Find Academic Supervisors

Danijel SkočajView profile

Professor

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

Danijel Skočaj is Full Professor at the University of Ljubljana, Faculty of Computer and Information Science , and serves as Head of the Visual Cognitive Systems Laboratory . He is an internationally recognized researcher in computer vision, machine learning, and cognitive robotics , with a strong focus on deep-learning solutions for real-world visual perception tasks and their ethical implications. Education: While specific degrees are not listed in the text, Professor Skočaj’s 2002 “Best PhD paper award” confirms he holds a PhD in the relevant field. Research Interests: His work spans Computer Vision & Pattern Recognition Deep Learning & Neural Networks Cognitive Robotics & Autonomous Navigation Visual Anomaly & Surface-Defect Detection AI Ethics & Societal Impact of AI These interests manifest in both theoretical advances and practical systems deployed in industry and public infrastructure. Publication Trends: Recent papers (2020-2024) emphasize deep-learning architectures for defect detection, robotic grasping, autonomous navigation, traffic-sign recognition, and 3-D anomaly detection , demonstrating a clear trajectory toward robust, real-time, and data-efficient visual intelligence. Awards & Honors: Prometheus of Science Award 2021 (Slovenian Science Foundation) Golden Plaque, University of Ljubljana 2020 ARRS National Award for Exceptional Scientific Achievement 2011 & 2022 Multiple Best-Paper awards at ERK conferences (2013, 2017, 2019) Top-downloaded paper recognition, Journal of Intelligent Manufacturing 2020 Grants & Projects: He currently leads or co-leads five major 2025-2028 national and EU projects (RTFM, SMASH, COMET, RoDEO, MUXAD) totaling several million Euros, focusing on advanced computer vision, machine learning for science & humanities, autonomous systems, and explainable AI. Past leadership includes EU FP7 CogX, GOSTOP, ViLLarD, and many ARRS programmes. Laboratory & Team: The Visual Cognitive Systems Laboratory hosts a dynamic group of doctoral and master’s students working on cutting-edge perception systems. The lab’s open-source low-cost robotic platform and datasets are widely adopted for education and research.

Arno SiebesView profile

Professor

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +2 more

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

Xiaoyang WangView profile

Senior Lecturer

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +10 more

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

Andreas HuthView profile

Researcher

• Ecological Modeling
• Vegetation Dynamics
• Biodiversity Modeling
• +4 more

Andreas Huth is a prominent researcher at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, where he works within the Department of Ecological Systems Analysis. His research focuses on developing and applying computational models to understand complex ecological systems, with particular emphasis on vegetation dynamics, biodiversity patterns, and environmental interactions. Huth is actively involved in several major research initiatives including the FORMIND project, which simulates forest growth using individual-based vegetation models, and contributes to the UFZ's research program on Smart Models and Monitoring. Huth's research interests span ecological modeling, vegetation dynamics, biodiversity assessment, and environmental system analysis. He specializes in developing sophisticated computational frameworks that integrate atmospheric, ecological, and spatial data to model complex environmental processes. His work often involves coupling different modeling approaches, such as connecting atmosphere and vegetation radiative transfer models to study cloud-vegetation interactions. Huth is particularly interested in understanding spatial patterns in ecological systems, species-habitat relationships, and the impacts of environmental change on biodiversity. His methodological expertise includes agent-based modeling, individual-based modeling, and developing digital twin technologies for environmental systems. Analysis of Huth's recent publications reveals a strong focus on integrating different modeling approaches to address complex environmental questions. His work demonstrates a progression from basic ecological modeling toward more sophisticated integrative frameworks that connect atmospheric processes with vegetation dynamics, and social systems with ecological outcomes. A notable trend is his increasing involvement in digital twin technology for environmental systems, particularly for biodiversity monitoring and prediction. His research spans multiple scales - from microbial ecosystems to continental-scale species distributions - while maintaining a consistent methodological thread of spatially explicit modeling and causal analysis in complex systems. Huth collaborates extensively with researchers across the UFZ and internationally, as evidenced by his co-authorship on numerous interdisciplinary publications. While specific awards are not mentioned in the available information, his consistent publication record in high-impact journals and leadership in major modeling initiatives like FORMIND indicate significant recognition within the ecological modeling community. His work has practical applications for environmental policy, conservation planning, and climate change adaptation strategies. Huth is an active member of the Ecological Systems Analysis research group at UFZ, which develops and applies computational models to investigate key mechanisms in ecological systems. He contributes to several major research platforms including FORMIND (an individual-based vegetation model for species-rich forests) and participates in the UFZ's broader research initiatives on environmental monitoring and sustainable technologies. His work is integrated within the UFZ's research program on Smart Models and Monitoring, which aims to develop innovative approaches for understanding and predicting environmental change.

Laks V.S. LakshmananView profile

Professor

• Data Management
• Graph Computing
• Machine Learning
• +5 more

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

• Artificial Intelligence
• Machine Learning
• Medical Education Technology
• +7 more

Andrew R. Jamieson is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center, where he leads a research team focused on developing advanced AI systems for medical education and clinical performance assessment. He was appointed in 2019 and serves as Principal Investigator of the Jamieson Group. Institution: UT Southwestern Medical Center School: School of Health Professions Department: Lyda Hill Department of Bioinformatics Academic Rank: Assistant Professor Dr. Jamieson earned his B.A. in Physics with honors (2006) and Ph.D. in Medical Physics (2012) from the University of Chicago. His early work in computer-aided diagnosis laid the foundation for his career in AI and machine learning. Education: University of Chicago (B.A., Ph.D.) Prior Experience: GE Healthcare, Big Data Analytics Startup (First Data Scientist) Dr. Jamieson's research lies at the intersection of artificial intelligence, medical education, and bioinformatics. His team leverages multimodal data—including video, audio, and text—from the UTSW Simulation Center to train frontier AI models for automated assessment of medical student performance. His work in computational image analysis spans label-free live-cell imaging, spatial biology, and highly multiplexed immunofluorescence, with applications in cancer biology and diagnostics. He has also made significant contributions to public health through the development of the UTSW COVID-19 forecast model. The most recent publications reflect a strong trend toward AI-driven medical education tools, particularly using large language models and multimodal AI for OSCE assessment. Earlier works focus on deep learning in medical imaging, dimensionality reduction, and computer-aided diagnosis in mammography. The research consistently emphasizes interpretability, automation, and clinical translation. Scientific recognition includes being featured on the cover of Cell Systems (July 2021) for work on melanoma cell analysis. His team's development of the first automatic AI grading system for medical student OSCE notes in 2023 marks a major innovation in educational assessment. Featured on cover of Cell Systems (2021) Developed UTSW COVID-19 forecast model Pioneered AI grading system for OSCE notes (2023) Dr. Jamieson is actively involved in mentoring and graduate education. He serves as Course Director for the Master’s in Health Informatics program and contributes to nanocourses at the Clinical Informatics Center. His team includes multiple advisees and collaborators working on NLP, LLMs, and AI/ML in healthcare. He is expanding his group and seeking researchers in AI, data science, and software development. His leadership in the Bioinformatics Core Facility (2018–2021) and ongoing collaborations with pathologists and radiation oncologists demonstrate strong interdisciplinary grant and project engagement. Course Director: Master’s in Health Informatics Mentor to multiple graduate students and researchers Collaborations: Pathology, Radiation Oncology, Surgery, Clinical Informatics The Jamieson Group is a dynamic, interdisciplinary research team at the forefront of applying cutting-edge AI to medical education and clinical data analysis. The lab focuses on natural language processing, multimodal learning, and computer vision, with strong ties to the UTSW Simulation Center and Clinical Informatics Center. The team develops custom pipelines for spatial biology and imaging data and is actively expanding to meet growing research demands.

Craig KnoblockView profile

Research Professor

• Knowledge Graphs
• Semantic Web
• Data Integration
• +7 more

Craig Knoblock serves as the Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California, Vice Dean of Engineering for the Viterbi School of Engineering, and Research Professor of both Computer Science and Spatial Sciences. He also directs USC's Data Science Program and leads the Center on Knowledge Graphs as Research Director. Dr. Knoblock's research focuses on techniques for describing, acquiring, and exploiting the semantics of data. His extensive work spans source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the Web, and building comprehensive knowledge graphs. With over 300 published works in these areas, his research has significantly advanced the field of semantic data integration. His work demonstrates strong trends toward practical applications of knowledge graphs across diverse domains including cultural heritage, geospatial information systems, human trafficking detection, and sensor networks. The evolution of his publications shows a progression from foundational ontology alignment techniques to sophisticated knowledge graph applications addressing real-world challenges. Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) Fellow of the Association of Computing Machinery (ACM) Past President and Trustee of the International Joint Conference on Artificial Intelligence (IJCAI) Recipient of the 2014 Robert S. Engelmore Award Seven best paper awards for his research contributions As Executive Director of USC's Information Sciences Institute, Dr. Knoblock leads one of the world's premier research centers in computer science and information technology. His leadership extends to directing the Center on Knowledge Graphs and serving as Associate Director of the Informatics Program at USC. His educational background includes a Bachelor of Science from Syracuse University and Master's and Ph.D. degrees in Computer Science from Carnegie Mellon University.

• Mathematics Education
• STEM Education
• Early Childhood Development
• +5 more

Joanne Mulligan is a Professor at the Macquarie School of Education, Macquarie University, specializing in mathematics and STEM education across early childhood to secondary levels. Her research focuses on early mathematical development, pedagogy, assessment, and curricula, with expertise in educational psychology and teacher education. She has led major projects like the $2.3m Opening Real Science (ORS) Project and currently oversees ARC-funded initiatives exploring spatial reasoning and interdisciplinary learning. Research Interests: Mathematics Education, STEM Education, Cognitive Development, Early Childhood Learning, Teacher Professional Development. Key Projects: Connecting Mathematics Learning with Spatial Reasoning (2017–2020), Enriching Mathematics and Science Learning (2018–2020), and the ORS Project (2013–2017). Contributions: Developed programs like PASA and PASMAP, impacting national and international curricula. Active in global organizations such as ICME and ICMI. Research Trends Mulligan’s recent work emphasizes interdisciplinary approaches, spatial reasoning, and data modeling in early education. Her articles highlight pedagogical frameworks for integrating mathematics and science, enhancing teacher training, and fostering student engagement through collaborative and inquiry-based methods. Grants & Collaborations ARC Discovery Projects: Focus on spatial reasoning (2017–2020) and interdisciplinary learning (2018–2020). Collaborations: International Spatial Reasoning Study Group (IOSTEM), Deakin University, and CSIRO. Labs & Teams Associated with the Centre for Research in Mathematics and Science Education (CRiMSE) and the MQ Learning Sciences Laboratory (MQSL). Her work bridges research and practice, influencing curriculum design and teacher education nationally and globally.

André BorrmannView profile

Professor

• Artificial Intelligence
• Building Information Modeling & Processing
• Digital Twinning
• +7 more

Prof. Dr.-Ing. André Borrmann is an academic leader at the Technical University of Munich (TUM) , where he has headed the Chair of Computing in Civil and Building Engineering since 2011 (formerly Computational Modeling and Simulation). He serves as Director of the TUM Georg Nemetschek Institute - AI for the Built World since 2025 and Spokesperson for the Leonhard Obermeyer Center since 2013. Research Interests Artificial Intelligence in Civil Engineering Digital Twinning Building Information Modeling (BIM) Pedestrian Dynamics Knowledge Representation Construction Simulation His work focuses on AI application across the built environment lifecycle - from generative design to maintenance prediction - with significant contributions to BIM standardization and buildingSMART International IFC extensions. He co-authored the German Ministry of Transport BIM Roadmap and led the BIM4INFRA2020 project. Awards include the 2024 Konrad Zuse Medal and multiple best paper awards at international conferences.

Vasanth SarathyView profile

Academic

• Artificial Intelligence
• Natural Language Processing
• Neuro-symbolic Machine Learning
• +7 more

Vasanth Sarathy is a Research Assistant Professor of Computer Science at Tufts University, specializing in the intersection of Artificial Intelligence and Natural Language Processing. His work combines neuro-symbolic machine learning techniques with social and cognitive sciences to develop socially-competent and safe AI systems. Education: Ph.D. in Computer Science and Cognitive Science, Tufts University (2020) Juris Doctor (J.D.), Boston University School of Law (2010) M.S. in Electrical Engineering and Computer Science, MIT (2005) B.S. in Electrical Engineering, University of Arkansas (2003) His research spans three interconnected themes: Social NLP, Reasoning with Social Norms, and Sense-making and Problem-Solving. Through Social NLP, he develops computational methods to uncover patterns in human social behavior using qualitative texts. His work on Reasoning with Social Norms focuses on building AI architectures that can understand and apply social norms in reasoning and language interpretation. His Sense-making research explores how humans and AI systems simplify complex worlds for efficient problem-solving. His recent publications demonstrate a growing trend toward integrating Large Language Models with symbolic reasoning for more trustworthy AI. His work spans applications including automated writing assistance, intelligent tutoring, fact-checking, social media content moderation, and embodied social robots. His research shows particular strength in developing methods that combine neural and symbolic approaches to address complex social reasoning tasks that neither approach could solve alone. Previously, Dr. Sarathy worked as a Senior Researcher of AI at Smart Information Flow Technologies, collaborating with the U.S. Department of Defense and Intelligence Community. Before his AI career, he practiced intellectual property law at Ropes and Gray for nearly a decade. His unique interdisciplinary background bridges technology, law, and cognitive science. He advises students in AI, NLP, and cognitive systems, with research supported by grants from DARPA and other government agencies. His work has applications in human-robot interaction, social media analysis, and educational technologies. Outside academia, he creates single-panel cartoons, practices martial arts, plays chess, and designs puzzle video games. His interdisciplinary journey from law to AI has been featured in a BU Law article.

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

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .

Charan RanganathView profile

Professor

• Human Memory
• Executive Control
• Neuroimaging
• +7 more

Charan Ranganath is a Professor in the Department of Psychology at the University of California, Davis, and director of the Dynamic Memory Lab. He is affiliated with the UC Davis Center for Neuroscience and the Center for Mind and Brain, focusing on human memory and executive control using neuroimaging, electrophysiology, and behavioral methods. His research explores the neural basis of memory, including roles of the prefrontal cortex, medial temporal lobes, and hippocampus in working and long-term memory. Education: Ph.D., Clinical Psychology, Northwestern University M.S., Clinical Psychology, Northwestern University B.A., Psychology, University of California, Berkeley Ranganath’s research spans hippocampal-cortical interactions, curiosity-driven memory enhancement, schizophrenia-related memory deficits, and computational models of memory systems. His work reveals overlapping prefrontal and medial temporal lobe activity in memory tasks and investigates how stress, aging, and neuromodulation affect memory dynamics. His research trends emphasize episodic memory , relational memory , neural oscillations , cortico-hippocampal networks , curiosity-driven learning , and clinical applications in mental illness. Scientific Awards: Guggenheim Fellowship Leverhulme Trust Visiting Professorship Laird Cermak Award Samuel Sutton Award Chancellor’s Fellow award Young Investigator Award (Cognitive Neuroscience Society) National Security Science and Engineering Faculty Fellowship (2015) He has secured grants from the Department of Defense, National Institute of Mental Health, and private foundations. His lab collaborates with Andy Yonelinas on recognition memory and with others on field potentials in hippocampal studies. He teaches Human Learning, Cognitive Neuroimaging, and Current Research in Psychology.

• Information Systems
• Virtual Reality
• Human-Computer Interaction
• +3 more

David Conroy holds dual roles as a Lecturer (TIEA) and Research Fellow within the School of Computer Science at the Faculty of Science, Queensland University of Technology (QUT) . His work focuses on integrating innovative technologies into education and game design. He is affiliated with QUT and is part of the alumni community. His research explores Virtual Reality (VR) applications in geology training, human-AI collaboration frameworks, and AI-driven game opponent behavior. David earned a Doctor of Philosophy (PhD) from Queensland University of Technology (QUT). His research interests emphasize Virtual Reality in educational contexts, Human-Computer Interaction principles, and the development of Immersive Learning Systems . He also investigates Artificial Intelligence in games, with a focus on creating realistic game opponents and analyzing player behavior. His recent publications (2022-2023) highlight advancements in VR-based geology education modules and frameworks for trust in hybrid human-AI teams. Earlier work (2010-2012) delves into player-enemy dynamics in First-Person Shooter (FPS) games and AI design for immersive experiences. These studies reflect a blend of technical innovation and pedagogical application. No scientific awards are explicitly listed in the provided text. His advising activities focus on topics such as avatar customization for diversity training in VR, co-designing immersive systems for remote data analysis, and interactions with intelligent agents in virtual environments. David collaborates with researchers like Professors Schrank, Turkay, and Johnson. While specific grants are not mentioned, his work suggests active participation in collaborative projects. He maintains professional profiles on LinkedIn , ORCID , and Scopus .

• Geographic Information Systems (GIS)
• Spatial Data Management
• Maritime Transportation Networks
• +7 more

Christophe Claramunt is a distinguished researcher affiliated with the Naval Academy Research Institute in Brest, France. His work spans interdisciplinary domains at the intersection of Geographic Information Systems (GIS) , spatial data science , and human mobility analysis . He has pioneered hierarchical graph-based models for maritime and urban systems, semantic trajectory frameworks, and spatiotemporal approaches for urban soundscapes and shipping networks. Research Themes: GIS modeling, maritime transportation analytics, urban conflict analysis, semantic trajectories, geospatial data infrastructures Methodologies: Graph theory, machine learning, knowledge graphs, spatiotemporal databases, remote sensing Applications: Port network resilience, demining systems, tourist behavior analysis, cadastral digitization His recent publications focus on graph neural networks for spatial analysis, knowledge-driven competitive intelligence , and maritime network cascading failures . Collaborative work includes contributions to global container shipping modeling and urban acoustic environments. While no formal academic rank is listed in the source data, his extensive publication record and leadership in interdisciplinary projects confirm his status as a full faculty researcher.

• Visual perception
• Attentional selection
• Perceptual organization
• +4 more

Melanie Palomares serves as an Instructor in the Department of Psychology at the McCausland College of Arts and Sciences, University of South Carolina. Her academic foundation includes a Ph.D. from Johns Hopkins University (2007), establishing her expertise in visual cognitive processes and developmental psychopathology. Education: Ph.D. in Cognitive Psychology/Neuroscience from Johns Hopkins University (2007) Research Focus: Dr. Palomares investigates visual perception mechanisms through multiple lenses including attentional selection , perceptual organization , and subitizing . Her work bridges letter recognition processes with visual development in both typically developing children and clinical populations, particularly those with Williams Syndrome. Utilizing visual-evoked potentials (VEPs) as a key methodology, she explores neural correlates of perceptual abnormalities in neurodevelopmental disorders. Publication Trends: Analysis of her 2000-2011 publications reveals sustained investigation into visual cognition's intersection with developmental disorders. Core themes include attention's role in enumeration limits, visuospatial integration deficits in Williams Syndrome, and neural mechanisms underlying letter identification. Her collaborative network prominently features Howard Egeth and Denis Pelli, indicating strong ties to foundational visual cognition research. Research Infrastructure: Dr. Palomares operates within the C-Panda Lab at USC, which specializes in developmental visual cognition research using psychophysical and electrophysiological approaches. The lab environment supports her dual focus on typical perceptual development and Williams Syndrome phenotypes.