Find Academic Supervisors

• Deep Learning
• Robustness in Machine Learning
• Label Noise
• +4 more

Erik Englesson is a Researcher at the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology. His work is supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP-AI/MLX). He holds a PhD from KTH Royal Institute of Technology, focusing on label noise in image classification from an aleatoric uncertainty perspective. His research interests center on robustness and uncertainty quantification in deep learning, particularly addressing label noise and combining aleatoric and epistemic uncertainties. He teaches Probabilistic Graphical Models (DD2420) at KTH. Recent publications explore topics like gradient-based explanation methods, autoencoder parameterization, and noise-robust classification strategies. His work frequently intersects with computer vision and theoretical deep learning principles. Englesson collaborates with prominent researchers like Hossein Azizpour and receives funding from strategic initiatives in AI and autonomous systems. His research bridges foundational theory with practical applications in healthcare imaging and robust model design.

Tatjana PavlenkoView profile

Professor

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +4 more

Tatjana Pavlenko is a Professor in Statistics at Uppsala University, affiliated with the Department of Statistics. Her research bridges mathematical statistics, probability theory, and computational methods, focusing on high-dimensional data analysis in biomedical and machine learning contexts. Key research areas include: High-dimensional statistical inference and Bayesian graph structure learning Sparse signal detection and adaptive thresholding methods Statistical machine learning with applications to biomedical datasets Her recent publications demonstrate expertise in: Bayesian model averaging and junction tree sampling Asymptotic theory for high-dimensional classifiers Testing independence and covariance structures L2-type statistics and empirical process theory She supervises PhD students working on: High-dimensional causal inference in media Bayesian graphical models Sparse classification algorithms Currently active in Uppsala University's AI4Research initiative, Pavlenko develops adaptive data-driven procedures for statistical learning problems with complex sparsity patterns.

Bruna G. PalmView profile

Lecturer

• Statistical Modeling
• Signal Processing
• Remote Sensing
• +4 more

Bruna G. Palm is a Lecturer at the Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), Sweden. She holds a BA (2014) and PhD (2020) in Statistics from Brazilian institutions and has held visiting and research fellow positions at BTH and the Aeronautics Institute of Technology (ITA). Education: BA in Statistics (2014), PhD in Statistics (2020) Current Role: Lecturer at BTH Collaborations: Saab AB, Sweden Her research bridges statistical modeling with remote sensing and signal processing , focusing on SAR image analysis , change detection , and machine learning applications. She has also contributed to environmental data forecasting and educational pedagogy in STEM. Key trends include Bayesian inference , autoregressive models , and Rayleigh distribution techniques for SAR imagery. Her work on resin composite masking extends into dentistry , highlighting interdisciplinary reach. She has no listed scientific awards but maintains active profiles on LinkedIn , Google Scholar , and ORCID .

Sonia-Florina HorchidanView profile

Researcher

• Graph Databases
• Machine Learning on Graphs
• Stream Processing
• +3 more

Sonia-Florina Horchidan is a doctoral researcher at the KTH Royal Institute of Technology in Stockholm, Sweden. She is affiliated with the Division of Software and Computer Systems within the School of Electrical Engineering and Computer Science (EECS), working under the supervision of Associate Professor Paris Carbone in the Data Systems Lab . Her academic activities include teaching assistant roles for courses in distributed systems and data storage paradigms. Research Focus: Graph Databases Machine Learning on Graphs Stream Processing Federated Learning with Privacy Guarantees Approximate Query Processing via ML Serverless Streaming Graph Analytics Scientific Contributions: Her recent publications at venues like EDBT, VLDB, and AI4DB explore intersections between graph data management and machine learning. Key themes include ML inference optimization in streaming systems, privacy-preserving health data forecasting, and innovative graph query execution models. Honors & Service: Co-authored Best Paper at DEEM 2022 Best Paper Award at VLDB 2023 PhD Workshop Program Committee Member, aiDM 2024 Reproducibility Committee Member, ACM SIGMOD/PODS

• Formal Verification
• Model Checking
• Concurrent Systems
• +6 more

Parosh Abdulla is a Professor in the Division of Computer Systems within the Department of Information Technology at Uppsala University, Sweden. His research focuses on formal methods and program verification with emphasis on concurrent systems, memory models, and verification techniques. With an extensive publication record spanning over three decades, he has established himself as a leading researcher in his field. His primary research interests include formal verification, model checking, concurrent systems analysis, memory models (particularly TSO and PSO), string constraints, timed automata, parameterized verification, and program analysis. His work addresses fundamental challenges in verifying complex software systems, especially those involving concurrency and weak memory models. In recent years, he has expanded his research to include quantum systems verification and the application of machine learning techniques to formal methods. Analysis of his recent publications (2023-2025) reveals a continued focus on advancing verification techniques for concurrent systems, with growing interest in quantum computing verification and the integration of graph neural networks with traditional formal methods. His work demonstrates consistent innovation in reducing the state space explosion problem while maintaining verification completeness. Professor Abdulla has established significant collaborations with researchers including Mohamed Faouzi Atig, Bengt Jonsson, and others, resulting in numerous high-impact publications in top-tier venues. His research has practical implications for developing reliable concurrent software systems and advancing formal verification methodologies. While specific details about his advising activities and grants aren't explicitly mentioned in the available information, his extensive publication record suggests active supervision of graduate students and involvement in multiple research projects. His work likely contributes to formal methods research groups at Uppsala University, focusing on verification tools and theoretical foundations of program analysis.

• Theorem proving
• Decision procedures
• SAT/SMT solving
• +8 more

Philipp Rümmer is a Professor of Theoretical Computer Science at the University of Regensburg and a Senior Lecturer/Associate Professor at the Department of Information Technology , Uppsala University. His career spans over two decades of academic and industrial collaboration in formal verification, SMT solvers, and program analysis. Professor (2022–present), Faculty of Informatics and Data Science, University of Regensburg Senior Lecturer/Associate Professor (2018–present), Department of Information Technology, Uppsala University His research focuses on formal verification of software and embedded systems, SMT solvers (e.g., Princess, Norn), model checking (Eldarica), and integrating machine learning with verification techniques. He has pioneered tools like JayHorn for Java verification and Sloth for string constraint solving. Recent publications emphasize probabilistic verification of parameterized systems, timing analysis in embedded updates, heap invariants via Horn clauses, and graph neural networks in solving word equations. These works reflect his commitment to advancing formal methods through interdisciplinary approaches. Awards include: Oscarspris (2013), Uppsala University SAP Award for top Computer Science graduate (2005) Bosch Telecom Award for natural science excellence (1998) He has secured significant grants from the Swedish Research Council , Knut and Alice Wallenberg Foundation , and Microsoft Research , supporting projects like UPDATE (2020–2024) and WebSec (2018–2023). His leadership in tools such as Princess and Eldarica underscores his influence in automated reasoning and verification.

Natalya Pya ArnqvistView profile

Associate Professor

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
• +7 more

Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

Robert LagerströmView profile

Professor

• Cyber Security
• Information Systems
• Threat Modeling
• +6 more

Robert Lagerström is a Professor at KTH Royal Institute of Technology specializing in Industrial Information Systems. His research focuses on developing automated cyber security frameworks and threat modeling methodologies for large-scale IT environments. Key areas include vulnerability analysis of IoT, automotive systems, and critical infrastructure, with a particular emphasis on probabilistic modeling and simulation languages like vehicleLang and powerLang. He collaborates extensively with industry partners across automotive, banking, energy, and cloud sectors to validate practical security solutions. His work emphasizes model-based security analysis that avoids disrupting live systems, enabling safe testing of cyber defenses. Notable contributions include the securiCAD tool for enterprise cyber security management and the PatrIoT framework for agile threat research in IoT ecosystems. Lagerström's research integrates enterprise architecture modeling with security behavior analysis, addressing challenges in digitalization and agile development environments. He has pioneered methods for quantifying attack resilience through probabilistic threat simulations and has published widely on topics such as LPWAN security, SCADA system vulnerabilities, and ethical considerations in offensive cyber training. His work bridges theoretical cyber security concepts with real-world implementation challenges in both traditional IT and emerging technologies like blockchain and microservices architectures.

• Computer Vision
• 3D Reconstruction
• Medical Image Analysis
• +3 more

Christopher Zach is a Research Professor at Chalmers University of Technology, affiliated with the Signal Processing and Medical Technology department within the Digital Image Systems and Image Analysis research group . His work focuses on 3D reconstruction , real-time computer vision , and numerical optimization for machine learning. Develops 3D image understanding techniques Specializes in robust optimization for vision systems Leads research in medical image analysis Recent publications demonstrate expertise in low-light text enhancement , out-of-distribution detection , and domain adaptation for industrial applications. Active in Chalmers' Wallenberg AI and ÅForsk funded projects. Collaborates with researchers from Volvo Group , Volvo Cars , and SAFER Vehicle Safety initiatives.

• Robotics
• Machine Learning
• Tactile Sensing
• +4 more

Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.

• Probability Theory
• Statistics
• Gaussian Processes
• +5 more

Jonas Wallin is a Senior Lecturer and Project Manager at the Department of Statistics, Lund University. His research bridges probability theory, statistics, and computational methods, with applications to public health, climate modeling, and spatial data analysis. He actively contributes to interdisciplinary projects addressing global challenges. Key Research Areas : Gaussian processes, Bayesian inference, disease risk mapping, climate-health interactions, statistical modeling on metric graphs. Projects : Leading Horizon Europe and Swedish Research Council-funded initiatives on antimicrobial resistance, spatio-temporal modeling, and protein degradation in immunity. Supervision : Primary supervisor for PhD students Erik Karlsson Strandh and Mattias Jönsson, focusing on climate-driven disease dynamics and Gaussian processes. Collaborations : Engaged in international partnerships and peer-review activities for journals and conferences. Wallin’s work aligns with UN Sustainable Development Goals, particularly in health and environmental resilience. Recent publications highlight methodological innovations in Markov random fields and regularization techniques, while his expertise in data quality control impacts disease surveillance practices.

• Machine Learning
• Artificial Intelligence
• Cyber-Physical Systems
• +3 more

Ning Xiong is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering and the Division of Intelligent Future Technologies. His research focuses on advanced artificial intelligence, machine learning, optimization algorithms, and cyber-physical systems. He explores applications ranging from digital twin frameworks in distributed systems to predictive maintenance using explainable AI and anomaly detection in timeseries data. His work integrates techniques like federated learning, Bayesian classifiers, and bio-inspired computing (e.g., membrane clustering) to address challenges in smart systems and data science. Key research areas include: Machine Learning & Deep Learning Cyber-Physical Systems Optimization Algorithms Smart Systems & IoT Data Science & Big Data Recent publications highlight advancements in digital twin frameworks for resilient distributed systems, ensemble learning for imbalanced data, and lightweight object detection methods for UAV imagery. His contributions emphasize practical applications in energy grids, predictive maintenance, and industrial automation while addressing theoretical challenges in model explainability and scalability. His research is characterized by interdisciplinary collaboration, combining software engineering, systems architecture, and domain-specific expertise to develop innovative solutions for dynamic environments.

Ignacio Torroba BalmoriView profile

Researcher

• Underwater Robotics
• Simultaneous Localization and Mapping (SLAM)
• Autonomous Underwater Vehicles (AUVs)
• +3 more

Ignacio Torroba Balmori is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Department of Aerospace, Mobility and Naval Architecture and the Robotics, Perception and Learning (RPL) division. He holds a PhD from RPL under the supervision of John Folkesson, focusing on Simultaneous Localization and Mapping (SLAM) for autonomous underwater vehicles (AUVs). His current research emphasizes underwater SLAM with sonar and camera systems, path planning, control strategies, and system identification for AUVs operating in open waters and confined spaces. His work centers on applications such as autonomous seabed surveying, algae farm monitoring, and seaweed mapping. He is actively involved in developing the AUV SAM, Kongsberg Hugin, BlueROV2, USV FloatSAM, and the AUV Lolo. As a mentor, he has supervised master’s theses on topics including underwater SLAM algorithms and bathymetric informative path planning. He teaches courses like 'Introduction to Robotics' (assistant) and 'Underwater Technology' (teacher). Key technical contributions include system identification for hydrobatic AUVs using physics-informed machine learning and tools for bathymetric SLAM, such as the SubmapsRegistration repository. His research is hands-on, prioritizing field robotics and problem-driven solutions.

Arunselvan RamaswamyView profile

Senior Lecturer

• Reinforcement Learning
• Multi-agent Learning
• Explainable Learning
• +5 more

Arunselvan Ramaswamy is a Senior Lecturer in Data Science, AI, and Machine Learning Research at Karlstad University. His research focuses on the intersection of Computer Science and Mathematics, with primary interests in reinforcement learning, multi-agent systems, stochastic optimization, and control theory. He has held prior roles as Lecturer at Paderborn University and Junior Research Head at the Heinz Nixdorf Institute, Germany. His work spans theoretical foundations of learning algorithms and practical applications in cyber-physical systems, wireless sensor networks, and Industry 4.0. Key contributions include distributed optimization frameworks, reinforcement learning for autonomous systems, and stability analysis of stochastic approximation methods. He has published widely in top venues such as IEEE Transactions on Automatic Control, NeurIPS, and IFAC conferences. Ramaswamy has contributed to interdisciplinary projects including automated side-channel detection in cryptographic protocols and deep reinforcement learning for microgrid control. His teaching includes courses on machine learning fundamentals and project-based ML applications at Karlstad University. Research highlights include developing the DeepCAS algorithm for control-aware scheduling, analyzing convergence properties of distributed optimization under communication constraints, and exploring gradient-based methods with unbounded delays. Current work emphasizes scalable multi-agent learning and explainable AI systems.

• Machine Learning
• Visual Analytics
• Explainable AI
• +7 more

Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.