Find Academic Supervisors

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

Dongmei ChenView profile

Professor

• Geomatics
• Environmental Science
• GIS Applications
• +4 more

Dr. Dongmei Chen is a Professor in the Department of Geography and Planning at Queen's University, affiliated with the Faculty of Arts and Science. She holds cross appointments in the School of Environmental Studies and the Beaty Water Research Centre. Her research focuses on understanding human-environment interactions through GIS and remote sensing, with specialties in land use modeling, climate change impacts, and spatial data analysis. Education: PhD in Geography (2001, San Diego State University/UCSB); M.Sc. in GIS/Remote Sensing (Chinese Academy of Sciences); B.Sc. in Geography (Peking University). Pre-Queen's career included work as a GIS product specialist at ESRI. Research interests include geomatics, environmental science, and applying machine learning to remote sensing challenges. Recent work addresses flood susceptibility modeling, urban sprawl analysis, and spatial-temporal disease risk. Her interdisciplinary approach bridges geospatial technologies with environmental and social science. Active in research centers like LaGISA, she explores topics like air pollution monitoring and spatial data privacy. Grants and advising are integral to her role, though specific details are not detailed here. Collaborations span global contexts, including Brazil, Thailand, and China.

• Control Systems
• Machine Learning
• Robotics
• +6 more

Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.

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.

Morteza Haghighat SefatView profile

Associate Professor

• Well Decommissioning
• Advanced Well Completion Technology
• Intelligent Well Design and Optimisation
• +4 more

Morteza Haghighat Sefat is an Associate Professor at the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering at Heriot-Watt University. His research focuses on well decommissioning, advanced well completion technologies, intelligent well design, and integrated asset modeling with a particular emphasis on CO2 storage integrity and optimization under uncertainty. He teaches Drilling Engineering and Integrated Asset Modelling. His research outputs span topics such as risk-based well integrity assessment for CO2 storage, flow control device optimization in multilateral wells, and methane emissions from decommissioned petroleum wells in British Columbia. He employs hybrid optimization techniques, machine learning, and numerical simulation to address challenges in reservoir management and environmental impact assessment. Key collaborations involve institutions exploring CO2 storage applications and advanced well technologies. His work bridges theoretical models with practical field applications, emphasizing proactive optimization and sustainability in energy systems.

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.

R. Edward BeighleyView profile

Professor

• Hydrologic Modeling
• Remote Sensing
• Climate Change Impacts
• +3 more

R. Edward Beighley is the COE Distinguished Professor and Interim Chair of Civil and Environmental Engineering at Northeastern University. He is also affiliated with the Marine and Environmental Sciences program. His research focuses on hydrologic and hydraulic modeling, remote sensing of the hydrologic cycle, and flood hazard assessment. He holds a Ph.D. from the University of Maryland (2001), an M.S. from Pennsylvania State University (1996), and a B.S. from the same institution (1995). Key honors include the 2024 Distinguished Faculty Award, 2019 Fostering Engineering Innovation Award, and leadership roles in NASA’s SWOT Satellite Mission. His work emphasizes sustainable water resource management through interdisciplinary approaches combining remote sensing, field data, and hydrologic models. Research projects include evaluating microplastic accumulation in floodplains, global river baseflow analysis via GRACE/GRACE-FO satellites, and improving SWOT discharge estimation algorithms. The Beighley Lab collaborates on flood risk assessment and climate change impacts at local to global scales. Grants: SWOT Science Team (NASA), NSF Microplastics Project, North Carolina Hurricane Relief Research. Students: Advises PhD student Max Rome on floating wetlands and urban water quality. Labs/Teams: Beighley Lab focuses on terrestrial water systems and satellite applications.

Mark GriffinView profile

Professor

• Organizational Behavior
• Workplace Safety
• Leadership
• +5 more

Mark Griffin is a Professor and Director of the Future of Work Institute at Curtin University’s Faculty of Business and Law. His research focuses on organizational performance, leadership, workplace safety, and employee well-being. He holds a PhD from Pennsylvania State University and has held academic positions at institutions including the University of Queensland and the University of Sheffield. Griffin’s research spans multilevel modeling, safety climate, and leadership behaviors. He has managed large-scale projects across industries like healthcare, energy, and construction, emphasizing practical applications of theory. His work has been published in top-tier journals such as the Journal of Applied Psychology and Academy of Management Journal . Key awards include Fellowships from the Australian Academy of Social Sciences and the US Society for Industrial/Organizational Psychology. He has also served as an editor for major journals including the Journal of Management and Organizational Psychology Review . Griffin’s current role at the Future of Work Institute promotes productive and meaningful work in the digital age, addressing societal and economic challenges through collaborative research. His work integrates psychological and systems perspectives to advance organizational practices and workplace well-being.

In-Sue OhView profile

Professor

• Personnel Selection
• Meta-Analysis Methods
• Strategic Human Resource Management
• +3 more

Dr. In-Sue Oh is the Charles E. Beury Professor of Management at Temple University's Fox School of Business. He specializes in personnel selection, meta-analysis, and strategic human resource management. With nearly 100 scholarly publications, his work appears in top-tier journals such as the Academy of Management Journal and Journal of Applied Psychology . Dr. Oh has received prestigious awards, including SIOP's Distinguished Early Career Contributions Award (2016) and the AOM HR Division Scholarly Achievement Award (2016). He currently serves as Associate Editor of the Journal of Applied Psychology . Education: PhD in Business Administration from Tippie College of Business, University of Iowa Research Interests: Advances in meta-analytic methods, including second-order meta-analysis and range restriction corrections Personnel selection validity and high-performance work systems Personality traits (Five-Factor Model) and their impact on job performance, organizational citizenship behaviors, and turnover Cross-cultural comparisons of person-environment fit Recent Focus: Crisis of confidence in psychological research reproducibility Methodological innovations in handling range restriction and publication bias HR systems' strategic alignment (vertical/horizontal fit) Awards & Recognition: Two-time Joyce and Robert Hogan Award (2013, 2024) Elected Fellow of SIOP, APA, and APS Recipient of SIOP's William A. Owens Award (2017-2018) Labs/Teams: Leads research teams focused on meta-analytic methodology and HR strategy optimization. Collaborates internationally on cross-cultural HR studies.

Gregory V CesanaView profile

Researcher

• Atmospheric Science
• Climate Change
• Cloud Physics
• +3 more

Dr. Gregory V Cesana is an Associate Research Scientist at Columbia University's Center for Climate Systems Research (CCSR), affiliated with the Columbia Climate School. He holds a B.S. (2005) and M.Sc. (2007) in Atmosphere/Ocean/Soil Remote Sensing from the University of Toulon and a Ph.D. from Sorbonne Université (2013). His career includes postdoctoral work at Caltech/NASA JPL and current collaboration with NASA GISS to improve climate models using satellite data. Research focuses on cloud processes, radiative feedbacks, and model evaluation using A-train satellite observations (CALIPSO, CloudSat). Key interests include low-cloud feedbacks, cloud-phase transitions, and reducing climate uncertainty. Projects include applying satellite data to constrain climate model biases and developing observational frameworks for future missions. Education: University of Toulon (B.S./M.Sc.), Sorbonne Université (Ph.D.) Affiliations: Columbia CCSR, NASA GISS, and international collaborations Publications highlight advancements in cloud-radiation interactions, model evaluation techniques, and Arctic/Southern Ocean climate dynamics. Expertise spans remote sensing (lidar/radar), climate modeling, and satellite data integration.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +3 more

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Noel CressieView profile

Professor

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Alexandros TaflanidisView profile

Professor

• Uncertainty Quantification
• Disaster Risk Reduction
• Bayesian Model Updating
• +3 more

Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.