Find Academic Supervisors

Ana MijicView profile

Professor

• Water Systems Integration
• Sustainable Water Management
• Urban Water Resilience
• +5 more

Professor Ana Mijic is a leading academic in water systems integration at Imperial College London's Department of Civil and Environmental Engineering (Faculty of Engineering). Her work focuses on developing systems tools to balance economic growth with sustainable water use, flood management, and water quality under future uncertainties. She leads high-profile projects like the EPSRC VENTURA initiative and the NERC CAMELLIA impact programme, emphasizing adaptive planning and grey/green infrastructure integration in urban systems. Education: BEng (First Class) in Civil Engineering from the University of Belgrade (Serbia), MSc (Distinction) in Hydrology for Environmental Management, and PhD in Earth Science & Engineering from Imperial College London (2013). Previously served as a teaching assistant in Fluid Mechanics and Hydrometry at Belgrade University. Research interests include whole-water system modelling, socio-hydrological interactions, and nature-based solutions for resilient urban water systems. Her work bridges academia and practice through partnerships with regulators like the UK Environment Agency and global initiatives like the IAHS HELPING programme. Awards include the Satish Dhawan Visiting Chair Professorship (2022) and the 2019 Imperial President’s Award for Excellence in Research (as part of her Hydrology team). She co-leads Imperial’s Transition to Zero Pollution (TZP) Urban Ecosystems theme and serves on editorial boards for journals like Water Security . Her contributions span over 50 peer-reviewed articles (2020–2025), focusing on integrated water management frameworks, AI-driven monitoring, and systemic approaches to urban resilience. She advocates for participatory decision-making and policy coherence in water governance.

Jiaqi GongView profile

Associate Professor

• Artificial Intelligence
• Smart and Connected Health
• Cyber-Physical Systems
• +13 more

Dr. Jiaqi Gong serves as Associate Professor in Computer Science and Adjunct Associate Professor in Mechanical Engineering at The University of Alabama's College of Engineering, while directing the Alabama Center for the Advancement of Artificial Intelligence. His academic foundation includes: B.S. in Engineering, China University of Geoscience (2004) Ph.D. in Engineering, Huazhong University of Science and Technology (2010) Dr. Gong's research pioneers human-AI convergence through cyber-physical systems and smart health technologies, developing mobile/wearable platforms to enhance human perceptual, cognitive, and physical capabilities. His work spans artificial intelligence, machine learning, computer vision, and IoT with applications in healthcare, environmental monitoring, and education. The Sensor-Accelerated Intelligent Learning (SAIL) laboratory he founded drives innovation in behavior change interventions, human movement modeling, and educational data mining. Recent publications reveal strong interdisciplinary trends: healthcare AI dominates with medication adherence prediction and surgical classification systems, while environmental applications feature flood-risk communication and drought analysis. His work increasingly integrates generative AI and LLMs across domains, demonstrating methodological innovation in federated learning, knowledge graphs, and explainable storytelling frameworks. Notable recognitions include: Best Student Paper Award, IEEE/ACM Connected Health Conference (2022) Best Student Paper Award, Body Sensor Networks Conference (2019) Data Challenge Win, IEEE Biomedical Health Informatics (2018) Best Paper Award, Body Area Networks Conference (2014) Best Demonstration Award, IEEE Wireless Health Conference (2014) Dr. Gong leads significant funded projects including a $2M CDC/NIOSH grant for first responder safety and $3M NSF funding for hydrologic research. As SAIL laboratory director, he mentors students in developing clinically deployed technologies for multiple sclerosis, dementia, and mental health. Future work focuses on scaling AI applications in chronic disease management and climate resilience through the Alabama AI Center. The SAIL laboratory (founded 2017) operates as a multidisciplinary hub developing wearable/mobile systems for health applications, with active collaborations across medical clinics and engineering departments for real-world deployment of behavior change interventions and movement analysis tools.

Farha SattarView profile

Lecturer

• Mathematics Education
• Geoscience Education
• STEM Education
• +11 more

Dr. Farha Sattar is a Lecturer in Education (Mathematics) at the Faculty of Arts and Society, Charles Darwin University. With over thirty years of teaching experience in Mathematics, Science education, GIS, Remote Sensing, and e-Learning technologies at undergraduate and postgraduate levels, Dr. Sattar brings extensive expertise to her academic role. She holds a PhD in Geospatial Science from Charles Darwin University, focusing on Spatial Mathematical Modelling for 3D Gully Mapping and Erosion Quantification. Her educational background includes: PhD in Geospatial Science (Three Dimensional Gully Mapping and Erosion Quantification within a Geoinformatics Framework), Charles Darwin University (2012) Prior academic experience at several Asian and European universities Dr. Sattar's research spans multiple interdisciplinary fields with a strong focus on the intersection of education and technology. Her primary research interests include Mathematics Education, STEM Education, Geoscience Education, Spatial Mathematical Modelling, Drone Technology, Remote Sensing, Geographic Information Systems, and Innovative Pedagogies such as experiential learning and inquiry-based approaches. She has developed expertise in cognitive development, particularly in fostering critical and creative thinking skills through iSTEM approaches. An analysis of Dr. Sattar's recent publications reveals a consistent trajectory toward integrating emerging technologies like drones, augmented reality, and geospatial artificial intelligence into educational contexts. Her work demonstrates a progression from foundational geospatial research on erosion mapping to innovative applications in STEM education. Recent publications increasingly focus on drone technology for educational purposes, spatial data infrastructure for policy development, and computational thinking development through technology-enhanced learning. Dr. Sattar has received numerous accolades for her contributions to education and research: NT Science Week Awards – Inspired NT STEM Hero of the Year award (2021) Long Service Recognition Award (2023) Finalist, CDU Alumni Award (2023) Multiple Best Presentation Awards (2008, 2010, 2017) Australian College of Educators - 2021 College Medal Nominee As a supervisor, Dr. Sattar mentors postgraduate research students including Ayesha Farhan, who is working on "A Framework for Cognitive Development: Fostering Critical and Creative Thinking Skills, Using iSTEM Approach." She actively secures research funding through various projects such as "DBELaSTEM: Drone-Based Experiential Learning and STEM Education," "STEM_D & VR: Youth STEM Learning and Empowerment with Drones and VR," and "CodProg Drone Lab." Her research is supported by grants from NT Government projects and other external funding sources. Dr. Sattar leads the CodProg Drone Lab, an innovative research space exploring drone technology applications in education. She is a certified drone pilot and aeronautical radio operator, which enables her to bridge theoretical knowledge with practical applications. Her work with the "Flying Forward: Drones, STEM Equity, and Indigenous Empowerment" initiative demonstrates her commitment to community engagement and addressing educational disparities through technology.

Padhraic SmythView profile

Professor

• Machine Learning
• Artificial Intelligence
• Pattern Recognition
• +3 more

Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.

Tiancheng ZhaoView profile

Researcher

• Multimodal Foundation Models
• AI Agents
• Vision-Language Models
• +3 more

Tiancheng Zhao is a principal researcher at the Binjiang Institute of Zhejiang University and founder of the Om Artificial Intelligence Laboratory (Om AI Lab), dedicated to frontier open multimodal AGI research for building next-generation agents that transform work and life through advanced human-machine interaction. His academic credentials include: Ph.D. in Computer Science from Carnegie Mellon University (2016-2019) under Prof. Maxine Eskenazi, Prof. Louis-Philippe Morency, Prof. William W. Cohen, and Dr. Dilek Hakkani-Tur, with pioneering dissertation “Learning to Converse With Latent Actions” in end-to-end generative conversational models M.S. in Computer Science from Carnegie Mellon University (2014-2016) B.S. in Electrical Engineering from UCLA (2010-2014) with Summa Cum Laude, focusing on speech signal processing under Prof. Abeer Alwan Dr. Zhao’s research centers on multimodal foundation models and agents, tackling three core challenges: Multimodal Models for cross-modal representation learning in high-dimensional data, Learning to Learn for effective skill acquisition from diverse signals (supervised labels, rewards, meta-learning), and AI Agents for open-world understanding and complex decision-making. His work bridges computer vision, natural language processing, and real-world applications including healthcare analytics and remote sensing. Analysis of his 50+ publications reveals accelerating innovation in multimodal large language models (2024-2025), with emphasis on stable vision-language architectures (VLM-R1), agent orchestration frameworks, and domain-specific applications in geospatial analysis and healthcare. Key trends include solving long-tail distribution challenges in satellite imagery, developing human-like zooming capabilities for multimodal LLMs, and creating unified benchmarks for autonomous GUI testing. His scientific recognition includes: National Breakthrough Technology Award by Ministry of Science and Technology (2021) Microsoft Research Best & Brightest PhD (2018) BEST PAPER AWARD at SIGDIAL 2018 Best Paper Nomination at SIGDIAL 2016 Top 1 Outstanding Bachelor of Science Award at UCLA (2014) As Om AI Lab founder, Dr. Zhao leads research teams developing computational building blocks for human-AI collaboration. While specific student mentorship details aren’t public, his extensive publication record with junior co-authors indicates active research supervision. Current projects focus on practical system implementations for real-world multimodal agent deployment across diverse domains.

Jiao LichengView profile

Professor

• Artificial Intelligence
• Machine Learning
• Image Understanding
• +6 more

Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.

Jan Dirk WegnerView profile

Associate Professor

• Machine Learning
• Computer Vision
• Remote Sensing
• +7 more

Jan Dirk Wegner is an Associate Professor at the University of Zurich, holding the chair in 'Data Science for Sciences' and leading the EcoVision Lab. He previously served as a Postdoc (2012–2016) and senior scientist (2017–2020) at ETH Zurich's Photogrammetry and Remote Sensing Group, following his PhD (with distinction) from Leibniz University Hannover (2011). His research bridges machine learning, computer vision, and remote sensing to address environmental and geoscience challenges, focusing on large-scale environmental data analysis, vegetation monitoring, and climate change mitigation. Education: PhD (with distinction) in Geodesy, Leibniz University Hannover (2011) Postdoc, ETH Zurich (2012–2016) Senior Scientist, ETH Zurich (2017–2020) Research Interests: Machine Learning, Computer Vision, Remote Sensing, Environmental Science, Climate Science, Geosciences, Explainable AI, Uncertainty Quantification, and Applications in Sustainability. The EcoVision Lab develops data-driven methods for global-scale environmental monitoring, including vegetation parameter mapping, flood prediction, forest degradation detection, and AI-driven ecological modeling. Awards: ETH Postdoctoral Fellowship (2012–2016) Science Prize of the German Geodetic Commission WEF Young Scientist Class 2020 (Top 25 globally under 40) Advising & Leadership: Director of the University of Zurich's Doctoral School in Data Science, leading the EcoVision Lab, and coordinating the CVPR EarthVision Workshops. His roles include Vice President of ISPRS Technical Commission II, member of the ETH AI Center, ELLIS, and UN-ETH Partnership. Labs/Teams: EcoVision Lab focuses on interdisciplinary AI applications for environmental challenges, collaborating with NGOs, governments, and industry to translate research into societal impact.

• Geology
• Geoscience Education
• Science Education
• +3 more

Brett Gilley is an Associate Professor of Teaching in the Department of Earth, Ocean and Atmospheric Sciences at The University of British Columbia (UBC), Vancouver Campus. His primary affiliation is within the Faculty of Science. He specializes in geoscience education with a focus on inclusive fieldwork practices, collaborative learning strategies, and accessibility in STEM education. He is currently not accepting students for advising. His research interests span geology and geoscience education, with a strong emphasis on making field experiences accessible for students with disabilities, developing innovative teaching methodologies, and fostering inclusive learning environments. Gilley has contributed to projects like the Earth Science Experiential and Indigenous Learning (EASEIL) initiative, which integrates Indigenous knowledge and community partnerships into curriculum design. Notable trends in his publications include advancing universal design in geoscience field courses, evaluating collaborative testing strategies, and exploring the impact of technology (e.g., ChatGPT) on student research practices. His work often bridges pedagogical innovation with practical implementation in large undergraduate science programs. While no formal awards are listed, his contributions to inclusive education and curriculum development reflect a commitment to equitable STEM education. His professional activities include mentoring faculty through UBC’s evidence-based pedagogy graduate course and participating in interdisciplinary curriculum development teams.

He WangView profile

Associate Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +6 more

Dr. He Wang is an Associate Professor in the Department of Computer Science at University College London (UCL), affiliated with the Virtual Environment and Computer Graphics (VECG) group and the UCL Centre for Artificial Intelligence. He holds a Visiting Professorship at the University of Leeds and previously served as an Associate Professor and Lecturer there, as well as a Senior Research Associate at Disney Research Los Angeles. His research focuses on computer graphics, vision, and machine learning, with notable contributions to crowd simulation, generative models, and physics-informed neural networks. Dr. Wang earned his BEng from Zhejiang University and his PhD from the University of Edinburgh, followed by postdoctoral work at the University of Edinburgh's School of Informatics. He has been recognized as a Turing Fellow and serves as an Academic Advisor to the Commonwealth Scholarship Council and an Associate Editor of Computer Graphics Forum . His research spans cutting-edge topics including 3D reconstruction, adversarial attacks on motion recognition, and AI-driven groundwater modeling. He has supervised six PhD students to completion and actively engages in collaborative projects, consultancy, and grant evaluations. His lab welcomes students through dedicated recruitment channels.

• Machine Learning
• Data Mining
• High-Performance Computing
• +5 more

Prof. Dr. Fabian Gieseke is a Professor and Chair of Machine Learning and Data Engineering at the University of Münster. He holds a PhD in Computer Science from Carl von Ossietzky University of Oldenburg and a dual degree in Mathematics and Computer Science from the University of Münster. His research focuses on Machine Learning, High-Performance Computing, and their applications in Geosciences, Smart Cities, and Astrophysics. Education: PhD in Computer Science (2012), Carl von Ossietzky University of Oldenburg University studies in Mathematics and Computer Science (2006–2011), University of Münster Research Interests: Data Mining and Machine Learning High-Performance Computing & Distributed Systems Deep Learning Applications in Environmental Science and Astrophysics Geospatial Data Analysis using Satellite Imagery Publications Trends: His recent work emphasizes large-scale environmental monitoring via deep learning, including canopy height estimation, forest biomass prediction, and national-scale tree counting. He also explores interactive systems for geospatial data retrieval and optimization of machine learning models for resource-constrained environments. Advising & Grants: Supervised over 30 theses on topics like satellite image analysis, deep learning on microcontrollers, and data marketplaces for smart grids. Active in securing grants for interdisciplinary projects combining AI with Earth observation. Labs/Teams: Leads the Machine Learning and Data Engineering group at the University of Münster, focusing on scalable AI solutions for real-world challenges in science and industry.

• Differential Privacy
• Machine Learning
• Privacy-Preserving Data Analysis
• +3 more

Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.

Søren Munch KristiansenView profile

Associate Professor

• Geoarchaeology
• Soil Science
• Groundwater
• +7 more

Søren Munch Kristiansen is an Associate Professor in the Department of Geoscience at Aarhus University, Faculty of Natural Sciences. His interdisciplinary research bridges geoscience and archaeology, focusing on the complex interactions between soil, water, and human societies across time. He is actively engaged in transdisciplinary projects involving geoarchaeology, groundwater, and public health. Research Interests: His work centers on how soil and groundwater have shaped human prehistory and continue to influence modern life. Key areas include safe drinking water from a lifelong perspective, geoarchaeological prospection, Viking Age settlements, and the application of geophysical and geochemical methods in archaeological contexts. He is particularly interested in novel, interdisciplinary methodologies. Recent Research Trends: His recent publications reveal a strong focus on integrating geophysical data (e.g., GPR, borehole databases) with archaeological interpretation, especially in 3D urban modeling and landscape change. Themes include Viking Age sites, interglacial deposits, and anthropogenic soil modifications. His work increasingly employs machine learning and large-scale data synthesis. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students are not listed, he leads and participates in multiple funded research projects, indicating an active supervisory role. His grants span from 2016 to 2024, including projects like TITAN, SAGA, and 'Fingerprinting displaced molecular substances,' highlighting sustained funding and leadership in interdisciplinary geoscience-archaeology research. Labs and Teams: He is involved in collaborative networks such as the Soil Science & Archaeo-geophysics Alliance (SAGA), suggesting leadership in interdisciplinary research teams. His work often involves multi-proxy analyses and international collaborations, particularly in Scandinavian archaeological geophysics.

Hyuck Jin ParkView profile

Professor

• Landslide Hazard Assessment
• Geotechnical Engineering
• Machine Learning Applications
• +5 more

Hyuck Jin Park is a Full Professor in the Department of Energy Resources and Geosystems Engineering at Sejong University, South Korea, where he has been teaching and conducting research since 2003. With a Ph.D. in Engineering Geology from Purdue University, his expertise spans geotechnical engineering, landslide analysis, and geospatial technologies. Professor Park has built a distinguished career in landslide hazard assessment, combining traditional geotechnical approaches with modern machine learning techniques to improve prediction accuracy and risk management. His educational background includes: B.S. in Geology from Yonsei University (1990) M.S. in Geophysics from Yonsei University (1993) Ph.D. in Engineering Geology from Purdue University (2011) Professor Park's research focuses on the spatial and temporal probability of landslide occurrence, utilizing fuzzy logic, probabilistic analysis, GIS, Monte Carlo simulation, and machine learning for landslide hazard assessment. His work integrates physically based models with statistical approaches to better understand landslide mechanisms and improve prediction capabilities. He has made significant contributions to the development of methodologies that account for geological uncertainties in hazard assessment, with applications ranging from rock slope stability to rainfall-induced shallow landslides. His recent publications demonstrate a clear trend toward integrating explainable artificial intelligence with traditional geotechnical approaches for natural hazard assessment. Professor Park's work increasingly focuses on making machine learning models transparent and interpretable while maintaining high predictive accuracy. The research spans multiple hazard types including landslides, earthquakes, and floods, with a growing emphasis on climate change impacts and data-scarce environments. With an h-index of 28 and over 3,421 citations, Professor Park has established himself as a leading researcher in his field. His work has been published in high-impact journals including Engineering Geology, Landslides, and Catena, reflecting the significance and quality of his contributions to geotechnical engineering and natural hazard assessment. Professor Park has mentored numerous researchers through collaborative projects and has secured funding for his innovative work in landslide prediction and hazard assessment. His research has involved significant international collaboration, particularly with researchers from Malaysia, Australia, and Yemen, addressing landslide and flood risks in diverse geographical contexts. He leads research activities within the Department of Geoinformation Engineering at Sejong University and has contributed to the development of specialized tools like DEWS (Distance, Elevation, Watershed, and Slope unit) for landslide early warning systems.

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).

• Game Development
• Human-Computer Interaction
• Accessibility
• +5 more

T.C. Nicholas Graham is a Professor of Computer Science at Queen's University's School of Computing, specializing in Human-Computer Interaction (HCI) and gaming technology. With a Dr.-Ing. from TU Berlin (1995), M.Sc. (1988) and B.Sc. (1985) from Queen's University and the University of Toronto respectively, he has held academic roles at York University and industry positions at Namzak Labs since 2000. Research Focus : Next-generation digital games, exergames combining physical activity with entertainment, and educational games using augmented reality. His EQUIS Lab explores heart rate power-ups, nudging mechanisms, and AI-assisted accessibility solutions for motor-impaired users. Teaching : Courses in Game Architecture (CISC 326) and Advanced Game Development (CISC 877), emphasizing game engine fundamentals, physics, networking, and AI algorithms. Labs & Collaborations : Director of the EQUIS Lab , co-founder of the Software Technology Laboratory , and former Chair of IFIP Working Group 2.7 on User Interface Engineering. Notable Projects : Liberi exergame for children with cerebral palsy, GAIM Exergaming Toolkit, and Partial Automation techniques for accessible gaming. His lab actively involves PhD, MSc, and undergraduate students in both technical and applied research.