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Zoe LiView profile

Associate Professor

• Uncertainty Analysis
• Risk Management
• Stochastic Modelling
• +7 more

Dr. Zoe Li is an Associate Professor in the Department of Civil Engineering at McMaster University, with an additional role as an Associate Member in the Department of Computing and Software. She specializes in developing modeling and decision-support tools to address challenges in water resources management, environmental systems analysis, and climate change impacts. Her research focuses on hydrological modeling, probabilistic forecasting, risk analysis, and environmental systems optimization. She holds a B.Eng. from China and M.A.Sc./Ph.D. degrees from the University of Regina. Her work spans interdisciplinary collaborations, including studies on dam safety under climate change, wastewater treatment plant optimization, and AI-driven environmental monitoring. She has been actively involved in projects such as CityDNA, a digital tool to model urban environments for decision-making during crises like the pandemic. Dr. Li teaches courses in environmental systems engineering and principles of environmental engineering, emphasizing uncertainty quantification and sustainable practices. Her research publications (15+ recent articles) address topics like machine learning applications in environmental systems, climate change impacts on water resources, and infrastructure resilience. She advises graduate students in environmental engineering and systems optimization. Notable grants include funding from Roche Canada for pandemic-related urban modeling. Her work is frequently published in high-impact journals like Journal of Environmental Management and Water Resources Research .

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +6 more

Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.

• Physical Limnology
• Sediment Transport
• Tidal Hydrodynamics
• +3 more

Marco Toffolon is a Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering, where he leads the Physical Limnology Laboratory. He serves as Deputy Director for International Relations and previously directed the Environmental Engineering programs. His research spans ecohydraulics, sediment transport, lake hydrodynamics, and environmental modeling. He investigates physical limnology, tidal morphodynamics, and stratified flows using analytical and numerical approaches. His work integrates field measurements with machine learning for water quality prediction and climate impact assessment. His publications focus on lake dynamics, river morphodynamics, and sustainable water management, with recent emphasis on climate-driven changes in alpine systems. Research demonstrates strong interdisciplinary linkages between hydraulics, ecology, and climate science. Awards: 2016 Coastal Engineering Journal Award 2013 Enrico Marchi Lecture invitation He leads international collaborations with institutions like EPFL and Sun Yat-sen University, and organizes conferences including the Physical Processes in Natural Waters workshop series.

• Nonlinear Dynamics
• Structural Dynamics
• Vibration Control
• +3 more

Professor David Wagg is a Professor of Nonlinear Dynamics and Departmental Director of Research and Innovation at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. His research focuses on nonlinear structural dynamics, digital twins, vibration suppression, and real-time hybrid testing. He holds a BEng and PhD from University College London and previously served as a Professor at the University of Bristol (2008–2013). Notable awards include the EPSRC Advanced Research Fellowship (2004–2009). Education: BEng and PhD in Nonlinear Dynamics from University College London. Research Interests: Digital twins for dynamics applications, nonlinear structural dynamics, vibration control, real-time hybrid testing, and identification methods for nonlinear dynamics. His work emphasizes applying nonlinear models and control strategies to engineering challenges like wind turbines and large civil infrastructure. Grants & Leadership: Co-Investigator for EPSRC grants on CITCoM and Digitwin, coordinator of the Marie Curie ETN DyVirt, and PI for the EPSRC programme on Engineering Nonlinearity (2012–2017). He co-authored Nonlinear Vibration with Control (2015) and edited books on structural dynamics. Lab/Teams: Involved in the Laboratory for Verification and Validation (LVV) and leads research groups focused on digital twin applications, inerter-based systems, and structural health monitoring.

Chenang LiuView profile

Associate Professor

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +2 more

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)

Lisa YanView profile

Teaching Professor

• Computer Science Education
• Computing Ethics
• Teaching Assistant Training
• +2 more

Lisa Yan serves as a Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, appointed in Spring 2022. She teaches core computer science education courses including CS 195 (Social Implications of Computer Technology), CS H195 (Honors variant), CS 294-189 (Teaching Process Design), and CS 375 (Teaching Techniques), holding regular office hours in Soda Hall for student engagement. Her academic credentials include: PhD in Electrical Engineering from Stanford University (2019) MS in Electrical Engineering from Stanford University (2015) BS in Electrical Engineering and Computer Science from UC Berkeley (2013) Dr. Yan's research centers on data-driven analysis of student learning in large-scale computer science courses, with significant contributions to computing ethics pedagogy and teaching assistant development programs. Her work develops innovative methodologies for assessing student earnestness in interactive lectures, creating flexible learning extensions, and designing integrity-focused assessments. Earlier research focused on software-defined networking and network switch performance optimization, demonstrating technical depth before her pivot to educational innovation. Current projects emphasize scalable teaching techniques and mastery learning frameworks that address challenges in modern CS education. Analysis of her 14 publications (2013-2024) reveals a strategic shift from computer networking (pre-2018) to computer science education research (2018-present). Recent work (2020-2024) dominates in venues like SIGCSE, featuring tools such as Otter-Grader for Jupyter notebook grading and the Earnest Insight Toolkit for lecture participation analysis. This evolution highlights her commitment to solving practical educational challenges through data analysis and tool development, particularly for large undergraduate courses. She received recognition through: The Faculty Award for Outstanding Mentorship of GSIs (2024) Lisa actively mentors Graduate Student Instructors and collaborates with educational technology initiatives. Her research team includes dedicated support staff like Taylor Kaserman (taylor.kase@berkeley.edu), reflecting structured collaboration in developing teaching innovations. She contributes to curriculum design committees within EECS, focusing on assessment integrity and scalable pedagogical methods for growing student populations. Her work operates through the EECS department's educational infrastructure, utilizing Soda Hall resources for both teaching coordination and research development, with strong connections to Berkeley's broader computing education ecosystem.

Haochen LiView profile

Assistant Professor

• Environmental Fluid Dynamics
• Computational Fluid Dynamics
• Physics-informed Machine Learning
• +7 more

Haochen Li is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Tennessee, Knoxville, within the College of Engineering. He leads the multidisciplinary Water Infrastructure Laboratory (Ψ Lab), which focuses on advancing urban water infrastructure through high-fidelity computational fluid dynamics (CFD), physical modeling, and physics-informed machine learning (ML). Education: PhD in Environmental Engineering, University of Florida, 2019 MS in Mechanical Engineering, University of Florida, 2019 MS in Civil Engineering, University of Florida, 2015 BS in Coastal Engineering, Hohai University, 2013 His research centers on environmental fluid dynamics , particularly multiphase and multiphysics flows in urban water systems. He investigates turbulence, particulate matter transport, pathogen fate, and chemical dynamics using advanced CFD simulations, volumetric particle image velocimetry (PIV), and AI-driven models. His lab develops open-source tools like InterAdsFoam for adsorption systems and integrates ML with CFD to optimize infrastructure design, retrofit, and regulatory frameworks. The recent publications reflect a strong trend toward hybrid CFD-ML frameworks for water infrastructure, with applications in clarifier design, stormwater basin optimization, and real-time sensing. His work emphasizes model validation, scalability, and practical deployment, including web-based tools for engineers. Scientific Awards: Rudolph Hering Medal, ASCE, 2023 Editor choice, Journal of Environmental Engineering ASCE, 2021 Editor choice, Journal of Environmental Engineering ASCE, 2020 Graduate School Fellowship, University of Florida, 2015 Academic Achievement Award, University of Florida, 2013 Haochen Li actively advises researchers and students in his lab, including Kai Liu, Mohamed Shatarah, and Ahmed Abdelmeguid. His team works on AI-empowered reactive flows, physics-informed ML, and CFD applications in energy and environmental systems. He has served as a reviewer for top journals and is a member of the ASCE/EWRI Computational Fluid Dynamics Committee. His lab is equipped with state-of-the-art HPC platforms and physical modeling facilities for experimental validation.

Hui LuView profile

Assistant Professor

• Operating Systems
• Systems Virtualization
• Cloud Computing
• +6 more

Dr. Hui Lu is an Assistant Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA), where he has been serving since September 2023. Prior to joining UTA, he was an Assistant Professor at SUNY Binghamton from 2017 to 2023. His academic journey includes a Ph.D. in Computer Science from Purdue University (2017), and Master’s and Bachelor’s degrees in Electronic Engineering from Shanghai Jiao Tong University. Ph.D., Computer Science, Purdue University, 2017 M.S., Electronic Engineering, Shanghai Jiao Tong University, 2009 B.S., Electronic Engineering, Shanghai Jiao Tong University, 2006 Dr. Lu's research centers on systems software with a focus on operating systems, virtualization, cloud computing, file and storage systems, and computer networks. His work emphasizes performance optimization and security in cloud-native environments. He has collaborated with leading industrial research labs including HPE Labs, IBM Research, Microsoft Research, AT&T Labs, and NEC Labs. His recent publications span top-tier venues such as OSDI, SOSP, USENIX ATC, and VLDB. The article trends reflect a strong emphasis on secure container technologies, memory tiering, packet processing optimization in virtualized networks, and efficient cloud storage systems. His work increasingly integrates hardware-aware optimizations and lightweight security mechanisms. NSF CAREER Award (2023) UT System Rising STARs Award (2023) Summer Faculty Fellowship, Air Force Research Lab (2019) Dr. Lu has successfully advised multiple Ph.D. students, including Jiaxin Lei, who is now an Assistant Professor at Kean University. His research is supported by major grants from the National Science Foundation (NSF) and the Air Force Research Lab (AFRL), focusing on secure containers, non-volatile memory management, and cloud-native virtualization. He has served as Principal Investigator (PI) on multiple funded projects, demonstrating strong leadership in research and innovation. He is actively involved in teaching core courses such as Operating Systems and advanced topics in systems and architecture. He mentors a growing group of Ph.D. students and welcomes motivated individuals to join his research group.

• Distributed control
• Autonomy and Control
• Aerospace Systems
• +6 more

Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.

• Structural Engineering
• Resilient Infrastructure Design
• Fire Safety Engineering
• +6 more

Maria Garlock is the Daniel Tsui Professor in Engineering at Princeton University, serving as Co-Director of the Program in Architecture and Engineering and Head of Forbes College. Her roles also include membership in the Executive Committee of the Council on Science and Technology, Associated Faculty in the School of Architecture, and Associated Faculty in the Program in Latin American Studies. Garlock holds a PhD in Structural Engineering (Lehigh University, 2002), an MS in Civil Engineering (Cornell University, 1993), and a BS in Civil and Environmental Engineering (Lehigh University, 1991). Her research focuses on resilient structural design for extreme hazards like fires, earthquakes, and storm surges. She explores both isolated and cascading multi-hazard scenarios while also analyzing historical structural designs (e.g., Félix Candela’s thin-shell concrete umbrellas) and improving STEM education for non-technical majors through innovative teaching methods, including MOOCs and scale model exhibitions. Recent work emphasizes coastal defense systems using hyperbolic-paraboloid forms and steel-concrete girder performance under shear stress. Garlock has received notable honors including the ASCE SEI Fellowship (2016 T.R. Higgins Lectureship), President’s Award for Distinguished Teaching (2012), and the Emerson Electric Co. Faculty Advancement Award (2006). In education and grants, she teaches courses like Structures and the Urban Environment and Advanced Design of Steel/Concrete Structures , and has secured government funding for STEM literacy initiatives. Her research collaborations include the BRITE Pivot project and studies on Cuba’s historic National School of Ballet domes. She also leads efforts in deploying kinetic umbrellas as flood barriers and advancing probabilistic models for fire fragility in multi-hazard contexts. Garlock’s work bridges engineering and art, exemplified by her preservation studies of Candela’s architectural masterpieces and pedagogical innovations that emphasize creativity in structural design.

• Artificial Intelligence
• Smart Cities
• Sensor Technology
• +5 more

Cormac Fay is a Research Fellow in Artificial Intelligence for Smart Cities at the School of Computing and Information Technology (SCIT), University of Wollongong, within the Faculty of Engineering and Information Sciences. His roles include affiliations with the SMART Infrastructure Facility and the ARC Centre of Excellence for Electromaterials Science. Previously, he held positions at Dublin City University, including post-doctoral roles in sensor research and data analytics. He holds a PhD in Engineering from Dublin City University (2013), an M.Eng. in Telecommunications Engineering (2007), and a B.Eng. in Mechatronic Engineering (2005). His research focuses on AI-driven smart city technologies, sensor systems for environmental monitoring, and advanced 3D printing materials. Key areas include IoT-enabled carbon-emission tracking, wearable biomedical devices, and sustainable sensor networks for landfill gas management. He has developed innovative solutions such as cryogenic 3D printing techniques for biocompatible inks and LED-based optical sensing platforms. Dr. Fay has secured grants totaling over $X million, including projects on military diver monitoring, blue carbon ecosystems, and low-cost sensor networks for agriculture and environmental safety. His work integrates interdisciplinary approaches, bridging materials science, biomedical engineering, and environmental engineering. Grants: Led projects on carbon-emission IoT systems, oyster farming sensors, and vibration monitoring. Supervision: Advised a Master's project on biomimetic microfluidic fabrication (2017–2019). Labs/Teams: Collaborates with the SCIT, SMART Infrastructure Facility, and global institutions like École Polytechnique Fédérale de Lausanne.

Ehsan PashajavidView profile

Senior Lecturer

• Stochastic modeling/optimisation
• Microgrids/Smartgrid
• Renewable Energy Resources
• +3 more

Dr. Ehsan Pashajavid is a Senior Lecturer at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, within the Faculty of Science and Engineering. His research focuses on stochastic optimization, renewable energy integration, microgrid control, and electric vehicle systems. Research interests include: Microgrid and smart grid control algorithms Renewable energy resource management Power system stability and operation Energy storage optimization Electric vehicle-grid integration His publications demonstrate significant contributions to power system resilience, with recent work emphasizing battery storage economics, fault-tolerant converters, and model predictive control for grid stability. Article trends show strong focus on renewable integration challenges and optimization techniques for modern energy systems. Awards include Senior Member status in IEEE and its Power & Energy, Industrial Applications, and Power Electronics societies. Teaching responsibilities encompass graduate courses in Renewable Power Generation Systems, Smart Grid Control, and Renewable Energy Principles.

• Uncertainty Quantification
• Civil Engineering Systems
• Resilience Engineering
• +5 more

Rui Teixeira is an Assistant Professor in the School of Civil Engineering at University College Dublin (UCD). He leads UCD's Centre for Critical Infrastructure Research (CCIR) and focuses on Uncertainty Quantification, Safety, and Risk in civil engineering systems, with applications to infrastructure resilience. His research emphasizes reliability analysis, multi-fidelity modeling, and AI-driven risk assessment. Education: MSc in Civil Engineering, University of Porto, Portugal PhD in Civil Engineering, Trinity College Dublin Professional Certificate in University Teaching and Learning, UCD Research Interests: Development of novel reliability analysis techniques Resilience of infrastructure systems Artificial intelligence applications for risk assessment Probabilistic system evaluation and safety standards Grants & Projects: Smart Enforcement of Transport Operations (SETO), Horizon Europe (2023–2026) Optimality-Tracking Civil Engineering Systems, Enterprise Ireland (2023–2025) Floating Offshore Wind Dynamic Cables (FlOWDyn), Sustainable Energy Authority of Ireland (2024–2027) Teaching: Coordinates courses such as 'Civil Engineering Systems' and 'Design of Structures 1'. Labs/Teams: Director of the Centre for Critical Infrastructure Research (CCIR), focusing on interdisciplinary approaches to infrastructure resilience.

Pamela Murray-TuiteView profile

Professor

• Transportation Systems
• Disasters and Hazards
• Resilience
• +12 more

Dr. Pamela Murray-Tuite is a Professor of Civil Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. Her work bridges transportation systems, disaster resilience, and emergency response modeling. Education: B.S. (1998), M.S. (1999), and Ph.D. (2003) in Civil Engineering from the University of Texas at Austin and Duke University Teaching: Courses include CE 3110 Transportation Engineering and CE 8930 Mathematical Modeling for Civil Infrastructure Systems Analysis Her research focuses on transportation resilience , with emphasis on evacuation modeling, emergency response, and infrastructure interdependence. She explores the impact of disasters on mobility patterns, utilizing mathematical modeling and simulation tools to enhance urban systems' robustness. The 15 most recent articles span hurricane evacuation behavior, agent-based modeling, and infrastructure resilience under climate change. Keywords include transportation systems, disaster management, and behavioral modeling, while subfields cover evacuation routing, network analysis, and risk perception dynamics. Dr. Murray-Tuite's work integrates statistical methods with transportation policy, addressing challenges in both natural disasters and public sector disruptions. Her contact details include office location (210 Lowry Hall) and phone (864-656-3802).

M.Z. NaserView profile

Assistant Professor

• Machine Learning
• Structural Engineering
• Fire Engineering
• +5 more

Dr. M.Z. Naser is an Assistant Professor in the Glenn Department of Civil Engineering at Clemson University. His research focuses on causal and explainable machine learning methodologies applied to structural engineering, materials science, and fire safety. He holds a PhD from Michigan State University and an M.S. from the American University of Sharjah. Naser teaches courses such as Machine Learning for Civil Engineers and Structural Fire Engineering, emphasizing interdisciplinary innovation. His work bridges data-driven analysis with domain-specific knowledge to address challenges in resilient infrastructure design, including fire-resistant materials, structural retrofits, and AI-driven decision-making. Education: PhD, Michigan State University; M.S., American University of Sharjah Research Themes: Explainable AI, Fire Engineering, Structural Materials, Causal Inference Key Projects: Developing SPINEX framework, wildfire classification models, and cognitive infrastructure systems Recent publications analyze over 1000 fire tests to uncover spalling mechanisms, explore synthetic fire tests via GANs, and benchmark automated ML platforms. His work on causal diagrams for civil engineers and firefighter algorithms highlights contributions to both theory and practical applications. Naser also advocates for integrating AI into engineering education, emphasizing ethical and transparent model deployment.