جست‌وجوی استاد و پژوهشگر

Miroslav Krsticمشاهده پروفایل

استاد

• Control Theory
• Nonlinear Systems
• Adaptive Control
• +۵ مورد دیگر

Miroslav Krstic is a Distinguished Professor of Mechanical and Aerospace Engineering at the University of California, San Diego (UCSD), and serves as Senior Associate Vice Chancellor for Research overseeing 17 research institutes, postdoctoral affairs, and shared facilities. He leads the Center for Control Systems and Dynamics and the Naval Innovation, Science, and Engineering Center (NISEC). Education: PhD (1994) and MS (1992) from University of California, Santa Barbara, under advisor Petar Kokotovic. BSc (1989) from University of Belgrade, Yugoslavia. Research Interests: Pioneered methods in control theory including PDE backstepping, extremum seeking, nonlinear adaptive control, and delay compensation. Focuses on applications in chip manufacturing, aircraft carriers, particle accelerators, Mars rovers, and traffic congestion. Integrates machine learning with control design for PDE systems. Awards: Over 30 major honors including the Bellman Award, Reid Prize, Oldenburger Medal, Bode Lecture Prize, and Fellowships from AAAS, SIAM, ASME, IEEE, and IFAC. Recognized as the world's top control theorist by ScholarGPS. Service & Grants: Editor-in-Chief of IEEE Transactions on Automatic Control and Systems & Control Letters . Directed over $100M in research funding annually. Advised 30+ PhD students and postdocs, many in industry leadership roles. Industry Impact: Technologies deployed in EUV lithography (Cymer/ASML), US Navy aircraft carrier arresting gear (General Atomics), and NASA's Mars Curiosity Rover laser system. Contributions to fusion control, battery estimation, and combustion optimization.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

Andreas Grotheyمشاهده پروفایل

مدرس ارشد

• Stochastic Programming
• Interior Point Methods
• Decomposition Methods
• +۵ مورد دیگر

Andreas Grothey is a Senior Lecturer in the School of Mathematics at The University of Edinburgh, a position he has held since 2011. He completed his MSc in Numerical Algebra and Mathematical Computing at the University of Dundee (1995) and his PhD in Optimization at the University of Edinburgh (2001), supervised by Ken McKinnon. His research focuses on stochastic programming, interior point methods, decomposition approaches, high-performance computing, and energy systems optimization. He has contributed to energy planning, power grid reliability, and emergency response strategies for power networks. Grothey has advised seven PhD students, including work on unit commitment, top-percentile traffic routing, and power flow optimization. His projects include the OOPS solver, CESI energy integration center, and the Structured Modelling Language (SML). Recent work addresses pandemic policy optimization and exascale computational challenges. Education: MSc in Numerical Algebra and Mathematical Computing (University of Dundee, 1995) PhD in Optimization (University of Edinburgh, 2001) Research Interests: Stochastic Programming Interior Point Methods Decomposition Methods High-Performance Computing Energy Systems Optimization Advising & Projects: PhD Supervision (7 students, 2007–2022) OOPS Parallel Solver Development CESI Energy Systems Integration SML Structured Modelling Language Labs/Teams: Member of the Edinburgh Research Group on Optimization, leading projects in power grid stability and energy planning.

• Stochastic Processes
• Queueing Theory
• Probability Theory
• +۷ مورد دیگر

Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.

Cathy Wuمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Machine Learning
• Large-scale Optimization
• +۶ مورد دیگر

Cathy Wu is the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering at MIT, affiliated with the Institute for Data, Systems, and Society (IDSS). Her research bridges machine learning, optimization, and urban systems, with a focus on mixed autonomy systems in mobility. She holds degrees from MIT (B.S., M.Eng in EECS) and a Ph.D. from UC Berkeley (EECS). Education: B.S. and M.Eng in Electrical Engineering and Computer Science, MIT (2012-2013) Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2018) Research Interests: Reinforcement Learning and Machine Learning Large-scale Optimization and Control Theory Mobility Systems and Urban Infrastructure Implications of AI and Automation Her work emphasizes interdisciplinary collaboration, involving transportation, computer science, and public policy. She founded the Interdisciplinary Research Initiative within the ACM Future of Computing Academy to advance cross-disciplinary computing research. Key Projects: Includes Flow (open-source RL framework for traffic control), eco-driving incentive mechanisms, and mixed autonomy traffic optimization. Her articles address congestion mitigation, autonomous vehicle integration, and scalable supervision strategies. Awards: Recipient of fellowships, best paper awards, and teaching honors (specific names unlisted). Engagement: Collaborations with institutions like Microsoft Research, OpenAI, and Caltrans. Active in policy-oriented initiatives and education through IDSS programs.

• Security and Privacy
• Active Cyber Defense
• Security Analytics
• +۶ مورد دیگر

J. Haadi Jafarian is an Assistant Professor in the Department of Computer Science and Engineering at the University of Colorado Denver, where he leads the Active Cyber and Infrastructure Defense (ACID) Lab. He earned his Ph.D. from the University of North Carolina Charlotte in 2017. Research Interests: Active Cyber Defense (Moving Target Defense, Cyber Deception) Big Data Analytics for Cyber Threat Intelligence Security for Cyber-Physical Systems & Critical Infrastructures Cyber Resilience and Automation Recent Publications highlight innovations in traffic obfuscation, adversarial machine learning, and deception-based threat detection. His work spans network security, cybersecurity analytics, and scalable defense frameworks. Teaching includes: CSCI 4743/5743: Cyber and Infrastructure Defense (Fall 2023) CSCI 4742/5742: Cyber Programming and Analysis (Spring 2023) CSCI 4741: Cybersecurity Principles (Spring 2022) CSCI 4800: Web Application Development (Spring 2021) CSCI 3761: Computer Networks (Spring 2020) Labs & Teams: The ACID Lab focuses on developing proactive cyber defense strategies, including moving target defense, deception techniques, and security analytics for critical infrastructure.

• Optical Networking
• High-Speed Networks
• Data Center Technologies
• +۶ مورد دیگر

Emmanouel (Manos) Varvarigos is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), where he leads the High Speed Communication Networks Laboratory. He holds a Diploma from NTUA (1988) and an M.S./Ph.D. from MIT (1990/1992). Prior roles include Professorships at UC Santa Barbara (1992-1998) and Delft University (1998-1999), and leadership at the University of Patras (1999-2015). He has coordinated over 30 EU research projects, including H2020 initiatives like ORCHESTRA and FLEXGRID. His research focuses on optical networking, cloud computing, smart energy grids, and high-speed network protocols. He has published over 450 papers and serves on numerous conference committees. Awards include the NSF Research Initiation Award. His lab employs 5 postdocs and 10+ PhD students, addressing challenges in networking, data centers, and grid computing. Education: Ph.D., Electrical Engineering and Computer Science, MIT, 1992 M.S., Electrical Engineering and Computer Science, MIT, 1990 Diploma in Electrical and Computer Engineering, NTUA, 1988 Research Interests: Optical networking, high-speed network protocols, data center architectures, cloud computing, smart energy grids, wireless networks, and distributed systems. Grants & Projects: Coordinated 7 EU projects (e.g., FLEXGRID, ORCHESTRA) and participated in 35+ others. National projects include roles as Scientific Director of the Greek School Network and CTI’s Network Technologies Division. Labs & Teams: Directs the High Speed Communication Networks Lab (HSCNL), collaborating with global institutions on optical networks, edge cloud, and smart grids. The lab’s work includes EU-funded projects on 5G, data centers, and renewable energy integration.

Gabor Oroszمشاهده پروفایل

استاد

• Nonlinear dynamics and control
• Time delay systems
• Connected and automated vehicles
• +۶ مورد دیگر

Gabor Orosz is a Professor at the University of Michigan in both the Department of Mechanical Engineering and Department of Civil and Environmental Engineering . His work bridges nonlinear dynamics and control , time delay systems , and connected automated vehicles , with a focus on traffic flow optimization and vehicle safety . Education: PhD in Engineering Mathematics, University of Bristol, UK (2006) MSc in Engineering Physics, Budapest University of Technology and Economics, Hungary (2002) Research Focus : Orosz's research explores the intersection of vehicle automation , connectivity , and nonlinear dynamics . He investigates time delay effects in teleoperation , intent-sharing protocols for cooperative maneuvering , and control barrier functions for safety-critical systems . His work spans theoretical analysis, numerical validation, and real-world experimentation. Article Trends : Recent publications highlight advancements in latency mitigation for remote driving , nonholonomic vehicle control , intent-sharing frameworks , and energy-efficient strategies for connected vehicle systems . Themes include delayed feedback , stochastic communication , and safety-guaranteed control . Awards & Appointments : NSF CAREER Award (2014) Fulbright Scholar at Budapest University of Technology (2023-2024) Editorial roles in Vehicle System Dynamics (2020) and Time Delay Systems (2017) Student Mentorship : Orosz has advised numerous PhD students, including Anil Alan (2024, TU Delft), Chaozhe He (2018, University at Buffalo), and Tamás Molnár (2020, Wichita State University). His alumni work at institutions like Toyota Research Institute , Ford Motor Co. , and Zoox . Labs & Teams : He leads research at the University of Michigan's Mechanical Engineering Department and collaborates with international institutions such as Caltech and Budapest University of Technology . His team focuses on experimental validation of connected vehicle systems and delay-tolerant control .

• Corporate Finance
• Fintech
• Environmental Economics
• +۵ مورد دیگر

Vesa Pursiainen is an Associate Professor at the School of Business and Economics , University of St. Gallen (SBF-HSG). His research focuses on corporate finance, fintech, environmental economics, crowdfunding, and behavioral finance, with a particular emphasis on how technology, social dynamics, and crises impact financial decisions and market behavior.

• Optical Networks-on-Chip
• Wavelength-Routed Optical Networks
• Electronic Design Automation
• +۴ مورد دیگر

Zhidan Zheng is a researcher at the Technical University of Munich (TUM), working within the Chair of Electronic Design Automation led by Prof. Ulf Schlichtmann. His office is located in room 0509.05.911 at Arcisstr. 21, 80333 Munich, with direct contact available via email zhidan.zheng@tum.de and phone +49 (89) 289 - 23692. Zheng holds a Master of Science degree as indicated by his academic title M.Sc. and has been actively contributing to the field of optical interconnects and network-on-chip design. Zheng's research focuses on wavelength-routed optical networks-on-chip, with particular expertise in network topology optimization, fault tolerance mechanisms, waveguide routing algorithms, and bandwidth allocation strategies. His work addresses critical challenges in photonic integrated circuit design, including thermal variation effects, crosstalk mitigation, and lifetime extension for communication-intensive systems. Zheng has developed several innovative methodologies including ToPro+ for topology projection, LightR for fault-tolerant architectures, and WROXIM for network-level simulation. Analysis of Zheng's publication trends from 2021-2025 reveals a consistent focus on practical implementation challenges of optical networks-on-chip. His research has evolved from foundational topology design (Light, 2021) to increasingly sophisticated solutions addressing reliability (LightR, 2023) and comprehensive system integration (ToPro+, 2025). The work demonstrates strong collaboration with researchers including Mengchu Li, Tsun-Ming Tseng, and Ulf Schlichtmann across multiple high-impact venues including DAC, DATE, ICCAD, and ASP-DAC. Zheng actively contributes to the Electronic Design Automation research group at TUM, participating in projects related to analog EDA, emerging technologies, and optical networks. His research is situated within TUM's broader initiatives in photonic integration and high-performance computing architectures, working closely with Prof. Schlichtmann's team on funded projects in the optical NoC domain.

Kaidi Yangمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +۵ مورد دیگر

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Li Songمشاهده پروفایل

استاد

• Building Energy System Optimization
• HVAC System Fault Detection
• Energy Efficiency Technology
• +۳ مورد دیگر

Li Song is a Professor and holds the Lesch Centennial Chair & Lloyd G. and Joyce Austin Presidential Professor at the University of Oklahoma's Aerospace & Mechanical Engineering Department. He leads the Building Energy Efficiency Lab and serves as AME Associate Director for Research. His expertise spans building energy systems, HVAC optimization, and fault detection technologies. Education: Ph.D. (Thermal/Fluid Science, 2004) from University of Nebraska-Lincoln; M.S. (Thermal/Fluid Science, 1996) from Harbin Institute of Technology; B.S. (Thermal Energy Systems, 1993) from Shengyang University of Civil Engineering and Architecture. Research focuses on energy-efficient HVAC systems, fault detection algorithms, and building performance analytics. Notable contributions include the ULEM-FDD system for high-performance buildings and virtual sensor technologies for airflow/water flow measurement. Awards include the ConocoPhillips Energy Prize (2011 finalist) and Bes-Tech Innovation Award (2006). Publications emphasize HVAC control strategies, energy modeling, and IoT-enabled diagnostics. Courses taught include Thermodynamics, Energy Efficient Building Systems Design, and HVAC Systems Engineering.

Kaiqun Fuمشاهده پروفایل

استادیار

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.

• Foundation Engineering
• Structural Engineering
• Working Platform Design
• +۷ مورد دیگر

Brian Solan is a Lecturer in Foundation Engineering at the Belfast School of Architecture & the Built Environment, Ulster University. His expertise spans foundation engineering, structural engineering, and environmental resilience, with particular focus on infrastructure stability under extreme conditions. Dr. Solan received his education at: Regional Technical College Dundalk (now Institute of Technology) - Civil Engineering studies beginning in 1986 Queen's University Belfast - BEng Honours (1st Class) in June 1991 Queen's University Belfast - PhD in Structural Engineering in 1996 Dr. Solan's research interests center on foundation engineering and infrastructure resilience. His work examines working platform design, traffic loading of infrastructure structures, implementation of Eurocode 7 standards, and the impact of extreme weather flooding on historic arch structures. He has expanded his research into environmental engineering, particularly wastewater treatment and sustainable methods for removing contaminants from water systems. His interdisciplinary approach bridges traditional civil engineering with contemporary environmental challenges, addressing critical infrastructure needs in the face of climate change. Dr. Solan's recent publications demonstrate a shift toward environmentally focused research while maintaining his foundation in structural engineering. His work increasingly addresses the intersection of infrastructure engineering and environmental sustainability, with particular attention to wastewater treatment technologies, emerging contaminants, and climate change adaptation for built heritage. This evolution reflects broader trends in engineering research toward more sustainable and resilient infrastructure solutions. Dr. Solan has received recognition for his work, including: Adrian Long Award (December 3, 2020) Royal Society Travel Scholarship for collaborative work with Professor Robert Ettema at the University of Colorado Dr. Solan has been actively involved in research funding and collaboration. He served as Principal Investigator for the 'Short-span bridge scour under submerged flood flow conditions' project (2015-2017) and was a Co-Investigator on the 'Infrastructure Compromise Avoidance' project (2018-2020). His research has attracted funding supporting both structural engineering and environmental sustainability initiatives. He contributes to the academic community through peer review activities, including for the Proceedings of the Institution of Civil Engineers - Ground Improvement journal. Dr. Solan maintains professional affiliations with the Institution of Engineers of Ireland and is a graduate member of the Institution of Structural Engineers. His work contributes to UN Sustainable Development Goals related to clean water and sanitation, sustainable cities and communities, and climate action. His research bridges traditional engineering disciplines with contemporary environmental challenges, positioning him at the intersection of infrastructure resilience and sustainable development.

Fiona Sloothaakمشاهده پروفایل

استادیار

• Stochastic Operations Research
• Network Theory
• Cascading Failures
• +۶ مورد دیگر

Fiona Sloothaak is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds the Eurandom Assistant Professor title and contributes to the Stochastic Operations Research group. Her research focuses on cascading failures in complex systems, power grids, battery swapping networks, and stochastic processes. Education: Fiona earned a PhD in Stochastic Operations Research from TU/e in 2020 and completed a Master's in Mathematics (2015). Her doctoral thesis, Criticality in power networks: a probabilistic approach , explores cascading failures in power systems. Research Interests: Fiona’s work addresses systemic risk in infrastructure systems, including power grids and transportation networks. She investigates cascading failure dynamics, load balancing in battery swapping stations, and resource pooling strategies. Her studies often employ probabilistic modeling and queueing theory. Key Projects: She co-authored the MARCONI project (2019–2022), focusing on integrated planning for maritime logistics and service systems. This involved optimizing supply chain resilience and remote control tower operations. Scientific Award: Applied Probability Trust Prize (2020) Teaching: Courses include Stochastic Networks, Insurance and Credit Risk, and Probability and Stochastics 1. Grants: MARCONI project funding for maritime logistics optimization. Labs/Teams: Active in TU/e’s Stochastic Operations Research group and Eurandom institute. Collaborations span network topology analysis and interdisciplinary systems research.