Find Academic Supervisors

David N. JansenView profile

Associate Professor

• Computer Science
• Formal Methods
• Model Checking
• +5 more

David N. Jansen is an Associate Professor at the Institute of Software , Chinese Academy of Sciences , Beijing, China. Previously, he held an Assistant Professor position at Radboud University Nijmegen (2007–2016) and a Postdoc role at RWTH Aachen University (2007). His work bridges academic research, software development, and international collaboration. Education : PhD in Computer Science from University of Twente (1998–2003); Diploma in Mathematics (with minors in Computer Science and Comparative Linguistics) from University of Bern (1990–1997); Certificate of Proficiency in English from University of Cambridge (2000). Research interests include stochastic model checking , UML extensions for probabilistic systems , Markov reward models , and formal verification of embedded systems . He has contributed to tools like MRMC and TCM , focusing on efficient data structures and lumping techniques for system validation. Publication trends show expertise in formal methods , probabilistic verification , and equivalence relations . His work spans automated analysis of probabilistic programs , stuttering equivalence algorithms , and applications to real-world systems like the European Train Control System. Leadership and Service : He has served on doctoral advisory boards (SIKS), supervised student projects (e.g., XML/XMI interface for TCM), and volunteered in Christian organizations. His technical skills include C, C++, UML, model checking, and database systems like MySQL.

Ryan WoodsView profile

Assistant Professor

• Epidemiology
• Biostatistics
• Disease Surveillance
• +5 more

Ryan Woods is an Assistant Professor in the University of British Columbia's Faculty of Health Sciences and an Executive Director, Data and Analytics at BC Cancer. He holds a Scientist position in the Department of Cancer Control Research at BC Cancer Research Institute. His academic journey includes a PhD in Population and Public Health from UBC, an MSc in Statistics, and a BSc in Mathematics from the University of Guelph. Dr. Woods' research focuses on leveraging administrative and registry data to address cancer control challenges, particularly in oncology outcomes, cancer screening equity, and health system performance. His work emphasizes international benchmarking, equity gaps in service utilization, and data-driven health policy. Key areas include evaluating disparities in breast cancer screening participation among immigrant populations, analyzing pandemic impacts on cancer diagnosis timing, and optimizing team-based cancer care models. His publications span over two decades, with recent emphasis on pandemic-related healthcare disruptions, multimorbidity trends, and global cancer survival disparities. He has contributed to CONCORD-3 and ICBP initiatives analyzing international cancer outcomes. Dr. Woods also leads BC Cancer's data strategy, integrating biostatistics and epidemiology to advance population health surveillance. His expertise bridges clinical and population-level research, with a focus on translating data insights into actionable strategies for improving cancer care equity and system efficiency. Current projects include spatial access analysis for lung screening programs and modeling pandemic-driven health burden shifts.

Nikolaus AugstenView profile

Professor

• Database Systems
• Data-Centric Applications
• Approximate Matching
• +4 more

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

Gerald RobertsView profile

Professor

• Earthquake Geology
• Structural Geology
• Seismic Hazard Assessment
• +3 more

Gerald Roberts is a full Professor of Earthquake Geology at Birkbeck, University of London, within the School of Natural Sciences. He has held leadership roles as Head of the Department of Earth and Planetary Sciences (2012–2018) and serves on the NERC Core Panel. His research focuses on active fault dynamics, seismic hazard assessment, and Mars geology, supported by a £811,925 NERC grant (2021–ongoing). Education: PhD, University of Durham, 1991 BSc Geology, University of Wales, Cardiff, 1987 Research Interests: Active fault slip-rates and earthquake recurrence Cosmogenic 36Cl and U/Th dating of fault scarps Stress modeling of fault systems Seismic hazard quantification Planetary geology of Mars Grants & Projects: NERC Standard Grant NE/V012894/1 (2021–2025) : Investigates fault interactions in Greece using fieldwork, cosmogenic dating, and Coulomb stress modeling. Past projects include studies in Italy, Crete, and Mars, with a focus on linking fault geometry to seismic behavior. Students & Supervision: Current PhD students: Sam Mitchell (Greece), Tom Wigley (Greece), Brian Kneller (Mars), and Jason Brown (Mars). Alumni include Jennifer Robertson (2021), Francesco Iezzi (2020), and Marco Meschis (2019). Teaching: Leads modules on structural geology, field mapping, and planetary science.

Raj SunderramanView profile

Professor

• Databases
• Data Mining
• Big Data
• +6 more

Raj Sunderraman is a Professor of Computer Science at Georgia State University, specializing in databases, data mining, and logic programming. His research focuses on deductive databases, semantic web technologies, bioinformatics, and graph data modeling. He holds a B.E. (Honors) in Electronics Engineering from Birla Institute of Technology and Science, an M.Tech. in Computer Technology from Indian Institute of Technology Delhi, and a Ph.D. in Computer Science from Iowa State University. His research projects include NeuronBank—a tool for cataloging neuronal circuitry—and work on scalable graph storage systems for big data. He has developed a programming environment for protein structure data and contributed to the paraconsistent relational data model. His teaching and research emphasize practical applications in bioinformatics, geoinformatics, and software systems. Key areas of exploration include reasoning with incomplete/inconsistent data, deductive database semantics, and graph query languages. Recent work involves lambda calculus visualization tools and 3D perception benchmarks for UAVs. He has authored over 150 publications and a textbook on Oracle 10g programming.

• In-Memory Databases
• Data Stream Processing
• Database Systems
• +12 more

Matthias Uflacker is a Professor at the Hasso Plattner Institute (HPI), University of Potsdam, Germany, where he conducts research in database systems, in-memory computing, and data-driven enterprise applications. His work bridges systems research and practical applications in Industry 4.0, education, and bioinformatics. University: University of Potsdam School: Hasso Plattner Institute Department: Department of Digital Engineering Research Focus: In-Memory Databases, Stream Processing, Causal AI, and Database Education His research interests span in-memory database systems , real-time data stream processing , GPU-accelerated analytics , and causal structure learning . He also contributes to computer science education , particularly in agile software engineering and interactive learning tools for K-12. The recent publications reflect a strong trend in enterprise data management , with a focus on benchmarking (e.g., ESPBench), performance optimization (e.g., Kafka, Hyrise), and intelligent decision support using causal models in industrial contexts. His work increasingly integrates AI for monitoring and automation in manufacturing and e-commerce. He has co-authored numerous publications in top-tier venues such as VLDB, ICDE, EDBT, and KDD, often in collaboration with Hasso Plattner and other HPI researchers. Matthias Uflacker leads and contributes to research projects on autonomous database systems , dynamic pricing simulations , and educational technology platforms for programming exercises. His team develops tools for real-time data integration, causal analysis, and classroom software engineering assessment.

• Literary and Environmental Humanities
• Settler Colonial Literature
• Nineteenth-Century Literary Culture
• +13 more

Sarah Comyn is an Associate Professor in the School of English, Drama and Film at University College Dublin, specializing in Economic and Environmental Humanities. She leads the IRC-funded Imperial Minerals project and co-leads a Mellon-funded Global Humanities Institute on post-extractivist legacies. Her research bridges literature, political economy, and colonial history, with a focus on the British settler colonies of Australia, New Zealand, and South Africa. PhD, University of Melbourne Professional Certificate in University Teaching and Learning, University College Dublin (2022) Graduate Certificate in Advanced Leadership and Learning, University of Melbourne Her research interests include 19th-century literary culture, settler colonial literature, the literary representation of mineral extraction, and the transhistorical relationship between literature and political economy. She employs hemispheric, southern, and postcolonial theories to challenge metropolitan-centered literary paradigms and explore south-south literary networks. Her work emphasizes the role of public institutions like mechanics’ institutes and libraries in shaping colonial citizenship and literary value. Her recent publications span monographs, edited collections, and journal articles in venues such as Journal of Victorian Culture and Journal of Commonwealth Literature . Her recent book Political Economy (2024) traces the evolution of economic thought through literary representation, while her earlier works examine Austen’s engagement with political economy and the cultural role of colonial libraries. Humanities Research Centre Visiting Fellow, ANU (2020) Chawton House Library Visiting Fellowship (2016) Australian Postgraduate Award (2010) Amy Gaye Cowper Tennent Memorial Scholarship (2011) Dwight Final Examination Prize (2009) Comyn supervises PhD students and mentors postdoctoral researchers in 19th-century global literary studies, the economic humanities, and ecocriticism. She has secured major grants including the IRC Starting Laureate Award for Imperial Minerals and an Ad Astra Fellowship. Her teaching emphasizes research-led pedagogy, anti-racist curriculum development, and the application of literary analysis to non-traditional 'texts' such as currencies and colonial maps. She coordinates modules including Fiction and Financial Crises , Un/Settling Global Literatures , and Reading South Africa . She is affiliated with the UCD Humanities Institute and leads the Literary Mines Database project, contributing to digital humanities and collaborative research on extractive industries and literary culture.

George FletcherView profile

Professor

• Data Systems
• Graph Data Management
• Query Languages
• +7 more

George Fletcher is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), where he chairs the Database Group in the Data and Artificial Intelligence Cluster. His research focuses on data systems, particularly the social aspects of data systems and the theoretical and engineering foundations of query and schema languages. He is currently investigating graph data management in domains such as social networks, knowledge graphs, biological networks, and communication networks, as well as data systems education. His academic training includes a PhD in Computer Science from Indiana University Bloomington and undergraduate studies in Mathematics and Cognitive Science at the University of North Florida. His research interests lie at the intersection of database systems, logic, and artificial intelligence, with a strong emphasis on the formal foundations of graph data querying. He explores expressiveness of navigational query languages, transitive closure, bisimulation, and synthetic workload generation for benchmarking graph databases. His recent publications demonstrate deep theoretical contributions to query language design and analysis. The trend in his recent articles (2015–2016) shows a concentrated effort on understanding the expressive power of query languages over graph-structured data, particularly through formal methods such as relation algebra and logical characterizations. These works contribute to both theoretical computer science and practical database system design, especially in the context of unlabeled graphs and Boolean queries. Executive Board Member, EDBT Association Associate Editor, ACM SIGMOD Record Associate Editor, Transactions on Graph Data and Knowledge He advises research within the Database Group and leads projects related to graph data systems. Though specific grants are not listed, his leadership in major research initiatives and editorial roles suggests active grant involvement and academic mentorship. He teaches courses such as Capita Selecta Databases, Seminar Datamanagement, Datamodeling and Databases, Knowledge Engineering, and Data Management for Data Analytics. He is a core member of the Database Group at TU/e and part of EAISI (Eindhoven Artificial Intelligence Systems Institute), contributing to foundational AI research with a focus on data infrastructure and intelligent data systems.

Matthias SpringsteinView profile

Researcher

• Computer Vision
• Deep Learning
• Multimedia Information Retrieval
• +5 more

Matthias Springstein is a researcher at the Technical Information Library (TIB), which is affiliated with Leibniz University Hannover. He is part of the Visual Analytics research group within TIB's Research & Development department, focusing on advanced computer vision and multimedia information retrieval techniques. His research interests span multiple areas of artificial intelligence with emphasis on computer vision applications. Springstein specializes in web-supervised learning for visual concepts, incremental learning approaches, and methods to minimize manual labeling efforts for training data. His work bridges theoretical machine learning with practical applications in digital humanities, particularly in art-historical image analysis and film/video studies. His publication record shows a consistent focus on multimodal analysis, with recent work exploring knowledge graphs for image classification, large-scale hierarchical classification of art-historical images, and computational tools for film analysis. The research demonstrates progression from foundational work in image-text relations and depth estimation toward increasingly sophisticated applications in cultural heritage and scholarly media analysis. Notable achievements include receiving the Best Paper Award at the International Conference on Multimedia Retrieval (ICMR) in 2019 for his work on semantic image-text relations. Springstein has developed TIB AV-Analytics, a computational platform for scholarly video analysis that has been presented at multiple major conferences (SIGIR 2023, SCSMI 2024), indicating significant impact in both the information retrieval and film studies communities.

• Age-Related Macular Degeneration
• Diabetic Retinopathy
• Retinal Imaging
• +4 more

Dimitra Skondra, MD, PhD, is a Professor at the University of Chicago's Department of Ophthalmology and Visual Science, specializing in vitreoretinal diseases. A board-certified retina specialist, she focuses on surgical and medical treatments for conditions including diabetic retinopathy, retinal detachment, and age-related macular degeneration (AMD). Her clinical expertise is complemented by active research in retinal imaging, gut microbiome interactions with ocular health, and therapeutic innovations. Education: Fellowship in Vitreoretinal Surgery, Harvard Medical School (2013) Residency in Ophthalmology, Weill Cornell Medical College (2011) PhD/Postdoctoral Research, University of Crete Medical School (2007) MD, University of Crete Medical School (2001) Research Focus: Dr. Skondra leads translational studies investigating the gut-retina axis in AMD pathogenesis, metformin's protective effects against retinal degeneration, and advanced imaging for early disease detection. Her work integrates microbiology, pharmacology, and epidemiology to develop targeted interventions. Publication Trends: Recent articles emphasize metformin's role in AMD prevention, racial disparities in retinal vascular health, and non-surgical treatments for macular holes. Her research consistently bridges clinical practice with molecular mechanisms, particularly in microbiome-related therapies. Awards: Retina Society Raymond Margherio Award (2013) Harvard/Alcon Clinical Research Scholar Award (2013) ARVO/Alcon Early Career Clinician Scientist Award (2006) Knights Templar Eye Foundation Award (2006) Dr. Skondra collaborates with multidisciplinary teams to advance retinal disease management, though specific lab/student details are undisclosed in the source text.

Sidharth KumarView profile

Associate Professor

• High-performance computing
• Data visualization
• Scalable algorithms
• +7 more

Sidharth Kumar is an Associate Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), where he leads research in high-performance computing and data visualization. He joined UIC in August 2023 after previously working at the University of Alabama at Birmingham. His research focuses on developing scalable algorithms and data structures for data-intensive applications, intersecting HPC, visualization, databases, and machine learning. Education: Ph.D. in Computing (2016) from the University of Utah's Scientific Computing and Imaging Institute, advised by Valerio Pascucci. Bachelor of Technology in Information and Communication Technology (2009) from DAIICT, Gandhinagar, India. Research Interests: Dr. Kumar's work centers on parallel I/O, GPU acceleration, big data processing, and scientific visualization. His projects include: 1) Exascale data management systems, 2) GPU-accelerated web visualization, 3) Declarative analytics frameworks, and 4) Topology-driven analysis for neuroscience and virology. He develops solutions for memory-constrained environments and heterogeneous systems. Publication Trends: Recent works (2023-2025) demonstrate strong focus on GPU-accelerated databases (Datalog optimizations), parallel communication algorithms (all-to-all collectives), memory-efficient visualization techniques (speculative raycasting), and applied topological analysis (brain networks, virus taxonomy). His publications consistently appear in top-tier HPC and visualization venues. Awards & Honors: Best Paper Awards: IEEE HiPC (2019), ISC Hans Meuer (2020), LDAV (2023) Honorable Mention: PacificVis (2025) Poster Awards: SC23 Finalist, HiPC SRS (2021) NSF EPSCoR Research Fellow (2022) Grants & Advising: NSF PPoSS Large: Declarative Analytics ($960K PI) NSF SHF: Scalable I/O Runtime ($300K PI) NSF EPSCoR: Relational Algebra ($265K PI) Advises 6 PhD students in HPC and visualization research Lab & Service: Leads a research team working on exascale computing challenges. Serves on technical committees for SC, ISC, IPDPS, and HiPC conferences. Teaches courses in Database Systems, Algorithms, and Data Visualization.

Birgit WildView profile

Assistant Professor

• Arctic Climate Change
• Permafrost Studies
• Carbon Cycling
• +5 more

Birgit Wild serves as an Assistant Professor in the Department of Environmental Science at Stockholm University. Her research focuses on Arctic climate change impacts on biogeochemical cycles, with particular emphasis on carbon and nitrogen dynamics in permafrost regions. She maintains her office in Room R 511 at Svante Arrheniusväg 8 C, Geohuset, Stockholm. Dr. Wild's primary research interests center on how climate warming affects carbon and nitrogen cycling in Arctic ecosystems. Her work investigates the mechanisms by which thawing permafrost releases greenhouse gases, with specific attention to plant-soil interactions ("priming effect"), carbon-nitrogen feedbacks on plant productivity, greenhouse gas release from subsea permafrost, nitrogen cycling and N2O production in Arctic Ocean shelves, and the transfer of permafrost-derived carbon along the land-river-ocean continuum. Her research integrates field studies across Siberian and Arctic regions with laboratory experiments and large-scale data synthesis. Her publication record demonstrates a strong focus on understanding permafrost carbon dynamics under climate change. The most recent articles reveal significant contributions to understanding the resistance of peat soils to priming effects, temperature controls on lignin decomposition, nitrous oxide dynamics in Arctic waters, and molecular-level degradation of organic matter in Arctic shelf seas. Her work shows consistent attention to both terrestrial and marine components of the Arctic carbon cycle, with particular emphasis on how past warming events can inform our understanding of future climate-carbon feedbacks. Dr. Wild leads and participates in multiple significant research projects including the Circum-Arctic Sediment Carbon Database (CASCADE), which curates data from the entire Arctic Ocean on organic carbon and related parameters; studies on ocean acidification in Siberian Seas; research on mercury remobilization from degrading permafrost; and projects examining plant-soil interactions in permafrost systems (NITROPRIME and PRIMETIME). She also works on quantifying methane emissions from subsea permafrost systems (SuPerTip project). Her research methodology combines field sampling across Arctic regions, laboratory incubation experiments, molecular biomarker analysis, and isotope-based source apportionment techniques. This multi-faceted approach allows her to address complex questions about carbon and nutrient cycling across spatial and temporal scales in the rapidly changing Arctic environment.

Souvick GhoshView profile

Assistant Professor

• Conversational Search Systems
• Database and Other Retrieval Systems
• Human Information Behavior
• +10 more

Souvick Ghosh is a Tenure-Track Assistant Professor at the School of Information, San Jose State University. His research focuses on conversational search systems, human-computer interaction, and information retrieval, integrating machine learning and natural language processing to enhance interactive information-seeking environments. Ph.D. : Communication, Information, and Media Studies, Rutgers University (2020) M.S. : Computer Science and Engineering, Jadavpur University (2016) B.S. : Computer Science and Engineering, Jadavpur University (2011) His research interests include conversational search systems, database and retrieval systems, human information behavior, human-computer interaction, information retrieval theory and practice, information systems and technologies, information visualization, and research methods. He applies machine learning, natural language processing, and deep neural networks to develop intelligent functionalities in voice-based and interactive environments. His recent publications span topics such as conversational agents, question quality in social Q&A, citation sentiment, and learning-oriented search. These works appear in top venues like HICSS, ASIS&T, SIGIR, and CHIIR, reflecting a consistent focus on improving user-system dialogues, modeling search behavior, and designing human-centered conversational systems. Outstanding Graduating Student Award, Rutgers University (2020) Ghosh has taught graduate and undergraduate courses in data science, programming, and information technology at both SJSU and Rutgers. He leads the ICANN Lab, where he mentors students and conducts ongoing research in conversational AI and information systems. His work emphasizes technology for social good, with applications in education, accessibility, and equitable information access.

Mathieu PicardeauView profile

Professor

• Bacteriology
• Leptospirosis
• Genomics
• +7 more

Mathieu Picardeau is Head of the Biology of Spirochetes Unit at the Institut Pasteur in Paris, France. The unit serves as the French National Reference Center (NRC) for Leptospirosis and a WHO Collaborating Center for Research and Reference on Leptospirosis. He leads research on the genomics, epidemiology, and diagnostics of Leptospira , a significant zoonotic pathogen affecting tropical and underserved populations globally. His research focuses on: Whole-genome sequencing and cgMLST genotyping of Leptospira strains Development of improved diagnostic tools, including ELISA and point-of-care tests Characterization of Leptospira biodiversity and discovery of novel species Epidemiological surveillance in France and overseas territories Application of targeted genome capture to overcome cultivation challenges His recent publications reveal strong trends in molecular epidemiology, diagnostic validation, and global pathogen surveillance. He contributes to international taxonomic efforts and One Health approaches, with studies spanning human, animal, and environmental health across multiple continents. His work is central to improving healthcare outcomes for neglected tropical diseases. Scientific contributions and recognitions include: Leadership of the French NRC and WHO Collaborating Center for Leptospirosis Development of the publicly accessible cgMLST database at https://bigsdb.pasteur.fr/leptospira/ Principal investigator on multiple national and international research projects Active participation in the International Committee on Systematics of Prokaryotes As a principal investigator, Dr. Picardeau mentors junior scientists and collaborates extensively with teams in endemic regions such as Laos, Costa Rica, Algeria, and French Guiana. His research is supported by institutional funding and international collaborations. The Biology of Spirochetes Unit maintains a large, curated collection of Leptospira strains and plays a key role in public health response and outbreak investigation. Future work is expected to focus on real-time genomic surveillance, vaccine development, and expanding diagnostic accessibility in low-resource settings.

Tim SeppeltView profile

Researcher

• Graph Theory
• Theoretical Computer Science
• Algorithmic Complexity
• +3 more

Dr. Tim Seppelt is a postdoctoral researcher at the IT University of Copenhagen , working under the mentorship of Prof. Radu Curticapean. Previously, he earned his PhD from RWTH Aachen University with supervisors Prof. Martin Grohe and Prof. Michael Schaub. His research focuses on theoretical computer science, specifically homomorphism indistinguishability , a framework connecting graph isomorphism, quantum information, and logical equivalences. Current Role: Postdoc in Theoretical Computer Science, ITU Education: PhD in Computer Science, RWTH Aachen University Tim's work addresses algorithmic meta-theorems for homomorphism indistinguishability over minor-closed and treewidth-bounded graph classes. He has extended Lovász-type results to CMSO2 logic, resolved complexity conjectures for the Lasserre hierarchy, and classified quantum group-induced indistinguishability relations. His research spans quantum computing , graph algorithms , and descriptive complexity , often intersecting with applications in machine learning and finite model theory. Recent publications include a 2025 paper on quantum group-driven homomorphism indistinguishability and a 2024 journal article on logical equivalences and forbidden minors. He presented at workshops like the Graph Learning Meets TCS (Simons Institute, 2025) and delivered tutorials on finite model theory at Finite and Algorithmic Model Theory 2025 (Les Houches, France).