جستجوی استاد راهنمای دانشگاهی

Hazem Torfah is a tenure-track Assistant Professor at the Computer Science and Engineering Department at Chalmers University of Technology, where he leads the Safe and Trustworthy Autonomous Reasoning lab (STARlab). Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley, USA, and obtained his Ph.D. in 2019 from Saarland University, Germany. His research focuses on developing theoretical foundations and techniques for constructing safe and reliable autonomous cyber-physical systems. Hazem earned his Ph.D. from Saarland University in 2019, followed by postdoctoral research at UC Berkeley. His educational background has positioned him at the forefront of formal methods research for autonomous systems. His research interests center on the formal specification, verification, and synthesis of cyber-physical systems, with a particular focus on quantitative approaches for verifying and explaining system behavior. He investigates methods to ensure safety and reliability in autonomous systems through runtime monitoring, specification mining, and assurance frameworks. His work bridges theoretical computer science with practical applications in autonomous driving and robotics. An analysis of his recent publications reveals a strong focus on runtime monitoring techniques for operational design domains, specification mining for predictive safety, and frameworks for runtime assurance of cyber-physical systems. His work frequently combines formal methods with machine learning to address safety challenges in autonomous systems, with applications primarily in autonomous driving. Hazem's research is funded by the Wallenberg AI, Autonomous Systems, and Software Program, reflecting the significance and potential impact of his work in the field of assured autonomy. He has supervised several Master's students, including Samuel Collier Ryder and Johannes Holmgren (2025), and Lucas Karlsson and George Kayembe (2024). His teaching includes courses on Formal Methods for Security, Oracle-Guided Inductive Synthesis, and Data Structures and Algorithms. As the leader of STARlab, Hazem directs research on safe and trustworthy autonomous reasoning, focusing on developing methods to ensure the reliability and explainability of autonomous cyber-physical systems through formal verification techniques.

• cyber-physical systems
• formal verification
• runtime monitoring
• +۵ مورد دیگر

Juliette Griffié is a DDLS Fellow at Stockholm University, affiliated with the Computational Microscopy for Cell Biology (CMCB) laboratory. Her research focuses on developing advanced image analysis strategies and machine learning tools to address fundamental questions in cell biology using light microscopy. Research Interests include: Advanced light microscopy techniques Machine learning applications in image analysis Immunology, particularly autoimmune diseases and immune cell interactions Generative models for dynamic cellular imaging Bayesian statistical methods in microscopy data analysis Her work spans molecular-level studies, computational modeling, and software development for super-resolution and single-molecule localization microscopy (SMLM). Key publications appear in Nature Methods , Cell Reports , and Nature Protocols . Scientific Awards : Wallenberg National Program for Data-Driven Life Science (DDLS) Fellow

• Light Microscopy
• Image Analysis
• Immunology
• +۴ مورد دیگر

Shiva Sander Tavallaey serves as an Adjunct Professor at the Royal Institute of Technology (KTH) within the School of Engineering Sciences, Department of Automotive Technology and Technical Acoustics. With over 30 years of combined academic and industrial experience spanning KTH, VOLVO, and ABB, he brings substantial expertise to his position. His educational background includes a PhD in Sound and Vibration from KTH and an MSc in Mechanical Engineering from Chalmers University of Technology. His primary research interests encompass Advanced Signal Analysis , Model-based Analysis (including uncertainty and probabilistic methods), and Data-driven Approaches utilizing AI and ML techniques. Over the past two decades, his work has focused on developing service and diagnostic solutions for optimized maintenance systems. Dr. Tavallaey teaches the course "Signals and Mechanical Systems (SD2125)" at KTH, where his office is located at Technology Ring 8. His research particularly emphasizes predicting the Remaining Useful Lifetime (RUL) of assets through sophisticated diagnostic algorithms and analytical methods.

• Sound and Vibration
• Mechanical Engineering
• Advanced Signal Analysis
• +۷ مورد دیگر

Martin Törngren is a Professor of Embedded Control Systems at the Department of Mechatronics and Department of Mechanical Engineering at KTH Royal Institute of Technology. He is actively engaged in research and teaching within Embedded Systems , Mechatronics , and Cyber-Physical Systems , focusing on architecture, model-based development, and system integration. His work bridges control systems and embedded systems, emphasizing reliability and safety in industrial applications. Research Leadership: Technical coordinator of the Artemis project iFEST, focusing on functional safety for automated systems. Education: Teaches courses like Cyber-physical Systems (MF2140) , Mechatronics Higher Course , and doctoral-level courses in embedded systems. Research Interests: Törngren's work spans model-based systems engineering , distributed control systems , co-design of control and computer systems , and system safety . He emphasizes industry-university collaboration , particularly with the automotive and automation sectors. His publications highlight trends in automated driving safety , edge computing , and toolchain integration . Collaborative Initiatives: He co-founded the Innovative Centre for Embedded Systems (ICES) and participates in networks like Artist2 and ArtistDesign. His research projects often involve partnerships with global companies and academic institutions.

• Cyber-Physical Systems
• Embedded Control Systems
• System Reliability
• +۵ مورد دیگر

Jose M Pena is a Senior Associate Professor and Head of Unit at Linköping University's Department of Computer and Information Science, specializing in the Division of Statistics and Machine Learning (STIMA). His research focuses on probabilistic graphical models including Bayesian networks, Markov networks, and chain graphs within machine learning and artificial intelligence. His research interests center on causal inference, machine learning, and statistical modeling. He actively develops methods for causal discovery, counterfactual reasoning, and sensitivity analysis using normalizing flows and graphical models. His work bridges theoretical statistics with practical applications in complex data analysis. His recent publications (2020-2025) demonstrate a strong focus on causal machine learning, with particular emphasis on directed acyclic graphs (DAGs), counterfactual modeling, and handling unobserved confounding. Key trends include the integration of deep learning with causal structures, robust causal inference under distribution shifts, and scalable methods for high-dimensional causal analysis. As Head of Unit within STIMA, he contributes to the international master's programme in Statistics and Machine Learning. The division hosts significant research activities in modern data analysis and is part of Linköping University's Department of Computer and Information Science—one of northern Europe's largest departments in this field.

• Machine Learning
• Artificial Intelligence
• Probabilistic Graphical Models
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Oleg Sysoev is a Senior Associate Professor in the Department of Computer and Information Science (IDA) at Linköping University, Sweden. He specializes in Statistics and Machine Learning, contributing to the Master's program 'Statistics and Machine Learning'. His academic background includes a Master's in Applied Mathematics from Russia and a doctoral thesis in Statistics at Linköping University (2010), focusing on monotonic regression for large datasets. Research Interests: Monotonic regression and interpolation Machine learning methods (decision trees, kernel smoothing, neural networks) Data mining in public health Bootstrap methods and computational statistics Optimization techniques Grants & Collaborations: Lead researcher in a SEK 13 million project (Swedish Research Council) applying AI to forensic analysis for solving murders. Member of the Wallenberg AI, Autonomous Systems and Software Program (WASP), a major Swedish research initiative. Associated with the Division of Statistics and Machine Learning (STIMA), focusing on data-driven methodologies. Teaching & Academic Leadership: Responsible for teaching at undergraduate and graduate levels in statistics and machine learning. Supports curriculum development for the Master's program in Statistics and Data Mining.

• Monotonic Regression
• Data Mining
• Machine Learning
• +۴ مورد دیگر

Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.

• Wireless Networking
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Mohammad Kahangi is a Postdoctoral Fellow in Structural Engineering at Lund University's Faculty of Engineering, actively contributing to the Circular Building Sector profile area through research on sustainable maintenance and reuse strategies for masonry structures. His research centers on sustainable brick facade maintenance, repointing techniques, moisture management in masonry, and circular economy principles in construction. Key focus areas include historical brick structures (1930-2000), mould growth prevention in cavity walls, and energy efficiency optimization in brick-AAC systems, directly supporting UN Sustainable Development Goals for sustainable cities. Recent publications (2024-2025) demonstrate interdisciplinary approaches combining probabilistic modeling, machine learning, and experimental analysis to address water penetration, structural durability, and resource efficiency in brick masonry systems. Kahangi secures research funding through active projects with the Swedish Energy Agency and Stiftelsen J. Gust. Richerts Minne, focusing on reclaimed brick utilization and sustainable management of existing masonry, alongside industry collaborations with the Development Fund of the Swedish Construction Industry.

• Civil Engineering
• Structural Engineering
• Sustainable Construction
• +۳ مورد دیگر

Beatrice Alenljung is an Associate Professor of Information Technology at the University of Skövde, Sweden, leading the Interaction Lab. She specializes in human-centered technology research with a focus on user experience (UX) design, human-robot interaction, and decision support systems. Her work bridges theoretical insights with practical applications in fields like healthcare robotics, industrial automation, and educational technology. She teaches in the User Experience Design program and actively contributes to interdisciplinary projects addressing challenges in aging populations, industrial collaboration, and sustainable digital transformation. Key ongoing projects include exploring socially assistive robots for older adults (RoSA and RO-LIV), optimizing human-robot collaboration in manufacturing (KOMPARO and AIHURO), and risk reduction strategies for emergency services through digital simulation. Her research emphasizes empirical studies on UX evaluation frameworks (e.g., ANEMONE), human factors in robotics, and the societal impacts of emerging technologies. Alenljung has authored over 40 peer-reviewed publications, spanning conference papers and book chapters in HCI, robotics, and requirements engineering. She holds a PhD in Decision Support Systems from the University of Skövde and has maintained collaborative ties with industries and institutions across Europe.

• User Experience Design
• Human-Computer Interaction
• Human-Robot Interaction
• +۳ مورد دیگر

Joe Steinhauer is a Senior Lecturer in Computer Science at the University of Skövde, affiliated with the School of Informatics and the Department of Information Technology. His research focuses on Artificial Intelligence, Machine Learning, and their applications in driver intention recognition, telecommunication networks, and healthcare systems. Research Interests: Joe’s work spans AI-driven solutions for transportation safety (e.g., driver and pedestrian behavior prediction), anomaly detection in telecommunication networks, and data fusion techniques. His recent projects include developing advanced driver assistance systems (ADAS) for trucks and AI methods for climate-adapted water management. Publications: Joe has published extensively on topics such as deep learning models for driver intention recognition, topic modeling for network monitoring, and uncertainty quantification in AI systems. His work bridges theoretical advancements and real-world applications in automotive safety and smart infrastructure. Grants/Projects: Key projects include I2Connect (2023–2026), STRATUS (2021–2023), and CogniDrive (2023–2024). These initiatives aim to enhance road safety, water infrastructure resilience, and driver situation awareness through AI and data-driven approaches. Labs/Teams: He collaborates within research groups focusing on vehicular AI, telecommunication analytics, and applied machine learning at the University of Skövde and industry partners like Volvo and CIDAN.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Najmeh Abiri holds roles as a Research Engineer, Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), Postdoc in the Department of Statistics, and Researcher in Computational Science for Health and Environment at Lund University. She is affiliated with eSSENCE: The e-Science Collaboration. Her primary research interests include variational inference, Bayesian inference, deep learning, computational biology, and machine learning, contributing to UN Sustainable Development Goals related to environmental sustainability and health. Abiri has conducted impactful research on probabilistic multivariate time series imputation using diffusion models, disease risk mapping with AI, and quantum mechanics applications of deep learning. She has also pioneered denoising autoencoder techniques for handling missing data across diverse datasets. Notable outputs include a 2022 study predicting West Nile virus outbreaks using eco-climatic drivers and a 2021 paper on inverse problems in quantum mechanics. Her work spans interdisciplinary collaborations, addressing global challenges through data-driven approaches. Abiri organized workshops on AI/ML technologies and GAN applications, fostering academic engagement. She led the 2014-2019 project on statistical inference with deep latent variable models, underscoring her commitment to advancing computational methodologies in science and health.

• Machine Learning
• Bayesian Inference
• Deep Learning
• +۵ مورد دیگر

Akram Abdul Hamid is an Associate Senior Lecturer in the Division of Building Physics at Lund University's Faculty of Engineering (LTH). His research focuses on sustainable building design, energy conservation, and moisture management in construction materials. He contributes to UN Sustainable Development Goals, particularly in affordable and clean energy and sustainable cities. His expertise includes hygrothermal analysis, building envelope performance, and field measurements. He has participated in projects like ASSURE (Adaptation of Urban Space through Sustainable Regeneration) and RECONCILE, addressing energy efficiency and environmental impacts. His recent work explores probabilistic modeling for mould growth, machine learning in construction, and strategies for moisture-safe building designs. Abdul Hamid has authored or co-authored over 28 publications, including peer-reviewed articles and conference proceedings. He actively collaborates internationally, with recent work presented at the International Building Physics Conference and the International Brick and Block Masonry Conference.

• Energy Engineering
• Multifamily Building
• Environmental Engineering
• +۴ مورد دیگر

Volker Krueger is Professor at Lund University's Department of Computer Science, with joint appointments in Robotics and Semantic Systems. He leads research at the intersection of artificial intelligence, computer vision, and autonomous systems through the ELLIIT initiative and NEXTG2COM Competence Centre. His research group focuses on developing robust perception and decision-making systems for industrial and service robotics applications. Research interests span robot perception under challenging conditions, uncertainty quantification in machine learning models, and human-robot interaction frameworks. Recent projects investigate vision-language models for robotic failure handling, domain-specific languages for robot programming, and optimization methods for logistics systems. His laboratory coordinates multiple European projects including RIACT (Radical Innovation of Adaptive Cobot Technology) and NextG2Com (Next Generation Communication Infrastructures). He actively promotes robotics education through initiatives like the Robotics Week for Schools and maintains extensive industry collaborations in manufacturing and automation sectors.

• Robotics
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.

• Robotics
• Computational Geometry
• Topology in Robotics
• +۳ مورد دیگر