Find Academic Supervisors

Ingela HolmströmView profile

Professor

• Sign Language Studies
• Bilingualism
• Language Ideologies
• +5 more

Ingela Holmström is a full-time Professor at the Department of Linguistics , Stockholm University , specializing in Swedish Sign Language (STS) and bilingualism . She serves as Head of the Sign Language Department and leads multiple research initiatives including the MULDER project (2024) on deaf migrants' multilingual situation , the LäsTecken project on deaf children's reading development , and the UTL2 project examining STS as a second language for hearing students . Research Themes : Deaf migrant language acquisition Bilingual education policy Visual-gestural modality instruction Language ideologies in Nordic countries Technological mediation in deaf education Key Methodologies include ethnographic classroom observations, longitudinal studies of cochlear implant users, and mixed-methods analysis of sign lexicon acquisition patterns. Her work demonstrates how able-bodied norms in Swedish bureaucracy create systemic barriers for deaf migrants, while highlighting translanguaging practices as both facilitators and complicators in multilingual classrooms.

Patrick NormanView profile

Professor

• Theoretical Chemistry
• Molecular Response Theory
• Computational Spectroscopy
• +5 more

Patrick Norman is a Professor and Head of the Division of Theoretical Chemistry and Biology at KTH Royal Institute of Technology, Stockholm, Sweden. He also serves as Director of the PDC Center for High Performance Computing. His research focuses on advanced time-dependent molecular response theory methods, with applications in optical and X-ray spectroscopy. Coordinates VeloxChem and Gator HPC software projects Leads EU-EIC project OMICSENS (2023–2026) for lung cancer biosensors Principal Investigator in EU-ITN COSINE (2018–2021) and KAW network CoTXS (2014–2019) His theoretical work includes resonance convergent response theory via Liouville equations and Ehrenfest theorem, implemented in quantum chemistry software for linear/nonlinear spectroscopy. He co-authored the graduate textbook Principles and Practices of Molecular Properties (Wiley, 2018) and organizes international PhD schools on molecular response properties. Recent publications emphasize fragment state optimization, solvation effects, chiral luminescent materials, and amyloid ligand binding simulations. His work bridges quantum chemistry, HPC, and biomedical applications through tools like Gator and VeloxChem .

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Ulf MaunsbachView profile

Professor

• Private International Law
• Intellectual Property Law
• IT Law
• +3 more

Ulf Maunsbach is a Professor of Private International Law and Deputy Head of the Department of Law at Lund University. He holds an Associate Professor title and is affiliated with the Business Law Center and Dispute Resolution research groups. His primary roles include teaching, research supervision, and managing the undergraduate program in international private and procedural law. Research Focus: Cross-disciplinary studies in private international law, intellectual property, IT law, and international family law. Notable Projects: EU Private International Law harmonization, digital jurisdiction, and family law migration issues. He co-authored foundational textbooks like Grundläggande Immaterialrätt and Private International Law in Sweden . His work emphasizes practical applications, with frequent collaborations in Nordic legal networks and expert engagements. Key contributions include analyses of EU jurisdiction rules, internet-related IP disputes, and cross-border family law. His research bridges theory and practice, addressing modern legal challenges like digitalization and globalization.

Björn LisperView profile

Professor

• Computer Science
• Software Engineering
• Real-Time Systems
• +5 more

Björn Lisper is a Professor at Mälardalen University, Sweden, affiliated with the School of Innovation, Design and Engineering and the Division of Computer Science and Software Engineering. His research focuses on formal methods, real-time systems, embedded software, static analysis, and high-level synthesis. He has contributed to advancements in WCET analysis, machine learning applications in testing, and compiler optimization techniques. His work emphasizes practical industrial applications, particularly in automotive and multi-core systems. Research Interests: Real-Time Systems & WCET Analysis Static Program Analysis High-Level Synthesis for FPGAs Machine Learning in Software Testing Formal Methods & Verification Embedded Systems Design Publications span topics like neural network accelerators, automated testing frameworks, and compiler optimizations, reflecting a blend of theoretical and applied computer science. His work is characterized by collaboration between academia and industry to bridge gaps in embedded software predictability and performance.

• Theoretical Computer Science
• Distributed Computing
• Approximation Algorithms
• +4 more

Mohit Daga is a Researcher at KTH Royal Institute of Technology, Sweden, specializing in theoretical computer science, distributed computing, and algorithmic research. He holds an MS in Computer Science from IIT Madras (2018). His current role involves research engineering and supervising master thesis projects across Machine Learning, AI, and GPU Programming. Education: MS in Computer Science, Indian Institute of Technology Madras (2018) Research Interests: His work focuses on distributed algorithms, approximation methods, and dynamic systems. He has explored applications in network analysis, optimization, and medical imaging. Recent projects include developing consensus protocols for DAG-based systems and analyzing multi-shot broadcast stability in distributed networks. Awards: Google Summer of Code Scholarship (2020) Advising & Grants: Supervised 9 master’s theses since 2020. Research internships include Cystar Labs (IIT Madras), IMDEA Networks Lab, and Xerox Research. His work on resource allocation at Xerox led to a patented solution. Labs/Teams: Contributed to projects at KTH’s Department of Computer Science and collaborated with IMDEA Networks and Cystar Labs. Part of the team developing the AciSpace CubeSat payload integration system.

• Numerical Analysis
• High-Performance Computing
• Parallel Computing
• +5 more

Carl C. Kjelgaard Mikkelsen is an Associate Professor at the Department of Computing Science, Umeå University. He specializes in numerical analysis and high-performance parallel computing, focusing on robust algorithms that cannot fail. He co-authored the StarNEig library for nonsymmetric eigenvalue problems and works on parallel constraint solvers for molecular dynamics and ultra-sparse linear systems. Research Interests: Numerical analysis, parallel computing, eigenvalue problems, linear systems, algorithm design. His recent publications emphasize robust task-based algorithms, GPU acceleration, and precision requirements in Newton's method. He teaches numerical analysis and scientific computing, emphasizing finite precision arithmetic and software documentation.

Jan Erik MoströmView profile

Lecturer

• Computer Science Education
• Programming Didactics
• Software Engineering Education
• +3 more

Jan Erik Moström is a Lecturer and Director of Studies at the Department of Computing Science, Umeå University. His work focuses on Computer Science Education , particularly in programming pedagogy, software design education, and student learning strategies. Contact: jan.erik.mostrom@umu.se . Research Interests include software engineering education , concurrency education , object-oriented programming pedagogy , and transformative learning experiences . His publications explore student challenges in programming, conceptual understanding, and educational frameworks. Recent trends in his work emphasize informal learning , imitation as a pedagogical tool , and threshold concepts in computing. He has contributed to conferences like ICER, ITiCSE, and SIGCSE, focusing on practical teaching methods and student skill development. Teaching involves programming courses and curriculum oversight as Director of Studies. No scientific awards or lab affiliations are explicitly mentioned in available data.

Stefan JohanssonView profile

Associate Professor

• Visualization
• Numerical Linear Algebra
• Computer Graphics
• +4 more

Stefan Johansson is an Associate Professor at Umeå University's Department of Computing Science, where he also serves as Deputy Head of Department overseeing finance and personnel. He maintains a part-time research affiliation with Curiosum Science Center, leading technical development for exhibitions and dome theaters. His research focuses on visualization , numerical linear algebra , and interactive educational tools . Current projects include VisA, which develops dome-theater visualizations of atmospheric data for science education. His work bridges computer graphics, software engineering, and didactic methods. Johansson's publications reflect dual expertise: earlier works emphasize matrix computations and control theory (2004-2017), while recent research shifts toward scientific visualization applications (2020-2025), particularly in geosciences and educational technology. Awards & Leadership Recognised University Teacher (Meritorious Teacher) 2021 Program Director, MSc in Computing Science (2022-2023) Deputy Director, Curiosum Science Center (2019-2021) He contributes to research groups in Matrix Computations with Applications and Parallel and Scientific Computing , and develops STEM curricula for primary/secondary schools.

Dejan KosticView profile

Professor

• Distributed Systems
• Computer Networks
• Operating Systems
• +6 more

Dejan Kostic is a Chair Professor of Internetworking at KTH Royal Institute of Technology, leading the Networked Systems Laboratory. He is affiliated with RISE Research Institutes of Sweden and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on distributed systems, computer networks, operating systems, and machine learning applications in networking. Education: Ph.D. in Computer Science, Duke University M.S. in Computer Science, University of Texas at Dallas B.S. in Computer Engineering, University of Belgrade Research Interests: His work spans distributed systems , high-performance networking , smartNIC offload , AI-driven systems , and network function virtualization (NFV) . Recent projects include energy-efficient AI inferencing (Wallenberg Scholar Project) and scalable federated learning for healthcare applications. Key contributions include the PacketMill framework, FAJITA stateful processing, and the LineFS distributed file system. Publications & Awards: ERC Starting (2010) and Consolidator (2018) Awards Wallenberg Scholar (2024) Best Paper at SOSP 2021 and Community Award at NSDI 2022 Advising & Grants: Supervised over 15 doctoral students, including notable alumni like Hamid Ghasemirahni and Alireza Farshin. Leads projects funded by ERC, WASP, and Swedish Research Council (VR). Active in grant review panels for ERC and Swedish Foundations. Labs & Teams: Heads the Network Systems Laboratory (NSLab) at KTH, collaborating with Ericsson and RISE on industrial projects. Key initiatives include the Networked Systems for Machine Learning course and the Connected Intelligence unit at RISE.

Jan KronqvistView profile

Assistant Professor

• Optimization
• Machine Learning
• Convex MINLP
• +7 more

Jan Kronqvist is an Assistant Professor at KTH Royal Institute of Technology and a faculty member of Digital Futures, a cross-disciplinary research center focused on digital technologies. His research emphasizes optimization methodologies, including convex MINLP, machine learning integration, and algorithm design for complex systems. He actively contributes to projects like SMART (Predictive Maintenance), SimOPT (Process Design), ADAPCOS (Undersea Swarm Optimization), and cybersecurity in power systems. Key research areas include global optimization for clustering problems, robustness in neural networks, GPU-accelerated machine learning, and ensemble model optimization. He has developed algorithms like the Extended Cutting Plane and SHOT solver for convex MINLP, with applications in chemical engineering, power systems, and control systems. His work bridges theoretical optimization and practical industrial challenges, addressing topics such as cyberattack resilience, multimodal mobility systems, and ensemble learning verification. Projects often involve collaboration with industry partners to translate mathematical models into real-world solutions. Jan has advised initiatives like the SEC scholar program for Calvin Tsay and contributed to research on radiation therapy scheduling and distillation process optimization. His research outputs span over 40 peer-reviewed articles, focusing on advancing optimization algorithms and their applications across engineering and data science domains.

Ivy PengView profile

Associate Professor

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Computing
• +6 more

Ivy Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology’s School of Electrical Engineering and Computer Science (EECS), where she leads the Scalable Computing Laboratory (ScaLab). She also holds a Docent title in Parallel Computing and is affiliated with the Digital Futures Faculty and the Cooperate Working Group. Prior to Sweden, she worked at the Lawrence Livermore National Laboratory (LLNL) as a Computer Scientist and as a post-doctoral fellow at Oak Ridge National Laboratory (ORNL) in the USA. Her research focuses on large-scale parallel systems, system-level optimization through workload and architecture awareness, performance modeling and analysis, and memory subsystem optimization. Key areas include converged HPC and cloud computing, heterogeneous accelerators (e.g., GPUs, RISC-V Vector units, QPUs, DPUs), and heterogeneous memories (e.g., persistent memory, HBM). She also explores disaggregated resources to enhance computing efficiency and scalability. Ivy Peng coordinates the EU Horizon 2022 Project OpenCUBE, which develops open-source cloud-based services for EPI systems, and leads the Swedish Research Council (VR) Grant on Disaggregated Memory for Emerging Parallel Systems. She actively contributes to technical committees and review boards for journals like IEEE TPDS and conferences including SC, ICS, HPDC, and IPDPS. She advises Ph.D. students Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, and Daniel Medeiros, while co-supervising Jeremy Williams and Måns Andersson. Her academic leadership extends to multiple courses at KTH, including Applied GPU Programming, Introduction to High Performance Computing, and Quantum Computing for Computer Scientists.

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Systems Optimization
• +5 more

Bo Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology in Stockholm, leading the Scalable Computing Laboratory (ScaLab). Previously, he held roles as a Computer Scientist at Lawrence Livermore National Laboratory (LLNL) and a postdoctoral fellow at Oak Ridge National Laboratory (ORNL). He coordinates the EU Horizon 2022 Project OpenCUBE and leads a Swedish Research Council (VR) grant on Disaggregated Memory for Emerging Parallel Systems. His research focuses on optimizing large-scale parallel systems through architecture-aware design, performance modeling, and memory subsystem innovation for heterogeneous computing environments. He teaches courses such as Applied GPU Programming, High-Performance Computing, and Operating Systems. As an academic leader, he supervises multiple PhD students and co-supervises additional researchers. His service includes roles on technical program committees for top conferences like IEEE TPDS, SC, and ICS, and journals like IEEE Transactions on Parallel and Distributed Systems. Bo Peng’s work has been recognized with prestigious awards, including the R&D 100 Award in 2024. His research bridges HPC and cloud computing, emphasizing scalable systems and resource efficiency in next-generation architectures.

Vincenzo GulisanoView profile

Associate Professor

• Distributed Systems
• Data Streaming
• Concurrent Data Structures
• +7 more

Vincenzo Gulisano is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology. He holds a Ph.D. from the Technical University of Madrid and master's and bachelor's degrees from the University of Trieste. His research focuses on distributed systems, data streaming, and concurrent data structures with applications in cyber-physical systems such as Advanced Metering Infrastructures (AMI) and DDoS detection. He leads the Master Program in Computer Systems and Networks (MPCSN) and teaches courses like Operating Systems (EDA092/DIT400). His work emphasizes scalable and fault-tolerant stream processing frameworks like STRETCH and Erebus, which address challenges in real-time analytics and elastic resource management. Gulisano collaborates on projects integrating machine learning (e.g., IP-LSH-DBSCAN for parallel clustering) and cybersecurity (e.g., Metis for AMI intrusion detection). He has published widely in top-tier conferences like VLDB, Euro-Par, and IEEE TPDS, with contributions to stream synchronization, scheduling policies, and energy-sharing communities. Gulisano’s research bridges theoretical foundations and practical implementations, addressing scalability, determinism, and explainability in data-driven systems. His work often involves interdisciplinary collaborations, combining algorithms, hardware, and middleware design for efficient data processing in edge and cloud environments.

Stefan PålssonView profile

Lecturer

• University Pedagogy
• Higher Education Teaching
• Computational Science Education
• +7 more

Stefan Pålsson is a Lecturer at the Department of Information Technology; Computational Science, Uppsala University, within the Faculty of Science and Technology. He serves as director of studies and is deeply engaged in university pedagogy, contributing through teaching, educational development, and leadership in pedagogical initiatives. His research and professional work focus on enhancing teaching quality in higher education, particularly in computational science and engineering disciplines. Key interests include active learning, gender-aware curriculum reform, and institutional support for pedagogical excellence. The recent publications reflect a strong trend in educational research, emphasizing faculty development, compulsory training for teachers, and systemic reforms in engineering education. The work spans journals and conference proceedings in education and engineering education, with a clear focus on practical, scalable pedagogical interventions. Scientific Awards: Excellent teacher, Uppsala University since 2013 Stefan Pålsson provides pedagogical support at the faculty level through TUR (the Faculty of Science and Technology's University Pedagogical Council) and the Unit for University Pedagogy. He advises on teaching practices and curriculum development, though no formal PhD or Master’s students are listed. No grants are mentioned in the provided text. He is actively involved in institutional educational development teams, including TUR and the Unit for University Pedagogy, where he contributes to shaping teaching standards and supporting academic staff in adopting evidence-based teaching methods.