Find Academic Supervisors

• Power Systems
• Renewable Energy Integration
• Optimization
• +6 more

Lei Wu is a Professor and Anson Wood Burchard Chair Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. He holds a B.S. (2001) and M.S. (2004) in Electrical Engineering from Xi'an Jiaotong University, and a Ph.D. (2008) in Electrical Engineering from Illinois Institute of Technology. His research focuses on power system optimization, renewable energy integration, microgrid control, and cyber-physical systems resilience. Education: B.S. Electrical Engineering, Xi'an Jiaotong University (2001) M.S. Systems Engineering, Xi'an Jiaotong University (2004) Ph.D. Electrical Engineering, Illinois Institute of Technology (2008) Research Interests: Dr. Wu's work addresses challenges in power system operations, including optimization of renewable energy integration, electricity market design, and resilient microgrid control. He develops advanced algorithms for unit commitment, stochastic modeling of renewable resources, and cyber-physical security. His research emphasizes practical applications in grid resilience, demand response, and multi-energy system coordination. Awards: Fellow of IEEE (2022) NSF CAREER Award (2013) IBM Smarter Planet Faculty Innovation Award (2011) Grants & Professional Service: He leads grants on smart grid optimization, including projects from NSF, DOE, and industry partners. He serves as Editor for IEEE Transactions on Smart Grid and other journals, and has advised numerous students on energy-related research. His work on microgrid control and cyber-physical security has been widely recognized in industry and academia. Labs & Teams: Leads the Stevens Energy Systems Lab, focusing on advanced grid technologies and interdisciplinary collaborations between power systems, AI, and cybersecurity.

Ragib HasanView profile

Professor

• Cybersecurity
• Cloud Security
• Internet of Things (IoT) Security
• +8 more

Ragib Hasan is a Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), affiliated with the College of Arts and Sciences. His research focuses on cybersecurity, with specialties in cloud security, IoT systems, digital forensics, and biomedical device security. He leads the Secure and Trustworthy Computing Lab (SECRETLab) and contributes to the UAB Center for Cyber Security and NIST Cloud Forensics Working Group. Education: M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, followed by a postdoctoral fellowship at Johns Hopkins University. Affiliations: NIST Cloud Forensics Working Group, UAB Center for Cyber Security. His research addresses threats in smart cities, autonomous vehicles, and healthcare technologies. Key interests include securing IoT networks, mitigating cyberattacks on critical infrastructure, and advancing forensic methodologies in cloud environments. Recent work emphasizes threat modeling for connected vehicles, medical devices, and AI-driven systems. Dr. Hasan’s funding comes from the Department of Homeland Security, NSF, ONR, and industry partners like Facebook, Google, and Amazon. His awards include the NSF CAREER Award (2014), Google RISE Award (2013), and Deutsche-Welle Best of Blogs (2014) for his BanglaBraille initiative. Grants & Projects: Supported by DHS, NSF, and corporate collaborations. Outreach: Founded Wikimedia Bangladesh, Shikkhok.com (STEM education platform), and contributed to Bangla and English Wikipedia. His lab develops frameworks like StreetBit for pedestrian safety and InSight for emergency alert systems, integrating Bluetooth beacon technology to enhance urban security and sustainability.

Albert ChengView profile

Professor

• Real-Time Systems
• Cyber-Physical Systems
• Internet of Things (IoT)
• +3 more

Professor Albert Cheng is a faculty member in the Department of Computer Science at the University of Houston. His research focuses on real-time systems, cyber-physical systems, smart cities, and embedded systems with societal impacts. He has authored over 270 publications and a textbook on real-time systems. Cheng holds roles as an Associate Editor for the IEEE Transactions on Knowledge and Data Engineering and ACM Computing Surveys. His research interests span real-time scheduling, machine learning applications, and systems optimization. Recent work includes vehicular traffic modeling for epidemiological risk reduction, quantum computing response time analysis, and satellite mission planning. Awards include Fulbright Specialist, Distinguished ACM membership, and IEEE Senior Member status. Cheng’s articles demonstrate expertise in real-time scheduling algorithms, cyber-physical systems development, and smart city infrastructure. His contributions bridge theoretical computer science with practical implementations in transportation, healthcare, and aerospace domains. Ongoing efforts include fault-tolerant systems, energy-efficient scheduling, and CPS education initiatives. Awards: Fulbright Specialist, ACM Distinguished Member, IEEE Senior Member, Institute of Physics Fellow Labs/Teams: Hewlett Packard Enterprise Data Science Institute (HPE DSI), Research Computing Data Core (RCDC)

Rajesh KarkiView profile

Professor

• Power Systems
• Renewable Energy Integration
• Grid Reliability
• +3 more

Rajesh Karki is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan’s College of Engineering. He holds a B.E., M.Sc., and Ph.D. in related fields. His research focuses on power system reliability, renewable energy integration, and microgrid resilience, with particular emphasis on addressing challenges posed by extreme weather, cyber threats, and decarbonization targets. Dr. Karki’s work spans theoretical modeling, probabilistic analysis, and practical implementation strategies for smart grids, energy storage systems, and distributed generation. His educational background includes advanced degrees in electrical engineering, complemented by professional engineering licensure (P.Eng.). His research has explored diverse topics such as wind energy curtailment mitigation, energy storage optimization, and demand response mechanisms in developing economies like Nepal. He has authored numerous peer-reviewed publications on grid resilience, reliability economics, and cyber-physical system security. Key themes in his work include: (1) quantifying the reliability value of energy storage in active distribution systems, (2) modeling cyber-physical threats to microgrids, and (3) developing frameworks for extreme weather-resilient infrastructure. Despite the volume of his publications (over 50 articles), no specific awards or grants are explicitly listed in the provided materials. His research often intersects technical, economic, and policy dimensions of sustainable energy systems.

Stephen FerrignoView profile

Assistant Professor

• Cognitive Development
• Comparative Cognition
• Number Cognition
• +4 more

Stephen Ferrigno is an Assistant Professor and Chair of the Biology of Brain and Behavior Area Group at the University of Wisconsin-Madison's Department of Psychology. His research investigates the origins of uniquely human cognition through developmental, comparative, and cross-cultural methodologies. He focuses on foundational cognitive abilities such as number sense, recursive grammar, logical reasoning, and metacognition, exploring their evolutionary and developmental bases. Ferrigno's work bridges disciplines including cognitive neuroscience, developmental psychology, and cultural anthropology, with practical applications in early childhood education and curriculum design. His research employs cross-cultural studies with indigenous populations (e.g., the Tsimane) to assess cultural variability in cognitive development. Key themes include spatial mappings of numerical concepts, recursive sequence processing in non-human primates, and the metacognitive capacities shared between humans and monkeys. The Cognitive Origins Lab, led by Ferrigno, seeks to uncover universal cognitive principles while identifying culturally mediated differences. No scientific awards or grants are explicitly listed in the provided materials. While no advisees are mentioned, his research program suggests involvement in mentoring graduate students through the Department of Psychology's PhD program. Current projects include investigating the developmental trajectories of logical reasoning, the neural correlates of recursive sequence processing, and the design of culturally adaptive educational interventions targeting early math and language skills in preschool settings.

Yuping HeView profile

Professor

• Autonomous Driving
• Vehicle System Dynamics
• Vehicle Chassis Design
• +7 more

Dr. Yuping He is a Professor in the Department of Automotive and Mechatronics Engineering at the University of Ontario Institute of Technology (UOIT). He holds a PhD in Mechanical Engineering from the University of Waterloo (2002) and has extensive academic and industry experience, including postdoctoral fellowships at the University of Windsor and University of Waterloo. His research focuses on autonomous driving, vehicle dynamics, chassis design, and active safety systems, with expertise in modeling and simulation techniques. Education: PhD (Mechanical Engineering), University of Waterloo, 2002 MASc (Automotive Engineering), Tsinghua University, China, 1991 BASc (Automotive Engineering), Hubei Automotive Industries Institute, China, 1985 Research interests include automated design synthesis, multidisciplinary optimization, and driver-hardware-in-the-loop simulations. He has contributed to advancements in heavy vehicle stability control, trailer steering systems, and energy-saving strategies for steer-by-wire vehicles. His work bridges mechanical systems, control engineering, and real-time simulation technologies. Awards include the 2010 Research Excellence Award from UOIT’s Faculty of Engineering and Applied Science, and a nomination for the Governor-General’s Gold Medal (2003). His publications span journals like Vehicle System Dynamics and ASME Journal of Computational and Nonlinear Dynamics , with a focus on improving vehicle safety and performance through advanced control strategies. Advising and Grants: Dr. He has advised student teams in capstone projects, including the 2010 FEAS Capstone Design Competition-winning team. His research integrates industrial collaboration, as seen in roles like Senior Product Engineer at American Axle & Manufacturing (2005). His work emphasizes practical applications in automotive and mechatronic systems.

Anthony Lising AntonioView profile

Associate Professor

• Higher Education
• Race and Ethnicity
• Sociology
• +5 more

Anthony Lising Antonio is an Associate Professor at the Stanford Graduate School of Education and Associate Director of the Stanford Institute for Higher Education Research . He serves as the founding faculty director of LifeWorks , an undergraduate integrative learning program. His work focuses on postsecondary access, racial diversity in education, student development, and social stratification. PhD, University of California, Los Angeles (1998) MA, University of California, Los Angeles (1994) MS, Stanford University (1992) BS, University of California, Berkeley (1988) Dr. Antonio's research spans stratification in higher education, racial and ethnic minority student development, and the impact of diversity on institutions. Recent work explores college admissions essays, synthetic text analysis, engineering equity, and social network applications in educational research. His 2024-25 courses include Diversity and Equity Issues in Higher Education and Holistic College Student Development. His publications address critical themes like racialization in STEM, gender dynamics in undergraduate applications, intersectionality in student status, and college access program partnerships. Articles frequently employ social network analysis, critical race theory, and interdisciplinary methodologies to examine educational equity and institutional change. Principal investigator for educational RCTs Co-author on large language model demography studies Contributor to affirmative action policy debates Dr. Antonio advises doctoral students in education and sociology, with affiliations to the Center for Comparative Study in Race and Ethnicity and the Asian American Studies program. He resides in EAST House, a Stanford residential fellowship focused on equity and societal issues.

Li CaiView profile

Professor

• Psychometrics
• Latent Variable Models
• Item Response Theory
• +3 more

Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.

Khalid M. MosalamView profile

Professor

• Earthquake Engineering
• Structural Health Monitoring
• Bridge Engineering
• +9 more

Khalid M. Mosalam is the Taisei Professor of Civil Engineering and Director of the Pacific Earthquake Engineering Research (PEER) Center at the University of California, Berkeley, where he has served in the Department of Civil and Environmental Engineering since 1997. He also chairs the Al Falah Program in Science and Engineering, facilitating academic exchange between UC Berkeley and Middle Eastern/OIC institutions. His educational background includes: Ph.D. in Civil Engineering from Cornell University (1996) M.S. in Structural Engineering from Cairo University (1991) B.S. (Honors) in Civil Engineering from Cairo University (1988) Mosalam's research centers on structural health monitoring using vibration-based and vision-based techniques, with expertise in experimental methods including hybrid simulations. His work spans earthquake engineering, bridge engineering, and the assessment/rehabilitation of critical infrastructure like bridges and electrical substations. He investigates structural behavior of concrete, masonry, steel, and wood systems under extreme events (earthquakes, blasts, wind), integrating machine learning tools for predictive modeling and developing solutions for energy efficiency and sustainable infrastructure resilience. Current projects include Building Efficiency and Sustainability in the Tropics (BEST) and AI applications in vision-based structural health monitoring. His scientific achievements include: ASCE Huber civil engineering research prize (2006) UC-Berkeley chancellor award for public services (2013) EERI outstanding paper award (2015) Mosalam has secured continuous research funding for 26+ years from NSF, Caltrans, FEMA, Intel, US-DOE, NRF-Singapore, and private industries, leading major initiatives like the $5.075M BRACE2 bridge research program (2025). He advises graduate students and directs multiple research entities including the STAIRlab, PEER Center, StEER Network, and SinBerBEST program. His leadership encompasses the PEER Center's large-scale structural testing facilities, the StEER Network for post-disaster response, and international collaborations through SinBerBEST, focusing on experimental validation, computational modeling, and practical implementation of resilient infrastructure systems.

• Lean construction
• Takt planning
• Industrialized Construction
• +12 more

Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.

Joseph P. RomanoView profile

Professor

• Statistics
• Econometrics
• Bootstrap Methods
• +5 more

Joseph P. Romano is a distinguished Professor of Statistics and Economics at Stanford University, where he has been on the faculty since 1986. He holds joint appointments in both the Department of Statistics and the Department of Economics, reflecting his interdisciplinary research that bridges statistical theory with economic applications. Romano has established himself as a leading scholar in mathematical statistics with significant contributions to econometrics, climate science, and multiple testing methodologies. Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) A.B. in Statistics, Princeton University (1982), Summa Cum Laude Romano's research focuses on the theoretical foundations and practical applications of statistical methods, particularly in nonparametric statistics, bootstrap and resampling techniques, and multiple testing procedures. His work addresses the challenges of analyzing massive datasets with complex structures, such as those found in biotechnology, clinical trials, and econometrics. He has developed universal statistical tools applicable across diverse fields including climate science, genetics, finance, and education. His recent work emphasizes methods for multiple testing and multivariate inference driven by the availability of massive datasets, where he tackles issues like unknown dependence structures, heterogeneity, and high dimensionality. Analysis of Romano's recent publications reveals a consistent focus on developing robust statistical methodologies for complex data structures. His work spans theoretical advances in U-statistics with growing dimensions, practical applications in seroprevalence studies, and innovative approaches to ranking inference across various domains. The interdisciplinary nature of his research is evident in publications spanning economics journals, statistics journals, and even behavioral science preprints, demonstrating the broad applicability of his methodological contributions. 2021 LGBTQ+ Scientist of the Year, Out to Innovate Fellow, International Association of Applied Econometrics (2020) Fellow, Institute of Mathematical Statistics Presidential Young Investigator Award, National Science Foundation The Canadian Journal of Statistics Award Romano has mentored dozens of doctoral students throughout his career at Stanford, serving as dissertation advisor, co-advisor, and committee member for numerous PhD candidates in Statistics. His research has been consistently supported by National Science Foundation grants, including recent funding for computer-intensive inference with applications to social sciences (2020-2023) and randomization inference for contemporary statistical problems (2013-2016). He has served in various administrative roles at Stanford including Associate Chairman and Chair of Committee on Faculty Affairs. Beyond his academic pursuits, Romano is actively involved in the 500 Queer Scientists visibility campaign and maintains a balanced life with passions in music (having performed at Carnegie Hall), competitive tennis (ranked nationally in his age group), cooking, and architecture.

Carl Henrik EkView profile

Professor

• Machine Learning
• Artificial Intelligence
• Bayesian Non-parametric Methods
• +6 more

Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.

Ekaterina HertogView profile

Associate Professor

• Digital Sociology
• Family Sociology
• Parental Control Technologies
• +4 more

Ekaterina Hertog is an Associate Professor of AI and Society at the University of Oxford's Oxford Internet Institute (OII) and the Institute for Ethics in AI. Her research focuses on the intersection of digital technologies and family life, exploring how AI and digital tools transform parenting, childcare, and family dynamics. She holds a DPhil and MSc in Sociology from the University of Oxford. Education: DPhil in Sociology, University of Oxford MSc in Sociology, University of Oxford Research Interests: Digital technologies in family contexts, parental control technologies, future of unpaid work, gender practices in households, and time use patterns. Her work includes studies on data-driven parenting, domestic AI automation, and societal implications of digital monitoring. Projects: DomesticAI: Explores AI's potential to transform unpaid domestic work in the UK and Japan. Research on digital technologies in childcare, including parental control apps and ethical frameworks. Awards & Media: Her work has been featured in media outlets like BBC Ideas, The Guardian, and Newsweek. She contributes to policy discussions on digital parenting and AI ethics. Advising & Grants: Supervises doctoral students on topics like digital disconnection, remote work, and AI ethics. Leads ESRC-funded projects and collaborates with international institutions like the Dieter Schwarz Foundation. Labs/Teams: Active in the OII's AI & Society research cluster and collaborates with interdisciplinary teams on AI ethics and societal impacts.

Hong HuaqingView profile

Professor

• Corpus Linguistics
• Computer-Assisted Instruction
• Machine Translation
• +3 more

Hong Huaqing is a Professor and doctoral supervisor at the Corpus Research Institute of Shanghai International Studies University. He holds roles as honorary director of the Chinese Corpus Linguistics Research Association and international expert at Peking University's Education Development Center. Formerly, he worked at Nanyang Technological University (Singapore) in roles such as researcher at the Learning Research and Development Center and director of the e-Learning Center of the Lee Kong Chian School of Medicine. His research spans machine translation, natural language processing, corpus linguistics, and educational technology. He supervises master's and doctoral students, co-supervises postdoctoral researchers, and focuses on smart education driven by big data analysis and innovative learning ecosystems. Research emphasizes corpus-based methods applied to language education, including computational frameworks for student engagement, wearable sensors in learning analytics, and cross-linguistic rhetoric studies. His work bridges technological innovation (e.g., AI-driven tutorial systems) with pedagogical practice, addressing challenges in non-English language education and teacher training.

Jonathan HauensteinView profile

Professor

• Numerical Algebraic Geometry
• Scientific Computing
• Numerical Solution of Differential Equations
• +5 more

Jonathan Hauenstein is the Robert and Sara Lumpkins Collegiate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, serving as Department Chair. He holds a Ph.D. from Notre Dame (2009) and M.S. from Miami University (2005). His research focuses on numerical algebraic geometry and computational methods for solving nonlinear equations, implemented in the Bertini software package. Applications span engineering, ecology, sports science, and machine learning. Education: Ph.D., Applied and Computational Mathematics, University of Notre Dame (2009) M.S., Mathematics, Miami University (2005) Research Interests: Development of numerical algorithms for polynomial systems, real algebraic geometry, and scientific computing. Key areas include homotopy continuation methods, parameter space decomposition, and applications in mechanism design, ecological modeling, and sports biomechanics. His work bridges theoretical mathematics with practical computational tools. Awards: Sloan Research Fellowship DARPA Young Faculty Award Army Research Office Young Investigator Award Office of Naval Research Young Investigator Award College of Science Research Award Advising & Grants: Advised numerous undergraduates, graduate students, and postdoctoral researchers. Active in securing grants for computational mathematics projects, including NSF-funded initiatives. His work emphasizes interdisciplinary collaboration between mathematics and engineering. Labs/Teams: Leads computational algebraic geometry research groups at Notre Dame, focusing on software development (e.g., Bertini) and numerical methods innovation.