formula's limitations for fixed income derivatives. His work spans commodity derivatives, foreign exchange instruments, and R&D investment dynamics, all rooted in theoretical frameworks. Recent research focuses on dynamic debt policies, second mortgages' valuation, tax-driven corporate cash holdings, and state-contingent capital budgeting. He co-authored influential papers on callable debt renegotiations and the interaction between financing and investment decisions under varying industry structures. Teaching includes elite courses on asset pricing, energy markets, and capital structure. As program director for CBS' Advanced Economics and Finance MSc, he shapes advanced academic programs. Outside academia, he serves as an expert witness and consultant for Analysis Group, advising on financial litigation and complex economic issues. His over 40 publications since 1989 reflect deep engagement with financial modeling, spanning interest rate guarantees, commodity price dynamics, and corporate debt optimization. Current research continues exploring financial frictions' impact on corporate policy and market structures.

• Optimization under Uncertainty
• Stochastic Programming
• Risk Analysis
• +6 more

Giovanni Pantuso is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, specializing in stochastic programming and optimization under uncertainty . His work bridges mathematical methods with practical applications in transportation, logistics, and production planning. Education : PhD in Operations Analysis from the Norwegian University of Science and Technology (Feb 2014) Research Focus : Developing mathematical frameworks for decision-making under risk, with applications to maritime fleet renewal, car-sharing systems, and ride-sharing logistics. Teaching : Courses in Advanced Operations Research: Stochastic Programming, Risk Optimization, and Introduction to Numerical Analysis. His methodological contributions include novel algorithms for stochastic programming and decomposition methods, while applied work spans electric car-sharing systems, first-mile transportation challenges, and production planning under uncertainty. Current research explores dynamic fleet management and cost-service tradeoffs in shared mobility.

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Mogens FosgerauView profile

Professor

• Transport Economics
• Discrete Choice Modeling
• Travel Behavior
• +7 more

Mogens Fosgerau is a Professor in the Department of Technology, Management and Economics at the Technical University of Denmark (DTU), where he conducts research in transport policy and transportation science. His work spans econometrics, travel behavior modeling, and transport economics, contributing to sustainable urban mobility and policy design. Institution: Technical University of Denmark Department: Department of Technology, Management and Economics Email: mogens.fosgerau@econ.ku.dk ORCID: https://orcid.org/0000-0002-6452-5215 His research focuses on discrete choice modeling, travel time valuation, scheduling preferences, and congestion pricing. He develops theoretical and empirical models to understand how individuals make travel decisions under uncertainty and how these behaviors affect urban transport systems. His work integrates economic theory with data-driven methods, often using large-scale datasets and advanced econometric techniques. The recent trend in his publications highlights innovations in perturbed utility models for route choice, stochastic traffic assignment, and the analysis of induced demand for cycling. His research bridges transportation science, behavioral economics, and operations research, with applications in urban planning and policy evaluation. Scientific awards received include: The International Choice Modeling Conference (ICMC) award for Most Innovative Application (2022) Best Overall Paper Award, ITEA Conference (2015) Best Paper Awards from BIVEC-GIVET (2007), Kuhmo-Nectar (2008) Hedorfs Fonds Pris for Transportforskning (2011) Mogens Fosgerau has supervised PhD students such as Fentie Abegaz and has been involved in multiple externally funded research projects, including URBAN (Innovation Fund Denmark), IRUC (Danish Council for Strategic Research), and Horizon 2020 initiatives. He has also served on review panels, including for the Norwegian Research Council, and contributed to peer review and editorial duties. He is actively engaged in research networks and has presented his work at international conferences. His projects often involve interdisciplinary collaboration with researchers in economics, engineering, and urban planning.

Manfred LaubichlerView profile

Professor

• Theoretical Biology
• Evolutionary Developmental Biology
• History of Biology
• +7 more

Manfred Laubichler is President’s Professor of Theoretical Biology and History of Biology at Arizona State University (ASU), where he serves as Director of the School of Complex Adaptive Systems and the Global Biosocial Complexity Initiative. He is also an External Professor at the Santa Fe Institute (SFI) and co-director of the ASU-SFI Center for Biosocial Complex Systems. His academic affiliations include the Center for Social Dynamics and Complexity, the Center for Biology and Society, the Center for Evolution and Medicine, and the Water Institute at ASU. His educational background spans zoology, philosophy, and mathematics from the University of Vienna; biology from Yale University; and history/history of science from Princeton University. Laubichler's research centers on evolutionary novelties across scales—from genomes to knowledge systems—alongside the structure of evolutionary theory and the evolution of scientific knowledge. His work bridges theoretical biology, philosophy of science, and complex systems, with a strong emphasis on biosocial complexity and sustainability. He has contributed extensively to understanding the conceptual foundations of evolutionary developmental biology (Evo-Devo) and the historical and philosophical dimensions of biological theory. His recent publications reflect an interdisciplinary trend, integrating epidemiology, public health, and social behavior during crises such as the COVID-19 pandemic, while maintaining a core focus on theoretical biology, history of science, and complex systems. Themes include trust, scientific responsibility, epistemology of science, and modeling biosocial dynamics. Elected Fellow of the American Association for the Advancement of Science Fellow at the Wissenschaftskolleg zu Berlin Vice Chair of the Global Climate Forum Laubichler has led multiple NSF-funded research initiatives in digital humanities, history and philosophy of science, and complex adaptive systems. He has advised numerous doctoral projects and co-led training programs such as the Embryo Project. He is actively involved in interdisciplinary labs and centers, including the Information and Competition Lab, Complexity Economics Lab, and the Social Insect Research Group. His leadership extends to editorial roles in journals like Biological Theory , Journal of Experimental Zoology Part B , and the Archimedes series.

Jalal KazempourView profile

Professor

• Energy Markets
• Power Systems
• Renewable Energy Integration
• +13 more

Jalal Kazempour is a Full Professor at the Technical University of Denmark (DTU) in the Department of Wind and Energy Systems (DTU Wind), where he leads the Energy Markets and Analytics (EMA) section and serves as Head of Studies for the MSc program in Sustainable Energy Systems. He is an Associate Editor for Operations Research and a Senior Member of both IEEE and INFORMS, and he contributes to EU energy policy through ACER’s Expert Group on Flexibility Needs Assessment. His research lies at the intersection of optimization, game theory, control, and machine learning, focusing on data-driven approaches for modern power systems with high renewable penetration. He investigates market design, grid services, and coordination mechanisms for integrated energy systems involving electricity, hydrogen, natural gas, and district heating, aiming to improve system efficiency and decision-making. Recent publications highlight trends in privacy-preserving optimization, bidding strategies for wind and hydrogen, flexibility aggregation, and market clearing in coupled energy systems, reflecting a strong emphasis on stochastic and robust optimization, machine learning, and real-world applicability in energy markets. Scientific Awards: Best Paper Award of IEEE SmartGridComm 2023 Best Paper Award of IEEE Transactions on Power Systems (2019–2021) Best Teacher Award, DTU Electrical Engineering Department (2019) IEEE Senior Member (2018) INFORMS Senior Member (2025) Outstanding Editor, International Transactions on Electrical Energy Systems (2017) Advising and Grants: He supervises multiple PhD students across projects on power-to-X, virtual power plants, and AI for market design. He has secured major funding, including a 9.5 million DKK EUDP grant for privacy-preserving data sharing and an Industrial PhD project with Energinet funded by Innovation Fund Denmark. Labs and Teams: He founded and leads the Energy Markets and Analytics (EMA) section at DTU, formerly known as the Energy Analytics and Markets (ELMA) group, which hosts over 10 researchers and organizes the annual DTU PES Summer School.

Peyman Afzali GorouhView profile

Researcher

• Power Engineering
• Smart Grids
• Renewable Energy Systems
• +5 more

Peyman Afzali Gorouh is a Postdoctoral Researcher in the Applied Power Electronic Systems group within the Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on developing innovative models for energy communities, smart grids, and renewable energy integration. Dr. Afzali's research interests span power engineering, smart grid technologies, renewable energy systems, and energy communities. His work particularly emphasizes prosumer economics, peer-to-peer energy trading, risk modeling in power systems, and energy democracy frameworks. He has developed novel approaches for optimizing energy communities while considering socio-economic-environmental factors, demand response, and uncertainty management. His recent publications (2020-2024) demonstrate a consistent focus on energy community modeling, with particular emphasis on peer-to-peer trading mechanisms, risk-constrained optimization, and multi-objective planning. His work bridges technical power system challenges with socio-economic considerations, creating integrated models that address both engineering and human aspects of modern energy systems. Dr. Afzali maintains an active research profile with numerous publications in high-impact journals including IEEE Transactions on Engineering Management, Energy and Buildings, Applied Sciences, and Sustainable Cities and Society. His research shows strong international collaboration, particularly with researchers from Iranian institutions.

Alvaro TorralbaView profile

Associate Professor

• Symbolic Search
• Artificial Intelligence
• Machine Learning
• +3 more

Alvaro Torralba is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with the Technical Faculty of IT and Design. His research focuses on symbolic search, heuristic functions, and planning algorithms within artificial intelligence and machine learning. Notable projects include the ConAn initiative exploring contrastive analysis for state-space exploration. He has contributed extensively to classical planning, probabilistic planning, and automated planning competitions, earning awards such as the First Prize in the Agile Track of the 10th International Planning Competition (IPC’23). His work often bridges theoretical advancements with practical applications, including game-based network update synthesis and believable non-player character development. Research outputs include over 60 publications since 2011, with a focus on optimizing search algorithms and enhancing planning efficiency through techniques like operator-potential heuristics and bidirectional search strategies. His scientific contributions span algorithmic innovation, verification methodologies, and large-scale abstraction evaluation. Collaborations and datasets include foundational work on PDDL generators and pattern databases, with open-access resources available via Zenodo. As a program committee member and award-winning researcher, Torralba actively contributes to advancing the frontiers of AI planning and decision-making systems.

Henrik MadsenView profile

Professor

• Energy Systems
• Stochastic Processes
• Demand Response
• +7 more

Henrik Madsen is a Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). His work focuses on energy systems, stochastic processes, and mathematical modeling. He leads research in areas such as demand response in district heating, probabilistic forecasting, and integration of renewable energy sources. Madsen has supervised multiple PhD students and contributes to projects like the IEA DHC Annex TS9 and SEEDS initiative. His expertise includes statistical analysis, time series modeling, and data-driven approaches for energy systems. He actively engages in collaborative research on smart grids, thermal energy storage, and sustainable energy solutions. Education: Academic qualifications from DTU, though specific details are not provided in the text. Research Interests: Mathematical modeling of energy systems Probabilistic forecasting methodologies Integration of wind and solar power Dynamic modeling for district heating Data assimilation in hydrological systems Optimization of energy flexibility Publications & Trends: Recent works emphasize demand response strategies, district heating optimization, and machine learning tools like the nabqr Python package and evalprob4cast R package. His research bridges theoretical stochastic methods with practical applications in energy infrastructure. Grants & Projects: IEA DHC Annex TS9: Digitalization of district heating SEEDS: RES-integrated electrified heating systems Data-Driven Methods for Demand-Side Flexibility Labs/Teams: Collaborates with DTU's Dynamical Systems group and participates in interdisciplinary initiatives like the Frigg 2.0 energy system analysis framework.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.