Find Academic Supervisors

Jürgen PfefferView profile

Professor

• Computational Social Science
• Big Data Analysis
• Social Media Dynamics
• +4 more

Prof. Jürgen Pfeffer is a Professor of Computational Social Science & Big Data at the Technical University of Munich (TUM), affiliated with the TUM School of Social Sciences and Technology. His research bridges social and computer science, focusing on large socio-technical systems, algorithmic challenges, and dynamics like online firestorms and hate speech. He holds a doctorate from Vienna University of Technology and has postdoctoral experience at Carnegie Mellon University. Key research areas include computational social science, network analysis, and critical data studies. He leads interdisciplinary projects on online misogyny, disaster communication, and AI ethics. His work addresses methodological rigor in handling big data, visualization of complex networks, and policy implications of social media dynamics. Pfeffer has authored numerous publications in top journals like Science and IEEE, and serves on editorial boards for Frontiers in Big Data and EPJ Data Science. He actively participates in global conferences, advocating for ethical AI and responsible data practices. His research projects span collaborations with institutions worldwide, addressing societal challenges through data-driven insights.

Samuel C BooneView profile

Lecturer

• Geology
• Tectonics
• Thermochronology
• +4 more

Dr Samuel C Boone is a Lecturer in Geology at the School of Geosciences, University of Sydney, and Honorary Fellow at the University of Melbourne. His research focuses on continental tectonics, thermochronology, and the integration of geochemical Big Data with numerical geodynamic models. He holds a PhD in Geology from the University of Melbourne (2019) and has held postdoctoral roles at Joannes Gutenberg University, University of Adelaide, and University of Melbourne. Education: BSc in Physics (University of Massachusetts, 2009), MSc in Geology (University of Melbourne, 2013), PhD in Geology (University of Melbourne, 2019). Research interests include the thermal and tectonic evolution of continental crust, with a focus on rift systems (East Africa, New Zealand), supercontinent breakup, and the interplay between tectonics, climate, and resource preservation. He leads the Melbourne Thermochronology Research Group and co-developed the AusGeochem platform for global geochemical data synthesis. Recent articles emphasize the application of thermochronology to rift dynamics, continental breakup mechanisms, and the development of geospatial data platforms. Awards include the 2021 University of Melbourne Early Career Research Grant and DAAD fellowships. Advising: Supervises PhD and MSc students across multiple institutions, including projects on Australian tectonic evolution, monazite fission-track techniques, and Cobar Basin mineral exhumation. Grants include leadership in AuScope infrastructure projects and IODP collaborations. Labs/Teams: EarthByte Group (Sydney), Melbourne Thermochronology Research Group, University of Adelaide Tectonics and Earth Systems Group, and the AuScope Geochemistry Network.

• Violent Extremism
• Community Resilience
• Counter-Radicalization
• +3 more

Professor Michele Grossman AM holds the Research Chair in Diversity and Community Resilience at Deakin University's Alfred Deakin Institute. She co-convenes the AVERT Research Network and directs the Centre for Resilient and Inclusive Societies (CRIS), focusing on community contributions to countering violent extremism. Her research examines how communities build resilience against violent extremism through bystander reporting mechanisms, youth resilience measurement, and cross-cultural trust relations. Recent projects include international studies on intimate bystander reporting thresholds in Canada, the UK, and Australia. Publications demonstrate methodological innovation in terrorism research ethics, mixed-methods approaches to extremist discourse analysis, and arts-based interventions for radicalization prevention. Scientific Awards: Member of the Order of Australia (AM) for service to tertiary education and social welfare initiatives Walter McRae Russell Prize for 'Entangled Subjects: Indigenous Cross-Cultures of Talk, Text and Modernity' She leads major grants including $875,247 from the Australian Research Council for 'Understanding the drivers and motivators of extremist violence' and $210,123 from the US Department of Homeland Security for bystander reporting research. Current doctoral supervision focuses on clinical assessment of high-risk individuals.

George AzzopardiView profile

Associate Professor

• Pattern Recognition
• Image Processing
• Machine Learning
• +5 more

George Azzopardi is an Associate Professor in Pattern Recognition at the University of Groningen, affiliated with the Bernoulli Institute. He leads the PRISMA team, focusing on robust AI, medical imaging, and interdisciplinary applications in astronomy and forensics. His roles include coordinating the Applied AI theme at the Jantina Tammes School and leading WG4 of the EU GLITSS COST Action. Education: PhD in Computer Science (University of Groningen, 2013), MSc in Advanced Methods in Computer Science (Queen Mary University London, 2008), BSc in Computer Science (Goldsmiths College London, 2006). Research Interests: Brain-inspired algorithms, image processing, predictive analytics, and applications in radio astronomy, forensics, and medical diagnostics. His work emphasizes robustness in AI models and cross-domain collaborations. Publications span advancements in COSFIRE filters, deep learning robustness, and interdisciplinary applications like radio galaxy classification and biometric systems. Recent trends include enhancing model explainability and combating adversarial attacks. Awards: Ben Feringa Impact Award (2023), ENLIGHT Impact Award (2024) Advising: Supervises PhD students in AI-driven medical imaging, radio astronomy, and computer vision. Collaborates with institutions like ASTRON, UMCG, and industry partners Ziel NL and KAIOS. Labs/Teams: PRISMA team develops robust AI tools for medical and astrophysical data analysis, with projects on biostructure segmentation and camera forensics.

• Computational Geometry
• Geometric Algorithmics
• GIScience
• +5 more

Prof. Marc van Kreveld is a Professor and Head of the Department of Computer Science at Utrecht University since February 2023. His research focuses on computational geometry, geometric algorithmics, and their applications in GIScience, cartography, and puzzle design. He leads the Geometric Computing group and has made significant contributions to algorithms for trajectory data, geometric similarity, and spatial data analysis. Education: PhD in Computer Science from Utrecht University (1992). Research Interests: Computational geometry, GIScience, graph drawing, LIDAR point cloud reconstruction, and geometric puzzle analysis. His work bridges theoretical foundations and real-world applications, such as route data analysis and environmental modeling. Publications: Over 290 peer-reviewed papers, including foundational work on Fréchet distance algorithms, geometric spanners, and trajectory grouping. Recent contributions include advancements in collision detection for modular robots and mobility data science. Awards & Editorial Roles: Editor-in-Chief of Computing in Geometry and Topology , and editorial board member of Journal of Computational Geometry . Active in program committees for conferences like GIScience and SoCG. Teaching: Specializes in computational geometry and its applications, with a focus on algorithm design and geometric computing. Labs & Teams: Leads the Geometric Computing group at Utrecht University, collaborating on projects like braided river network modeling and automated puzzle generation.

Rui HenriquesView profile

Assistant Professor

• Data Mining
• Machine Learning
• Bioinformatics
• +4 more

Rui Henriques is an Assistant Professor at the Department of Electrical and Computer Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon. He is also an Associate Researcher at INESC-ID. His research focuses on data mining, machine learning, bioinformatics, and urban analytics, with applications in healthcare, transportation systems, and pattern recognition. He has advised numerous PhD and MSc students, contributing to impactful projects in areas like healthcare decision support, mobility optimization, and environmental data analysis. Education: PhD in Learning from High-Dimensional Data (2016) from IST, University of Lisbon. Research interests include developing algorithms for high-dimensional data analysis, integrating spatiotemporal data for urban planning, and applying machine learning to biomedical challenges. Notable contributions include tools like BicPAMS for biological data analysis and software frameworks for transportation analytics. His work spans over 100 publications in top journals and conferences, addressing topics from triclustering algorithms to fraud detection in online tests. He leads or contributes to projects such as ILU (city mobility optimization) and Data2Help (emergency medical services optimization). His research emphasizes interdisciplinary approaches, combining data science with real-world applications in health, transport, and sustainability.

• Cybersecurity
• Cybercrime Analysis
• Digital Forensics
• +3 more

Nigel Phair is a Professor in the Department of Software Systems & Cybersecurity at Monash University, specializing in cybersecurity, cybercrime analysis, and digital forensics. His research focuses on global cybersecurity trends, corporate cybersecurity practices, and the intersections of law enforcement with digital platforms. Phair’s work frequently addresses critical issues such as cybercrime indices, boardroom cybersecurity competency, and the societal impact of digital technologies. He has contributed to national cybersecurity strategy discussions in Australia and explored challenges in law enforcement’s adaptation to digital crime landscapes. His publications highlight innovative methods like Automated Social Media Intelligence Analysis (ASMIA) for detecting illicit activities online and emphasize the disconnect between corporate cybersecurity claims and actual practices. He has also analyzed encryption’s role in balancing national security and privacy rights. While no awards or grants are explicitly listed, his extensive output reflects a commitment to advancing cybersecurity policy, education, and technical solutions. His research extends to global cybersecurity comparisons, digital financial service vulnerabilities, and the evolving threat landscapes in cyberspace.

Trisalyn NelsonView profile

Professor

• GIScience
• Urban Planning
• Transportation Planning
• +5 more

Trisalyn Nelson is a Professor and Department Chair in the Department of Geography at the University of California, Santa Barbara (UCSB), holding the Jack and Laura Dangermond Endowed Chair of Geography. Previously, she served as Director of Arizona State University's School of Geographical Sciences and Urban Planning (2016–2020) and Lansdowne Research Professor at the University of Victoria, Canada. Her leadership emphasizes fostering inclusive academic cultures, innovation through industry partnerships, and advancing diversity in STEM. Her research focuses on active transportation, urban planning, and big data applications, with a particular emphasis on bicycle safety and infrastructure equity. She pioneered BikeMaps.org, a crowdsourced platform mapping cycling collisions and near-misses, and developed methodologies using Strava fitness app data to analyze ridership patterns. Nelson also explores spatial ecology, including grizzly bear behavior and salmon ecosystems, and co-leads the Spar Lab at UCSB. Recent work includes evaluating bicycle network connectivity, equity in cycling infrastructure access, and the impacts of bike theft on ridership. Her articles highlight themes like data-driven urban design, transportation equity, and geospatial education innovation. Awards include the prestigious Dangermond Chair, recognizing her contributions to geographic sciences. Nelson's leadership during her ASU tenure included doubling research revenue, expanding student diversity by over 200%, and generating $2M in new revenue through partnerships. She advises five graduate students and collaborates with over a dozen industry partners. Key projects include the INTERACT urban intervention study and WalkRollMap.org for mobility barriers.

Christine YuenView profile

Assistant Professor

• Data Science
• Statistics Education
• Programming for Data Science
• +3 more

Christine Yuen is an Assistant Professor (Education) in the Department of Statistics at the London School of Economics and Political Science (LSE) and serves as the BSc Data Science Programme Director. She earned her PhD from LSE in 2021 and has been teaching since 2016. Her roles include lecturing in Programming for Data Science (ST101) and Managing and Visualising Data (ST115), as well as serving as a teaching assistant for courses like Distributed Computing for Big Data (ST446). Her expertise focuses on statistics education and data science pedagogy, reflecting her contributions to curriculum development and teaching innovation. She has overseen the BSc Data Science programme since her appointment, ensuring alignment with industry and academic standards. No scientific awards or grants were explicitly mentioned in the provided text.

Radmila SazdanovicView profile

Associate Professor

• Low-dimensional topology
• Algebraic topology
• Categorification
• +3 more

Radmila Sazdanovic is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), part of the College of Sciences. She holds a Ph.D. from George Washington University, where she worked under Jozef Przytycki. Her research focuses on low-dimensional topology, applied algebraic topology, categorification, and their interdisciplinary applications in areas like data science and biomedical engineering. She is affiliated with the Faculty Research Group on Topology, Geometry, and Mathematical Physics. Her expertise spans knot theory, homology theories (Khovanov, chromatic, magnitude), and topological data analysis (TDA). Notable projects include collaborations on vascular network analysis with pulmonary hypertension studies and development of tools like the TAaCGH Suite for cancer genomics. She advises students on interdisciplinary projects, such as topology and fluid dynamics in vascular networks, alongside Dr. Olufsen. Recent work explores data-driven knot theory, torsion in homology, and applications of TDA in biomedical contexts. Her research bridges pure mathematics with applied problems, emphasizing categorification and computational methods. She actively disseminates findings through conferences and publications, contributing to both theoretical and applied advancements in topology.

Georgios J. FakasView profile

Associate Professor

• Big Data
• Keyword Search
• Spatial Databases
• +9 more

Georgios J. Fakas is an Associate Professor (Docent) in the Department of Information Technology at Uppsala University, Sweden, where he leads the Uppsala DataBase Laboratory (UDBL). He has held academic positions at prestigious institutions including the Hong Kong University of Science and Technology, EPFL, UMIST, University of Cyprus, and Manchester Metropolitan University, where he served as a Senior Lecturer. Education: Ph.D. in Computation, UMIST, Manchester, UK (1998) M.Phil. in Computation, UMIST, Manchester, UK (1996) B.Sc. in Computation, UMIST, Manchester, UK (1995) His research spans big data, keyword search on structured and graph data, spatial databases, web and geo-social search, workflow management, and data summarization . He also explores applications in educational technologies, distance learning, and health informatics. His work emphasizes efficient, user-centric data access and intelligent querying paradigms. The 15 most recent publications highlight a sustained focus on keyword search, object summarization, spatial and semantic data, and ranking , published in top venues like SIGMOD, VLDBJ, IEEE TKDE, and PVLDB. His work integrates algorithmic innovation with practical database applications. Scientific Awards: International Sabbaticals grant from Teknat (2023), hosted by Boston University Fellowship 'Hosting of Experienced Researchers from Abroad' from RPF, Cyprus (2009) ERCIM Fellowship (2001), hosted by EPFL, Switzerland He is actively involved in PhD supervision and grant leadership. He currently serves as Principal Investigator for an eSSENCE-funded project on Data-intensive analytics for discovering critical minerals using Machine Learning , in collaboration with industry partners. He has supervised multiple PhD students to completion, including Georgios Kalamatianos, Khalid Mahmood, Zhi Cai, and Ben Cawley. He is a frequent reviewer for leading journals and serves on program committees of major conferences such as ACM SIGMOD and PVLDB. He leads the Uppsala DataBase Laboratory (UDBL) , a research group focused on advanced data management systems, where he mentors students and drives innovation in database technologies.

Jeff GrayView profile

Professor

• Software Engineering
• Model-Driven Development
• Domain-Specific Languages
• +7 more

Jeff Gray is a University Distinguished Professor in the Department of Computer Science at the University of Alabama, where he also directs the Randall Research Scholars Program. He holds a Ph.D. in Computer Science from Vanderbilt University and has built a distinguished career in software engineering, model-driven development, and computing education. Education: Ph.D. in Computer Science, Vanderbilt University, 2002 M.S. in Computer Science, West Virginia University, 1993 B.S. in Computer Science (summa cum laude), West Virginia University, 1991 Dr. Gray's research focuses on model-driven software development, domain-specific languages, blocks-based programming environments, and computer science education outreach. His work bridges theoretical software engineering with practical applications in K–12 education and accessibility. He has developed tools like Spherly, Pixly, and Tunely to make programming more accessible. His research interests include software modeling, tool integration, debugging techniques, and inclusive programming environments. His recent publications reflect a strong trend in computing education, particularly in blocks-based programming, unit testing for beginners, and accessible language design. He has also contributed significantly to model-driven engineering and domain-specific language research. Scientific Awards: NSF CAREER Award Professor of the Year (Alabama, 2008) – Carnegie Foundation McMahon-Pleiad Prize (2025) Morris L. Mayer Premier Award Distinguished Member of the ACM IEEE Computer Society Distinguished Contributor (Inaugural Class) Dr. Gray has advised numerous doctoral students and has served as Editor-in-Chief of Software and Systems Modeling (Springer). He has secured over $24 million in external funding as PI or co-PI from agencies including NSF, Department of Education, and Google. He is actively involved in national initiatives such as the AP CS Principles course and Alabama’s Computer Science Advisory Council. He leads outreach programs including the Alabama Summer Computer Camps, Alabama Robotics Competition, and Code.org workshops. He also co-chairs the Development Committee for the College Board's AP CS Principles course and serves on Alabama Governor Kay Ivey’s Computer Science Advisory Council.

• Mathematical Optimization
• Statistical Modeling
• Machine Learning
• +13 more

Vanesa Guerrero Lozano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Flores de Lemus Institute and the UC3M-Santander Big Data Institute. Her work bridges mathematical optimization, statistical modeling, and data science, with applications across disciplines including biomedicine, fluid mechanics, and sustainable development. Her research focuses on developing advanced statistical methodologies using mathematical optimization. Key interests include shape-constrained regression, P-splines smoothing, sparse modeling, clustering of categorical data, and interpretable machine learning. She applies these techniques to complex datasets in turbulence modeling, biological age imputation, and pandemic forecasting. The recent publications reveal a strong trend in integrating optimization techniques with statistical learning, particularly in nonparametric and semiparametric models. There is a consistent emphasis on interpretability, robustness, and scalability, especially for high-dimensional and dynamic datasets. Applications span from fluid dynamics to public health, demonstrating interdisciplinary impact. Scientific Awards and Recognition: Ayuda adicional within the Juan de la Cierva Incorporación Program (2020), awarded by the State Research Agency (AEI) Research Leadership and Advising: She has served as principal investigator on multiple competitive research projects funded by national and regional agencies, including the State Research Agency (AEI), the Jacques Hadamard Mathematical Foundation, and the Community of Madrid. Her projects cover topics such as constrained additive models, machine learning for sustainable fishing, turbulence control, and ADHD diagnosis using data science. She has supervised at least one doctoral thesis on constrained smoothing models, indicating active mentorship in methodological statistics and optimization. Laboratories and Research Groups: She is a member of the Energy Analytics research group and conducts her work within the UC3M-Santander Big Data Institute, which supports interdisciplinary data science research. Her affiliation with the Flores de Lemus Institute further underscores her engagement with advanced statistical and computational methodologies.

Hassan DehghanpourView profile

Professor

• Enhanced Oil Recovery
• Hydrogen Storage
• Produced Water Treatment
• +3 more

Dr. Hassan Dehghanpour is a Professor in the Faculty of Engineering at the University of Alberta, specializing in Civil and Environmental Engineering with a focus on Petroleum Engineering. He holds a PhD from The University of Texas at Austin (2011), an MS from the University of Alberta (2008), and dual BSc degrees in Petroleum and Mechanical Engineering from Sharif University of Technology (2006). His research centers on fluid-rock interactions in unconventional reservoirs, including hydraulic fracturing, enhanced oil recovery (EOR), and hydrogen storage in salt caverns. Key projects include laboratory experiments on CO2 and natural gas interactions in tight rocks, flowback data analysis for fracture characterization, and hydrogen permeability studies in salt formations. He leads the Dehghanpour Lab, equipped with advanced facilities like HPHT visualization cells and spinning drop tensiometers. His work bridges experimental, analytical, and data-driven approaches to optimize energy extraction and storage technologies. Education: PhD, Petroleum Engineering, UT Austin; MS, University of Alberta; BScs in Petroleum and Mechanical Engineering, Sharif University. Research Interests: Enhanced Oil Recovery (EOR), Hydrogen Storage, Produced Water Treatment, and Energy Data Analytics. Notable projects include developing protocols for evaluating chemical additives in fracturing fluids and assessing the feasibility of storing hydrogen in salt caverns. His lab collaborates with industry partners on projects like cyclic solvent injection for bitumen recovery and AI-driven production optimization. Recent advancements include a workflow for estimating fracture volume using flowback data and studies on wettability alteration using nanoparticles. He advises on well productivity via flowback analysis and has contributed to understanding mechanisms in gas huff-n-puff processes. His work on salt cavern hydrogen storage received Alberta Innovates funding.

Xiao Hui TaiView profile

Assistant Professor

• Statistics
• Data Science
• Machine Learning
• +12 more

Xiao Hui Tai is an Assistant Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. Her research sits at the intersection of statistics, data science, and social science, with a focus on global public health, conflict dynamics, and socioeconomic development. She leverages large-scale, granular data sources such as mobile phone records, satellite imagery, and geospatial datasets to study the impacts of violence, displacement, and environmental hazards. Her research interests include statistical and machine learning methods for causal inference, spatiotemporal modeling, and interdisciplinary applications in public policy and development economics. She is particularly interested in how data science can inform decision-making in humanitarian and low-resource settings. Her work bridges technical rigor with real-world impact, often involving collaboration across disciplines such as political science, economics, public health, and environmental science. The recent publications reflect a strong trend in using novel data sources to address pressing global challenges. Themes include conflict and education, air pollution and mortality, displacement due to violence, and illicit crop monitoring. Her methodological expertise spans record linkage, hierarchical clustering, natural language processing, and satellite-based environmental monitoring. These works demonstrate a consistent focus on both methodological innovation and policy-relevant applications. Hellman Fellow (2024–25) Tai advises students through her research and teaching, having developed and taught courses such as STA 35A (Introductory Statistical Data Science), STA 160 (Capstone in Data Science), and STA 250 (Data Science for International Development). She has mentored student-led research, including a project on air pollution in Chile that led to a publication in Communications Earth & Environment . Her current projects involve interdisciplinary collaborations funded by the L&S Unites Initiative, including automated text analysis of lobbying influence on global health policy. She is actively engaged in the academic community, presenting her work at major conferences such as the Households in Conflict Network, WNAR/IMS, and the Australasian Development Economics Workshop. Tai leads research that integrates data-intensive methods with social science questions, often in collaboration with labs and centers such as the UC Davis DataLab. She previously worked with the Global Policy Lab at UC Berkeley and CyLab at Carnegie Mellon University, maintaining connections to interdisciplinary research teams focused on data for development and security.