Find Academic Supervisors

Jonas UngerView profile

Professor

• Computer Graphics
• Image Processing
• Artificial Intelligence
• +3 more

Jonas Unger is a Professor at Linköping University's Department of Engineering and Natural Sciences (ITN) and Media and Information Technology (MIT). As a research leader in computer graphics and image processing, he contributes to advanced visualization techniques, GPU computing, and AI applications in healthcare and climate adaptation. Specializes in BRDF modeling, spectral light field imaging, and augmented intelligence Active in AI4Climateadaptation and Wallenberg Autonomous Systems Program (WASP) Recipient of the 2023 Chester Carlson Research Prize Develops AI methods for medical imaging (SCAPIS dataset) and autonomous systems His recent work focuses on deep learning for material acquisition, real-time rendering optimizations, and visual analytics pipelines for disaster response systems. Current projects include multi-agent reinforcement learning for air traffic control and GPU-accelerated signal processing techniques. Scientific awards include: Chester Carlson Research Prize (2023) He collaborates with Visual Sweden's Augmented Intelligence platform, co-leads major visualization conferences, and contributes to Horizon 2020 Marie Skłodowska-Curie funded research. Contact: jonas.unger@liu.se

David BromanView profile

Professor

• Programming Languages and Compilers
• Real-Time Systems and Cyber-Physical Systems
• Probabilistic Machine Learning
• +2 more

David Broman is a Professor at KTH Royal Institute of Technology in Sweden, leading the Division of Software and Computer Systems (SCS) and serving as Associate Director Faculty for Digital Futures. He holds a Ph.D. in Computer Science from Linköping University (2010), with visiting roles at UC Berkeley (2012–2016) and Stanford University (2023–2024). His research spans programming languages, real-time systems, and probabilistic machine learning, emphasizing secure and efficient compilation techniques. Education: Ph.D. in Computer Science (Linköping University, 2010) Visiting Roles: UC Berkeley (2012–2014 part-time), Stanford University (2023–2024) Research Interests: David focuses on advancing programming language theory, compilers, and probabilistic methods. Key areas include transformer-based compilation, cyber-physical systems, and formal verification. His work bridges theory and practice, addressing challenges in software reliability and efficiency. Awards: Notable recognitions include the Best ETAPS Paper Award (2023), Distinguished Artifact Award (ESOP 2022), and Teacher of the Year (KTH 2017). His contributions also include grants from the Swedish Foundation for Strategic Research and Wallenberg AI, Autonomous Systems and Software Program (WASP). Advising & Grants: David mentors graduate students (e.g., Gizem Caylak, Viktor Palmkvist) and leads projects funded by WASP, focusing on next-generation compilation techniques. He also contributed to tools like Miking, Timed C, and FlexPRET. Labs & Teams: His team develops frameworks for probabilistic programming and real-time systems, emphasizing collaboration with industry and academia through initiatives like Digital Futures at KTH.

Sebastian MairView profile

Assistant Professor

• Statistical Machine Learning
• Unsupervised Learning
• Representation Learning
• +3 more

Sebastian Mair is an Assistant Professor at the Division of Statistics and Machine Learning (STIMA) at Linköping University, Sweden. He previously served as a postdoctoral researcher at the Division of Systems and Control at Uppsala University and completed his PhD in the Machine Learning Group at Leuphana University of Lüneburg. His academic journey includes a Master of Science in Computer Science and a Bachelor of Science in Mathematics from the Technical University of Darmstadt, as well as a Bachelor of Science in Computer Science from Hochschule Darmstadt University of Applied Sciences. His research focuses on statistical machine learning, with an emphasis on unsupervised learning, representation learning, and efficient data summarization. Key areas include generative modeling, probabilistic methods, and geometric data analysis. He has contributed to advancements in privacy amplification via importance sampling, task-specific graph subsampling, and self-supervised autoencoder architectures. Publications span topics like privacy-preserving techniques, graph algorithms, and medical treatment optimization. While no scientific awards are explicitly mentioned, his work demonstrates significant contributions to foundational machine learning research. As part of STIMA, he collaborates with a diverse team of researchers, including senior professors, postdocs, and PhD students. His teaching and research activities align with the division’s focus on modern data analysis and the international master’s program in Statistics and Machine Learning.

Michael HörnquistView profile

Professor

• Systems Biology
• Gene Regulatory Networks
• Computational Biology
• +4 more

Michael Hörnquist serves as Professor and Deputy Head of the Department of Science and Technology (ITN) at Linköping University, concurrently leading the Physics, Electronics and Mathematics (FEM) Division. His responsibilities encompass undergraduate education oversight and management of research activities within FEM, which focuses on communication electronics, mathematics, engineering didactics, physical electronics, and nanotechnology. Hörnquist's research bridges systems biology and computational physics, specializing in gene regulatory network analysis through machine learning techniques including Elastic Net and LASSO regression. His work extends to nonlinear dynamics in aperiodic electronic structures, reflecting FEM's interdisciplinary approach to physics, electronics, and mathematics. This dual focus enables innovative cross-domain applications in biological modeling and electronic systems. Publication trends since 2003 reveal consistent contributions to systems biology, particularly in reverse-engineering biological networks through integrated data approaches. His 2010 DREAM3 challenge-winning work on gene expression prediction exemplifies practical applications of computational methods, while later research (2016) connects molecular oncology with network dynamics. This trajectory demonstrates sustained innovation in translating theoretical models to biological insights across diverse systems. As an academic leader, Hörnquist emphasizes learning as the university's core mission: 'Learning is the essence of what we do as a university, whether it involves teaching, research, or collaboration. It is learning that allows us to grow and mature as individuals.' He actively fosters this philosophy through mentorship and institutional leadership.

Nikolas Benavides HöglundView profile

Researcher

• Hydrogeology
• Groundwater Modelling
• Environmental Remediation
• +2 more

Nikolas Benavides Höglund is a Postdoctoral Researcher at the Department of Geology, Lund University. His work focuses on hydrogeology, groundwater modeling, and environmental remediation, contributing to UN Sustainable Development Goals related to clean water and sustainable development. Research Interests: Groundwater modeling and numerical techniques Contaminated site characterization and remediation Data-driven approaches in hydrology Integration of academic research with industrial and policy applications Key Projects: A decade of applied groundwater modelling in Sweden (PI, 2024–2026) MIRACHL: In-situ remediation of chlorinated hydrocarbon contamination (Researcher, 2016–2024) Collaborations include international teams in hydrology and geophysics, with recent focus on Sweden's groundwater decision-support systems and industry partnerships.

• Machine Learning
• Deep Learning
• Computer Vision
• +5 more

Atsuto Maki is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Robotics, Perception and Learning. He holds a PhD from KTH and an MEng from the University of Tokyo. Previously, he served as an associate professor at Kyoto University and worked in industry for over a decade at Toshiba Research Cambridge Lab and Toshiba Corporate R&D, focusing on computer vision systems like radar image analysis. His research spans machine learning, deep learning, and computer vision, including motion/object recognition, clustering, subspace analysis, and representation learning. He teaches Machine Learning (DD2421) and co-chairs the Master’s Programme in Machine Learning. Maki has over 30 patents in image analysis and is a board member of the Swedish Society for Automated Image Analysis. Recent projects include grants like 'SeDECI' (semi-supervised learning with imbalanced data) and collaborations with NII, RIKEN AIP, and the University of Tokyo. He actively organizes conferences (e.g., ECCV 2026) and workshops, and holds leadership roles in academic partnerships. Key awards include the Niwa-Takayanagi Best Paper Prize (2018). His work emphasizes practical applications in manufacturing, medical imaging, and autonomous systems, reflected in papers on synthetic data for quality inspection, sonar beamforming, and deep learning regularization techniques.

Sasikumar PunnekkatView profile

Professor

• Dependable Software Engineering
• Real-time Systems
• Safety-Critical Systems
• +5 more

Roles & Affiliations: Professor in Dependable Software Engineering at Mälardalen University (MDU), leading the Dependable Software Engineering research group. Former Head of Software Test & Reliability Engineering at Indian Space Research Organization (ISRO), Director of BITS Pilani KK Birla Goa Campus (Mar 2015–Aug 2016), and Visiting Researcher at Ericsson since 2022 (funded by SSF's strategic mobility program). Education: PhD in Computer Science from University of York, UK (1997), focusing on schedulability analysis of fault-tolerant systems (awarded Commonwealth Scholarship). Research Interests: Real-time systems, dependability, software engineering, safety-critical systems, cybersecurity, and industrial IoT. Key focus areas include fault-tolerant scheduling, safety-critical software design, and system-of-systems analysis. Funding & Projects: Led major EU projects (InSecTT, SUCCESS, DAIS, EUROWEB series) and national initiatives. Coordinated large EU educational collaborations with Western Balkans/Asia. Active in projects like FORA fog computing platform and TSN networks. Awards & Recognition: 6 Best Paper Awards, Senior Member of IEEE, and frequent contributor to conference program committees (ETFA, HASE, DATE, etc.). Grants & Labs: Recipient of SSF strategic mobility funding. Research group develops solutions for automotive systems, industrial automation, and safety-critical infrastructure.

Kristina LundqvistView profile

Professor

• Formal Verification
• Autonomous Systems
• Safety-Critical Systems
• +3 more

Kristina Lundqvist is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Computer Science and Software Engineering. Her work focuses on formal verification, autonomous systems, and safety-critical systems. She holds a Professorship position and contributes to both academic research and industry-relevant solutions. Her research interests emphasize the intersection of formal methods, autonomous systems, and ontology-driven approaches for safety and security. She explores topics such as mission planning for multi-agent systems, hazard analysis, and assurance strategies for cyber-physical systems. Her work bridges theoretical frameworks with practical applications in automotive, aerospace, and industrial automation domains. Dr. Lundqvist’s publications span over two decades, with a strong focus on formal verification techniques, safety-critical software assurance, and ontology development for security and safety domains. Her recent work highlights advancements in scalable strategy synthesis for autonomous systems and the integration of safety and security ontologies for complex systems analysis. Her contributions include frameworks like ACICS for industrial CPS assurance and AQAF for architecture quality assurance in embedded systems. She collaborates on initiatives such as SafeML for training autonomous vehicles and has published extensively in top-tier venues.

Gizem ÇaylakView profile

Researcher

• Probabilistic Programming Languages
• Clinical Data Analysis
• Machine Learning
• +2 more

Gizem Çaylak is a doctoral researcher at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Software and Computer Systems. She is supervised by David Broman (principal) and Magnus Boman (co-supervisor). B.S. in Computer Engineering, Bilkent University (2012-2017) M.S. in Computer Engineering, Bilkent University (2017-2019) Ph.D. in Computer Science, KTH Royal Institute of Technology (2019-present) Her research focuses on deep probabilistic programming languages and their applications in clinical data analysis . She has contributed to bioinformatics research through her work on epistasis test prioritization using diverse SNP selection algorithms. Notable teaching experiences include workshops and courses in Computer Hardware Engineering , Computer Organization and Components , and Algorithms & Programming at KTH and Bilkent University. Her 2020 publication at the International Conference on Research in Computational Molecular Biology demonstrates her interdisciplinary work bridging computer science and computational biology.

• Wireless Communication
• Telecommunications
• Machine Learning
• +3 more

Mahmoud Zaher is a Postdoctoral Fellow at the Royal Institute of Technology (KTH) in Stockholm, Sweden. His academic work includes teaching roles as an assistant for Degree Project in Computer Science (II142X) and as a teacher for Communication Systems (IK2200) and Radioelectronics (IL2219) . His research focuses on wireless communication and network optimization, particularly in Cell-Free Massive MIMO systems, federated learning over wireless networks, and interference management techniques. Research Trends : His recent publications highlight advancements in energy-efficient communication, latency reduction in distributed learning systems, and interference modeling for improved spectral efficiency. Topics span full-duplex relaying, beamforming, power allocation, and mmWave mobility. Teaching Roles : He contributes to academic programs in computer science and radioelectronics, reflecting his expertise in telecommunications and signal processing.

• Mechatronics
• Control Systems
• Autonomous Vehicles
• +5 more

Paolo Falcone is an Associate Professor at the Mechatronics department of Chalmers University of Technology . His research focuses on constrained optimal control , verification methods , and Mechatronics , applied to autonomous and semi-autonomous mobile systems , cooperative driving , and intelligent vehicles . He teaches Model predictive control , Vehicle dynamics control , and Modeling and simulation of dynamical systems . Research Trends: His recent work explores multi-agent Markovian networks for vehicle coordination, safe MPC in uncertain environments, LSTM-based sensor fusion for heavy vehicles, and ADMM algorithms for intersection management. Collaborations with industry focus on autonomous driving , cooperative driving , and electronic stability control .

• Machine Learning
• Information Fusion
• Data Analytics
• +7 more

Alexander Karlsson is an Associate Professor at the Department of Information Technology, School of Informatics, University of Skövde. His research focuses on machine learning, information fusion, and their applications in transportation, healthcare, and industrial systems. He leads projects like I2Connect, developing advanced driver assistance systems using deep learning and situation awareness algorithms. Key research interests include driver intention recognition, predictive maintenance, telecommunication anomaly detection, and biomedical data analysis. He has published extensively in top venues like IEEE Transactions, ACM journals, and conferences like IPMU and MDAI. Education: PhD in Information Fusion (2010) from University of Skövde. Teaching: Coordinates courses in data analytics and AI at graduate and postgraduate levels. Projects: Active in industrial collaborations (e.g., automotive, steel production, telecommunication). His work bridges theoretical advances in machine learning with practical industrial challenges, emphasizing uncertainty quantification and robust decision-making frameworks.

• Control Engineering
• Robotics and Automation
• Vehicle and Aerospace Engineering
• +3 more

Björn Olofsson is a Senior Lecturer and Docent at the Department of Automatic Control, Lund University, within the Faculty of Engineering (LTH). He serves as Director of First and Second Cycle Studies, Project Manager, and Researcher in ELLIIT and the WASP research program. His research focuses on autonomy of robots and vehicles, motion planning, and optimal control. Education: M.Sc. in Engineering Physics (2010), PhD in Automatic Control (2015), Docent (2020) Roles: Senior Lecturer, Director of Studies, Project Manager Memberships: LTH Profile Areas (AI & Digitalization, Engineering Health), LU Profile Area (Natural and Artificial Cognition) Research Interests: Robotics, autonomous systems, motion planning, optimal control for vehicles and robots. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure (SDG9) and Sustainable Cities and Communities (SDG11). Teaching: Courses include Applied Robotics, Motion Planning and Control, and supervision of over 15 PhD/Master’s students. Active in curriculum development and educational leadership. Lab Affiliation: RobotLab LTH, part of the Center for Construction Robotics.

Adel DaoudView profile

Associate Professor

• Global Poverty
• Political Economy
• Development Studies
• +3 more

Adel Daoud is a Senior Associate Professor at Linköping University's Institute for Analytical Sociology (Department of Management and Engineering) and an Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences at Chalmers University of Technology. His research focuses on global poverty, political economy, and causal inference using machine learning. He leads the AI and Global Development Lab, combining AI and earth observation to study poverty dynamics in Africa. Education: PhD in Sociology (2011, University of Gothenburg), M.A. in Sociology (2006), and pedagogical training for higher education (2011-2012). Former positions include roles at Harvard University, the University of Cambridge, and the Alan Turing Institute. Research emphasizes causal mechanisms linking economic shocks, governance, and child poverty. Methodological innovations include integrating satellite imagery with machine learning for poverty measurement. Major grants include SEK 18 million for historical poverty analysis via satellite data. Awards include the Academy Research Fellowship (2018) and Young Academy of Sweden membership (2022-2027). His work spans 150+ publications in journals like PNAS, World Development, and Sociological Methods & Research. Teaching covers quantitative methods, political economy, and social theory. Labs: AI and Global Development Lab (focusing on satellite-based poverty analytics). Podcast: 'The Journeys of Scholars' interviews top academics on research strategies. Active in policy advocacy through the Young Academy of Sweden.

Dejiu ChenView profile

Associate Professor

• Cyber-Physical Systems
• Embedded Control Systems
• Automotive Systems
• +6 more

Dejiu Chen is an Associate Professor in the Unit of Mechatronics at KTH Royal Institute of Technology's Digital Futures Faculty. His research focuses on trustworthy human-compatible Cyber-Physical Systems (CPS), with applications in automotive and medical domains. He holds a Docent title and leads projects addressing system reliability, self-management, and safety assurance. Research interests include embedded control systems, anomaly detection, formal modeling, and AI integration for CPS. Key projects include TRUSST-E (trustable embedded systems), SALIENCE4CAV (autonomous vehicle safety), and SocketSense (IoMT wearable sensors). Publications span over 100 peer-reviewed articles, emphasizing topics like graph neural networks, digital twins, and fault management. He collaborates with industry partners through initiatives like the Mechatronics Twin Framework and condition monitoring methodologies. Teaches courses such as Embedded Systems Design and Smart CPS for Sustainability. Active in grants and industrial partnerships, with a focus on advancing CPS resilience and industrial relevance.