Find Academic Supervisors

Wayne MaddisonView profile

Professor

• Evolutionary Biology
• Systematics
• Phylogenetics
• +3 more

Dr. Wayne Maddison is a Professor in the Departments of Botany and Zoology at the University of British Columbia, within the Faculty of Science. His research focuses on jumping spider systematics, phylogenetic theory, and computational biology. He is known for contributions to software like Mesquite and MacClade, aiding phylogenetic analysis. His work integrates empirical studies of spider diversity with theoretical advancements in evolutionary inference. Key research areas include evolutionary and ecological theory, systematics, and biodiversity conservation. Maddison has led expeditions to diverse regions like Siberia, Mexico, and Madagascar, discovering new species and documenting spider behavior. His lab emphasizes the intersection of fieldwork, morphology, and molecular data to understand arthropod evolution. Publications span spider phylogeny, taxonomic revisions, and methodological innovations in systematics. Collaborative projects include global phylogenomic studies of jumping spiders and reclassification efforts within the Salticidae family. His work bridges traditional taxonomy with computational tools, advancing both empirical and theoretical aspects of evolutionary biology.

Alexander A. RazborovView profile

Professor

• Complexity Theory
• Circuit Complexity
• Proof Complexity
• +5 more

Alexander A. Razborov is the Andrew MacLeish Distinguished Service Professor of Computer Science and Mathematics at the University of Chicago and Adjoint Professor at the Toyota Technological Institute. His primary research focuses on complexity theory, including circuit complexity, proof complexity, quantum computations, and communication complexity. Previously he worked in combinatorial group theory and is actively exploring extremal combinatorics. He leads the Theoretical Computer Science Group, which connects computer science with physics, statistics, and mathematical sciences. His educational contributions include teaching Honors Discrete Mathematics, Mathematics of Quantum Computing, Complexity Theory, and other advanced topics. Razborov has received numerous prestigious awards including the Rolf Nevanlinna Prize (1990), Gödel Prize (2007), and election to the American Academy of Arts & Sciences (2020). He has supervised over 12 PhD and Master's students, with research spanning combinatorial open problems in proof complexity, continuous combinatorics, and communication complexity.

Wei LiuView profile

Associate Professor

• Natural Language Processing
• Generative AI
• Knowledge Graph Construction
• +6 more

Associate Professor Wei Liu is a full-time academic in the Department of Computer Science and Software Engineering at The University of Western Australia (UWA), leading the UWA Centre for Natural and Technical Language Processing. Her research focuses on knowledge discovery from natural language text, generative AI, semantic technologies, deep learning for knowledge graphs, and sequential data mining. She holds a PhD from the University of Newcastle (2003) and an MEng from Huazhong University of Science and Technology (China). Research Interests: Generative AI and Large Language Models (LLM) Multimodal Knowledge Graph Construction Human-AI Collaboration Systems Clinical Data Analysis (Ophthalmology) Technical Language Processing for Maintenance Data Neural-Symbolic Computation Recent Work Trends: Her publications emphasize multimodal systems integration, knowledge graph refinement, and adversarial AI applications. Notable 2025 work includes Docs2KG (human-LLM collaboration), Auto-Regressive Diffusion for 3D interactions, and spherical embeddings for logical queries. Scientific Awards: First Prize in IEEE ICDM/ICBK Knowledge Graph Contest (2020) Best Contribution to Science Award at DICTA 2020 2022 Students Choice Award for academic support Advising & Grants: Leads 25 active grants including ARC Training Centres in Critical Resources and Maintenance Data Science. Collaborates with industry partners like Aspire Digital Group and Minerals Research Institute of Western Australia. Labs/Teams: Directs UWA's Natural and Technical Language Processing Centre, focusing on projects like maintenance work order analysis, geological survey data fusion, and clinical data integration in ophthalmology.

Pekka OrponenView profile

Professor

• DNA nanotechnology
• Algorithmics of self-organisation
• Computational complexity theory
• +3 more

Pekka Orponen is a Professor of Computer Science at Aalto University's School of Science, Department of Computer Science. He holds academic appointments in both the Computational Life Sciences (CSLife) and Algorithms and Theoretical Computer Science (TCS) research areas. His roles include former Head of the Aalto Department of Information and Computer Science (2008–2014) and Vice-Head for Research of the Aalto Department of Computer Science (2015–2016). Education: MSc and PhD in Computer Science from the University of Helsinki (1983 and 1986) Postdoctoral research at the University of Toronto Research Interests: Focuses on algorithms and complexity theory, with contributions to computational complexity, neural network computation, stochastic algorithms, and nucleic acid nanostructure design. His notable work includes algorithmic design of 3D DNA nanostructures and RNA origami, recognized as an ISI highly-cited paper in biology/biochemistry (2015). Publications: Over 100 publications, emphasizing nanostructure design, computational methods, and interdisciplinary applications in bioengineering and materials science. Recent work includes automated DNA wireframe design tools and RNA secondary structure prediction via neural networks. Awards: 2021: Knight First Class of the Order of the White Rose of Finland 2018: Best Paper Award at UCNC 2023: Grant for Algorithmic Design of DNA Nanotechnology Advising & Grants: Led the ALBION project (2017–2021) on biomolecular nanostructure design. Supervised 4 theses and held leadership roles in CODATA and Informatics Europe. Engaged in academic service, including roles at the Finnish Academy of Science and Letters. Research Groups: Head of the Natural Computation research group at Aalto, focusing on DNA/RNA nanotechnology and algorithmic self-assembly. Maintains active collaborations in Europe and internationally.

Jeffrey BarrettView profile

Professor

• Philosophy of Science
• Philosophy of Physics
• Quantum Mechanics
• +5 more

Jeffrey Barrett is the Chancellor’s Professor in the Department of Logic and Philosophy of Science at the University of California, Irvine (UCI). He holds a Ph.D. from Columbia University (1992). His research focuses on resolving quantum mechanics' measurement problem by exploring collapse-free solutions and modeling empirical/mathematical inquiry through evolutionary game theory and self-assembling games. He collaborates with Brian Skyrms on coevolutionary models of descriptive language and predictive theory, examining how truth, probability, and priors interact with linguistic evolution. His work bridges philosophy of physics and cognitive science, addressing topics like the Many-Worlds Interpretation, algorithmic randomness, and the evolution of compositional language. Notable contributions include critiques of standard quantum mechanics' inconsistency and frameworks for understanding observer roles in quantum contexts. Barrett’s publications span foundational physics, epistemology, and social science methodologies, often employing mathematical formalisms to clarify conceptual issues. He has served as referee for journals in philosophy and physics, contributed to edited volumes on Hugh Everett’s work, and reviewed key texts in quantum mechanics pedagogy. His current projects extend evolutionary game theory to model complex interactions between self-assembling games and the emergence of higher-order cognitive frameworks.

Leonid LevinView profile

Professor

• Theory of Computation
• Randomness in Computing
• Algorithmic Complexity
• +6 more

Leonid Levin is a Professor in the College of Arts & Sciences, Department of Computer Science at Boston University since 1980, promoted to Full Professor in 1984. He holds dual PhDs from Moscow University (1972) and MIT (1979). His research focuses on computational complexity, randomness, algorithmic information theory, and foundational aspects of mathematics and computer science. Key roles include membership in the US National Academy of Sciences (2019) and the American Academy of Arts and Sciences (2014). He has held visiting professorships at institutions such as the Institut des Hautes Études Scientifiques (France), Heidelberg University (Germany), and Hebrew University (Jerusalem). Levin’s work bridges theoretical computer science and mathematics, with contributions to one-way functions, pseudorandomness, and Kolmogorov complexity. Notable publications include foundational papers on computational complexity and cryptography, such as A pseudorandom generator from any one-way function (1999) and The tale of one-way functions (2003). His research interests span algorithmic randomness, fault-tolerant systems, and interdisciplinary topics like taxation and valuation. Awards include the Guggenheim Fellowship (1993-94) and recognition as a Clay Mathematics Institute Scholar (2001-2002).

Karen RuebensView profile

Researcher

• Paleoarchaeology
• Proteomics
• Human Evolution
• +3 more

Dr. Karen Ruebens is a Principal Investigator of the COEXIST project at the University of Reading's Department of Archaeology, focusing on Neanderthal and early Homo sapiens coexistence in Central and Southeast Europe. She is an affiliated researcher at the Collège de France and serves as an editor for the Paleoanthropology journal. Her work integrates proteomics (e.g., ZooMS) with archaeological analyses to study fragmented bone assemblages. Key affiliations include cross-disciplinary collaborations with History, Classics, and Geography departments. Recent research emphasizes genomic studies, isotopic analyses, and sustainable methodologies in paleoproteomics. Her research interests span human evolution, subsistence strategies, and Middle/Upper Paleolithic technological transitions. Notable projects include investigations into early Homo sapiens dispersal into Europe and Neanderthal subsistence behaviors. She actively promotes open-access publishing through her editorial role and advocates for large-scale, interdisciplinary approaches in archaeological science. Recent publications highlight advancements in ZooMS applications, stable isotope interpretations, and chronological frameworks for key European sites. Her work bridges methodological innovation with paleoenvironmental and cultural context, contributing to debates on human adaptation and migration during the last glacial period.

Hagen HirschmannView profile

Researcher

• Corpus-based variety research
• Language acquisition and learner corpora
• Grammar of modifiers
• +4 more

Hagen Hirschmann is a Researcher at the Department of German Language and Linguistics at Humboldt University of Berlin, affiliated with the Faculty of Language and Literature. He currently holds a position as a research assistant at the Chair of Corpus Linguistics and Morphology. His past roles include substitute professorships for Corpus Linguistics and Morphology (2020/2021) and a visiting professorship for German as a Foreign Language (2018-2019). He is a member of the GESLA research group (German Second Language Acquisition) and collaborates with projects such as Falko (a learner corpus), Dulko (a German-Hungarian learner corpus), and the SFB 1412 Register project. His research focuses on corpus-based studies of language variation, syntax modeling, learner corpora, and diachronic analysis. He has contributed to the development of linguistic tools like the ANNIS search interface and annotation guidelines for corpora. His work spans academic committees, including the Promotionsausschuss and Kommission für Lehre und Forschung at Humboldt University. He has supervised doctoral students in second language acquisition and orthographic analysis. Hirschmann has taught at institutions worldwide, including guest professorships in China and workshops in Cuba, focusing on corpus linguistics and methodology.

Aravindan IlangovanView profile

Associate Professor

• Structural Biology
• Protein-nucleic acid complexes
• Helicases
• +3 more

Dr. Aravindan Ilangovan is an Associate Professor (Reader) in Structural Biology at Queen Mary University of London, affiliated with the School of Biological and Behavioural Sciences and the Centre for Molecular Cell Biology. His research focuses on structural biology of bacterial nucleoprotein complexes, including helicases and DNA replication mechanisms. His lab uses cryogenic electron microscopy (Cryo-EM) to study molecular mechanisms of bacterial processes such as DNA replication initiation, conjugative systems, and type IV/V secretion systems. Recent research highlights include the structural basis of DnaA-dependent DNA unwinding at bacterial replication origins (BUS system) and characterization of type VI secretion system components. His work spans bacterial genetics, protein-DNA interactions, and molecular microbiology, with applications in understanding antibiotic resistance mechanisms. Key grants include funding from MRC, BBSRC, and Wellcome Trust for projects on DNA processing in antibiotic resistance, helicase loading mechanisms, and linear ubiquitin signaling in innate immunity. His lab collaborates on structural studies of bacterial systems and hosts a research group including Dr. Yuanyuan Li and Dr. Ruina Liang. Publications span high-impact journals like Nature Communications, Nucleic Acids Research, and Cell, with a focus on structural and mechanistic insights into bacterial molecular systems. The lab website (www.ilangovan-lab.com) provides detailed research descriptions and ongoing projects.

• Second language acquisition
• Multilingualism
• Conceptual transfer
• +3 more

Hanna Solberg Andresen is a Research Fellow in Multilingualism at the Department of Linguistics and Nordic Studies, University of Oslo. She holds a PhD (2024) focusing on bilingual Norwegian-English children's conceptualization, and prior degrees including a Master's in Norwegian as a Second Language (2006) and a Cand. Mag. in Nordic Studies and interdisciplinary fields (2001). Her academic focus spans first/second language acquisition, multilingualism, conceptual transfer, and linguistic complexity. She has taught advanced courses in second language research methodologies and pedagogy at UiO. Her research examines how language structure influences thought processes, particularly in bilingual motion event descriptions. Publications include studies on Norwegian linguistic patterns and cross-linguistic conceptualization differences. Andresen is affiliated with the Language and Cognition research group and has contributed to media discussions on language learning and literacy. She has no listed awards but maintains active research collaborations in Nordic and international linguistics circles.

• Computational Linguistics
• Endangered Languages
• Indigenous Language Technologies
• +5 more

Antti Arppe is an Associate Professor in the Department of Linguistics at the University of Alberta, Faculty of Arts. His research focuses on computational methods in linguistic analysis, particularly applied to endangered and Indigenous languages. He teaches courses like LING 325 - Writing Systems, exploring the historical and typological dimensions of writing systems and their cognitive impacts. His work integrates formal linguistic theory with advanced computational tools, addressing challenges in morphosyntax, phonology, and language documentation. Key research areas include: computational modeling of polysynthetic languages (e.g., Plains Cree), finite-state methods for morphological analysis, and developing open-source tools like DescTools for descriptive statistics. Arppe collaborates with Indigenous communities to create language revitalization technologies, including speech databases and interactive learning platforms. His contributions bridge academic linguistics with practical applications in language preservation and pedagogy. Recent projects highlight innovations in AI for endangered languages, such as evaluating neural ASR robustness for morphologically complex systems and leveraging word embeddings to enhance low-resource language tools. He actively participates in workshops and conferences on computational methods in endangered language studies, emphasizing interdisciplinary collaboration between linguists, technologists, and community stakeholders. Teaching emphasizes connecting theoretical linguistic concepts to real-world applications, with courses addressing writing systems’ sociocultural roles and technical dimensions. His publications span over two decades, demonstrating sustained engagement with both foundational linguistic theory and cutting-edge computational methodologies.

Michael TautschnigView profile

Lecturer

• Software Verification
• Concurrency
• Decision Procedures
• +5 more

Dr. Michael Tautschnig is a Lecturer in Theoretical Computer Science at Queen Mary University of London, School of Electronic Engineering and Computer Science. He holds a PhD from Vienna University of Technology (2011) and a Master's from TU Munich (2006). His academic roles include Director of Undergraduate Admissions and teaching modules like Programming for Artificial Intelligence and Data Science. He has extensive industry experience as a Senior Software Development Engineer at Amazon Web Services (AWS), focusing on security and verification. Research interests include software verification, concurrency, decision procedures, formal methods, and embedded systems. He has contributed to projects like CBMC (C Bounded Model Checker) and FShell, emphasizing tools for program analysis and testing. His work spans formal verification of low-level software, weak memory models, and automated testing frameworks. Publications highlight contributions to model checking, concurrency analysis, and verification competitions. Awards include a patent for API optimization and a best paper award. He has secured grants such as the Google Faculty Research Award (2014-2015) and GCHQ Small Grant (2014-2015). Active in conference organization (e.g., CAV, TACAS) and program committees, he promotes academic-industrial collaboration in software verification. Labs/Teams: Collaborations include AWS Security, University of Oxford, and TU Wien. His work bridges academia and industry, focusing on scalable verification tools for large software systems.

Simon HadfieldView profile

Associate Professor

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

Dr Simon Hadfield is an Associate Professor (Reader) in Robot Vision and Autonomous Systems at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on advancing computer vision and machine learning techniques for real-world applications, particularly in robotics. He holds a PhD and certifications such as FHEA (Fellow of the Higher Education Academy) and AUS (Advanced University Skills). Research Interests: - Effective exploitation of novel visual sensors - Practical application of computer vision to robotics - Efficient dynamic reconstruction techniques - Real-time robotics applications - Sensor fusion and calibration systems Recent projects include the NIMROD initiative exploring analogue visual sensors and contributions to multimillion-pound research efforts in sustainable packaging and agricultural robotics. His work emphasizes bridging the gap between lab-based techniques and real-world deployment, with publications in top-tier conferences and journals. Notable achievements include developing paradigms for low-complexity 3D reconstruction and proposing methods for depth estimation and localization. His research has been funded by grants such as a £1m award for robotic applications in farming. Collaborations and lab affiliations include CVSSP, where he leads projects at the intersection of vision, robotics, and autonomous systems. Future work continues to focus on scalable vision techniques and ethical AI practices in representation learning.

Paul M. GartsideView profile

Professor

• Analytic Topology
• Descriptive Set Theory
• Continua Theory
• +3 more

Paul M. Gartside is a Professor at the University of Pittsburgh, affiliated with the Department of Mathematics within the Dietrich School of Arts and Sciences. He has been at Pitt since 2001, following postdoctoral research positions at the University of Oxford, the University of Auckland, Moscow State University, and NUI-Galway. Prior to joining Pitt, he held a Junior Lectureship at Oxford from 1999 to 2001. Education & Training: PhD (DPhil) in Mathematics, University of Oxford (1993) Research Interests: His work centers on analytic topology, particularly function spaces and hyperspaces, and their interplay with order structures. He explores descriptive set theory, continua theory, and geometric topology, including knot theory and foliations. A recurring theme in his research is the study of complexity and computability within these domains. Advising & Grants: No formal advisees (PhD/Master’s students) or grant details are explicitly listed in the provided text. Labs/Teams: No specific laboratories, research groups, or collaborative teams are mentioned in the text.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +3 more

Egoitz Laparra is a Researcher and Adjunct Instructor at the University of Arizona's College of Information Science since 2017. His work focuses on advancing natural language processing (NLP) methods for analyzing human language in medical and environmental domains. He specializes in machine/deep learning techniques for tasks like domain adaptation, information extraction, and semantic parsing. Research Interests: - Natural Language Processing (NLP) with emphasis on medical and environmental applications - Machine learning and deep learning methodologies - Domain adaptation and transfer learning in clinical and policy texts - Geographical ontology development and toponym resolution - Lexical resources like WordNet and predicate matrices Publications highlight innovations in NLP for environmental policy analysis, clinical text mining, and cross-lingual semantic interoperability. His recent work addresses challenges in metadata extraction from environmental impact statements and improving geolocation entity recognition through deep learning. Collaborations include the NewsReader project for cross-lingual news analysis. Laparra contributes to open-source projects like Eidos and INDRA, which transform free text into causal models. He maintains active involvement in international NLP competitions (e.g., SemEval tasks) and develops tools like Taxonomy Builder for knowledge organization.