Find Academic Supervisors

Aurélie LEBORGNEView profile

Lecturer

• Spatio-temporal Data Analysis
• Graph Modeling
• Frequent Pattern Mining
• +3 more

Aurélie Leborgne is a Lecturer in Computer Science at the University of Strasbourg's ICube laboratory, affiliated with the Department of Computer Science at IUT. She completed her PhD in Computer Science at INSA Lyon (2016), a Master's at the University of Caen (2012), and a University Diploma in Higher Education Pedagogy at Strasbourg (2022). Her research spans two domains: 1) Computer Science focusing on spatio-temporal graph modeling, pattern mining, and applications in medical imaging/environmental monitoring; and 2) Pedagogy investigating emotional awareness, collaborative learning, and teaching methodologies. She leads significant research projects including the ANR JCJC-funded MoS-T (2021-2025) on spatio-temporal pattern mining and METEC-Graphe (2019-2022) on spatio-temporal data modeling. She currently advises PhD candidates Assaad O. Zeghina and Romain Perrin, and has supervised three Master's students. Her teaching portfolio includes programming (C#, Java, C++), web technologies, AI, databases, and professional development courses at IUT. Research responsibilities include leading ANR projects and serving on laboratory councils. Teaching leadership includes implementing reflective practices across the department (2021-2023) and coordinating internships (2017-2018).

• Automatic text analysis
• Information search
• IT project management
• +2 more

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

Lauren WallnerView profile

Associate Professor

• Cancer health services research
• Implementation science
• Health equity
• +4 more

Dr. Lauren Wallner is a University of Michigan-based cancer health services researcher with dual appointments as Associate Professor in the Department of Internal Medicine (Medical School) and School of Public Health. She co-leads the Cancer Control and Population Sciences Program at Rogel Cancer Center, while affiliated with the Cancer Surveillance and Outcomes Research Team (CanSORT) and Institute for Healthcare Policy and Innovation. BS, MPH, and PhD from University of Michigan Postdoctoral training at Kaiser Permanente Southern California and UCLA Her research examines cancer care delivery systems across the continuum, focusing on: Health equity in diagnosis/treatment Implementation science for care models Aging and cognitive impairment impacts Multi-level interventions for care delivery Provider-patient decision-making dynamics Recent publications highlight: Population-based cancer survivorship studies (2025) across breast, prostate, and thyroid cancers End-of-life care disparities in ovarian and metastatic cancers Implementation challenges in screening and provider-facing interventions Psychosocial and communication barriers in urology and gastroenterology Health equity in clinical trial availability Geriatric oncology intersections with polypharmacy and cognitive status Dr. Wallner's funded research (NIH, American Cancer Society) emphasizes team-based care systems and equity-focused interventions. She actively mentors cancer care delivery researchers while collaborating with national cancer registries and large healthcare databases. She contributes to healthcare policy through her affiliations with the Institute for Healthcare Policy and Innovation, with clinical insights into multidisciplinary care models via CanSORT. Her work spans electronic health record analyses, survey research, and implementation trials across diverse patient populations.

• Linguistics
• Computational Linguistics
• Cognitive Science
• +7 more

Aniello De Santo is an Assistant Professor in the Department of Linguistics at the University of Utah, where he has been serving since July 2020. He earned his PhD in Linguistics from Stony Brook University in 2020 and is fluent in Italian, which informs his research on Italian syntax and processing. His research spans multiple disciplines with key interests in: Computational Linguistics and Syntax Cognitive Science and Theoretical Frameworks Artificial Intelligence and Machine Learning Applications Formal Language Theory and Complexity Psycholinguistic Processing Models De Santo's scholarly work demonstrates remarkable interdisciplinary integration, with publications in top journals across linguistics, cognitive science, and computer science. His recent work focuses on theoretical foundations in cognitive science (2024 publications on plausibility criteria and theoretical virtues), empirical investigations of Italian relative clause processing (2024), and innovative applications of machine learning techniques to linguistic problems. His research often bridges formal theoretical analysis with computational modeling and empirical validation. He has developed innovative educational approaches including an asynchronous general-education course "Language in the United States" (2023) and teaches a diverse range of courses in computational linguistics, syntax, and language studies. His extensive teaching portfolio includes Computers & Language, Introduction to Syntax, Computational Linguistics, and various research/thesis courses at both undergraduate and graduate levels. De Santo is actively engaged in community outreach as an organizer of the North American Computational Linguistics Open Competition (NACLO) for high school students, conducting practice sessions and hosting competition rounds since 2022. This work connects academic linguistics with pre-college education and demonstrates his commitment to promoting computational thinking in language studies.

Domenico LemboView profile

Professor

• Knowledge Representation and Reasoning
• Description Logics
• Ontology-Based Data Access
• +6 more

Domenico Lembo is a Full Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering. He is a leading figure in Knowledge Representation and Reasoning, focusing on Description Logics, Semantic Web, and Ontology-Based Data Access (OBDA). His research spans information integration, inconsistency-tolerance, and controlled query evaluation, with significant contributions to theoretical and applied data management. Academic Roles: Full Professor (since 2004), Program Committee Chair for major AI/DB conferences (IJCAI, AAAI, ISWC), Co-founder of OBDA Systems startup Leadership: Head of CAIRNE Rome Office (since 2023), General Chair of SEBD 2025, Vice-President of RR Conference Steering Committee Research Interests: Lembo's work bridges Description Logics with database theory, enabling efficient query answering over inconsistent data. He pioneered DL-Lite framework for tractable ontologies, developed tools like MASTRO and Graphol for visual ontology modeling, and advanced controlled query evaluation with epistemic policies for privacy-preserving systems. Awards: 2021 AAAI Classic Paper Award for DL-Lite 2024 IJCAI Distinguished Paper Award 2020 ISWC Best Poster/Demo Award Exceptional Guest Editor, Semantic Web Journal (2014) Teaching: He teaches courses on databases, data science, and computer science fundamentals. Currently coordinating 'Fondamenti di Programmazione I' and 'Data Management for Data Science', with extensive experience in course design and educational innovation.

Yann StrozeckiView profile

Associate Professor

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +7 more

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

Yee Ling BooView profile

Senior Lecturer

• Artificial Intelligence
• Image Processing
• Information Systems
• +5 more

Dr. Yee Ling Boo is a Senior Lecturer in the Department of Accounting, Info Sys & Supply Chain at RMIT University in Australia. Her research focuses on Artificial Intelligence, Image Processing, Information Systems, Finance, and Business Analytics . She actively supervises Masters and PhD students in projects related to Large Language Models for Policy Analysis, Machine Learning in Social Services, and AI Readiness . Her teaching interests include Data Mining, Big Data, Business Analytics, and Cognitive Computing with applications across Business, Health, and Education . Recent publications highlight her work in Automated vehicle damage detection using AI Configurational approaches to cloud service provider selection Multimodal frameworks for car damage localization Decision-making agility through embedded analytics Community detection in multi-layer dark networks She has developed innovative methods for unsupervised expertise estimation in crowdsourcing , feature selection in imbalanced data , and Shari'ah-compliant stock analysis , with applications spanning fraud detection, spammer analysis, and educational technology . Her work bridges theoretical advancements with practical implementations for industry partners like Carsales and Baptcare .

Petra W. RasmussenView profile

Professor

• Health Insurance
• Health Literacy
• Health Care Access
• +12 more

Petra W. Rasmussen is a policy researcher at RAND and Professor of Policy Analysis at the RAND School of Public Policy. Her work centers on health care coverage, insurance decision-making, health policy implementation, and health care access in the United States, with a focus on federal and state-level analysis. Education: Ph.D. in Health Policy and Management, UCLA Fielding School of Public Health M.P.H. in Health Policy and Management, Columbia University Mailman School of Public Health Bachelor's Degree, Duke University Her research interests include health insurance, health literacy, health economics, and health care access. She employs mixed methods, including analysis of national and state survey data, insurance enrollment and claims databases, and semi-structured interviews. Her work spans diverse areas such as Medicare-Medicaid integration, cervical cancer screening in low-resource settings, workers’ compensation payment models, and funding for youth exiting foster care. The recent body of her publications (2021–2025) reflects a strong focus on health policy evaluation, cost-effectiveness analysis, and health system financing. Key themes include Medicare and Medicaid alignment, alternative payment models, global health interventions, and consumer decision-making in insurance marketplaces. Her research combines rigorous quantitative modeling with real-world policy implications. Scientific Awards: No awards listed in the provided text. Dr. Rasmussen has led and contributed to numerous policy analyses and program evaluations. She has conducted environmental scans on Medicare quality, health plan value, social determinants of health, and public funding for foster youth. Her research has been supported by data from large national databases and has informed state and federal policy discussions. She is actively publishing and contributing to RAND’s mission of evidence-based public policy. She has been involved in projects examining the California workers’ compensation system, VA community care, and cervical cancer screening in Malawi. Her work often bridges domestic and international health policy challenges, demonstrating methodological versatility and policy relevance. She is affiliated with RAND’s health and public policy research teams.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. His research lies at the intersection of programming languages and artificial intelligence, with a current focus on neurosymbolic programming, trustworthy AI for healthcare, and AI-enabled software engineering tools. Education & Career: Ph.D. in Computer Science, Stanford University (2008) – advisor Alex Aiken M.S. in Computer Science, Purdue University (2003) – advisor Jens Palsberg B.E. in Computer Science, BITS Pilani (1999) Former faculty at Georgia Institute of Technology and researcher at Intel Labs, Berkeley Research Interests: Naik’s group develops languages, algorithms, and compilers for neurosymbolic programming, an emerging paradigm that unites symbolic reasoning with data-driven learning. Their flagship system is the open-source Scallop language and toolchain, applied to computer vision, cybersecurity, medicine, and bioinformatics. He also investigates AI-assisted programming tools that boost productivity and software quality by marrying traditional program analysis with modern machine learning. Recent Highlights: In 2024 he was named Misra Family Professor; his former student Elizabeth Dinella received the 2025 ACM SIGSOFT Outstanding Dissertation Award; his team released IRIS , an LLM-assisted static analysis framework for security vulnerabilities, and published the first comprehensive book on Neurosymbolic Programming in Scallop . Teaching: He regularly teaches CIS 5470 (Software Analysis) every Fall and CIS 5500 (Database Systems) every Spring, both of which are also delivered in Penn’s MCIT Online and Georgia Tech’s OMSCS programs. Advising & Service: Naik has graduated 8 Ph.D. students and mentored numerous postdocs and undergraduates; many alumni now hold faculty or research positions worldwide. He has served on organizing, program, and steering committees for premier venues such as PLDI, POPL, OOPSLA, SPLASH, ESEC/FSE, ISSTA, SAS, and others.

• Combinatorics
• Graph Theory
• Asymptotic Enumeration
• +7 more

Brendan McKay is a Professor in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of mathematics and computing. His work is deeply rooted in combinatorics, graph theory, and probabilistic methods in discrete structures. His research interests span a wide range of topics in discrete mathematics, including random graphs , asymptotic enumeration , Hamiltonian cycles , planar and bipartite graphs , matrix theory , and computational combinatorics . His work often combines theoretical depth with algorithmic applications, particularly in the analysis of complex networks and discrete systems. The recent publications (2015–2025) reflect a sustained focus on asymptotic enumeration techniques, structural properties of graphs, and algorithmic challenges in graph isomorphism and distance queries. Key themes include the use of probabilistic tools (e.g., cumulant expansions, martingales), enumeration under degree constraints, and symmetry-preserving operations on maps. His collaborations frequently involve researchers such as Mikhail Isaev, Catherine Greenhill, and Qing Wang. Scientific Awards and Recognitions: Fellow, Australian Mathematical Society (2000 → …) Fellow, Australian Academy of Science (1997 → …) McKay has led and co-led several major research projects, including Deep Learning for Graph Isomorphism and Hypergraph models for complex discrete systems , demonstrating leadership in both theoretical and applied directions. He has not explicitly listed advisees, but his role as Principal Investigator (PI) on multiple projects indicates active mentorship and grant leadership. His work often involves interdisciplinary applications in bioinformatics and network science. He is associated with research groups and projects focused on graph algorithms , random discrete structures , and computational combinatorics , often in collaboration with the mathematical sciences community at ANU and beyond.

Luc De RaedtView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +3 more

Luc De Raedt is a Full Professor of Computer Science at KU Leuven, where he serves as the Director of Leuven.AI - KU Leuven Institute for Artificial Intelligence and Head of the Subdivision Declarative Languages and Artificial Intelligence within the Faculty of Engineering Science. He is also a Wallenberg Guest Professor at Örebro University's Center for Applied Autonomous Sensor Systems. Professor De Raedt's research focuses on the intersection of Artificial Intelligence, Machine Learning, and Logic Programming. His work spans probabilistic logic programming, neurosymbolic AI, statistical relational learning, and applications in various domains. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which has been at the forefront of integrating symbolic and subsymbolic AI approaches. His research has evolved from traditional Inductive Logic Programming to Statistical Relational Learning and more recently to Neurosymbolic AI, which combines neural networks with symbolic reasoning for more transparent and trustworthy AI systems. His recent publications demonstrate a strong focus on neurosymbolic AI, with work spanning theoretical foundations as well as practical applications in areas like constraint learning, relational reinforcement learning, and probabilistic programming. His research integrates learning and reasoning in AI, with particular emphasis on creating systems that can both perceive patterns and perform logical reasoning. ERC Advanced Grant 2015 ERC Advanced Grant 2023 EurAI Fellow AAAI Fellow ELLIS Fellow Former IJCAI Trustee Elected Member of the Royal Flemish Academy of Belgium Wallenberg Guest Professor Professor De Raedt leads numerous research projects including "Bayesian Neurosymbolic AI for Trustworthy Machine Learning," "Neurosymbolic AI," "Theoretically Sound Neurosymbolic AI," and "Deep Probabilistic Logics." He is also involved in the Flanders Artificial Intelligence Research program (FAIR) as a work package leader. His research group includes PhD students and postdocs working on various aspects of neurosymbolic AI and probabilistic programming, with significant funding from European Research Council grants. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which consists of multiple research teams focusing on different aspects of AI, machine learning, and logic programming. The unit has a strong international presence and collaborates with researchers worldwide, maintaining leadership in probabilistic logic programming through systems like ProbLog.

Roderic F. CasaliView profile

Professor

• Phonology
• Phonetics
• African Languages
• +3 more

Roderic F. Casali is a Professor at Canada Institute of Linguistics (CanIL) affiliated with Trinity Western University's Faculty of Humanities & Social Sciences in the Department of Applied Linguistics since 2006. His academic career spans over three decades with significant contributions to African linguistics, particularly focused on phonology and phonetics of Niger-Congo languages. His educational background includes a PhD in Linguistics from UCLA (1996), MA in Linguistics from University of Texas at Arlington (1988), and BS in Physics with honors from University of Massachusetts (1980). His dissertation titled 'Resolving hiatus' was published by Garland Publishing in 1998. Research Interests: Casali specializes in phonological phenomena with particular expertise in vowel systems, ATR (Advanced Tongue Root) harmony, hiatus resolution, and vowel coalescence. His primary focus has been on African languages, especially Nawuri (Ghana), Akan, and other Niger-Congo languages. He has developed specialized software tools like Dekereke for phonological fieldwork and maintains active research in tongue root harmony systems and vowel inventory typology. His publications demonstrate consistent contributions to major linguistics journals including Language , Linguistic Typology , and Journal of West African Languages . Key trends show sustained focus on Niger-Congo phonology with increasing emphasis on typological frameworks and computational tools for field linguists. President's Fellowship, UCLA (1992-93, 1994-95) Casali serves as an International Linguistics Consultant for SIL International since 2000, previously working as Africa Area Linguistics Coordinator (1996-2004). He has advised numerous field linguists across Africa and developed training materials for phonological analysis. His teaching portfolio includes advanced courses in Acoustic Phonetics, Advanced Phonological Analysis, and Field Methods. Casali maintains active collaboration with the West African Linguistics Society and continues developing phonological analysis tools for field linguists.

Hannah SandeView profile

Associate Professor

• Phonological Processes
• Syntax-Phonology Interface
• Morphology-Phonology Interface
• +2 more

Hannah Sande is Associate Professor of Linguistics at UC Berkeley, specializing in phonological theory and its interfaces with syntax/morphology. She holds a PhD from UC Berkeley and conducts extensive fieldwork on West African languages, particularly Guébie (Kru) and Lobi (Gur). Her research investigates phonological domains, morphologically conditioned phonology, prosodic systems, and linguistic typology. Current NSF-CAREER funded projects focus on multiword tone and harmony systems across four languages. Publications center on tonal phonology, morphological conditioning, prosodic typology in African languages, and theoretical frameworks like Cophonologies by Phase. Recent work examines recursion in morphology and noun classification systems. Morris Halle Memorial Award for Phonology (2024) Peder Sather Grant Award (2023-2025) NSF CAREER Grant (2023-2028) Stanford CASBS Fellowship (2018-2019) NSF Documenting Endangered Languages Grant (2018-2022) She directs the Guébie Documentation Project, maintains the California Language Archive collections, and founded the TwistedTongues database tool. Current PhD students investigate topics in phonology and morphology.

• automata theory
• logic
• games
• +3 more

Anca Muscholl is a Professor at the University of Bordeaux, affiliated with LaBRI (Laboratoire Bordelais de Recherche en Informatique), a prominent computer science research laboratory in France. Her research focuses on theoretical aspects of computer science, particularly in automata theory, logic, games, verification and synthesis, distributed systems, and foundations of databases. Her research interests span across several key areas in theoretical computer science: Automata theory, with particular focus on string transducers and weighted automata Formal verification and synthesis of distributed systems Logic in computer science, including two-variable logic on data words Communication models and message passing systems Web services composition and negotiation protocols Professor Muscholl has been actively involved in the theoretical computer science community through numerous editorial and leadership roles. She currently serves on the editorial boards of TheoretiCS, Logical Methods in Computer Science, and LIPIcs. She is also a member of the Council of the European Association for Theoretical Computer Science (EATCS) and the Comité national de la recherche scientifique (section 06). Her recent publications demonstrate continued productivity and influence in her field, with significant contributions to string transducers, distributed systems verification, and automata theory. Her work shows a consistent focus on theoretical foundations with practical applications in verification and distributed computing. Her professional service includes significant leadership roles such as: Steering committee chair of the EATCS International Colloquium on Automata, Languages and Programming (2018-2024) Editorial board member of Discrete Mathematics & Theoretical Computer Science (2001-2023) Editorial board member of Information Processing Letters (2006-2015) Steering committee member of the Symposium on Theoretical Aspects of Computer Science (STACS) (2008-2014) Executive committee member of GDR Informatique-Mathématique (2017-2023) Professor Muscholl has made substantial contributions to the theoretical computer science community through her research, service, and mentorship. Her work bridges theoretical foundations with practical applications in verification and distributed systems, contributing to the advancement of formal methods and their applications.

Dominik SibbingView profile

Researcher

• Computer Vision
• 3D Reconstruction
• Image Synthesis
• +3 more

Dr. Dominik Sibbing is affiliated with the Department of Computer Science at RWTH Aachen University , Germany. His research focuses on 3D reconstruction , computer vision , and medical imaging . Developed markerless 3D face tracking systems using deformation models and smoothness priors Created volume-based fiber tracking techniques for diffusion MRI data Contributed to quad meshing methods for vascular structures Proposed SIFT-realistic rendering for laser-scanned point clouds His work combines statistical modeling with interactive GPU-based visualization . Notable award: VMV 2015 Honorable Mention . Collaborated with Leif Kobbelt and others on applications spanning facial animation, medical imaging, and physics-based simulations. Scientific Awards: VMV 2015 Honorable Mention Key Collaborations: Leif Kobbelt (RWTH Aachen) Martin Habbecke Robin Tomcin