جستجوی استاد راهنمای دانشگاهی

Saurabh Amin is a Professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as the Edmund K. Turner Professor and Undergraduate Officer. He is a Principal Investigator at the Laboratory of Information and Decision Systems and holds affiliations with the Operations Research Center and the Center for Computational Science and Engineering. His educational background includes: B.Tech. 2002, Indian Institute of Technology (IIT) Roorkee M.S. 2004, University of Texas (UT) Austin Ph.D. 2011, University of California (UC) Berkeley Saurabh Amin's research focuses on the design and control of infrastructure systems using game theory and optimization in networks. His work spans three main areas: resilient network control, information systems and incentive design, and optimal resource allocation in large-scale infrastructure systems. By concentrating on critical infrastructure domains including highway transportation, electric power distribution, and urban water networks, his research develops innovative theory and tools to enhance system performance against both stochastic and adversarial disruptions. His approach involves modeling cyber-physical interactions in infrastructures to assess vulnerabilities, developing detection and response tools for failures at various scales, and designing economic incentive schemes that improve aggregate public good while accounting for dependencies and private information among strategic entities. Amin's work bridges mathematical systems theory with practical civil engineering applications, creating a rigorous theoretical foundation for infrastructure resilience that addresses diverse failure mechanisms from natural disasters to deliberate malicious actions. His recent publications demonstrate a strong focus on decarbonization of energy systems, resilient infrastructure planning under climate uncertainty, optimization methods for complex networked systems, and game-theoretic approaches to sustainable infrastructure management. His work increasingly integrates artificial intelligence and machine learning techniques with traditional control theory to address contemporary challenges in infrastructure resilience and sustainability. The research shows a clear trajectory toward addressing climate change impacts on infrastructure systems while maintaining economic efficiency and operational reliability. Professor Amin has received numerous prestigious awards and honors: Common Ground Excellence in Teaching Award, 2025 HSCC Test-of-Time Award, 2024 MIT CEE, Distinguished Service and Leadership Award, 2023 Samuel M. Seegal Prize (SoE) – inspiring students in pursuing and achieving excellence, 2022 Earll M. Murman for Excellence in Undergraduate Advising, 2022 C3.ai Digital Transformation Institute Research Award, 2020 MIT, Ole Madsen Mentoring Award, 2020 MIT, Energy Initiative Research Award, 2020 National Academy of Engineering, China-America Frontiers of Engineering Symposium speaker, 2019 MIT, Robert N. Noyce Career Development Professor, 2015-2018 Google Faculty Research Award, 2015 National Science Foundation CAREER Award, 2015 Siebel Energy Institute Research Award, 2015 MIT, Solomon Buchsbaum AT&T Research Fund Award, 2012 Professor Amin has been actively involved in significant research projects including the C3.ai DTI project on Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems and another on Learning in Routing Games for Sustainable Electromobility. He serves as the chief scientist on multi-institutional NSF grants, including the $9 million Foundations of Resilient Cyber-Physical Systems (CPS) project. His teaching portfolio includes courses such as 1.008 Engineering for a Sustainable World, 1.104 Sensing and Intelligent Systems, 1.020 Engineering Sustainability: Analysis and Design, and 1.208 Resilient Networks. As Undergraduate Officer, he plays a key role in shaping the educational experience for civil and environmental engineering students at MIT. Professor Amin leads the Resilient Infrastructure Networks Lab at MIT, where his team develops theoretical foundations and practical tools for infrastructure resilience. The lab focuses on the intersection of control theory, game theory, and optimization applied to cyber-physical infrastructure systems. Current research directions include pandemic-resilient urban mobility and hurricane-resilient smart grid operations, reflecting the lab's commitment to addressing pressing societal challenges through rigorous systems engineering approaches.

• Infrastructure Resilience
• Cyber-Physical Systems Security
• Game Theory Applications
• +۷ مورد دیگر

Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.

• Mathematical Finance
• Probability Theory
• Stochastic Analysis
• +۵ مورد دیگر

Simon Harvey is a Professor in Energy Engineering at Chalmers University of Technology, Sweden. With a background in Mechanical Engineering and over two decades of academic leadership, he focuses on industrial energy systems optimization and sustainable process integration. PhD from Thayer School of Engineering, Dartmouth College (1994) Assistant Professor at Chalmers (1997-2011) Full Professor since 2011 His research spans : Energy efficiency in chemical processes Biomass-based industrial energy systems Biorefinery techno-economics Climate impact assessment methodologies Energy market scenario modeling Recent publications demonstrate expertise in carbon capture integration, heat recovery optimization, and biomass utilization for negative emission technologies. Collaborative work emphasizes cross-sectoral energy synergies and uncertainty analysis in industrial retrofits. Key trends include: Decarbonization of pulp/refinery operations Hybrid electric/steam generation systems Excess heat valorization in district heating Process flexibility under uncertain parameters

• Energy efficiency analysis of industrial chemical processes
• Rational use of biomass in industrial energy systems
• Process integration and techno-economic analysis of biorefinery concepts
• +۲ مورد دیگر

Anna Gautier is an Assistant Professor in the Department of Computer Science at Chalmers University of Technology, affiliated with the Division of Data Science and AI. Previously, she was a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology (2023–2025), focusing on mechanism design for multi-robot systems. Her research emphasizes planning under uncertainty, multi-agent systems, and human-robot interaction. She holds a PhD from the University of Oxford (2023), an MSc from the London School of Economics, and dual undergraduate degrees from Washington University in St. Louis. Education Background: PhD in Computer Science, University of Oxford (2023) MSc in Applied Mathematics, London School of Economics BA in Mathematics and BS in Computer Science, Washington University in St. Louis Research Interests: Dr. Gautier explores planning algorithms for multi-agent systems, particularly in uncertain environments. She designs mechanisms to coordinate robots and humans, leveraging game theory and formal methods. Her work addresses challenges like resource allocation, risk-aware decision-making, and trust in autonomous systems. Recent projects include contingency planning for autonomous vehicles and auction-based resource distribution. Professional Activities: She co-chairs the ECAI 2025 Demonstration Track and teaches the course Safe Robot Planning and Control at KTH. Her projects include collaborations with WASP-Nest (PerCorSo) and TECoSA on trustworthy autonomy. She actively publishes in top venues like AAMAS and AAAI. Labs and Teams: Affiliated with Chalmers' Data Science and AI division, she leads research in multi-agent systems and human-AI collaboration.

• Multi-Agent Systems
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Sophie Isaksson Hallstedt is a Full Professor at Chalmers University of Technology, conducting research in Sustainable Product Development (SPD) with a focus on strategic sustainability integration in product innovation. She holds appointments at both Chalmers and Blekinge Institute of Technology, and serves on international Design Society committees. Key Research Areas: Strategic socio-ecological sustainability, digital decision support tools, circular value chains, and sustainability in emerging technologies. Notable Projects: SUSTAIN (aerospace sustainability), Circular Design Nexus (user behavior analysis), and Digital Materials Ecosystems. Awards & Recognition: Featured in Royal Swedish Academy of Engineering Sciences (IVA) 100-list (2020, 2023). Publications: Over 80 peer-reviewed works demonstrating methods for sustainability maturity assessment, impact evaluation, and progress visualization. Teaching & Education: Developed master's programs and PhD courses in sustainability-driven product development. Collaborates extensively with industry partners like GKN Aerospace and VINNOVA. Current work examines predictive models for user behavior and sustainability implications of additive manufacturing.

• Sustainable Product Development
• Strategic Sustainability
• Lifecycle Solutions
• +۴ مورد دیگر

Morteza Haghir Chehreghani is a Professor of Artificial Intelligence and Machine Learning at the Data Science and AI Division of Chalmers University of Technology , Sweden. He leads the Machine Learning and Decision Making Lab and is affiliated with WASP , CHAIR , and ELLIS . Education : PhD in Computer Science (2014) from ETH Zurich under Prof. Dr. Joachim M. Buhmann Prior Roles : Staff Research Scientist at Naver Labs Europe (2014-2018) Research spans Interactive Machine Learning , Sequential Decision Making , Federated Learning , Efficient Deep Learning , and Graph-Based Learning . Key application areas include Transport , Autonomous Systems , Energy , Drug Discovery , and Computational Biology . Selected Publications (2020-2025) demonstrate expertise in Reinforcement Learning for drug design, Minimax Distance Measures for clustering, and Graph Neural Networks for trajectory analysis. Current work focuses on Combinatorial Bandits and Human-in-the-loop AI . Teaching includes graduate courses like Advanced Topics in Machine Learning (DAT441/DIT41), Algorithms for Machine Learning (TDA233/DIT382), and PhD-level Advanced Reinforcement Learning . He has also taught Statistical Methods for Data Science and Theoretical Foundations of ML . Patents include systems for Autonomous Vehicle Motion Control , K-NN Search via Minimax Distances , and Trip Prediction Algorithms . Collaborative projects involve Nature Communications (2022) and multiple ICML / CVPR publications.

• Machine Learning
• Decision Making
• Reinforcement Learning
• +۱۷ مورد دیگر

Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)

• Total Productive Maintenance
• Artificial Intelligence
• Multi-Criteria Decision Making
• +۳ مورد دیگر

Malin Göteman is an Associate Professor at the Department of Electrical Engineering, Uppsala University. Her research focuses on offshore renewable energy systems, particularly modeling and optimizing large-scale wave power farms and analyzing their resilience to extreme weather conditions. Deputy Director, Center for Natural Disaster Studies (CNDS), Sweden Specialized in wave energy converter dynamics and hybrid offshore energy systems Collaborates on SPH-based numerical wave-current tanks and CFD validation Research Interests: She investigates wave energy farm interactions, hydrodynamic performance of floating platforms, extreme wave load modeling, and survivability strategies using machine learning. Her work spans renewable energy integration, coastal protection, and power system stability under extreme conditions. Recent Publications: Her 2025 articles address resilience of offshore energy systems to metocean extremes and reduced-order modeling via Bayesian design. Earlier works (2023-2024) cover SPH validations for floating wind-wave systems, neural network survivability approaches, and hybrid energy-water supply solutions. Collaborations: She works with international teams on projects like Lysekil wave energy test sites and DeepCwind floating platforms. Key areas include grid-connected wave parks, multi-fidelity surrogate modeling, and comparative studies on offshore wind dependencies.

• Offshore Renewable Energy Systems
• Wave Power Farm Modeling
• Extreme Weather Resilience
• +۳ مورد دیگر

Jana Tumova is an Associate Professor at the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology. Her research focuses on designing algorithms for safe, purposeful autonomous systems using formal methods to ensure rigorous specifications and guarantees. Applications span autonomous driving, UAV exploration, and network control. She teaches courses like Artificial Intelligence (DD2380) and leads the Robotics, Reading Group (FDD3316) . Her work emphasizes formal methods integration with AI, safety-critical control, and human-robot interaction. Notable research directions include risk-aware planning, belief space control, and contingency planning under uncertainty. She has published extensively on motion planning, robust control synthesis, and multi-agent systems. Key technical contributions include techniques like Belief Control Barrier Functions , Backward Underapproximate Reachability (BURNS) , and Transitional Grid Maps . She actively participates in interdisciplinary initiatives like the Control for Societal-Scale Challenges: Roadmap 2030 .

• Robotics
• Autonomous Systems
• Formal Methods
• +۵ مورد دیگر

Anders Forsgren is a Professor of Optimization and Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology since 2003. His research focuses on nonlinear programming, particularly Newton-type methods for smooth optimization, with applications in radiation therapy, cell biology, and telecommunications. PhD in Optimization and Systems Theory (KTH, 1990) MS in Operations Research (Stanford, 1987) MSc in Engineering Physics (KTH, 1985) Research Interests: Anders develops methods for constrained optimization and applies them to intensity-modulated radiation therapy, metabolic networks, and wireless communication systems. His work bridges algorithmic innovation with real-world clinical and engineering challenges. Recent Publications: Focus on robust optimization for radiation therapy under uncertainty, quasi-Newton methods, and applications in medical physics. His 2025 papers address interplay-robust optimization and scenario positioning in proton therapy. Scientific Leadership: Co-chair, 8th SIAM Conference on Optimization (2005) Editorial board member, Computational Optimization and Applications (since 1998) Member, Mathematical Optimization Society and SIAM Mentorship: Supervises PhD students in optimization and systems theory, with former advisees working on radiation therapy robustness, metabolic modeling, and network design.

• Nonlinear Programming
• Optimization Methods
• Radiation Therapy Optimization
• +۲ مورد دیگر

Martina Maggio is a Professor at the Department of Computer Science, Saarland University (full-time since 2020) and holds a 20% position at Lund University's Department of Automatic Control (since 2023). She serves as Coordinator of LTH's AI and Digitalization Profile Area and is a member of Lund University's Natural and Artificial Cognition initiative. Her research integrates control theory, real-time systems, and cybersecurity in cyber-physical systems. She has supervised over ten PhD students and postdoctoral researchers, contributing to advancements in resource allocation, fault-tolerant control systems, and self-aware computing. Education: PhD in Control Theory from Politecnico di Milano (with MIT visiting research), postdoctoral work at Lund University. Key affiliations: ELLIIT, Bosch Corporate Research (sabbatical 2019). Research focuses on robust control strategies under computational uncertainties, cyberattacks, and sensor misalignment. Notable work includes influential papers at the intersection of software engineering and control theory (e.g., 2015 SEAMS most influential paper). Grants and advising: Supervised 10+ students, including alumni Dr. Nils Vreman (2023) and Dr. Gautham Nayak Seetanadi (2021). Active in collaborative projects like Bosch's control system verification initiatives. Labs/Teams: Leads research groups at Saarland and Lund, focusing on real-time systems, embedded systems security, and AI-driven control architectures.

• Control Theory
• Cyber-Physical Systems
• Real-Time Systems
• +۵ مورد دیگر

Anders Brandt is an Associate Professor (Docent) in Geospatial Information Science and Senior Lecturer in Geomatics at the University of Gävle, Sweden, within the Department of Computer and Geospatial Sciences under the Faculty of Engineering and Sustainable Development. His research focuses on flood risk mapping uncertainties, agent-based modeling of pedestrian movement, and spatial decision-support systems for sustainable urban planning. He holds a PhD in Physical Geography from the University of Copenhagen and has extensive international collaboration experience. Education: PhD in Physical Geography (University of Copenhagen, Denmark), focusing on fluvial geomorphology of Costa Rica’s Reventazón River Master’s Degree in Physical Geography (Uppsala University, Sweden) Research Interests: Brandt’s work addresses uncertainties in flood modeling, spatial decision analysis, and geospatial data applications for urban resilience. His projects include the 'Big Data Methodology for Experiential and Cognitively Sustainable Urban Growth' initiative, emphasizing ecosystem service mapping and spatial MCDA methods. His agent-based modeling research explores emergent urban path systems to optimize pedestrian infrastructure. Publications Trends: Recent work spans flood risk visualization, blue-green infrastructure roles in climate resilience, and urban form complexity metrics. His 2025 papers highlight innovative applications of agent-based modeling and systematic reviews of climate hazard mitigation strategies. Advising & Grants: While no formal advisee list is provided, his collaborative publications suggest involvement in student projects. His grants include international initiatives like Mongolia’s land administration capacity-building programs. Labs/Teams: Co-founded GeoVega , a consulting firm specializing in flood risk mapping and geospatial solutions. Active in educational initiatives like harmonizing GIS curricula in Swedish universities.

• Geospatial Information Science
• Flood Risk Mapping
• Uncertainty Analysis in Environmental Modeling
• +۶ مورد دیگر

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +۷ مورد دیگر

Jonas Fredriksson is a Professor in the Mechatronics research group at the Department of Systems and Control Engineering, Chalmers University of Technology. His work focuses on electric/hybrid vehicles, vehicle dynamics, active safety systems, and optimization-based coordination of automated vehicles. Academic Rank: Professor Affiliation: Chalmers University of Technology Department: Systems and Control Engineering Email: jonas.fredriksson@chalmers.se Research Themes: Powertrain control and energy management for electric/hybrid vehicles Advanced control strategies for heavy articulated vehicles Autonomous driving in confined environments Battery thermal management and charging optimization Vehicle stability and safety systems using Newtonian mechanics Article Trends: Recent publications emphasize 1) optimization algorithms for electric vehicle coordination, 2) aerodynamic modeling under crosswind conditions, 3) stochastic approaches to longitudinal vehicle dynamics, and 4) robust control systems for articulated heavy vehicles. The work combines classical mechanics with modern machine learning techniques. Teaching & Leadership: Supervises doctoral students and leads research projects in mechatronics. Manages the master's program in Systems, Control and Mechatronics. Teaches courses in mechatronics and vehicle control systems.

• Vehicular Control Systems
• Electric and Hybrid Vehicle Technology
• Vehicle Dynamics
• +۴ مورد دیگر

Katarina Elofsson is a Professor at the Department of Social Sciences, Södertörn University. Her research focuses on environmental and resource economics, with applications spanning international, national, and regional policy frameworks. She specializes in interdisciplinary collaborations with biologists, soil scientists, hydrologists, and stakeholders to address complex environmental challenges. Research Interests: Her work centers on the economics of carbon sequestration in land use, biodiversity conservation, wildlife management, water quality improvement, and climate policy. She develops bioeconomic models to optimize natural resource decisions and environmental policies under ecological uncertainty, applied across EU, national, and local scales. Honors & Memberships: Elected member of The Royal Swedish Academy of Agriculture and Forestry Academic Contributions: She has served as course director for 22 Bachelor's/Master's courses and one PhD course in economics. She supervised 15 Master's theses, 6 completed PhDs (as main supervisor), and mentors one ongoing PhD candidate. Her consultancy work includes projects for Swedish/Danish agencies on marine ecosystems, biodiversity offsetting, biochar sequestration, and economic impacts of large carnivores. Collaborations: Leads interdisciplinary teams integrating economics with ecology, forestry science, and marine modeling to advance sustainability solutions.

• Environmental Economics
• Resource Economics
• Climate Policy
• +۶ مورد دیگر