Find Academic Supervisors

• Agent-Based Modelling
• Computational Social Science
• Natural Language Processing (NLP)
• +3 more

Dr. Sjoukje Osinga is an Assistant Professor in the Information Technology group at Wageningen University's Department of Social Sciences. Her research focuses on computational social science, natural language processing (NLP), and big data applications in agriculture. She holds a PhD from Wageningen University on agent-based modelling of knowledge management in the pig sector, with fieldwork in China. She contributed to EU H2020 projects like Cybele (big data in agriculture) and Dragon (knowledge transfer of ABM tools). She is a member of the SiLiCo Centre, specializing in simulating complex systems through agent-based simulations. Education: Artificial Intelligence and Cognitive Science (Groningen and Leuven, 1991) Research interests include agent-based modelling, big data analytics for agriculture, machine learning, and knowledge management. She explores topics like digital twins in health and agriculture, and sentiment analysis in policy-making. Her work bridges technical innovation with societal challenges, such as sustainable farming practices and compliance strategies in regulatory environments. Publications span agent-based models for pork supply chains, machine learning applications in crop forecasting, and digital twin frameworks for agriculture. She actively engages in interdisciplinary projects addressing data integration and policy implications of emerging technologies.

Wenqi ShiView profile

Assistant Professor

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +3 more

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.

• Quantitative Finance
• Machine Learning in Finance
• Sustainable Finance
• +3 more

Guillaume COQUERET is a Professor of Finance and Data Science at emlyon business school since 2018 and Director of the AIM Institute, which coordinates research and teaching in artificial intelligence applied to management. His academic qualifications include an HDR (2022) from Université Lumière Lyon 2 and a PhD in Business Administration from ESSEC Business School (2012). His research focuses on quantitative finance, machine learning in capital markets, sustainable finance, and heterogeneous agent models. He previously served as a Quantitative Researcher at EDHEC-Risk Institute (2013–2015). Education : 2022: HDR, Université Lumière Lyon 2 2012: PhD in Business Administration, ESSEC Business School 2008: Master in Probability and Finance, Université Pierre et Marie Curie (Paris 6) 2007: Master in Management, ESSEC Business School Research Interests : Machine learning applications in finance, factor investing, climate risk modeling, ESG integration, and algorithmic portfolio strategies. Publications : Over 30 peer-reviewed articles in journals such as The Journal of Portfolio Management , European Journal of Operational Research , and Quantitative Finance . Notable works include studies on climate betas, biodiversity premiums, and supervised learning in equity investing. Books : Machine Learning for Factor Investing: Python Version (2023) Perspectives in Sustainable Equity Investing (2022) Awards : None explicitly mentioned, but recognized for contributions to quantitative finance and AI in management. Labs/Teams : Leads the AIM Institute, fostering AI-driven research in management and finance.

Xingpeng LiView profile

Associate Professor

• Power Systems
• Machine Learning
• Renewable Energy Integration
• +5 more

Dr. Xingpeng Li is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Houston (UH), where he has held this position since 2024, following his tenure as Assistant Professor (2018–2024). His research focuses on smart grid technologies, renewable energy integration, and optimization of power systems. He holds a Ph.D. in Electrical Engineering from Arizona State University (2017) and multiple master’s degrees in Computer Science (Georgia Tech, 2023), Industrial Engineering (ASU, 2016), and Electrical Engineering (Zhejiang University, 2013). His work emphasizes machine learning applications in power systems, including battery degradation modeling, microgrid design, and climate-resilient grid planning. He leads the RPG Lab (https://rpglab.github.io), advancing solutions for offshore renewable energy systems, hydrogen transmission, and grid cybersecurity. His research has been funded by the NSF CAREER Award (2024) and the NAS Gulf Research Program (2023). Education : Ph.D., Electrical Engineering, Arizona State University (2017) M.S., Computer Science, Georgia Institute of Technology (2023) M.S., Industrial Engineering, Arizona State University (2016) M.S., Electrical Engineering, Zhejiang University (2013) B.S., Electrical Engineering, Shandong University (2010) Dr. Li’s recent publications highlight advancements in AI-driven grid optimization, battery lifecycle modeling, and offshore microgrid planning. He has received awards such as the Early-Career Research Follow (NAS Gulf, 2023) and the IEEE Phoenix Section Student Scholarship (2016). Awards : NSF CAREER Award (2024) Early-Career Research Follow, NAS Gulf Research Program (2023) Emerging Leader, Offshore Technology Conference (2023) His teaching includes courses like ECE 6327 (Smart Grid Systems) and ECE 6379 (Power System Operations), bridging theoretical research with practical grid challenges. His lab collaborates on projects like 100% renewable offshore platforms and hydrogen energy transmission systems, addressing climate resilience and decarbonization goals.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

Saumen MandalView profile

Professor

• Optimal experimental design
• Biostatistics
• Data science
• +7 more

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Rickard SandbergView profile

Associate Professor

• Machine Learning
• Deep Learning
• Data Analytics
• +9 more

Rickard Sandberg is an **Associate Professor** and **Center Director** at the **Department of Entrepreneurship, Innovation and Technology** at the **Stockholm School of Economics (SSE)**. His work bridges econometrics, statistics, and business analytics with a focus on time series analysis, machine learning applications, and sustainability measurement. **Research Interests**: Machine Learning, Deep Learning, Data Analytics, Predictive Analytics, Forecasting, Nonlinear Time Series Modelling, Structural Economic Modelling, Econometrics, and Measuring Sustainability. His research emphasizes theoretical advancements in statistical methods and their practical application in economic and business contexts. **Key Contributions**: His publications explore unit root testing in nonlinear models, ESG rating challenges, and the impact of energy policies. Notable works include analyzing Scandinavian unemployment trends, cartel damage calculations, and Nordic companies' data-driven transformations. His 2023 paper on ESG ratings proposes solutions for consistency in ambiguous evaluation systems. **Teaching & Outreach**: Teaches advanced econometric time series courses (e.g., MSc 5314) and actively engages in international academic collaborations through presentations in Japan and Brazil. His work on AI for sustainability highlights interdisciplinary outreach efforts. **Labs/Teams**: Leads research initiatives within SSE’s Department, focusing on entrepreneurship and innovation through data and economic modeling frameworks.

Ayman El-HagView profile

Associate Professor

• Electrical Engineering
• High Voltage Engineering
• Power Systems
• +5 more

Ayman El-Hag is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is affiliated with the Outdoor Insulation and Condition Monitoring Research Group, focusing on advancing technologies for high-voltage insulation systems and smart grid infrastructure. His work integrates machine learning, signal processing, and materials science to improve condition monitoring and diagnostic methods for power equipment. Research interests include partial discharge detection using UHF and acoustic sensors, machine learning applications for defect classification in outdoor insulators, and the development of non-invasive sensing techniques for real-time monitoring. He also explores energy management systems leveraging fuzzy logic and smart meter data analysis for residential and grid-level applications. Recent publications highlight advancements in capsule networks for insulator discharge prediction, deep learning-based hydrophobicity classification, and novel antenna designs for partial discharge localization. His work emphasizes practical solutions for power system reliability and environmental resilience of insulation materials under extreme conditions. El-Hag is a Full-time faculty member and holds Adjunct faculty status, contributing to interdisciplinary research projects. He actively engages in promoting condition monitoring methodologies through educational initiatives and industry partnerships.

Andrea ScottView profile

Associate Professor

• Fluid Dynamics
• Machine Learning
• Remote Sensing
• +8 more

Andrea Scott is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Faculty of Engineering. Her research focuses on fluid dynamics, remote sensing, and machine learning applications in environmental systems. She holds a Doctorate in Mechanical Engineering from the University of Waterloo (2008) and has taught courses such as ME 351 (Fluid Mechanics) and SYDE 621 (Numerical Methods). Notable awards include the 2022 Outstanding Performance Award and 2021 Distinguished Performance Award from the University of Waterloo. Research interests span turbulence modeling, data-driven approaches, and physically inspired neural networks. Her work includes developing algorithms for sea ice concentration estimation, SAR imagery analysis, and fluid flow simulations. She collaborates with groups like the Vision and Image Processing Lab and the Remote Sensing of Environmental Change group. Current projects involve small object detection in remote sensing and graph neural networks for unstructured grid problems. Education: PhD, Mechanical Engineering, University of Waterloo, Canada (2008) MASc, Mechanical Engineering, McMaster University, Canada (2001) BASc, Mechanical Engineering, University of Waterloo, Canada (1999) Teaching responsibilities include undergraduate and graduate courses in fluid mechanics and systems engineering. She actively mentors students through the IEEE GRSS Women-to-Women Mentorship program and serves as an Associate Editor for the AGU Journal of Machine Learning and Computation. Her lab oversees over 20 current and past graduate students, focusing on topics like space debris tracking, AI-driven environmental modeling, and sea ice dynamics. Research outputs include over 40 publications in journals such as Physical Review Fluids and IEEE Transactions on Geoscience and Remote Sensing .

Salem S. GharbiaView profile

Professor

• Climate Resilience
• Environmental Monitoring
• Environmental Modeling
• +3 more

Dr. Salem S. Gharbia is the Head of the Department of Environmental Science and a Principal Investigator at Atlantic Technological University (ATU). He leads the Centre for Environmental Research Innovation and Sustainability (CERIS) and co-founded the Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE). His research focuses on climate resilience, environmental monitoring, and modeling, with expertise in Geographic Information Systems (GIS), wireless sensor networks, and climate change impacts. Dr. Gharbia coordinates major EU projects like Horizon 2020 SCORE and has secured over €14M in research funding. He holds a PhD in Environmental Engineering from Trinity College Dublin, where he received the prestigious Ussher Award. His work spans collaborations with the Irish EPA, Horizon Europe, and the Marine Institute, addressing coastal flooding, microplastic detection, and sustainable agriculture. Education: PhD in Environmental Engineering, Trinity College Dublin. Research interests include climate adaptation strategies, urban resilience, and innovative sensor technologies for environmental monitoring. His projects emphasize interdisciplinary approaches to address global challenges such as rising sea levels, extreme rainfall, and coastal erosion. Awards: Trinity College Ussher Award. Grants: Over €14M secured over five years for projects like EmpowerUS, Pro-climate, and WaterFutures. Advising: Accepting PhD students in environmental science and engineering. Labs: CERIS and MISHE, focusing on climate modeling and environmental systems.

• Neurosymbolic AI
• Answer Set Programming
• Visual Question Answering
• +3 more

Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains

Pedro GaleanoView profile

Associate Professor

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +3 more

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

Laurie Gaskins BaiseView profile

Professor

• Geotechnical Earthquake Engineering
• Seismic Hazard Mapping
• Natural Hazards
• +3 more

Laurie Gaskins Baise is a Professor and Chair of the Department of Civil and Environmental Engineering at Tufts School of Engineering, Tufts University. She leads the Geohazards Research Lab and directs the BSCE degree program. Her expertise lies in geotechnical earthquake engineering, seismic hazard mapping, and natural hazards mitigation. Education: Ph.D., University of California, Berkeley (2000) M.S., University of California, Berkeley (2000 and 1997) B.S.E., Princeton University (1995) Research Interests: Dr. Baise focuses on integrating predictive models with observational data to address geohazards like earthquakes and extreme wind events. Her work emphasizes regional seismic hazard mapping, liquefaction risk assessment, and rapid damage detection via remote sensing. She develops machine learning frameworks for post-disaster damage mapping and geospatial models for site response analysis. Labs/Teams: She directs the Geohazards Lab at Tufts, which collaborates on projects involving satellite imagery, geostatistical methods, and deep learning for hazard response. Recent work includes liquefaction modeling in Türkiye and Ukraine, wildfire spread forecasting, and ensemble-based seismic hazard assessments. Grants/Advising: Her research is supported by national and international grants. She mentors students in developing predictive models and remote sensing techniques for disaster resilience. Notable collaborations include work with the U.S. Geological Survey and global seismic networks.

Michael MorrisView profile

Professor

• Human Centered Leadership
• Human Centered Design
• Strategic HR
• +5 more

Michael Morris is the Associate Dean of Undergraduate Studies and Student Affairs and the John W. Fisher Professor of Innovative Learning at the Haslam College of Business , University of Tennessee, Knoxville. His career includes leadership roles such as Director of the Smith Global Leadership Scholars program and department head at Haslam, alongside corporate experience as a metallurgical R&D special projects engineer at Teledyne Firth Sterling. His research spans Human Centered Leadership , Strategic HR , Leadership Development , and Work-Life Harmony . His work, appearing in top-tier journals like Organizational Research Methods and Family Relations , integrates machine learning for disease forecasting, HRD theories, and interdisciplinary approaches to workplace wellness. His recent publications focus on infectious disease modeling , fMRI reliability , and socioemotional wealth in family firms. He serves on editorial boards for Academy of Management Learning & Education and contributes to certifications from the National Council on Family Relations and IDEO. Past President, Academy of Human Resource Development