جستجوی استاد راهنمای دانشگاهی

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

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.

Elina Rönnbergمشاهده پروفایل

استاد

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +۱۸ مورد دیگر

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

Mattias Villaniمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Ilie Sarpeمشاهده پروفایل

پژوهشگر

• Graph Mining
• Temporal Networks
• Algorithm Design
• +۵ مورد دیگر

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.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +۸ مورد دیگر

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.

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

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
• +۶ مورد دیگر

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.

Tatjana Pavlenkoمشاهده پروفایل

استاد

• high-dimensional statistical inference
• statistical machine learning
• sparse signal detection
• +۴ مورد دیگر

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.

Roghayeh Hajizadehمشاهده پروفایل

مدرس

• Discrete Optimization
• Operations Research
• Mathematical Modeling
• +۴ مورد دیگر

Roghayeh Hajizadeh is a University Lecturer at Linköping University's Department of Mathematics (MAI) and Applied Mathematics (TIMA) research group. Her work focuses on discrete optimization techniques for real-world problems like urban snow removal and air traffic control, using mathematical modeling and algorithm development. PhD in Optimization of Snow Removal in Cities (2023) Member of Mathematics and Algorithms for Intelligent Decision Making research group Key projects: Optimization of Snow Removal in Cities (2017-present) and OPT@ATM (2023-present) Research keywords include: Discrete Optimization, Operations Research, Sustainable Urban Planning, Air Traffic Control Algorithms . Teaching includes Combinatorial Optimization for Computer Science/Software Engineering programs and Supply Chain Optimization for Industrial Economics/Mechanical Engineering students. Publications (2020-2024) demonstrate expertise in: Lagrangian relaxation techniques , vehicle coordination algorithms , and tree elimination methods for snow removal; Dantzig-Wolfe decomposition for aircraft scheduling.

Robert Lagerströmمشاهده پروفایل

استاد

• Cyber Security
• Information Systems
• Threat Modeling
• +۶ مورد دیگر

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.

• Control Theory
• Game Theory
• Machine Learning
• +۴ مورد دیگر

Muhammad Umar B Niazi is a Marie-Curie Postdoctoral Fellow at both KTH Royal Institute of Technology and Massachusetts Institute of Technology , working under the guidance of Karl H. Johansson and Munther Dahleh respectively. Holding a Ph.D. in Automatic Control Engineering from Université Grenoble Alpes, Niazi's research spans secure monitoring of cyber-physical systems and dynamic incentive design for sociotechnical systems . Education : Ph.D. (Grenoble INP, 2021); M.Sc. & B.Sc. (Bilkent University, COMSATS) Research Directions : Secure estimation against cyberattacks in transportation networks and epidemic models Physics-informed learning for observer design in nonlinear systems Eco-driving incentives using Stackelberg game theory Aggregated monitoring of large-scale systems Scientific Contributions : 2025 publications on distributed observers and reachability analysis 2024 work on incentive mechanisms and sensor fault detection 2023 developments in physics-informed epidemic control 2022-2019 foundational work on network observability and opinion dynamics Awards : Marie-Curie Postdoctoral Fellowship (2022-2025) Best Student Paper Finalist, European Control Conference 2019 Niazi's interdisciplinary work combines control theory , game theory , and machine learning to address resilience in transportation, epidemic monitoring, and social network dynamics. His methods integrate theoretical rigor with practical implementations through tools like SUMO simulations and physics-informed neural networks.

Fredrik Sävjeمشاهده پروفایل

دانشیار

• Causal Inference
• Econometrics
• Experimental Design
• +۳ مورد دیگر

Dr. Fredrik Sävje is a Senior Lecturer and Associate Professor at the Department of Economics, Uppsala University , specializing in causal inference and econometrics. His research focuses on experimental design, variance estimation, and treatment effect analysis under complex interference structures. Academic Affiliation: Department of Economics, Uppsala University Email: fredrik.savje@nek.uu.se Dr. Sävje’s research interests span causal inference, econometrics, and statistical methodology. Key areas include: Design-based frameworks for causal inference Threshold blocking and covariate balancing Exposure-response models in bipartite experiments Robust variance estimation under interference His recent work emphasizes: Foundations of causal inference in randomized controlled trials Optimized covariate balancing via Gram–Schmidt Walk Design Relaxing assumptions in exposure mapping for treatment effects Generalizing inference methods to sparse and complex experimental graphs Dr. Sävje has co-authored software packages including GSWDesign.jl for covariate balancing and quickmatch for generalized full matching in R. His publications appear in top journals like Biometrika , Journal of the American Statistical Association , and Electronic Journal of Statistics .

• Visualization
• Data Science
• Human-Computer Interaction
• +۴ مورد دیگر

Miriah Meyer is a Professor in the Department of Science and Technology (ITN) at Linköping University, supported by the Wallenberg Autonomous Systems Program (WASP). Her research focuses on designing visualization tools to enhance data analysis and understanding through interdisciplinary approaches integrating computer science, design, social science, and humanities. Previously, she held positions as an Associate Professor at the University of Utah’s School of Computing and completed a postdoctoral fellowship at Harvard University. Education: Bachelor’s in Astronomy and Astrophysics, Penn State University PhD in Computer Science, University of Utah Postdoctoral Fellowship in Visualization, Harvard University Research Interests: Meyer’s work emphasizes human-centered design of visualization systems to address societal challenges. Key areas include: Exploratory and reflective data analysis Interdisciplinary methodologies Design studies in visualization Impact of technology on societal perceptions Publications: Her recent work explores feminist theory in visualization (2025), preregistration in research design (2024), and socio-technical transitions in dashboarding (2024). She advocates for rigorous, ethical practices in visualization design. Awards: Recognitions include TED Fellow (2013), MIT TR35 (2012), and Microsoft Research Faculty Fellowship (2011). Labs & Teams: Active in the Visualization and Interaction Design Group at LiU and former contributions to the Scientific Computing & Imaging Institute (SCI) at the University of Utah.

Berkant Savasمشاهده پروفایل

دانشیار

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +۳ مورد دیگر

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

• Information Network Analytics
• Decentralized Machine Learning
• Data Mining
• +۵ مورد دیگر

Sarunas Girdzijauskas is a Professor at the Department of Computer Science within KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), where he leads the Graph Analytics and Network Learning group (GALE) . He also holds a Senior Researcher position at RISE (Research Institutes of Sweden) and has served as Program Director for EECS's doctoral program in Information and Communication Technology (2019-2022) and Deputy Director of Third-Cycle Education at EECS. PhD from Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2016 Docent in Computer Science at KTH (2016) Coordinated multiple large-scale projects: H2020 Marie Curie RAIS, Marie Curie ITN iSocial Research Focus : His work spans decentralized machine learning systems, graph analytics, and blockchain infrastructure. Key areas include: Information network analysis for social and technical systems Federated learning with adaptive personalization Graph partitioning and distributed algorithms Privacy-preserving models with differential privacy Knowledge distillation for evolving graph representations Scalable solutions for big data and IoT botnet detection Article Trends : Recent work demonstrates convergence of graph neural networks with federated learning, emphasizing privacy (differential privacy in 5/15 articles), robustness (out-of-distribution detection in 3 articles), and decentralized infrastructure (blockchain, gossip protocols, and P2P overlays). The articles frequently address temporal modeling, knowledge distillation, and heterogeneous graph representations. Scientific Leadership : Coordinated H2020 Marie Curie European Training Network 'RAIS' (2018-2023) Coordinated Marie Curie ITN 'iSocial' (2012-2017) Activity Leader for EIT Digital Innovation Activities Teaching & Advising : Has supervised/examined over 60 master theses and currently advises 8 PhD students, including Adam Orucu (Main Supervisor, 2024-), Filip Cornell (Main Supervisor, 2020-), and Ahmed Emad Samy Youssif (Main Supervisor, 2019-). Former supervisees include 9 PhDs like Dr. Edvin Listo Zec (2025) and Dr. Debaditya Roy (2024).