Find Academic Supervisors

Dana PorterView profile

Professor

• Agricultural Water Management
• Agricultural Irrigation
• Salinity Management
• +3 more

Dana Porter is a Professor, Extension Program Leader, and Associate Department Head at Texas A&M University, affiliated with the College of Agriculture & Life Sciences and the College of Engineering. She holds a B.S. (1987), M.S. (1989) in Agricultural Engineering from Texas A&M, and a Ph.D. in Agricultural and Biological Engineering from Mississippi State University (1993). Her research focuses on sustainable agricultural water management, including irrigation optimization, salinity control, and precision agriculture technologies. She leads efforts to integrate sensor networks, machine learning, and climate models to enhance water efficiency and crop productivity in arid regions like the Texas High Plains. Her work emphasizes bridging scientific advancements with practical agricultural practices through extension programs and technology transfer initiatives. Key contributions include improving SWAT model applications for watershed management, developing irrigation scheduling tools, and exploring genotype-based adaptation strategies for climate resilience. She collaborates with interdisciplinary teams to address challenges in water governance, land-use change impacts, and big data integration in agriculture. Porter’s outreach efforts focus on educating stakeholders on sustainable practices, including water conservation, sensor-based irrigation, and subsurface drip irrigation systems. Her leadership in Texas A&M AgriLife Extension ensures research findings translate into actionable solutions for farmers and policymakers.

Jyh-Charn LiuView profile

Professor

• Real-time distributed computing systems
• Behavior modeling and simulation
• Cyber physical systems security
• +5 more

Jyh-Charn 'Steve' Liu is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on real-time distributed systems, cyber-physical security, and interdisciplinary applications such as mathematical expression analysis and GNSS spoofing mitigation. Education: Ph.D., Electrical & Computer Engineering, University of Michigan (1989) M.S., Electrical Engineering, National Cheng Kung University (1981) B.S., Electrical Engineering, National Cheng Kung University (1979) Research Interests: Real-time distributed computing systems Cyber-physical systems security Behavior modeling and simulation Mathematical expression analysis and tools for STEM education GNSS spoofing detection and mitigation Blockchain-based supply chain management Key Publications Trends: Recent work emphasizes AI-driven solutions for mathematical document processing (e.g., LaTeX conversion from images), cybersecurity in navigation systems, and interdisciplinary applications like STEM education tools. Early-career contributions include real-time scheduling algorithms and embedded systems design. Awards: Senior Member, IEEE Computer Society (2014) Nominated for ACM Eugene L. Lawler Award (2013) Conference leadership roles (RTAS 2006/2007) Lab & Collaborations: Leads the Real Time Distributed Systems Lab, exploring areas such as verifiable credentials, urban navigation systems, and medical image analysis. Collaborates on projects like DIME (mathematical expression tool) and MOP (mathematical PDF labeling).

Elena NafievaView profile

Researcher

• Remote Sensing
• Spatio-temporal analysis
• Synthetic aperture radar
• +3 more

Elena Nafieva is a Researcher at the Department of Geoinformatics (Z_GIS) within the Faculty of Digital and Analytical Sciences at the University of Salzburg. Her work focuses on remote sensing , spatio-temporal analysis , and synthetic aperture radar applications for monitoring natural hazards and glacial dynamics using Big Earth Observation data . Current projects include: LEONA : Landslide Information from Earth Observation for Humanitarian Aid (2024-2027) ROGER : EO-based Rock Glacier Mapping (2024-2025) ReHIKE : Glacier Retreat Effects on Alpine Hiking Infrastructure (2024-2026) Her recent publications demonstrate expertise in landslide mapping , rock glacier characterization , and glacial foreland evolution using earth observation technologies. Project partners include Austrian Federal Ministry for Climate Action (FFG-ASAP) and European Space Agency (ESA). Research interests span: Remote Sensing of geohazards Spatio-temporal modeling of alpine environments Synthetic Aperture Radar processing Climate Change Impacts on mountainous regions Big Data Analytics in earth observation Digital Elevation Modeling for hazard assessment

Carlos Tirado CortesView profile

Lecturer

• Human-Computer Interaction
• Virtual Reality
• Mixed Reality Technologies
• +6 more

Carlos Tirado Cortes is a Lecturer in Interaction Design at the Discipline of Design Lab , Faculty of Architecture, Design and Planning, University of Sydney. As a virtual environments researcher, he focuses on Human-Computer Interaction and data visualization in immersive systems. Ph.D in Human-Computer Interaction (University of Technology Sydney, 2021) M.S. in Computer Game Engineering (Newcastle University, UK, 2015) B.S. in Engineering and Information Technology (Monterrey Institute of Technology, Mexico, 2012) His research explores immersive visualization for wildfire training (iFire project), VR sickness analysis, and brain-body dynamics during virtual navigation. Recent publications examine: AI-powered wildfire visualization systems Metaverse safety for children Postural instability in VR environments Fire-atmosphere interaction modeling Balance recovery techniques in immersive spaces

Haizhong WangView profile

Professor

• Transportation Engineering
• Disaster Resilience and Emergency Management
• Intelligent Transportation Systems (ITS)
• +3 more

Haizhong Wang is a Professor in the Department of Civil and Construction Engineering at Oregon State University, affiliated with the College of Engineering. He holds a Ph.D. in Civil Engineering from the University of Massachusetts Amherst (2010), an M.S. in Applied Mathematics from the same institution (2010), and earlier degrees from Beijing University of Technology and Hebei University of Technology in China. His research focuses on transportation systems, disaster resilience, and intelligent infrastructure solutions. Education: Ph.D., Civil Engineering, UMass Amherst, 2010 M.S., Applied Mathematics, UMass Amherst, 2010 M.S., Civil Engineering, Beijing University of Technology, 2006 B.S., Civil Engineering, Hebei University of Technology, 2003 Dr. Wang's research integrates traffic flow modeling, agent-based systems, and interdisciplinary approaches to address challenges in emergency evacuation logistics, autonomous vehicle impacts, and climate-resilient infrastructure. He explores topics such as tsunami preparedness, wildfire evacuation behavior, and optimization of electric vehicle networks. His work emphasizes data-driven methodologies for enhancing transportation safety, efficiency, and disaster response capabilities. Recent studies highlight his contributions to understanding evacuation decision-making under time-critical scenarios, optimizing multi-modal transportation networks, and assessing infrastructure vulnerability to cascading disasters. Collaborative projects include developing frameworks for cooperative logistics systems and interdisciplinary models linking natural, built, and social systems for community resilience. His grants and collaborations focus on disaster preparedness, smart work zones, and connected vehicle technologies. He advises on transportation safety and serves on research initiatives addressing climate adaptation and emergency management. Current efforts include refining agent-based models for vertical evacuation strategies and evaluating the societal impacts of automated vehicles on urban mobility.

Shenghua GaoView profile

Associate Professor

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +4 more

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

René ForsbergView profile

Professor

• Geodesy
• Earth Observation
• Gravimetry
• +12 more

René Forsberg is a Professor in the Department of Space Research and Technology at the Technical University of Denmark (DTU), affiliated with the Geodesy and Earth Observation section and the Center for Quantum Technologies. His work integrates satellite and airborne remote sensing with geodetic modeling to study Earth's polar regions and gravitational field. His research spans geodesy, Earth observation, gravimetry, and quantum sensing, with a strong focus on polar ice sheets, satellite altimetry (CryoSat, ICESat-2), geoid modeling, and climate change impacts. He actively contributes to understanding mass balance in Greenland and Antarctica, Arctic sea ice dynamics, and the application of quantum technologies in airborne gravimetry. The recent publications highlight a strong trend in quantum-enabled gravity measurements, high-resolution ice sheet mapping, and multi-sensor integration for Arctic and Antarctic observations. His work leverages data from ESA and NASA missions and contributes to global datasets used in climate modeling and geophysical studies. René Forsberg has not been explicitly mentioned with any scientific awards in the provided text. He is actively advising multiple PhD students, including B. Dale, B. Jenny, R. M. F. Hansen, H. Teitsson, and A. R. Stokholm, on projects involving quantum gravimetry, Arctic sea ice, and geoid determination. He has secured and led several funded research projects, such as 'Gravimetry from aircraft and drones' and 'Earth Observation and Artificial Intelligence for Automatic Arctic Sea Ice Charting', indicating sustained grant support. He is involved in research teams and labs focused on geodesy and Earth observation at DTU Space, particularly those working on quantum gravity sensors, satellite altimetry data analysis, and Arctic/Antarctic field campaigns. His collaborations extend to international agencies like the European Space Agency and NASA, as well as pan-Arctic research networks.

Sangmi Lee PallickaraView profile

Professor

• Big Data
• Data Science
• Data Engineering
• +13 more

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

Gianluca AloiView profile

Associate Professor

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +7 more

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

• Educational Technology
• Collaborative Learning
• Computer-Supported Learning
• +3 more

Markus Kuhn is a researcher at the University of Cambridge with a diverse academic career spanning over two decades, evidenced by publications from 2003 to 2023. His work bridges educational technology, computer science, and autonomous systems, demonstrating significant interdisciplinary reach across multiple domains. Dr. Kuhn's research has evolved through distinct phases. Initially focused on educational technology (2003-2007), he published extensively on classroom scenarios, collaborative learning, and media integration. His work then broadened to include physics computation (2008) and process support for inquiry learning (2010). More recently (2016-2023), his research has shifted toward autonomous systems, with publications on self-localization technologies and map generation for autonomous vehicles. This publication trajectory reveals a researcher who has successfully adapted his expertise from educational applications to more technical domains while maintaining connections to his foundational work in learning systems. His research demonstrates consistent innovation in applying computational approaches to solve practical problems across different contexts. Dr. Kuhn has maintained long-term collaborations with researchers including Heinz Ulrich Hoppe, Andreas Harrer, and Andreas Lingnau, indicating strong interdisciplinary networks. His work has been published in diverse venues ranging from IEEE Access and Microelectronics Reliability to Research and Practice in Technology Enhanced Learning and International Conferences on Computer-Supported Collaborative Learning.

• Knowledge Representation
• Ontology Engineering
• Semantic Web
• +6 more

Cassia Trojahn is a permanent Lecturer at the University of Toulouse 2 Jean Jaurès , affiliated with IRIT - UMR 5505, Department of Mathematics and Computer Science. Her career spans institutions in France, Portugal, Brazil, and the Netherlands, with roles including Expert Engineer at Joseph Fourier University and Post-Doc at INRIA Rhône-Alpes. Education Semantic Web, Ontology Alignment, Business Intelligence (University of Toulouse) Functional Programming (Haskell, Scala), Information Extraction (Pierre Mendès University) Graph Theory and Complexity (University of Évora) Research Interests : Knowledge representation and semantic web technologies Ontology alignment methodologies (argumentation, complex alignments, holistic approaches) Earth observation data integration with contextual information Open science and FAIR data principles Argumentation frameworks for mapping reconciliation Query rewriting with complex correspondences Advising : Co-supervised 5 PhD theses across France and Brazil, including: Jordane Dorne (2018-2021) - Earth observation semantic change detection Élodie Thiéblin (2016-2019) - Complex ontology alignment Roger Granada (2011-2015) - Hierarchical relations extraction Projects : Participated in international (CAMELEON, MULTIFARM) and European (EFFECTOR, CANDELA, SEALS) projects focused on multilingual ontology alignment, maritime situational awareness, and semantic earth observation data analysis.

Jason J. JungView profile

Professor

• Computer Science
• Knowledge Engineering
• Social Media Analysis
• +3 more

Jason J. Jung is a Professor in the Department of Computer Engineering at Chung-Ang University, Seoul, Korea. His academic work focuses on knowledge engineering , social media analytics , and data mining within the Knowledge Engineering Laboratory. Research Interests : Computer Science, Social Knowledge, Data Modeling, Sentiment Analysis Projects : IoT-based cultural systems, real-time social event detection, transmedia storytelling models The 15 most recent publications (2014-2018) demonstrate expertise in social network analysis , multimodal data processing , and context-aware systems applied to urban services, cultural tourism, and digital storytelling. Key trends include real-time analytics , trust modeling , and collaborative frameworks for O2O services. Professional activities include editorial contributions, invited talks, and patent developments. Students at all levels (PhD/MSc/BSc) conduct research under his supervision at the Knowledge Engineering Laboratory. Personal interests include travel, film, painting, literature, and music.

• Geoinformatics
• Computer Graphics
• Computer Vision
• +5 more

Prof. Dr. rer. nat. habil. Stephan Kopf is a faculty member at the Faculty of Spatial Information, where he holds the Professorship in Informatics and Geoinformatics. He actively contributes to research in geographic information systems (GIS), computer graphics, computer vision, and multimedia technologies. His academic leadership roles include serving as Dean and as a member of the Faculty Council, with advisory roles in the Senate. His research spans crowd simulation , image/video retargeting , augmented reality platforms , and interactive learning systems . He focuses on algorithm development for GIS, temporal effects in re-captured video, and scalable solutions for classroom interactivity. His work frequently integrates machine learning, mobile computing, and geospatial data. For teaching, he oversees courses such as Internet Technologies, Informatics, and Programming in degree programs like Geoinformatics/Management and Surveying. He supervises theses involving programming-centric topics like mobile app development , VR applications , and astronomical analysis of geospatial data .

• Metabolomics
• Computer Vision
• Amino Acids
• +5 more

Ron Wehrens is a researcher at Biometris , part of Wageningen University & Research. His work spans interdisciplinary applications of machine learning, metabolomics, and computer vision in agricultural and biological sciences. Developing statistical tools in R (e.g., aaresponse , Kohonen packages) Applying AI/ML to problems in floriculture, animal behavior, and nutritional science Collaborating on projects involving remote sensing, plant phenotyping, and metabolic health studies Research Interests include metabolomics, deep learning for agricultural robotics, and behavioral analysis in livestock. His recent work focuses on self-organizing maps for data clustering and trait estimation in plants. Scientific Awards : No explicit awards mentioned in the text.

• LLM-assisted agent systems
• Explainable AI
• Multimodal artificial intelligence
• +12 more

Dr. Yifei Dong is a Research Fellow at the Data Science Institute , University of Technology Sydney, with expertise in LLM-assisted agent systems , explainable AI , and multimodal artificial intelligence . Holding a PhD in Computer Science from UNSW Sydney and over a decade of fintech industry experience including CTO roles, he has secured $2.4 million in competitive funding for AI solutions bridging academia and real-world applications in healthcare, education, and finance. Education : PhD in Computer Science from UNSW Sydney Current Role : Research Fellow at UTS Data Science Institute (2023–present) Past Academic Appointments : Lecturer at Southern Cross University and Western Sydney University Dr. Dong’s research focuses on making AI systems transparent and socially beneficial , with contributions to adversarial AI, trustworthy digital societies, and wireless sensor networks. His recent work includes: Developing AICAttack (2025) for adversarial image captioning attacks Creating the QMAD fairness metric (2025) for dynamic environments Advancing explainable ECG diagnosis systems (2025) via multimodal LLMs As a scientific awardee (2025 RegTech Social Impact of the Year), he has pioneered AI solutions for vulnerable populations, such as NDSI participants through the "My Complaint Assistant" tool. His supervision of PhD and Honours students emphasizes technical rigor and ethical responsibility.