جستجوی استاد راهنمای دانشگاهی

Istvan David McMasterمشاهده پروفایل

استادیار

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +۶ مورد دیگر

Istvan David is an Assistant Professor in the Department of Computing and Software at McMaster University , with research expertise spanning Digital Twins , Model-Driven Engineering , and Sustainability . His work bridges theoretical and applied domains, focusing on smart ecosystems , collaborative modeling , and AI-driven simulation . Key contributions include frameworks for digital twin evolution and interoperability in sustainable systems. Education : BSc, MSc, and PhD in Computer Engineering and Computer Science from Budapest University of Technology and Economics, and University of Antwerp. Research Areas : Digital Twins, Model-Driven Engineering, Reinforcement Learning, Smart Ecosystems, Sustainability, Collaborative Modeling, Cyber-Biophysical Systems, and Software Architecture. Recent Article Trends emphasize AI integration with digital twins, collaborative modeling in industrial contexts, and sustainable systems engineering . His work often combines machine learning with formal modeling to address challenges in technical sustainability and smart agriculture .

Uwe Bergmannمشاهده پروفایل

استاد

• Ultrafast X-ray Science
• Synchrotron Radiation
• X-ray Free-Electron Lasers
• +۵ مورد دیگر

Uwe Bergmann is the Martin L. Perl Endowed Professor in Ultrafast X-Ray Science at the University of Wisconsin-Madison's Department of Physics. His research leverages advanced X-ray techniques to study nonlinear phenomena, real-time chemical reactions, and structural changes in both biological and material systems, with a focus on photosynthesis and ancient cultural heritage. PhD in Physics from Stony Brook University Former affiliations: National Synchrotron Light Source, European Synchrotron Radiation Facility, Lawrence Berkeley National Laboratory, Stanford Synchrotron Radiation Lightsource, Linac Coherent Light Source, Stanford PULSE Institute His work spans ultrafast X-ray spectroscopy, synchrotron methods, and the development of novel instruments like the X-FAST tabletop spectrometer. Research themes include: Photosynthetic water oxidation mechanisms using femtosecond X-ray crystallography Electronic and structural dynamics in 2D materials and metalloenzymes Imaging ancient fossils and manuscripts with X-ray techniques Advancing X-ray laser and synchrotron instrumentation Recent publications highlight attosecond X-ray pulses, catalysis under extreme conditions, and structural intermediates in photosystem II. His work intersects physics, chemistry, and paleobiology. Scientific recognition includes the Martin L. Perl Endowed Professorship. He leads the Bergmann Research Group, mentoring graduate students and postdocs in ultrafast X-ray science.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Architectural Technology
• Bioclimatic Design
• Building Performance
• +۸ مورد دیگر

Giacomo Chiesa is a Full Professor at the Department of Architecture and Design (DAD) at Politecnico di Torino. He is a member of the Interdepartmental Center Ec-L - Energy Center Lab. His research focuses on Architectural Technology , Bioclimatic Design , Building Performance , and Urban Climate . Research Interests : Building Simulation, Passive Cooling, Smart Buildings, Digital Twin, Climate Change Adaptation Recent publications analyze urban weather datasets for energy simulations, shading control thresholds , and ventilation strategies in educational buildings. His work covers energy renovation roadmaps , thermal comfort , and climate-resilient building systems . Teaching : PhD courses in Human-Centric Methodologies and MSc courses in ICT in Building Design Research Leadership : Scientific Director for projects like Urban Generation and Prelude , EU-funded initiatives

Luis Duffaut Espinosaمشاهده پروفایل

استادیار

• Control Theory
• Estimation Theory
• Robotics
• +۷ مورد دیگر

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

Mahroo Eftekhariمشاهده پروفایل

استاد

• Building Energy Management Systems
• HVAC Systems Optimization
• Thermal Comfort Modeling
• +۵ مورد دیگر

Professor Mahroo Eftekhari is a Professor in Building Services Engineering at Loughborough University, leading the Low Energy Building Services Engineering MSc programme. Her research focuses on energy-efficient building systems, thermal comfort, HVAC optimization, and renewable integration. She has pioneered control systems for airports and buildings, including MPC-based strategies to reduce energy use while enhancing occupant well-being. Notable contributions include the development of BISPA (Building & Industrial Services Pipework Academy), a national center for BIM and pipework training. Education: Holds qualifications including CEng (Chartered Engineer), DPhil (Doctor of Philosophy), FCIBSE (Fellow of Chartered Institution of Building Services Engineers), and SFHEA (Senior Fellow of the Higher Education Academy). Her academic career is marked by collaborations with Tata Steel, Mitsubishi R&D, and Vexo, yielding applied research in sustainable building technologies. Research Interests: Indoor Air Quality, Thermal Comfort Modeling, Zero Energy Buildings, Digital Twins, and Advanced Control Systems. She has developed innovative solutions like AI-driven thermal management for Building Energy Management Systems (BEMS) and interfaces to synchronize airport operations with energy systems. Awards & Grants: Secured funding from diverse bodies for projects such as adaptive thermal comfort models, BISPA infrastructure, and energy-efficient HVAC strategies. Her work emphasizes practical applications, including reducing CO₂ emissions via airport terminal optimization and improving renewable energy use in buildings. Lab & Teams: Leads the Building Energy Research Group, managing projects in closed-loop heating systems, IEQ monitoring, and hydronic system efficiency. The Civil Engineering labs house interactive BIM rigs launched with institutional and industry support.

• Cybersecurity
• Malware Detection
• Machine Learning
• +۷ مورد دیگر

Hayretdin Bahsi is an Assistant Professor at the School of Informatics, Computing, and Cyber Systems at Northern Arizona University . His research focuses on cybersecurity, with expertise in malware detection, IoT security, and machine learning applications in defense mechanisms. He collaborates internationally on maritime cybersecurity, healthcare systems, and critical infrastructure protection. Research Interests include Android malware analysis, botnet detection, explainable AI in intrusion detection, and threat modeling for AI-driven systems. His work addresses challenges like concept drift in malware detection and privacy-preserving techniques for IoT networks. Publications span 66 scholarly works since 2009, emphasizing cybersecurity trends in AI, IoT, and healthcare. Recent contributions explore large language model (LLM) applications in vulnerability detection and cyber threat modeling for healthcare systems. Collaborations include projects on maritime cyber-insurance, cyber incident management in low-income countries, and datasets like MedBIoT for IoT botnet analysis. His work bridges theory and practice, addressing real-world cybersecurity challenges.

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.

Munther A. Dahlehمشاهده پروفایل

استاد

• Robust Control Theory
• Networked Systems
• Systemic Risk
• +۵ مورد دیگر

Munther A. Dahleh is the William A. Coolidge Professor in the Department of Electrical Engineering and Computer Science at MIT and Director of the Institute for Data, Systems, and Society (IDSS). He is also affiliated with MIT’s Laboratory for Information and Decision Systems (LIDS). Previously, he served as Associate Department Head of EECS and held visiting roles at Caltech. His research focuses on foundational theory for networked systems, systemic risk detection, social network dynamics, and transportation systems modeling. Education: BS in Electrical Engineering from Texas A&M University (undergraduate), PhD in Electrical and Computer Engineering from Rice University. Research interests span robust control theory, information-propagation dynamics, distributed decision-making, and the interplay between physical and information networks. He has advised on systemic risk mitigation strategies for interconnected systems and contributed to energy grid resilience frameworks. Labs/Teams: IDSS, LIDS, and cross-disciplinary MIT initiatives. His work bridges engineering, economics, and policy to address complex societal challenges.

Misa Faezipourمشاهده پروفایل

دانشیار

• System Dynamics Modeling
• Healthcare Systems Engineering
• Complex Systems Modeling
• +۳ مورد دیگر

Dr. Misa Faezipour is an Associate Professor in the Department of Engineering Technology at Middle Tennessee State University (MTSU) and director of the Systems Engineering Lab (SEL). She holds a Ph.D. and M.Sc. in Industrial Engineering and Engineering Management from the University of Texas at Arlington and a B.Sc. in Software Engineering from Islamic Azad University North Tehran Branch. Her research focuses on system dynamics modeling, healthcare systems engineering, complex systems, and sustainability optimization. Dr. Faezipour has advised numerous graduate students in M.S. and Ph.D. programs, focusing on topics such as healthcare disparities, AI robustness, and policy impact analysis. She teaches courses including Engineering Economy, Operations Management, and Engineering Management Systems. Her work spans collaborations with institutions like NASA, the Systems Engineering Research Center, and healthcare organizations. Her recent publications emphasize AI-driven healthcare solutions, system dynamics applications in policy analysis, and optimizing healthcare delivery through data-driven models. She actively contributes to professional organizations such as INCOSE, IEEE, and the System Dynamics Society. Dr. Faezipour’s lab, SEL, develops innovative tools for healthcare informatics, including diagnostic recommenders and policy simulation frameworks. Her research bridges systems engineering with healthcare challenges, addressing issues like racial health inequities and pandemic response strategies.

Heikki Handroosمشاهده پروفایل

استاد

• Mechatronics
• Robotics
• Machine Automation
• +۵ مورد دیگر

Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Christopher Mortonمشاهده پروفایل

دانشیار

• Fluid-Structure Interaction
• UAV Technology Development
• Energy Engineering
• +۷ مورد دیگر

Dr. Christopher Morton is an Associate Professor in the Department of Mechanical Engineering at McMaster University, specializing in fluid-structure interaction, UAV technology, and energy systems. His research focuses on aerodynamics, flow control, and sustainable energy solutions, with applications in aerospace and environmental engineering. Education background includes a BASc in Mechatronics Engineering (University of Waterloo, 2008), MASc (2010), and Ph.D. (2014) in Mechanical Engineering from the same institution. His work bridges experimental and computational methods, particularly in flow estimation and control using advanced diagnostics like PIV and spectral analysis. His research interests span vortex-induced vibrations (VIV), unsteady aerodynamics, and energy harvesting through fluid-structure interactions. Recent publications highlight innovations in flow field reconstruction, sensor-based monitoring, and turbulence control. His work has been recognized through awards such as the Departmental Research Excellence Award (2021-2022) and multiple teaching accolades, reflecting his dedication to both research and education. Dr. Morton currently teaches MECH ENG 4FM3 (Advanced Instrumentation for Thermo-Fluids) and MECH ENG 723 (Flow Induced Vibrations), emphasizing hands-on experimental techniques and theoretical analysis. He actively supervises graduate students and collaborates with industry partners like Atlantis Research Labs and Plains Midstream Canada. Key Research Clusters: Advanced Materials & Manufacturing, Digital & Smart Systems, Energy, and Environment. Teaching Excellence: Awarded “Professor of the Year” multiple times and recognized for outstanding teaching performance.