Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Data Science
• +7 more

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.

Karl Henrik JohanssonView profile

Professor

• Networked and distributed control
• Cyber-physical systems
• Cyber-secure control systems
• +4 more

Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.

Martin BerggrenView profile

Professor

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

Stefano MarkidisView profile

Professor

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +5 more

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Thermodynamics
• Diffusion
• Phase Transformations
• +3 more

Powder Metallurgy (MH2100) and has expertise in computational materials science. His research emphasizes predictive modeling of material behavior, including precipitation kinetics, sintering processes, and coating interactions. Notable areas include phase field modeling of discontinuous precipitation, spinodal decomposition in Fe-Cr alloys, and high-entropy alloy design. His studies bridge experimental data with computational tools like the YAPFI phase-field framework. Key themes in his publications span cemented carbides, Co-based entropic alloys, and tool wear mechanisms. He combines CALPHAD thermodynamic modeling with first-principles calculations to address challenges in materials processing and corrosion resistance. His work often addresses industrial applications, such as optimizing machining tools and additive-manufactured superalloys.

Panagiotis PapapetrouView profile

Professor

• Data Science
• Machine Learning
• Time Series Analysis
• +5 more

Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)

• Computational Fluid Dynamics
• Vehicle Aerodynamics
• Bluff-Body Flows
• +3 more

Sinisa Krajnovic is a Professor of Computational Fluid Dynamics and Head of the Department of Mechanics and Maritime Sciences at Chalmers University of Technology. His research focuses on vehicle aerodynamics, bluff-body flows, and time-dependent numerical simulations, particularly in ground vehicle flows (trains, cars, buses) and high-speed train dynamics. He leads studies on flow control mechanisms, bi-stable wake phenomena, and turbulence modeling using advanced CFD techniques like LES and PANS. Recent work emphasizes active flow control optimization, snow-resistance performance of bogies, and aerodynamic interactions in platoons. His 247+ publications span topics including high-speed train aerodynamics, ship airflow control, and bluff-body wake dynamics. Collaborations involve experimental validation and industrial applications in rail and marine transportation.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +8 more

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

• Radio Astronomy
• Astrophysics
• Black Hole Imaging
• +3 more

John Conway is a Professor of Radio Astronomy at Chalmers University of Technology , serving as Director of Onsala Space Observatory . His work spans multiple domains in observational astrophysics, focusing on: High-resolution VLBI imaging of black holes and AGN Instrumentation development for submillimeter telescopes Multiwavelength studies of M87 and Sgr A* black holes Large-scale radio surveys with LOFAR and SKA technologies As a key member of the Event Horizon Telescope collaboration, he contributes to polarization analysis and magnetic field studies around supermassive black holes. His instrumentation projects include work on the Onsala Twin Telescopes and SKA data stacking techniques. Current research involves black hole shadow characterization , jet dynamics , and machine learning applications for radio source detection. He collaborates extensively with international teams across projects like ALMA, LOFAR, and APEX.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +6 more

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +5 more

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

• Computational Fluid Dynamics
• High-Performance Computing
• Turbulence Simulation
• +3 more

Martin Karp is a Research Fellow and postdoctoral researcher at KTH Royal Institute of Technology's Department of Engineering Mechanics, working under Dan Henningson. His research focuses on high-fidelity numerical simulations of turbulence and transition, with a specialization in high-performance computing (HPC) and supercomputing architectures. He holds a PhD in computer science from KTH and an MSc in Engineering Physics from Lund University, complemented by studies at ETH Zürich's computer science department. His research interests explore computational limits in nonlinear chaotic systems and future computational advancements. He leads the development of the Neko framework, a scalable simulation tool for extreme-scale CFD with extensive accelerator support. Karp's work emphasizes GPU and FPGA acceleration, parallel computing, and optimizing algorithms for heterogeneous architectures. Key contributions include large-scale turbulence simulations using GPUs, reducing communication in conjugate gradient methods, and evaluating FPGA-based flow solvers. His publications span journals like Concurrency and Computation and Scientific Reports , with conference presentations at IEEE Cluster, PASC, and HPCAsia. Karp's research bridges theoretical computational limits and practical HPC implementation, addressing challenges in precision, scalability, and hardware utilization. His educational background combines engineering physics with computer science, enabling interdisciplinary approaches to fluid dynamics and high-performance simulation. Current projects aim to push the boundaries of computational fluid dynamics through novel algorithm design and leveraging emerging hardware capabilities.

Ricardo VinuesaView profile

Associate Professor

• Artificial Intelligence
• Fluid Mechanics
• Sustainability
• +7 more

Ricardo Vinuesa is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mechanics within the School of Engineering Sciences. He serves as the Principal Investigator (PI) for projects such as 'An AI-based framework for harmonizing climate policies and projects with the SDGs' and co-leads the 'Faster-than-real-time and high-resolution simulation of fluid flow in engineering applications' initiative. His work integrates artificial intelligence with fluid mechanics, sustainability, and climate policy. Education and Academic Roles: Vinuesa teaches courses including Data-driven methods in engineering mechanics, Mechanics I, and Turbulence, holding roles as examiner, course manager, and teacher. He is part of the Digital Futures Faculty and actively contributes to academic programs at KTH. Research Interests: His research focuses on AI-driven solutions for fluid mechanics challenges, turbulence modeling, sustainable development goals (SDGs), and climate policy alignment. He develops machine learning frameworks for flow control, sensor optimization, and climate risk assessment, emphasizing interdisciplinary applications. Projects and Applications: Key projects include AI-based frameworks for SDG harmonization, high-resolution fluid simulations for indoor climate, and turbulence control via reinforcement learning. His work bridges computational fluid dynamics with real-world sustainability challenges, leveraging deep learning and data-driven methodologies. Labs and Collaborations: Vinuesa leads the Artificial Intelligence Group, with a lab website at vinuesalab.com . His research is supported by collaborations with industry and academic partners, focusing on advancing fluid mechanics and environmental engineering through AI innovation.

• Artificial Intelligence
• Neuroevolution
• Evolutionary Dynamics
• +3 more

Arend Hintze is a Professor of Microdata Analysis at Dalarna University's Department of Information and Technology. His research bridges artificial intelligence, evolutionary biology, and computational psychiatry, focusing on neuroevolution, cellular automata, and digital health applications for bipolar disorder. Key research themes include: Evolutionary dynamics in computational models AI for mental health prediction Cellular automata and self-replication Large language model behavior analysis Recent publications demonstrate interdisciplinary work across computer science, genetics, and psychiatry, with emphasis on: Neuroevolution and information transfer Fitness landscape navigation Bipolar disorder early warning systems Evolutionary game theory applications His work frequently employs agent-based modeling, digital evolution, and deep learning techniques.