Find Academic Supervisors

Carlos MoralesView profile

Associate Professor

• Game-based Learning
• Interactive Educational Technology
• Digital Video Production
• +4 more

Carlos Morales serves as Associate Professor at Purdue University's Polytechnic Institute, specializing in digital video, animation, and interactive technology for visual training solutions. His research focuses on game-based learning applications in chemistry education and innovative distance learning systems, with significant contributions to educational technology through interdisciplinary collaboration. His primary research domains include: Game-based Learning for STEM education Interactive Educational Technology development Digital Video Production for distance learning Animation Technology pipelines Mobile Learning Applications design Chemistry Education Technology integration Dr. Morales' publication record (2002-2007) demonstrates consistent innovation in chemistry video games and mobile learning solutions, featuring multiple collaborative projects with chemistry educators. His work bridges engineering design and educational theory to create validated tools that enhance student engagement through visual realism and sustained interactivity. As Principal Investigator and Co-PI, he has secured substantial research funding including: National Science Foundation: $539,999 for "Effective Distance Learning Through Sustained Interactivity and Visual Realism" (2003-2006) National Science Foundation: $200,000 for "Game-based Learning in Chemistry" (2003-2006) ITaP MIDC: $15,000 for "Development of Mobile Games for Chemistry" (2006-2007) Discovery Learning Center: $20,000 for Chemistry Video Game Development (2006-2007)

Stavros TripakisView profile

Associate Professor

• Formal Methods
• Computer-Aided Verification and Synthesis
• Safety-Critical Systems
• +4 more

Stavros Tripakis is an Associate Professor at the Khoury College of Computer Sciences at Northeastern University , where he joined in 2018. He is on sabbatical during the 2024-2025 academic year. His research focuses on the foundations of software and system design , emphasizing formal methods , computer-aided verification and synthesis, with applications to safety-critical, embedded, and cyber-physical systems, security, and trustworthy AI. He leads a group developing theory and tools for designing better systems. Recent publications explore distributed protocol synthesis, neural network verification, and inductive invariant inference, reflecting trends in formal methods for AI and distributed systems. His work often intersects with automated reasoning, model checking, and tool development. Scientific awards include the Distinguished Artifact Award at TACAS 2018 for the Refinement Calculus of Reactive Systems (RCRS) toolset. He advises Derek Egolf , Daniel Melcer , and William Schultz (graduated 2025). Former postdocs include Rômulo Meira-Góes (now Penn State) and Eunsuk Kang (now CMU). Current projects include the NSF FMitF grant (2023-2027) on safe multi-agent reinforcement learning and the NSF SaTC grant (2018-2022) on protocol design.

Audun JøsangView profile

Professor

• Cybersecurity
• Artificial Intelligence
• Trust and Reputation Systems
• +4 more

Audun Jøsang is a Professor of Cybersecurity at the University of Oslo and holds a Visiting Professor position at Queensland University of Technology (QUT) . His work focuses on trust and reputation systems in digital environments, with notable contributions to subjective logic —a mathematical framework for reasoning under uncertainty. Education : MSc in Information Security from Royal Holloway, University of London , MSc and PhD in Telecommunications from Norwegian University of Science and Technology (NTNU) Books : Subjective Logic: A Formalism for Reasoning Under Uncertainty (2016), Cybersecurity: Technology and Governance (2011) His research spans cybersecurity , artificial intelligence , and risk assessment , with applications in online marketplaces , social media , and decision-making under uncertainty . He has developed mathematical models for uncertainty-aware influence propagation and competitive information spread using subjective logic. Audun Jøsang’s work bridges theoretical foundations and industrial applications of computational trust and security analytics . His recent publications explore ethical AI , fair federated learning , and dynamic intelligence assessment for artificial general intelligence development.

Steinar BøyumView profile

Professor

• Political Philosophy
• Ethics
• Philosophy of Education
• +3 more

Steinar Bøyum is a Professor and Head of the Department of Philosophy at the University of Bergen (Norway). He is a leading scholar in political philosophy , philosophy of education , and social justice , with a particular focus on the intersection of democratic theory , educational equity , and Wittgensteinian analysis . Email: steinar.boyum@uib.no Bøyum's research examines normative frameworks for educational policy, including admissions fairness (2023), critical thinking legitimacy in compulsory education (2006), and philosophical experience in childhood (2004). His work frequently engages with Rawlsian liberalism , Wittgenstein's philosophy , and democratic deliberation . Recent publications analyze intransitive learning (2012), philosophical education (2010), and professional epistemic virtues (2017). Despite extensive output, no scientific awards are explicitly mentioned in the source text.

James R. WilcoxView profile

Assistant Professor

• Programming Languages
• Computer Science
• Formal Methods
• +5 more

James R. Wilcox is an Assistant Professor of Computer Science at the University of Washington , where he teaches courses like Introduction to Programming , Foundations of Computing , and Operating Systems . His research focuses on programming languages , formal methods , and distributed systems verification . PhD, University of Washington BS, Williams College (2013) Wilcox's research bridges software engineering and formal verification , with applications to distributed systems, concurrent programming, and tools like mypyvy for verification. He has published extensively in top venues such as POPL , PLDI , and CAV , emphasizing compositional techniques and proof assistants like Coq. He has received two Distinguished Paper Awards (PLDI 2015, PLDI 2020) and contributes to frameworks like Verdi for verifying distributed systems. Outside academia, he is a baritone in Seattle's choral ensembles and an avid long-distance cyclist.

Bo HuiView profile

Assistant Professor

• Data Mining
• Machine Learning
• Graph Neural Networks
• +6 more

Bo Hui is an Assistant Professor of Computer Science at The University of Tulsa's College of Engineering & Computer Science. He received his Ph.D. in Computer Science and Software Engineering from Auburn University in 2023 and earned his B.S. in Computer Science from Xi'an Jiaotong University in 2013. Prior to his Ph.D., he worked as a senior software engineer in the industry. Education: Ph.D., Computer Science and Technology, Auburn University, 2023 B.S., Computer Science and Technology, Xi'an Jiaotong University, 2013 Dr. Hui's research focuses on data mining and machine learning, particularly in designing machine learning methods for complex real-world data while addressing social concerns such as privacy in AI. His work spans multiple application domains including traffic prediction, social recommendation systems, and biological data analysis. He has made significant contributions to graph neural networks, knowledge graph unlearning, and federated learning systems. Dr. Hui's publication record demonstrates a strong focus on machine unlearning (the 'right to be forgotten' in AI models), graph neural networks, and traffic prediction systems. His work bridges theoretical machine learning with practical applications, with a growing emphasis on privacy-preserving AI techniques. He frequently publishes in top-tier venues including ICCV, ICLR, KDD, and AAAI. Awards and Honors: NSF award #2348177 for machine unlearning research AAAI student scholarship award Dr. Hui currently advises two Ph.D. students (Ruimeng Ye and Yang Xiao) who began their studies in Fall 2024. His research is supported by an NSF grant (#2348177) focused on machine unlearning. He has served as a reviewer for major conferences including ICLR, AAAI, KDD, CVPR, ACL, and EMNLP. His teaching includes Data Mining (2024 Spring) and Interaction Design (2023 Fall). His research group appears to focus on data mining and machine learning with applications to real-world problems requiring privacy-aware solutions.

Konstantinos KyriakopoulosView profile

Senior Lecturer

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +4 more

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

Razieh Negin RahimiView profile

Assistant Professor

• Information Retrieval
• Explainable Search
• Cross-Language Information Retrieval
• +5 more

Razieh Negin Rahimi serves as an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, conducting research at the Center for Intelligent Information Retrieval (CIIR). Her work focuses on developing interpretable information access systems that reduce user cognitive burdens through advanced modeling techniques. She earned her PhD, MS, and BS in Computer Engineering from the University of Tehran. Her educational foundation in computational methods directly informs her research approach to complex information challenges. Dr. Rahimi's research spans explainable search systems, cross-language information retrieval, and entity representation. She pioneers methods for generating human-interpretable explanations of ranking models to build user trust, develops techniques for entity relationship inference from unstructured text, and creates frameworks for multilingual search in resource-constrained languages. Her work consistently bridges theoretical innovation with practical user experience improvements, particularly in handling diverse information needs across linguistic boundaries. Analysis of her recent publications reveals accelerating integration of large language models for explainable ranking tasks, with growing emphasis on bias discovery and search result diversification. Her 2023-2025 work shows sophisticated adaptation of neural architectures for listwise explanation generation and reasoning-aware retrieval. Her scientific contributions have been recognized through: 2022 Google Research Scholar Award Manning/IALS Innovation Award (with James Allan) 2019 Amazon Research Award (with James Allan) Dr. Rahimi has secured significant research funding including an NSF CAREER grant (2024) for explanation-based optimization of diversified retrieval systems. She actively mentors within the CIIR lab and collaborates with industry partners through her awarded research projects. Her teaching portfolio includes graduate courses in information retrieval where she emphasizes practical implementation of advanced ranking models. Within the Center for Intelligent Information Retrieval, her team develops next-generation search technologies focusing on interpretability and user-centered design, with current projects exploring the intersection of neural retrieval and human cognition.

Evan F. RiskoView profile

Professor

• Distributed Cognition
• Effort Perception
• Education and Training
• +3 more

Evan F. Risko is a Professor and Associate Chair (Graduate Affairs) at the University of Waterloo. His research focuses on Distributed Cognition, Effort Perception, and applying cognitive psychology principles to improve education and training. He holds the Canada Research Chair in Embodied and Embedded Cognition and has received multiple awards including Early Career Awards from the Psychonomic Society, Canadian Society for Brain, and Outstanding Performance Awards from the Province of Ontario. His work is funded by NSERC, SSHRC, and other major grants. He leads the Cognition and Natural Behaviour Laboratory, exploring how humans integrate body and environment to enhance thinking. Recent studies examine lecture design, cognitive offloading effects, and metacognition in digital contexts. Education: B.A., M.A., Ph.D. from the University of Waterloo in Cognitive Psychology. His research bridges theoretical and applied domains, with a focus on optimizing learning through lecture video design, understanding effort perception, and mitigating cognitive biases. Scientific Awards: Canada Research Chair in Embodied and Embedded Cognition Early Career Award (Psychonomic Society) Early Career Award (Canadian Society for Brain, Behaviour, and Cognitive Science) 2014 & 2018 Outstanding Performance Awards (Province of Ontario) Advising & Grants: Supervises graduate students in cognitive science and education research. Major grants include NSERC Discovery Grants, SSHRC Insight Development Grants, and Canada Foundation for Innovation funds. His work on video lectures and cognitive offloading has implications for educational technology and workplace training. Labs & Teams: Director of the Cognition and Natural Behaviour Lab, collaborating on projects like 'Distributed Metacognition' and 'Effort Perception in Technology Use'. Current initiatives explore how external tools (e.g., smartphones) shape cognitive processes and learning efficacy.

Igor IvkovicView profile

Professor

• Information Systems
• Software Engineering
• Knowledge Engineering
• +3 more

Igor Ivkovic was a Professor in the Department of Systems Design Engineering at the University of Waterloo, Canada. His work focused on integrating theory and practice in complex information systems, emphasizing system modeling, process modeling, and data modeling to bridge technical and business stakeholder needs. He taught courses such as Data Structures and Algorithms, Algorithms and Data Structures, and Digital Systems in recent years. Education: Holds a Doctorate in Electrical and Computer Engineering (2011), along with advanced certificates in university teaching (Certificate in University Teaching, 2011) and instructional skills (ISW, 2015). Earlier degrees include a Master of Mathematics in Computer Science (2003) and a Bachelor of Mathematics in Operations Research (2001), both from the University of Waterloo. Research Interests: Specialized in Information Systems, Software Engineering, and Knowledge Engineering. His work addressed challenges in model synchronization, software evolution, and architecture recovery, leveraging formal methods and model-driven approaches to enhance system consistency and interoperability. Notable Contributions: Authored influential papers on model synchronization for software evolution (2011) and improving the Gnutella protocol (2001), the latter earning a prize-winning award. He also pioneered educational innovations like Design Days Boot Camps (2017–2021), integrating remote learning and augmented reality into engineering education. Awards: Recognized for his 2001 research on the Gnutella protocol, which won a prestigious award through the LimeWire contest.

Annette ten TeijeView profile

Professor

• Artificial Intelligence
• Knowledge Representation
• Medical Knowledge Representation
• +7 more

Annette ten Teije is a Full Professor at Vrije Universiteit Amsterdam (VU Amsterdam) with appointments in the Faculty of Science, Artificial Intelligence department, the Network Institute, and the Knowledge Representation and Reasoning research group. Her academic career spans several decades with a strong focus on the intersection of artificial intelligence and healthcare applications. Professor ten Teije's research interests center around Knowledge Representation, particularly in medical contexts. Her work bridges multiple domains including Semantic Web technologies, Ontology development, Neuro-Symbolic AI systems, and Clinical Decision Support. She has made significant contributions to the formalization of clinical guidelines, handling multimorbidity in healthcare systems, and developing design patterns for hybrid AI systems. Her research integrates machine learning with symbolic reasoning to create explainable and reliable AI systems for healthcare applications. Analysis of Professor ten Teije's recent publications reveals a strong trajectory toward neuro-symbolic AI approaches that combine the strengths of neural networks and symbolic reasoning. Her work increasingly focuses on explainability in medical AI systems, with numerous publications on feature selection, interaction detection, and narrative-based understanding. She has developed frameworks for shared understanding in multi-agent systems and created design patterns specifically for medical decision-making contexts. Her research consistently bridges theoretical AI advances with practical healthcare applications. Professor ten Teije has supervised 5 PhD theses as indicated in her academic profile and teaches courses including "AI in Health" and "Machine Learning and Reasoning for Health" for the 2024-2025 academic year. Her academic contributions extend to editorial work, including serving as editor for conference proceedings and special issues on Knowledge Representation for Healthcare Processes.

• Formal Logic
• Philosophy of Logic
• Metaphysics
• +2 more

Prof Greg Restall is the Shelby Cullom Davis Professor of Philosophy at the University of St Andrews, affiliated with the School of Philosophy, Anthropology and Film Studies and the Department of Philosophy. Previously, he held positions at the Australian National University, Macquarie University, and the University of Melbourne, where he was Professor of Philosophy since 2013. His research focuses on formal logic, philosophy of logic, metaphysics, philosophy of language, and philosophy of religion. He has authored five books, including Logical Pluralism (with Jc Beall) and Proofs and Models in Philosophical Logic , and over 100 journal articles. His work has been funded by the Australian Research Council, and he is a Fellow of the Australian Academy of the Humanities. Restall’s research emphasizes logical pluralism and foundational questions in logic, semantics, and metaphysics. His recent publications explore topics like dialetheism in theology, proof theory, and the dialogical roots of deductive reasoning. He has supervised six PhD students, including Nathan Bray and Sophie Nagler. Beyond academia, his personal website consequently.org hosts extensive resources on his work.

Guy EmersonView profile

Research Fellow

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +3 more

Dr Guy Emerson is a Departmental Early-Career Academic Fellow and Affiliated Lecturer in the Department of Computer Science and Technology at the University of Cambridge. He also serves as a College Lecturer at Gonville & Caius College and Executive Director of Cambridge Language Sciences. His research focuses on computational linguistics, particularly functional distributional semantics, probabilistic models, and machine learning applications in natural language processing. He explores how computational models can illuminate linguistic knowledge and advance artificial intelligence. His work integrates formal linguistic theory with data-driven methods, addressing challenges like hypernymy detection, cross-linguistic syntax, and semantic representation. Recent projects include probing lexical semantics limitations, developing scalable distributional models, and leveraging visual data for semantic analysis. Key themes include natural language processing, machine learning, and cross-linguistic studies, with publications in top venues like SemEval workshops and computational linguistics journals. His contributions bridge formal syntax, probabilistic semantics, and scalable computational techniques, emphasizing both theoretical and applied outcomes. Emerson collaborates extensively on interdisciplinary initiatives through Cambridge Language Sciences, fostering connections between linguistics, computer science, and cognitive science. His research has implications for improving NLP systems, semantic modeling, and cross-linguistic computational frameworks.

• Machine Learning
• Graph Neural Networks
• Algorithmic Reasoning
• +3 more

Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.

Albert Qiaochu JiangView profile

Research Fellow

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +3 more

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.