Find Academic Supervisors

Fatma DeghimView profile

Research Fellow

• Urban microclimate
• Building energy simulation
• Green infrastructure integration
• +4 more

Fatma Deghim is a Research Fellow at the Technical University of Munich , affiliated with the Chair of Energy Efficient and Sustainable Design and Building. Her work focuses on urban microclimate, building energy simulation, and data-driven methods for sustainability. Education : Master’s Degree in Civil Engineering (2019–2022) and Bachelor’s Degree in Civil Engineering (2015–2019), both from TUM. Research Interests include: Urban microclimate and indoor-outdoor interactions Building energy simulation and comfort analysis Integration of green infrastructure in climate-resilient design Data-driven methods for environmental monitoring Publications highlight her expertise in applying machine learning to occupancy modeling, thermal comfort prediction, and multi-objective optimization frameworks for sustainable building design. Her work emphasizes uncertainty analysis, resource efficiency, and computational methods. Teaching contributions include assisting in courses on sustainable architecture, building energy principles, and urban water systems at TUM.

• Emergent Software Systems
• Runtime Adaptation
• Component-Based Software
• +7 more

Professor Barry Porter is a faculty member at Lancaster University in the School of Computing and Communications . His research focuses on emergent software platforms that address software complexity through component models , meta-software platforms , and machine learning . Key areas include distributed systems, cloud integration with sensor nodes, green computing, and real-time visualization. Research Interests : Runtime adaptation in complex systems Self-assembling software architectures Machine learning for code optimization Distributed emergent systems at scale Green computing for multi-core environments Edge-cloud continuum integration Recent Publication Trends : His 2025 work explores genetic improvement for software using speciation algorithms , program geometry projection , and multi-agent decision frameworks . Earlier studies (2022-2024) investigate edge-cloud systems , neural transfer learning , and ecosystem curation in emergent software. Supervision & Projects : He supervises PhD student Ben Craine and leads projects like B-EGI (Bio-Enhanced Genetic Improvement) and BBC Prosperity Partnership for media delivery. Collaborations span environmental IoT, multi-agent learning, and fog computing. Labs & Groups : Affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems Centre , Centre of Excellence in Environmental Data Science , and the Distributed Systems group.

Michael PyrczView profile

Professor

• Subsurface Data Analytics
• Geostatistics
• Machine Learning
• +3 more

Michael Pyrcz is a Professor in the Hildebrand Department of Petroleum and Geosystems Engineering and holds the rank of Associate Professor in the Jackson School of Geosciences at the University of Texas at Austin. He is the recipient of the B. J. Lancaster Professorship in Petroleum Engineering and the George H. Fancher Centennial Teaching Fellowship in Petroleum Engineering. His research focuses on subsurface data analytics, geostatistics, and machine learning applications in energy systems and CO2 sequestration. Pyrcz teaches widely, including through online lectures and GitHub workflows, and has authored over 50 peer-reviewed publications and a textbook on spatial data analytics. His work integrates machine learning with geoscience challenges, such as uncertainty quantification in reservoir modeling and CO2 storage site evaluation. He leads initiatives in energy data analytics through the Freshman Research Initiative and collaborates with industry on workflow development. Key research areas include generative AI for subsurface models, stochastic methods for fracture networks, and anomaly detection in geologic monitoring. Education: Background in petroleum engineering and geosciences (details not explicitly provided). Grants/Advising: Extensive industry collaboration and mentorship roles at Chevron prior to UT Austin. Labs/Teams: Maintains active GitHub repositories (GeostatsGuy), YouTube lecture series (GeostatsGuyLectures), and social media outreach (X/GeostatsGuy).

Onesun Steve YooView profile

Professor

• Operations Management
• Marketing Analytics
• Innovation Management
• +3 more

Onesun Steve Yoo is a Professor of Operations and Marketing Analytics at the UCL School of Management, University College London, and Co-Director of the UCL Centre for Sustainable Business. He holds a PhD from UCLA Anderson School of Management, alongside advanced degrees in Electrical Engineering and Applied Mathematics from UC Berkeley and UCLA. His research focuses on innovation and entrepreneurship, examining operational and marketing strategies for firms launching innovative products/services. Key areas include consumer behavior analysis, pricing policies, sequential product launches, and the impact of technologies like surge pricing and AI-driven data analytics on business operations. Recent work integrates sustainability initiatives with AI to enhance operational transparency in supply chains and regulatory compliance. Yoo’s research has been published in top journals such as Marketing Science , Operations Research , and Manufacturing & Service Operations Management . His findings have been cited by US policymakers and featured in media outlets like the Wall Street Journal . He serves as a senior editor at Production and Operations Management and associate editor at Manufacturing & Service Operations Management . His academic service includes grants from Innovate-UK (UKRI) to collaborate with industry on sustainable business practices. Yoo’s work bridges theoretical research with practical applications, emphasizing data-driven decision-making and interdisciplinary collaboration between operations, marketing, and sustainability domains.

Asher WolinskyView profile

Professor

• Microeconomic Theory
• Industrial Organization
• Imperfect Competition
• +4 more

Asher Wolinsky is the Gordon Fulcher Professor of Economics at Northwestern University's Weinberg College of Arts & Sciences. His research focuses on microeconomic theory and industrial organization, with emphasis on markets under imperfect competition and information asymmetry. He holds a PhD from Stanford University (1980). He is a Fellow of the Econometric Society and the American Academy of Arts and Sciences. His work has explored auction design, strategic market behavior, and institutional mechanisms. Recent research includes studies on auctions with frictions, bidder solicitation dynamics, and search theory under adverse selection. Educational background: PhD in Economics, Stanford University, 1980. His academic service includes editorial roles with the Econometric Society Annual Reports. His research portfolio spans over 50 years, addressing foundational questions in game theory and market design.

• Operations Research
• Operations Management
• Sequencing and Scheduling
• +6 more

Roel Leus is a full professor at KU Leuven's Faculty of Economics and Business (FEB), part of the Operations Research and Statistics Research Group (ORSTAT). He holds roles such as Program Director for the Business Engineering programs and Chairman of the KU Leuven Advisory Committee for the Chinese Region. He earned his PhD in Applied Economics from KU Leuven in 2003, focusing on project planning under uncertainty. His research emphasizes operations research and management, particularly scheduling, project planning, and decision-making under uncertainty. Education: PhD in Applied Economics (KU Leuven, 2003); Master's in Business Engineering (Handelsingenieur, KU Leuven, 1998). He has held academic positions since 2003, including adjunct professorships at Beijing Jiaotong University. His administrative roles include heading ORSTAT research group (2012–2016) and program directorships. Research Interests: Sequencing and scheduling, project planning under uncertainty, discrete optimization, and practical quantitative decision support. He has supervised 12 graduated PhD students as primary supervisor and contributed to numerous publications in top journals like INFORMS Journal on Computing and European Journal of Operational Research. Teaching: Courses include 'Introduction to Operations Research,' 'Operations Research,' and 'Applications of Operations Research.' He coordinates master's theses in Data Science and Business Analytics, focusing on practical optimization problems. Grants and Projects: Acquired over €2 million in research funding from private companies, the National Bank of Belgium, and KU Leuven. His work spans satellite scheduling, supply chain management, and cross-docking logistics. Labs/Teams: Active in ORSTAT, collaborating on projects like drone-assisted delivery and robust scheduling algorithms. His research bridges theoretical advancements with real-world applications in logistics, manufacturing, and aerospace.

Jason RightsView profile

Associate Professor

• Quantitative Methods
• Multilevel/Hierarchical Data Analysis
• Statistical Methods
• +4 more

Dr. Jason Rights is an Associate Professor in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His office is located in Kenny Room 2017 at 2136 West Mall, Vancouver, BC. He leads The Rights Lab, a quantitative methods research group dedicated to improving statistical practice in scientific research. His educational background includes: B.S. in Psychology and Mathematics from the University of North Carolina at Chapel Hill (2011) M.S. in Psychology (Quantitative Methods) from Vanderbilt University (2015) Ph.D. in Psychology (Quantitative Methods) from Vanderbilt University (2019) Dr. Rights' research focuses on addressing methodological complexities in multilevel/hierarchical data contexts where observations are nested (e.g., patients within clinicians, students within schools). His work spans several interconnected programs including developing R-squared measures for multilevel models, addressing issues with level-specific effects, exploring connections between multilevel and mixture models, and advancing latent variable model selection techniques. Analysis of his publication record reveals a consistent focus on methodological innovations in quantitative psychology, with particular emphasis on improving statistical techniques for hierarchical data structures. His work bridges theoretical statistical development with practical applications across psychology and related fields. Dr. Rights actively develops open-source software in R to implement his methodological contributions, making advanced statistical techniques accessible to researchers. The Rights Lab serves as the hub for his ongoing research program in quantitative methods development.

Karthika MohanView profile

Assistant Professor

• Causal Inference
• Graphical Models
• Missing Data
• +3 more

Dr. Karthika Mohan is an Assistant Professor of Computer Science in the College of Engineering at Oregon State University, affiliated with the School of Electrical Engineering and Computer Science. Her research bridges artificial intelligence and causal inference, focusing on graphical models, missing data, and non-IID data challenges. Her work has been recognized with the Google Outstanding Graduate Research Award. She serves as an associate editor for the Journal of Causal Inference and has secured NSF funding for research on incomplete data. Dr. Mohan mentors students in causal inference methods and maintains collaborations with institutions like UC Berkeley and UCLA. Her laboratory develops innovative approaches for causal reasoning in AI systems.

Birgitte Bak-JensenView profile

Professor

• Smart Grids
• Electric Power Distribution Systems
• Multi-Energy Systems Integration
• +6 more

Birgitte Bak-Jensen is a Professor at the Department of Energy, Aalborg University, where she has worked since 1988. She specializes in intelligent control of power distribution systems, with research focusing on grid stability, power quality, and integration of dispersed generation and smart grid technologies. Her work also addresses multi-energy system interactions between electrical grids, heating, and transport sectors. Projects : Led EU H2020 projects (SERENE, SUSTENANCE) and Danish initiatives (EFFORT, SMARTCE2H) Publications : Over 250 papers on distribution grid control and smart energy systems Her research combines renewable energy integration , electric vehicle grid interaction , and energy storage optimization . Recent work includes explainable AI for wind forecasting and voltage control strategies for EV charging. 2025 Awards : Best Student Paper Award (2023), Best Paper Award (2021), CIGRE Technical Council Award (2018) Organizational Roles : Vice Head of AAU Energy Research, leadership positions in IEEE and CIGRE

• 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.

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)

Kuanshi ZhongView profile

Assistant Professor

• Structural Engineering
• Earthquake Engineering
• Seismic Design and Safety
• +4 more

Kuanshi Zhong is an Assistant Professor in the Department of Civil and Architectural Engineering and Construction Management at the University of Cincinnati. He holds a PhD from Stanford University (2021) in Civil and Environmental Engineering, with prior degrees from Stanford (Master, 2017) and Tongji University (Bachelor, 2015). His research focuses on earthquake engineering, structural resilience, and advanced computational methods for infrastructure safety. Key research interests include seismic design of tall buildings, probabilistic modeling of structural response (e.g., using Probabilistic Learning on Manifolds), and material failure mechanisms in reinforced concrete. He also explores multi-hazard resilience, regional risk assessment, and software tools for disaster simulation (e.g., R2DTool and EE-UQ). Dr. Zhong has secured grant funding as PI/Co-PI, including a National Science Foundation grant (2023-2026) for equitable building decarbonization strategies and a Concrete Reinforcing Steel Institute grant (2024-2025) for bar performance improvements. He teaches graduate/undergraduate courses on concrete design and structural mechanics. His work spans collaborations with institutions like Stanford University and the SimCenter, contributing to open-source tools for regional loss assessments and hurricane impact modeling. Current projects address cascading hazards, steel reinforcement durability, and high-resolution seismic risk evaluation.

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.

Saman RazaviView profile

Associate Professor

• Hydrology
• Water Resources Management
• Climate Change Impact Analysis
• +3 more

Dr. Saman Razavi is an Associate Professor at the University of Saskatchewan, holding dual appointments in the School of Environment and Sustainability (SENS) and the Department of Civil, Geological and Environmental Engineering in the College of Engineering. He is a member of the Global Institute for Water Security and leads the Razavi EnviroFutures Lab. His research focuses on hydrological modeling, water resources management, climate change impacts, and the integration of machine learning with environmental science. Dr. Razavi has earned a PhD in Civil and Environmental Engineering from the University of Waterloo. Education: PhD in Civil and Environmental Engineering, University of Waterloo MS in Civil and Environmental Engineering, Amirkabir University, Iran BS in Civil Engineering, Iran University of Science and Technology Research interests include hydrologic model development, optimization, uncertainty quantification, climate change analysis, and socio-hydrological modeling. He emphasizes interdisciplinary approaches to address water challenges through integrated frameworks that bridge natural science, engineering, and socio-economic factors. Award: Walter L. Huber Civil Engineering Research Prize from ASCE (2024) Advising & Grants: Dr. Razavi leads the Integrated Modelling Program for Canada under Global Water Futures, focusing on transboundary water systems. His work involves developing decision-support tools for flood/drought management and climate adaptation. Labs/Teams: The Razavi EnviroFutures Lab advances research on water-human systems, leveraging AI and big data to enhance resilience against water-related hazards. Current projects include flood-prone area mapping, drought prediction, and socio-hydrological modeling in transboundary basins.

Ben Bloem-ReddyView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Bayesian Methods
• +3 more

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.