Find Academic Supervisors

• Signal Processing
• Wireless Communications
• MIMO Communications
• +4 more

Joakim Jaldén is a Professor at the Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He holds a Ph.D. in Electrical Engineering from KTH (2007) and completed post-doctoral studies at Vienna University of Technology (2007-2009). With affiliations at Stanford University and ETH Zürich, his academic journey reflects global expertise. 2002: M.Sc. in Electrical Engineering, KTH 2007: Ph.D. in Electrical Engineering, KTH 2007-2009: Post-Doctoral Researcher, Vienna University of Technology Jaldén's research spans Signal Processing , Wireless Communications , and Biomedical Data Analysis . He pioneered MIMO communications and later developed ELISpot/FluoroSpot analysis algorithms commercialized by Mabtech AB. His work on cell migration tracking (IEEE ISBI 2012) and distributed optimization (ECO-PANDA method) demonstrates interdisciplinary impact. Key publication trends include Hidden Markov Models for DNA sequencing, Reinforcement Learning in communication systems, and Low-Complexity Beamforming for MU-MIMO networks. His 2024 work on mmWave MIMO beam coherence showcases continued leadership in wireless channel modeling. Scientific recognition includes: IEEE Signal Processing Society 2006 Young Author Best Paper Award Ingvar Carlsson Career Award 2009 (Swedish Foundation for Strategic Research) IEEE ISBI 2012 Best Paper Award Bitplane Awards (2013-2015) for cell tracking challenges As Program Director of KTH's 5-year Electrical Engineering Degree Program (CELTE) since 2016 and Vice-Chair of EECS Faculty Board , Jaldén leads academic initiatives. His collaborations with industry (e.g., Mabtech AB) and roles as examiner for advanced courses in communication systems highlight his educational impact.

Ilie SarpeView profile

Researcher

• Graph Mining
• Temporal Networks
• Algorithm Design
• +5 more

Ilie Sarpe is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, mentored by Prof. Aristides Gionis. He is an active member of both VandinLab and AIDA Lab, focusing on developing rigorous algorithms for temporal network analysis. His educational background includes: PhD in Computer Engineering from the University of Padova (2019-2023), with thesis on 'Efficient and Rigorous Techniques for the Analysis of Large Temporal Networks' Master's Degree in Computer Engineering (summa cum laude) from the University of Padova (2017-2019) Bachelor's Degree in Computer Engineering from the University of Padova (2014-2017) Sarpe's research centers on the design of scalable algorithms for data-mining problems, particularly in graph-mining and clustering. He specializes in probabilistic algorithms with rigorous theoretical guarantees, with a strong focus on temporal networks. His work integrates tools from sampling theory, probability, concentration inequalities, and statistical learning theory to develop efficient solutions for complex network analysis problems. His publications demonstrate a consistent focus on temporal network analysis, with recent work accepted at top venues including KDD 2024, WWW 2022, CIKM 2021, and SIAM SDM 2021. The research trajectory shows increasing sophistication in handling temporal motifs, dense subnetwork discovery, and centrality measures in evolving networks. His scientific recognition includes: SoBigData TNA Fellowship (2022) 3 years Ph.D. Fellowship (2019) Award for Scientific Degrees (2017) Two 'Mille e una lode' awards (2016, 2017) Sarpe actively supervises master's thesis projects in data mining at KTH and has secured research funding through the SoBigData TNA Fellowship. His research group affiliation with VandinLab and AIDA Lab provides collaborative opportunities across multiple institutions. He maintains active laboratory work focused on developing practical implementations of his theoretical algorithms, as evidenced by his GitHub repositories containing C++ implementations of his published methods.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +3 more

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

• Computational Weld Mechanics
• Finite Element Analysis
• Fatigue and Fracture of Materials
• +3 more

Professor Zuheir Barsoum is a faculty member at KTH Royal Institute of Technology, serving as Vice Head (Research) in the Department of Engineering Mechanics. His research focuses on computational weld mechanics, fatigue assessment of materials, and structural integrity of welded joints. Key areas include high-frequency mechanical impact (HFMI) treatments for fatigue improvement, finite element analysis, and lightweight metal joining. Funded by VINNOVA, SSAB, Volvo, and others, his work addresses industrial challenges in structural durability. Current PhD students include Martin Edgren (bridge structural health monitoring), Mehdi Ghanadi (fatigue of high-strength steels), Yu Zhu (laser cladding simulations), and Kaushik Iyer (LCC modeling of welded structures). He teaches courses like Advanced Design of Welded Structures (SD2420) and oversees degree projects in Lightweight and Solid Mechanics. Notable achievements include the 2010 Henry Granjon Prize for fatigue design research. His startup Winteria AB commercializes digital quality assurance solutions for welding production, aligning with Industry 4.0 trends. Recent research emphasizes probabilistic fatigue modeling, machine learning for weld geometry analysis, and material defect characterization. Collaborations include Chalmers University and Swerim. His work bridges advanced manufacturing, computational mechanics, and industrial applications to enhance structural reliability and lifecycle cost optimization.

Christian RohnerView profile

Professor

• Wireless Sensor Networks
• Backscatter Communication
• Network Security
• +5 more

Christian Rohner is a Professor at the Department of Information Technology at Uppsala University, specializing in the Division of Computer Systems. His research spans over two decades with a clear evolution from early work in opportunistic networking to current cutting-edge research in backscatter communication and physical-layer security. Professor Rohner's research interests focus on wireless communication systems , particularly backscatter communication , sensor networks , and network security . His work on analog backscatter tags has pioneered techniques for channel estimation, reliable flooding protocols, and identification systems for battery-free devices. In wireless security , he has made significant contributions to radiometric fingerprinting, physical-layer authentication, and intrusion detection for IoT systems. His research in information theory applies theoretical frameworks to practical network analysis problems, including modularity computation in probabilistic networks and information decomposition. His recent publications (2020-2025) demonstrate a strong focus on enabling low-power wireless systems, with applications ranging from medical contexts (fat intra-body communication) to temperature sensing with RFID tags. The research shows a clear trajectory toward practical implementations of battery-free sensor networks that can operate without traditional power sources while maintaining security and reliability. Professor Rohner has maintained long-term collaborations, particularly with Thiemo Voigt at Uppsala University, resulting in numerous joint publications across multiple research domains. His work bridges theoretical foundations with practical implementations, making significant contributions to both academic research and potential real-world applications in wireless networking.

• Information Security
• Cybersecurity
• Software Architecture
• +5 more

Mathias Ekstedt is a Professor of Industrial Information and Control Systems at KTH Royal Institute of Technology in Stockholm, Sweden. Affiliated with the Software Systems Architecture and Security (SSAS) research group under the Division of Network and Systems Engineering in the Department of Computer Science, School of Electrical Engineering and Computer Science (EECS). He holds a Ph.D. (2004) and M.Sc. (1999) from KTH. Co-founder of foreseeti (acquired by Google in 2022) Director of KTH's Master Programme in Cybersecurity Member of the Digital Futures research center's Trust Working Group Affiliated with Center for Cyber Defense and Information Security (CDIS) Ekstedt's research focuses on information security and cybersecurity integrated with software/systems architecture modeling and analysis . His work emphasizes probabilistic attack/defense graphs for system-of-systems security evaluation, particularly in SCADA and Industrial Control Systems within power infrastructure. He has developed the Meta Attack Language (MAL) and collaborated extensively with the power industry. Recent research trends (2022-2025) include: Threat modeling languages for ICT and power systems Probabilistic attack graph applications in energy flexibility markets and smart cities Software Bill of Materials (SBOM) challenges in Java ecosystems Dynamic attack graph generation and lazy evaluation techniques Cyber-physical security for low-voltage grids and substation automation Ekstedt supervises 17 Ph.D. students (11 as main supervisor, 6 as co-supervisor) and has mentored numerous postdocs. He leads major projects like the SSF project CHAINS (software supply chain security) and coordinates initiatives within SweGRIDS , HONOR , and SOCCRATES .

Leif NilssonView profile

Associate Professor

• Statistical Learning
• Defect Detection
• Occupational Health
• +5 more

Leif Nilsson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. His research focuses on statistical learning methods for industrial quality control and occupational health risk assessment. Statistical learning for defect detection Exposure modeling for occupational hazards Biostatistical analysis of health outcomes Recent work applies probabilistic classifiers and spline smoothers to automate surface finish inspection. He also investigates exposure variability in hand-arm vibration and its health impacts through epidemiological studies. Nilsson collaborates with clinical teams on stress recovery interventions and contributes to methodological developments in biological monitoring. His statistical expertise spans Bayesian modeling, resampling techniques, and spatio-temporal data analysis.

Anna LukinaView profile

Assistant Professor

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +4 more

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

Fredrik GustafssonView profile

Professor

• Stochastic Signal Processing
• Adaptive Filtering
• Change Detection
• +5 more

Professor Fredrik Gustafsson at Linköping University is a leading researcher in sensor informatics, affiliated with the Department of Systems Engineering (ISY) and its Control Engineering division. His work bridges theoretical signal processing with real-world applications in conservation biology and autonomous systems. 1988: MSc in Electrical Engineering 1992: PhD in Control Engineering Research spans stochastic signal processing , adaptive filtering , and change detection , applied to marine automation, wildlife protection, and AI-driven conservation. Articles focus on machine learning for animal classification, seismic sensing, and pandemic data analysis. Key awards: Arnberg Prize (KVA, 2004), IVA member (2007), SAAB scholarship (2011). He co-leads the Smart Savannahs initiative and contributes to m-learning projects in Kenya.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +4 more

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

Måns MagnussonView profile

Associate Professor

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +4 more

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

• Robotics
• Autonomous Systems
• Spatial Cognition
• +6 more

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

• Expressive Speech Synthesis
• Multimodal Interaction
• Prosody Control
• +4 more

Éva Székely is an Assistant Professor and Researcher at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. Her work focuses on expressive speech synthesis, multimodal interaction, and the application of synthetic speech in human-robot and conversational systems. She teaches the course 'Human Perception for Information Technology (DM2350)'. Her research interests include adapting synthetic speech to situational context, analyzing prosody and pragmatic functions, and investigating ethical implications of synthetic voice design. Notable projects involve developing tools like ConnecTone for modular AAC systems and CreakVC for voice modulation. She has contributed to foundational studies on gender diversity in synthetic voices and the impact of disfluencies on speaker perception. Recent work explores spontaneous speech synthesis, controllable TTS with neural HMMs, and the integration of gesture and speech synthesis using flow matching techniques. Her research often bridges technical innovation with human-centered design principles.

Thomas SchönView profile

Professor

• Machine Learning
• Probabilistic Modeling
• Signal Processing
• +7 more

Thomas Schön serves as the Beijer Professor of Artificial Intelligence at Uppsala University's Department of Information Technology within the Faculty of Science and Technology. His research focuses on developing probabilistic models and algorithms for extracting knowledge from data, with particular emphasis on dynamical systems. His research interests span multiple disciplines at the intersection of Machine Learning and statistics, signal processing, automatic control, and computer vision . He takes a systematic approach to representing and manipulating uncertainty through probability theory. His work encompasses both basic and applied research, with strong collaborations with industry partners including ABB Crane Systems, Autoliv, Saab, Sectra, and Xsens Technologies. Schön's research output demonstrates significant contributions to probabilistic modeling of dynamical systems , with particular expertise in sequential Monte Carlo methods (particle filters), Markov chain Monte Carlo, Gaussian processes, and deep learning. His recent work includes advancements in incorporating background knowledge into machine learning models and developing flexible probabilistic frameworks. Schön actively supervises numerous PhD students working on diverse topics including Bayesian nonparametric models, deep learning applications, uncertainty-aware systems, and probabilistic computer vision. His research is funded by The Swedish Research Council (VR), The Swedish Foundation for Strategic Research (SSF), and Vinnova.

Mohamed Faouzi AtigView profile

Professor

• Model Checking
• Program Verification
• Concurrency
• +7 more

Mohamed Faouzi Atig is a Professor in Computer Systems at the Department of Information Technology, Uppsala University. His career spans roles as Senior Lecturer (2018-2021), Associate Senior Lecturer (2014-2018), and Researcher (2012-2018) at the same institution. He obtained his Doctoral Degree in Computer Science from the University of Paris Diderot-Paris 7 (2010) and a Master in Engineering from Tunisia Polytechnic School (2005). Current Role: Professor in Computer Systems Institution: Uppsala University Research Focus: Model checking, verification of infinite-state systems, weak memory models, automata theory, string constraints, concurrent program analysis His research explores formal methods for concurrent programs, weak memory models (TSO/PSO/POWER), automata theory for verification, and SMT solvers for string constraints. Recent work integrates graph neural networks with word equation solving and advances stateless model checking techniques. Key article trends include Weak Memory Model Verification (TSO, PSO, POWER) Stateless Model Checking Algorithms String Constraint Solvers (TRAU, Norn) Timed Automata and Multi-Pushdown Systems Mohamed Faouzi Atig has led collaborations on fence insertion procedures, timed pushdown automata, and database-driven system verification. His contributions are recognized through publications in top-tier conferences and journals.