Find Academic Supervisors

• Control Systems
• Robotics
• Embedded Systems
• +3 more

Alessandro Papadopoulos is a Professor of Electrical and Computer Engineering at Mälardalen University (MDU) and a QUALIFICA Fellow at the Institute for Software Technology and Software Engineering (ITIS), University of Málaga. He leads the Complex Real-Time Embedded Systems (CORE) research group and serves as Scientific Leader for Applied AI under the AI@MDU initiative. His research focuses on control theory, robotics, and real-time embedded systems, emphasizing interconnected systems and uncertainty management. Education: BSc (2008) and MSc (2010) in Computer Engineering from Politecnico di Milano; PhD (2014) in Information Technology, Systems and Control from the same institution. Postdoctoral fellowships at Lund University’s Department of Automatic Control and Politecnico di Milano’s Dipartimento di Elettronica, Informazione e Bioingegneria. Research interests include control theory applications to computing systems, distributed systems, and AI integration. His work addresses challenges in real-time, embedded, and edge computing. Notable contributions include VR starting grant (2020) and SSF strategic mobility grant (2020), and the Most Influential Paper Award at SEAMS 2025. He has advised PhD students like Anna Friebe and Daniel Bujosa Mateu. Grants and collaborations include projects with ABB industrial automation and leadership roles in conferences (e.g., DEBS 2025 Workshop/Tutorial Chairs). His research group explores autonomous systems, cybersecurity in industrial networks, and cloud manufacturing resilience. Labs/teams: CORE group, collaborating with Prof. Thomas Nolte.

Masoud DaneshtalabView profile

Professor

• Deep Learning Acceleration
• Hardware/Software Co-Design
• Interconnection Networks
• +7 more

Masoud Daneshtalab is a Professor at Mälardalen University, leading the Heterogeneous System research group (HERO). He previously held roles as a European Marie Curie Fellow at KTH Royal Institute of Technology (2014) and as a university lecturer and group leader at the University of Turku, Finland (2012-2014). His research focuses on interconnection networks, hardware/software co-design, deep learning acceleration, and evolutionary optimization. He specializes in fault-tolerant DNN accelerators, time-sensitive networking (TSN), and embedded systems. His work bridges theoretical advancements with practical implementations, emphasizing reliability and efficiency in edge computing and AI applications. Research interests include: Network-on-Chip (NoC) architectures and congestion prediction Fault resilience in deep neural networks (DNNs) Optimization of federated learning and homomorphic encryption for edge AI Integration of TSN with 5G and automotive systems Hardware acceleration techniques for computational efficiency Recent publications emphasize advancements in robust AI architectures, fault tolerance mechanisms, and TSN-based communication protocols. His work often addresses practical challenges in deploying machine learning models on resource-constrained devices. He actively contributes to interdisciplinary projects in autonomous systems, healthcare monitoring via FMCW radar, and neural architecture search for embedded applications. Labs/Teams: Leads the HERO group at Mälardalen University, focusing on heterogeneous computing systems and real-time embedded systems.

• Biostatistics
• Epidemiology
• Medical Statistics
• +7 more

Matteo Bottai is Professor of Biostatistics and Head of the Department of Biostatistics at Karolinska Institutet, where he also serves as Scientific Director of the Biostatistics Core Facility. He holds additional academic appointments as Visiting Professor in the Department of Mathematical Statistics at Stockholm University and Adjunct Professor in the Department of Biostatistics at the University of South Carolina. Education: Doctor of Science, Harvard University, USA His research focuses on advanced biostatistical methods, particularly in quantile regression, modeling of bounded and censored outcomes, longitudinal data analysis, and methods for handling missing data in distributed networks. His work bridges theoretical statistics and practical applications in epidemiology, environmental medicine, and public health. He has contributed to methodological developments in Laplace regression, parametric quantile modeling, and optimal probability weighting. His recent publications span a wide range of topics, including statistical methodology, cancer outcomes, respiratory health, cardiovascular disease, and mental health interventions. The articles reflect a strong emphasis on population-based cohort studies, clinical trials, and the use of complex statistical models to derive meaningful clinical and public health insights. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis Effect of internet-delivered cognitive behavioral therapy in pediatric OCD and Tourette syndrome Risk of thromboembolic events after cancer surgery Impact of air pollution on infant lung function Statistical methods for missing data and quantile regression Matteo Bottai actively collaborates across disciplines, working with researchers in oncology, neurology, cardiology, psychiatry, and environmental health. His role as Scientific Director of the Biostatistics Core Facility underscores his leadership in supporting high-quality statistical analysis in biomedical research. He has been involved in numerous clinical trials and large-scale cohort studies, contributing statistical expertise to study design, analysis, and interpretation. He leads the Biostatistics Unit at the Institute of Environmental Medicine, Karolinska Institutet, which provides statistical support and fosters methodological research in environmental and occupational health.

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

Marina TogerView profile

Senior Lecturer

• Spatial Analysis
• Urban Dynamics
• Human Mobility
• +7 more

Marina Toger is a Senior Lecturer/Associate Professor at Uppsala University's Department of Human Geography, where she conducts research at the intersection of spatial analysis, urban dynamics, and human mobility. Her work utilizes advanced geospatial technologies including GIS, agent-based modeling, and mobile phone data analysis to investigate contemporary urban challenges. Her research focuses on spatial inequality, urban mobility patterns, and the impact of external shocks like pandemics on human spatial behavior. Toger specializes in analyzing how people move through urban spaces, with particular attention to leisure mobility, spatial segregation, and accessibility to urban amenities. Her methodological expertise spans geosimulation, spatial network analysis, and the innovative use of mobile phone data for understanding complex urban systems. Analysis of her publication record reveals a strong trend toward studying mobility patterns during the COVID-19 pandemic using mobile phone data, with significant contributions to understanding how liberal lockdown policies affected spatial behavior in Stockholm. Her work consistently bridges theoretical spatial analysis with practical urban planning concerns, particularly regarding equity and accessibility. While no specific scientific awards are mentioned in the provided text, her extensive publication record in high-impact journals demonstrates significant scholarly contribution to the field of human geography and spatial analysis. Toger maintains active research collaborations with scholars including John Östh, Umut Türk, Karima Kourtit, and Ian Shuttleworth. Her research appears to be supported by various funding sources that enable data collection and analysis of large-scale mobility patterns, though specific grants are not detailed in the provided text.

Diego CalvaneseView profile

Visiting Professor

• Artificial Intelligence for Data Management
• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• +5 more

Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.

Chuan HeView profile

Assistant Professor

• Optimization
• Machine Learning
• Data Science
• +12 more

Chuan He is an Assistant Professor in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA) and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research bridges continuous optimization and machine learning, focusing on algorithmic efficiency and theoretical foundations. Education: Ph.D. in Industrial and Systems Engineering, University of Minnesota, USA (2019–2023) B.S. in School of Mathematical Sciences, Xiamen University, China (2015–2019) His research interests include deep learning, decentralized and large-scale optimization, high-order methods, and applications in healthcare, scientific computing, and engineering. He develops algorithms with strong theoretical guarantees, particularly in nonconvex optimization settings. His work emphasizes improving the speed, reliability, and scalability of machine learning training processes. His recent publications focus on Newton-CG based methods, augmented Lagrangian techniques, and federated learning under constraints. These contributions span top journals in operations research, optimization, and machine learning, reflecting a strong trend toward integrating second-order optimization with practical machine learning challenges. Scientific Awards: No awards explicitly mentioned in the text. Chuan He advises and collaborates within the WASP Mathematics research environment, particularly in the 'Optimisation for machine learning' group. He previously held a postdoctoral position at the University of Minnesota under Professor Ju Sun. His research is supported through institutional affiliations with WASP and Linköping University. He actively contributes to the academic community through conference presentations at INFORMS, SIAM, and NeurIPS workshops. He is involved in the 'Optimisation for machine learning' research group at MAI, which aims to develop more efficient and theoretically sound algorithms for machine learning. The group focuses on replacing heuristic methods with principled approaches, reducing computational costs in training models.

• Network Security
• Cybersecurity
• Privacy-Preserving Technologies
• +3 more

Cihan Eryonucu is a Ph.D. student and researcher at the KTH Royal Institute of Technology , affiliated with the Networked Systems Security (NSS) Group and the WASP - Autonomous Systems and Software track . He is supervised by Professor Panos Papadimitratos . Research Focus: Security and privacy in participatory sensing systems, Sybil attack detection, and privacy-preserving protocols for location-based services. Teaching: Assistant roles in courses like Advanced Networked Systems Security (EP2510) and Building Networked Systems Security (EP2520) , and a teacher in Communication System Design (IK2200) . Expertise: His work addresses vulnerabilities in crowdsourced systems (e.g., Google Maps manipulation) and develops cryptographic methods for secure data aggregation. Publications: Recent works explore Sybil attack mitigation via presence verification, privacy-enhancing frameworks for urban sensing, and applications of homomorphic encryption to genomic and location data. Contact: Email eryonucu@kth.se | Room 4444, Kistagången 16, KTH Royal Institute of Technology.

Sina SheikholeslamiView profile

Researcher

• Machine Learning
• Deep Learning
• Distributed Systems
• +3 more

Sina Sheikholeslami is a Researcher at the Division of Energy Systems, Department of Energy Technology at KTH Royal Institute of Technology. He works on leveraging AI for sustainability and climate action, focusing on projects like Beyond 2030 and OnStove. His affiliations include the KTH Climate Action Centre and Vinuesa Lab. He holds a PhD in Distributed Computing from KTH (2025), advised by Vladimir Vlassov, Amir Payberah, and Jim Dowling, with prior M.Sc. studies at Eindhoven University of Technology and KTH through the EIT Digital Master School. He also completed a B.Sc. in Computer Software Engineering at Amirkabir University of Technology. Research interests include distributed systems, machine learning, deep learning, and their applications in sustainable development. Notable work includes developing frameworks like AutoAblation for ablation studies and Importance-aware DPT for dataset partitioning, which earned the Best Artefact Award at DAIS 2023. His recent work explores using LLMs for ablation studies and weight initialization techniques from hyperparameter trials. Academic leadership roles include serving on the KTH PhD Chapter’s Board, EECS PhD Student Council, and committees such as the School Assembly and Third-Cycle Education Council. He is Sweden’s Local Representative for the EIT Digital Alumni Foundation. His teaching roles include assistant and teacher in courses like Data Mining and Data-Intensive Computing. He supervises multiple students, including those exploring topics like scalable model training with Ray and feature stores in Hopsworks. His research spans environmental monitoring (e.g., ExtremeEarth), public transit systems (DUGET), and interdisciplinary applications of ML in wood science and urban planning.

Silun ZhangView profile

Assistant Professor

• Nonlinear Control
• Large-Scale Complex Systems
• Privacy-Preserving Algorithms
• +3 more

Silun Zhang is an Assistant Professor in the Department of Mathematics at KTH Royal Institute of Technology, affiliated with WASP (Wallenberg AI, Autonomous Systems and Software Program) and the Digital Futures Faculty. He holds a Ph.D. in Optimization & Systems Theory from KTH (2019), an M.S. and B.S. in Automation from Harbin Institute of Technology, China. Prior to KTH, he was a Postdoctoral Associate at MIT's Laboratory for Information & Decision Systems (LIDS) and EECS Department (2019–2023), where he collaborated with Prof. Munther Dahleh and contributed to the MIT MicroMasters Program in Statistics and Data Science. His research focuses on nonlinear control , large-scale complex systems , privacy-preserving algorithms , and risk-averse reinforcement learning . Key areas include control on manifolds (e.g., rigid-body attitude coordination), privacy in federated learning, and moment-based modeling for collective systems. He leads projects such as 'Analysis and Synergy of Hyper-networked Autonomy' and 'Designing Interaction-Aware Multi-Robot Systems.' Current team members include Georgios Vasileiou (Ph.D.), Lantian Zhang (Postdoc), and Malintha Fernando Chakravarthige (Postdoc). His work is supported by the Wallenberg Foundations and Digital Futures. Open positions are available for motivated Ph.D. students and postdocs.

Georgios J. FakasView profile

Associate Professor

• Big Data
• Keyword Search
• Spatial Databases
• +9 more

Georgios J. Fakas is an Associate Professor (Docent) in the Department of Information Technology at Uppsala University, Sweden, where he leads the Uppsala DataBase Laboratory (UDBL). He has held academic positions at prestigious institutions including the Hong Kong University of Science and Technology, EPFL, UMIST, University of Cyprus, and Manchester Metropolitan University, where he served as a Senior Lecturer. Education: Ph.D. in Computation, UMIST, Manchester, UK (1998) M.Phil. in Computation, UMIST, Manchester, UK (1996) B.Sc. in Computation, UMIST, Manchester, UK (1995) His research spans big data, keyword search on structured and graph data, spatial databases, web and geo-social search, workflow management, and data summarization . He also explores applications in educational technologies, distance learning, and health informatics. His work emphasizes efficient, user-centric data access and intelligent querying paradigms. The 15 most recent publications highlight a sustained focus on keyword search, object summarization, spatial and semantic data, and ranking , published in top venues like SIGMOD, VLDBJ, IEEE TKDE, and PVLDB. His work integrates algorithmic innovation with practical database applications. Scientific Awards: International Sabbaticals grant from Teknat (2023), hosted by Boston University Fellowship 'Hosting of Experienced Researchers from Abroad' from RPF, Cyprus (2009) ERCIM Fellowship (2001), hosted by EPFL, Switzerland He is actively involved in PhD supervision and grant leadership. He currently serves as Principal Investigator for an eSSENCE-funded project on Data-intensive analytics for discovering critical minerals using Machine Learning , in collaboration with industry partners. He has supervised multiple PhD students to completion, including Georgios Kalamatianos, Khalid Mahmood, Zhi Cai, and Ben Cawley. He is a frequent reviewer for leading journals and serves on program committees of major conferences such as ACM SIGMOD and PVLDB. He leads the Uppsala DataBase Laboratory (UDBL) , a research group focused on advanced data management systems, where he mentors students and drives innovation in database technologies.

• Federated Learning
• Wireless Communications
• Communication Efficiency for Distributed Learning
• +7 more

José Mairton Barros da Silva Jr. is an Assistant Professor in the Division of Computer Systems at Uppsala University, Sweden, starting April 2023. Prior to this, he was a Marie Skłodowska-Curie Postdoctoral Fellow jointly at Princeton University (Department of Electrical and Computer Engineering) and KTH Royal Institute of Technology (Division of Network and Systems Engineering). He holds a Ph.D. in Electrical Engineering and Computer Science from KTH, supervised by Carlo Fischione and Gábor Fodor, and BSc and MSc degrees in Telecommunications Engineering from the Federal University of Ceará, Brazil. Ph.D. in Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 2019 MSc in Telecommunications Engineering, Federal University of Ceará, 2014 BSc in Telecommunications Engineering (with honors), Federal University of Ceará, 2012 His research lies at the intersection of wireless communications and machine learning, with a focus on federated learning, communication efficiency, full-duplex systems, millimeter-wave communications, and vehicular networks. He investigates how to optimize distributed machine learning over constrained wireless channels, leveraging techniques in signal processing, optimization, and network design to improve efficiency, fairness, and scalability. The recent publications highlight a strong trend toward integrating machine learning with wireless system design, particularly in enabling efficient federated learning over-the-air, reducing communication overhead via quantization and lazy aggregation, and enhancing full-duplex mmWave systems through low-resolution hardware and smart beamforming. His 2022 survey on Wireless for Machine Learning serves as a foundational reference in the field. He has been recognized with prestigious awards, including: Marie Skłodowska-Curie Fellowship (2022–2025) Grant from the Ericsson Research Foundation (2022) He has been actively involved in research leadership and dissemination, having served as Secretary for the Full-Duplex and Self-Interference Cancellation Emerging Technologies Initiatives (2018–2021), co-chaired workshops at IEEE GLOBECOM, and delivered tutorials on 'Wireless for Machine Learning' at major IEEE conferences (ICASSP, PIMRC, ICC, GLOBECOM). He has also taught in the KTH-Ericsson Data Science Micro Degree Program. His research has been supported by competitive grants and collaborative institutions including Ericsson, Princeton, and KTH. He contributes to open science through GitHub, where he shares code for full-duplex mmWave beamforming algorithms. He has been affiliated with research labs and teams at KTH, Princeton, GTEL (Brazil), and Rice University, working closely with leading experts in communication theory and machine learning. His future work is expected to advance intelligent, energy-efficient, and scalable wireless systems for distributed AI.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +4 more

Zahra Taghiyarrenani is a postdoctoral researcher at Halmstad University , affiliated with the School of Information Technology and the IT - Computer Department . Her work bridges theoretical advancements in machine learning with practical applications in industrial and cybersecurity domains. Education : PhD in Machine Learning (2024, Halmstad University), Licentiate Thesis on Multi-Domain Learning (2022) Her research focuses on domain adaptation , federated learning , and explainable AI , with emphasis on handling data scarcity and heterogeneity. Key projects include fault identification in industrial systems, intrusion detection, and physics-informed models for diagnostics. Her publications highlight trends in cross-domain learning , combining privacy-preserving AI with edge computing , and addressing data efficiency through few-shot/supervised methods . Recent work explores Industry 5.0 applications of Explainable AI. Projects: EVE : Extending vehicle lifespan in electromobility Federated Learning for Well-being : Privacy and fairness in AI FeelAI : Edge-based predictive logistics models

Naveen Kumar Dasanadoddi VenkategowdaView profile

Associate Professor

• Wireless Communications
• Internet-of-Things (IoT)
• Privacy and Security
• +4 more

Naveen Kumar Dasanadoddi Venkategowda is an Associate Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Physics, Electronics and Mathematics (FEM) division. His research focuses on wireless communications, IoT, privacy/security in IoT systems, distributed learning, and optimization. He holds a Ph.D. from IIT Kanpur (2016) and a B.E. from Bangalore University (2008). Prior roles include postdoctoral researcher at NTNU (2017-2021) and Research Professor at Korea University (2016-2017). His work emphasizes modular low-power solutions for wireless sensor networks, high-data-rate communication, and antenna design. Recent publications address distributed algorithms, privacy-preserving techniques, and robust optimization in decentralized systems. Notable awards include the TCS Research Fellowship (2011-2015), ERCIM Alain Bensoussan Fellowship (2017), and a Best Paper Award (Fusion 2020). Education: Ph.D. in Electrical Engineering, Indian Institute of Technology Kanpur (2016) B.E. in Electronics and Communication Engineering, Bangalore University (2008) Awards: TCS Research Fellowship (2011-2015) ERCIM Alain Bensoussan Fellowship (2017) Runner-up Best Paper Award at Fusion 2020 His research spans distributed learning frameworks, IoT security, and signal processing for MIMO systems. Collaborations include work on hybrid beamforming, resilient consensus algorithms, and privacy-aware distributed systems.

Anders EklundView profile

Associate Professor

• Medical Image Analysis
• Machine Learning
• Medical Informatics
• +3 more

Anders Eklund is a Senior Associate Professor at Linköping University, affiliated with both the Department of Biomedical Engineering (IMT) and the Department of Computer and Information Science (IDA). He holds roles in the Center for Medical Image Science and Visualization (CMIV), focusing on developing advanced methods for medical image analysis, including applications in pediatric brain tumor classification, synthetic image generation, and federated learning frameworks. His research bridges biomedical engineering, artificial intelligence, and data science to address clinical challenges such as improving diagnostic accuracy and reducing healthcare resource bottlenecks. Education: Master of Science (2007), PhD in Medical Informatics (2012), Postdoc at Virginia Tech (2012–2014), Docent (2016), Associate Professor (2021). Research Interests: Medical image processing, deep learning for healthcare, federated learning for sensitive data, synthetic medical image generation, and statistical validation of neuroimaging techniques. Notable contributions include exposing flaws in fMRI analysis software and advancing AI-driven solutions for radiation treatment planning. Publications: Recent work emphasizes deep learning applications in medical imaging, including tumor classification, federated learning for privacy-preserving models, and synthetic image techniques to address data scarcity. His articles reflect a focus on interdisciplinary solutions for healthcare challenges, such as reducing queues post-pandemic through automated brain image analysis. Grants & Advising: Secured a 3.6M SEK grant from the Swedish Research Council (2016). Supervised numerous PhD students, including main supervision of Xuan Gu (2019) and co-supervision of Per Sidén (2020). Active in training the next generation of biomedical engineers and AI researchers. Labs & Collaborations: Leads projects within CMIV and collaborates across departments at Linköping University. Involved in initiatives like the ASSIST project, which aims to automate brain image analysis to free clinician time.