Find Academic Supervisors

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +6 more

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Kaidi YangView profile

Assistant Professor

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +5 more

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 SongView profile

Professor

• Building Energy System Optimization
• HVAC System Fault Detection
• Energy Efficiency Technology
• +3 more

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.

Yongjie Jessica ZhangView profile

Professor

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +5 more

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

• Climate Change
• Environmental Hydrology
• Extreme Rainfall Patterns
• +3 more

Dr Conrad Wasko is a Sydney Horizon Fellow at the University of Sydney and an honorary fellow at the University of Melbourne. He specializes in environmental hydrology and climate change impacts on extreme rainfall and flooding. His work has been cited by the IPCC and he co-leads the update to Australia’s flood estimation guidelines. Previously, he held an ARC DECRA Fellowship and a McKenzie Fellowship. He has published over 50 articles and received awards including the Batterham Medal (2023) and the MSSANZ Early Career Research Excellence Award (2019). His research focuses on quantifying climate-driven changes in rainfall patterns and flood risks, with applications to infrastructure resilience and policy. Education: PhD in Civil Engineering, UNSW Sydney (2016) Bachelor/Master’s qualifications (not explicitly stated in text) Key Roles: Member of The Net Zero Institute Editor of Journal of Hydrology X and Advances in Water Resources Hydroclimate Stream Leader, Modelling and Simulation Society of Australia and New Zealand His research emphasizes the intensification of extreme rainfall events with global warming, particularly their implications for flood risk and urban infrastructure. He uses advanced statistical models and climate data to project future flood scenarios and improve design standards. Recent work includes analyzing non-stationary rainfall patterns and improving flood frequency analysis techniques. Awards and Honors: Batterham Medal (Australian Academy of Technological Sciences and Engineering, 2023) Veski Victoria Fellow (2020) MSSANZ Early Career Research Excellence Award (2019) Lorenz G Straub Award (2016) Grants and Projects: ARC DECRA Fellowship (University of Melbourne) Contributions to national climate change adaptation frameworks Labs and Collaborations: Partnerships with the Water Research Centre (UNSW) and the Net Zero Institute Collaborations on global flood risk assessments and hydrological modeling tools

Ioana JivetView profile

Research Professor

• Learning Analytics
• Educational Technology
• Artificial Intelligence in Education
• +5 more

Ioana Jivet is a Research Professor and Head of the Learning Analytics Research Professorship at FernUniversität in Hagen, Germany. She leads interdisciplinary research focused on leveraging learning analytics to enhance educational practices through data-driven insights. Her role in the CATALPA Graduate School involves collaboration across disciplines to translate research into actionable educational strategies. Educational Background: PhD in Learning Analytics from Open Universiteit Nederland (2021) Postdoc at TU Delft (2019–2021) Research roles at DIPF (2021–2024) and studiumdigitale (2021–2024) Research Interests: Self-regulated learning mechanisms and feedback systems Cultural and ethical dimensions of learning analytics adoption Design of human-centered learning dashboards Privacy concerns in educational technology AI-driven educational decision support systems Her work emphasizes practical applications, such as developing adaptive feedback tools and investigating cross-cultural usage patterns of learning analytics platforms. Professional Contributions: Secretary of the Society for Learning Analytics Research (SoLAR) General Chair for the 2024 European Conference on Technology Enhanced Learning (EC-TEL) Co-editor of Springer volumes on technology-enhanced learning Lab & Teams: Active in the Learning Analytics research group at FernUniversität, focusing on translational research between academic theory and classroom practice.

Misa FaezipourView profile

Associate Professor

• System Dynamics Modeling
• Healthcare Systems Engineering
• Complex Systems Modeling
• +3 more

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.

Weichao WangView profile

Professor

• Cybersecurity
• Wireless Networks Security
• Cloud Computing Security
• +7 more

Weichao Wang is a Professor and Chair of the Department of Software and Information Systems at the University of North Carolina at Charlotte (UNC Charlotte). He holds a Ph.D. in Computer Science from Purdue University (2005), with earlier degrees from Tsinghua University. His research focuses on securing pervasive systems, wireless networks, cloud computing, and critical infrastructures, integrating multi-disciplinary approaches like information theory and visualization. He leads efforts in cybersecurity education for K-12 and higher education. Key roles include organizing IEEE conferences (e.g., IPCCC) and editorial roles in journals like ITU Intelligent and Converged Networks. His awards include the 2024 AECT Crystal Award and multiple Distinguished TPC recognitions. Students under his advisement have contributed to areas like mobile cloud computing and network security. Professional contributions include service on over 50 conference committees and journal reviewing. Current research emphasizes cybersecurity in education, IoT defense, and AI-driven security systems. His lab explores immersive visualization for cybersecurity training and augmented reality applications.

Tianhua XuView profile

Associate Professor

• Optical Communication Networks
• Optical Sensing Systems
• Optoelectronics
• +4 more

Dr. Tianhua Xu is a Reader in the School of Engineering at the University of Warwick and an Honorary Lecturer in the Department of Electronic and Electrical Engineering at University College London (UCL). He holds a Ph.D. in Optical Communications and Intelligent Signal Processing from KTH Royal Institute of Technology in Sweden, followed by postdoctoral and research roles at KTH, RISE Acreo, DTU, and UCL. His research focuses on optical communication systems, intelligent signal processing, machine learning, optical sensing, and advanced energy systems. He has secured major grants, including a €1.8M EU Horizon Europe project (SPAR) and a £285K UK National Grid initiative. Dr. Xu serves as an Associate Editor for IEEE Transactions on Communications and the Journal of the European Optical Society-RP, and chairs technical groups in the Optical Society of America. He has authored over 200 publications, including invited book chapters, with a Google Scholar h-index of 32. Current projects involve smart photonic sensing, energy storage systems, and deep learning in optical networks. His lab oversees six PhD students and multiple postdoctoral researchers. He actively recruits students through global scholarships and oversees vibrant research teams in optical communications, sensing, and energy systems.

Scott T. M. DawsonView profile

Assistant Professor

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +5 more

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.

• Construction Safety and Risk Management
• Project Risk Assessment
• Railway and Highway Safety
• +3 more

Professor Min An is a Professor of Construction and Risk Management at the University of Salford, leading the Infrastructure Research Group within the School of Science, Engineering & Environment. He holds an honorary professorship at two overseas universities (China and Portugal) and serves on the editorial boards of 12 international journals. With over 40 years of experience, his career spans academic roles at Heriot-Watt University, Coventry University, and the University of Birmingham, alongside industry roles as a civil engineer and researcher. His research focuses on safety and risk management in construction, transportation systems, and energy sectors, with over 200 publications. Key areas include railway and highway safety, offshore oil & gas risk assessment, and nuclear reliability management. He has secured funding from EPSRC, EU, DfT, and industry partners, leading 20+ projects. Notable achievements include developing methodologies for infrastructure safety and maintaining collaborations with 30+ industrial partners. Professor An has supervised 30 PhD students and over 280 postgraduate projects, contributing to industry workshops and best practices. Awards include multiple science technology prizes and conference best paper/keynote recognitions. His teaching spans risk management, construction safety, and project management across MSc programs.

Young-Jin ChaView profile

Professor

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +5 more

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Daanika GordonView profile

Associate Professor

• Law and criminal legal system
• Racial inequality and racial formation
• Urban politics and cities
• +2 more

Daanika Gordon is an Associate Professor of Sociology at Tufts University, with a secondary appointment in Studies in Race, Colonialism, and Diaspora. Her research explores the intersection of racial inequality, urban governance, and policing through organizational and interactional lenses. PhD, University of Wisconsin–Madison (2018) MS, University of Wisconsin–Madison (2013) BA, Development Studies & Sociology, UC Berkeley (2009) Her work connects theories of race and racism with urban political economy and organizational behavior, focusing on how: Policing functions as a tool for urban governance Racial inequalities emerge from race-neutral policies Segregation is dynamically produced through relational processes Recent publications analyze police funding debates, data science applications in policing, and the bureaucratic dissociation of race. Her groundbreaking 2022 book Policing the Racial Divide won the 2023 Edwin H. Sutherland Book Award. She teaches sociology of race, criminal legal systems, and research design while serving on the advisory committee for the Tufts University Prison Initiative. Her research has been funded by: Institute for Citizens & Scholars Bernstein Faculty Fellowship Neubauer Faculty Fellowship National Science Foundation

Ben WilliamsonView profile

Senior Lecturer

• Digital Education
• Data-Intensive Practices in Education
• Educational Policy and Governance
• +4 more

Dr. Ben Williamson is a Senior Lecturer in Digital Education at the University of Edinburgh's Moray House School of Education and Sport, affiliated with the Institute for Education, Community & Society (IECS) and the Centre for Research in Digital Education. His work critically examines how digital technologies, data infrastructures, and biometric/neurotechnological practices shape educational futures. Key areas of focus include the role of venture capital in education, algorithmic governance, and the privatization of educational systems. His research has been funded by the Leverhulme Trust and the Economic and Social Research Council (ESRC). Notable publications include Big Data in Education: The Digital Future of Learning, Policy, and Practice (2017). He maintains the Code Acts in Education blog and is active on Twitter as @BenPatrickWill. Williamson’s research explores intersections between biology and education (e.g., educational genomics), financial markets in schooling, and the governance of educational data by tech corporations. He critiques how platforms like Google Classroom and investors such as Amazon reshape educational practices and policies. His work emphasizes sociotechnical and infrastructural analyses of educational change. He supervises PhD students focusing on data-centric education policies, digital technology in education governance, and emerging biometric sciences in learning contexts. His articles analyze global EdTech dynamics, pandemic-driven privatization, and the ethical implications of biosocial education. Williamson’s affiliations include the Edinburgh Futures Institute, where he collaborates on future-oriented educational research. He has contributed to policy innovation labs and studies the impact of corporate actors like Pearson and the Chan Zuckerberg Initiative on educational systems.

Lu SuView profile

Associate Professor

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +7 more

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.