Find Academic Supervisors

Stephen ThomasView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Big Data
• +4 more

Stephen W. Thomas is an Assistant Professor & Distinguished Teaching Fellow of Management Analytics at the Smith School of Business, Queen’s University. He holds a PhD in Computer Science from Queen’s University and has expertise in Natural Language Processing (NLP), machine learning, and data analytics. His research focuses on practical and fair AI applications, including bias detection and ethical considerations in NLP models. He has held roles as Executive Director of Smith's Analytics & AI Ecosystem and directs the Master of Management Analytics and Master of Management in Artificial Intelligence programs. His work bridges academia and industry, with consulting experience in big data and AI. Education: PhD (Computer Science, Queen’s University 2012), M.S. (Computer Science, University of Arizona 2009), B.S. (Computer Science, New Mexico State University 2006). Research Interests: NLP applications in messy real-world data, fair AI, AutoML for NLP, temporal databases, and mining software repositories. He has published in IEEE Transactions journals and led initiatives like τBench for temporal benchmarking. Awards: Scotiabank Scholar, multiple Professor of the Year awards, Queen’s Graduate Award Scholarship, and Raytheon Advanced Scholarship Program recipient. He has advised over 15 students in analytics and AI projects. Teaching: Courses include Machine Learning & AI, Text Analytics, Big Data, and Mathematical Analysis across Smith’s programs. He is a frequent speaker on AI ethics and management applications.

Ying ZouView profile

Professor

• Software Engineering
• Software Maintenance and Evolution
• Empirical Studies
• +7 more

Dr. Ying Zou is a Professor and Canada Research Chair (Tier II) in Software Evolution at Queen’s University’s Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computing. She is a Visiting Faculty Fellow at IBM Centers for Advanced Studies (CAS) and a member of IEEE. Her research focuses on software engineering, maintenance, empirical studies, and service-oriented computing, with notable contributions to refactoring patterns, performance analysis, and developer tooling. Education: BEng from Beijing Polytechnic University (China), MEng from Chinese Academy of Space Technology (China), and PhD from the University of Waterloo (Canada). Research interests include software evolution dynamics, code clone analysis, and optimizing developer workflows. Notable achievements include the 2014 IBM CAS Research Faculty Fellow of the Year and two IBM Faculty Awards (2007, 2008). Her work bridges empirical software engineering with practical industrial applications. Awards: Canada Research Chair, IBM Faculty Awards, IBM CAS Fellow Grants: IBM CAS collaborations, cross-disciplinary research funding Labs/Teams: Active contributor to the Software Evolution and Analysis Lab (SEAL), focusing on empirical studies and tool development for software maintenance challenges.

Dorothea BlosteinView profile

Professor

• Computer Science
• Biomechanics
• Graphics Recognition
• +6 more

Roles and Affiliations: Dorothea Blostein is a Professor in the School of Computing at Queen's University, part of the Faculty of Arts and Science. She has held this position since 1988 after earning her Ph.D. (1987) and B.Sc. (1978) from the University of Illinois and an M.Sc. (1980) from Carnegie Mellon University. Research: Her work focuses on the interface between paper and electronic documents, with specialties in graphics recognition, document analysis, and biomedical computing. Key areas include tensegrity structures for biomechanical modeling, adaptive systems, and artificial fascial networks. She also explores music notation recognition and software engineering document classification. Collaborations include projects with NASA on tensegrity robotics and studies on concussion prevention through helmet fitting systems. Teaching: She teaches courses such as CISC 859 (Pattern Recognition) and CISC 324 (Operating Systems). Her pedagogical approach emphasizes hands-on projects and theoretical foundations in AI and algorithms. Labs and Teams: Her research leverages tools like the NASA Tensegrity Robotics Toolkit and ArtiSynth. She collaborates with institutions like the Gordon Lab for embryonic modeling and biomechanical engineers on cytoskeleton simulations.

• Open Source Software
• ICT Standards
• Software Engineering
• +6 more

Björn Lundell is a Professor of Computer Science at the University of Skövde's School of Informatics. His research focuses on open source software, ICT standards, software engineering practices, and their applications in public sector and industrial contexts. He is actively involved in projects addressing challenges like standard compliance, lock-in effects, and sustainable digitalization. Lundell has collaborated extensively with organizations such as Combitech, Saab, and Husqvarna, and his work often bridges academic research with practical industrial needs. Key areas of research include open source ecosystems, governance of software standards, and cybersecurity in public sector IT. He has contributed to major initiatives like the EDU4Standards project (EU Horizon 2020) and the ORIOS project on open source reference implementations. His work emphasizes long-term maintenance of digital assets, interoperability, and compliance with regulations like the Cyber Resilience Act (CRA). Lundell has authored over 150 publications in journals like IEEE Software and Empirical Software Engineering, and conferences such as EGOV and OpenSym. His recent focus includes GenAI data governance, SaaS contract terms in public sectors, and IoT standardization. He serves as an advisor to governmental bodies, including the Swedish Public Procurement Agency, and has provided expert opinions on IT policy frameworks. Grants and projects include leadership in the LIM-IT (2016–2020) and SUDO (2020–2024) initiatives, addressing lock-in effects and sustainable innovation. His research team collaborates internationally, with partners in academia and industry across Europe and beyond.

• IT Security
• Technology Theory
• Open Source Software
• +3 more

Niels Jørgensen is an Associate Professor at Roskilde University's Department of Programming, Logic and Intelligent Systems Science Studies. His research focuses on IT security, technology theory, and the history of cryptography, particularly exploring how social movements shape technological advancements. He has contributed to projects like 'Situated Design Methods' and has participated in discussions on mobile technology security and open-source development. His work bridges technical analysis with societal impact, exemplified by studies on mobile phone hacking and cryptographic protocols. Jørgensen's publications span interdisciplinary topics, including open-source governance, educational design methodologies, and the integration of engineering principles in large-scale projects like the Boeing 777. He has engaged in activities such as lecturing on e-government curriculum design and collaborating on initiatives like the EU-supported CLIENT project. His research often highlights the interplay between technical systems and human agency, emphasizing the historical and social dimensions of technological evolution.

Frank SteinickeView profile

Professor

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +5 more

Professor Frank Steinicke is the Chair and Head of the Human-Computer Interaction (HCI) Lab at the University of Hamburg's Department of Informatics. He leads the Human-Computer Interaction Lab within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA) , focusing on merging natural and artificial intelligence to enhance interactions in virtual environments. His research spans virtual/augmented reality, perceptual interfaces, and immersive analytics, with over 400 publications. Education: Ph.D. and Venia Legendi in Computer Science (University of Münster) Mathematics with a Minor in Computer Science (University of Münster) Research Interests: Steinicke's work emphasizes human perceptual and cognitive limitations in XR, AI integration in virtual agents, and applications in healthcare, cultural heritage, and education. Recent projects include immersive tools for analyzing ancient artifacts and exergames for older adults. Awards: IEEE VGTC VR Technical Achievement Award (2023) Induction into IEEE VR Academy (2023) Over 30+ Best Paper Awards across ACM/IEEE venues Labs/Teams: Directs the Human-Computer Interaction Lab and collaborates on interdisciplinary projects like the Mobile Lab Container and Data Linking Lab . He chairs the ACM SUI Symposium and serves on editorial boards for IEEE Transactions on Visualization and Frontiers in Virtual Reality .

Michael GodfreyView profile

Professor

• Software Engineering
• Software Evolution
• Empirical Studies
• +5 more

Michael Godfrey is a Professor at the University of Waterloo's David R. Cheriton School of Computer Science, affiliated with the Department of Electrical and Computer Engineering. His work focuses on software engineering, empirical studies of software systems, code review practices, and open-source software ecosystems. He holds a Ph.D., M.Sc., and B.Sc. from the University of Toronto (1997, 1988, 1986). Research interests include software evolution, mining software repositories, provenance tracking, code duplication analysis, and program comprehension. He actively explores how developers interact with code reviews, documentation systems, and modern CI/CD pipelines. Recent work investigates app store software ecosystems, Bash scripting vulnerabilities, and deep learning API testing. Publications span empirical studies of developer communities (e.g., Stack Overflow analysis), architectural teaching methods, and automated testing techniques like documentation-guided fuzzing. His work bridges theory and practice, addressing challenges in large-scale systems maintenance and developer productivity. Leverages tools like Elasticsearch for repository mining and explores anomaly detection in software development. Engaged in curriculum development for software architecture education and has contributed to conferences like MSR and ICSE.

Shane McIntoshView profile

Associate Professor

• Empirical Software Engineering
• Software Delivery
• Release Engineering
• +2 more

Shane McIntosh is an Associate Professor and Associate Director of Graduate Studies at the Cheriton School of Computer Science, University of Waterloo. He holds a Ph.D. from Queen's University (2015) and specializes in empirical software engineering, software delivery processes, and software quality improvement through data mining techniques. His research focuses on analyzing historical and operational data from large-scale software systems to derive actionable insights. Key areas include release engineering, DevOps optimization, and software quality enhancement. McIntosh employs empirical methods to study build systems, continuous integration practices, and defect prediction. McIntosh's recent publications demonstrate a strong focus on improving software development workflows through empirical analysis of build systems, CI/CD pipelines, and code quality metrics. His work frequently addresses efficiency optimization in development operations and vulnerability detection in machine learning systems.

Matthew BeckmanView profile

Academic

• Statistics Education
• Data Science Education
• Educational Assessment
• +3 more

Matthew Beckman is an Associate Research Professor in the Department of Statistics at Pennsylvania State University and serves as Executive Director of the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE). He earned a PhD in Statistics Education from the University of Minnesota, an MS in Statistics from the same institution, and a BS in Mathematics from Penn State University. His research focuses on post-secondary statistics and data science education, particularly assessment development, NLP tools for educational feedback, and computational thinking in data science. He leads the Statistics & Data Science Education Research Lab at PSU and has held leadership roles in academic organizations like the Statistics Education Research Journal and international conferences. Beckman has received notable awards including the 2025 NZSA Visiting Lecturer and the 2017 Tombros Fellowship. He is Principal Investigator on NSF-funded projects like Project CLASSIFIES, which explores AI-assisted feedback in STEM education. His teaching spans courses from introductory statistics to graduate seminars in education research. During his 2024-2025 sabbatical at the University of Auckland, he is collaborating on NLP tool development and observing active learning practices. Professional service includes roles as Director of Undergraduate Programs in the PSU Statistics Department and leadership in CAUSE. His work bridges academia and industry, with prior experience as a statistician in medical technology companies like Medtronic and Nonin Medical.

• Cybersecurity
• Network Security
• IT Security Testing
• +3 more

Christian Schanes is a PostDoc Researcher at TU Wien's Department of Business Informatics. He holds a Dipl.-Ing. degree and a Dr.techn. from TU Wien, with a PhD titled 'Scope and depth efficient testing approach and framework for enhancing the detection of IT security bugs' (2013). His research focuses on cybersecurity, network security, automated security testing, and secure communication protocols. He teaches the course 'Introduction to Security' (2024W) and has supervised multiple graduate theses, including works on cybersecurity awareness, automated testing frameworks, and VoIP security. His research interests span IT security testing, data privacy, and vulnerability analysis in systems like VoIP and mobile payment frameworks. He has pioneered methodologies for automated security testing using machine learning and system behavior modeling. His work often addresses real-world threats, such as TLS vulnerabilities and honeynet-based attack analysis. Schanes has co-authored over 30 peer-reviewed publications, emphasizing practical solutions to cybersecurity challenges. Notable contributions include studies on mobile kernel subversion via TrustZone rootkits and large-scale VoIP threat analysis using honeynets. His research bridges theoretical frameworks with applied security solutions, impacting both academic and industry practices.

• Data Management
• Information Retrieval
• Machine Learning
• +3 more

Moritz Staudinger is a PreDoc Researcher at the Data Science department of Technische Universität Wien . His research focuses on reproducibility in machine learning and information retrieval, with particular emphasis on query generation, data citation, and evolving database schemas. Current projects: FAIR-AI (2024–2026) , HumRec (2021–2025) , and DoSSIER (2019–2024) Collaborations: Works with Andreas Hanbury , Andreas Rauber, and others Research interests include large language models for scientific applications, temporal information retrieval, and FAIR data principles. His recent publications examine reproducibility challenges across machine learning, systematic literature reviews, and environmental data management. Supervisions : Mentors students working on topics like data sovereignty, multilingual fact-checking, and quality indicators for data management plans. Collaborates on the DBRepo semantic repository framework.

• Model-Driven Engineering
• Model Transformation
• Web Engineering
• +3 more

Manuel Wimmer is a Lecturer in Business Informatics at TU Wien's Faculty of Informatics, specializing in model-driven engineering methodologies. His research develops foundations for model transformation, metamodeling, and interdisciplinary engineering. Research interests include model-driven software engineering, cyber-physical systems, web engineering, and industrial automation, with applications in smart production systems. Current work focuses on bridging IT/OT domains through standardized modeling approaches. Recent publications address quantum-edge cloud architectures, AI-enhanced modeling, and industrial security challenges. Article trends demonstrate strong focus on modeling language engineering, interoperability solutions, and quality assurance in complex systems. Leads the Christian Doppler Laboratory for Model-Integrated Smart Production and coordinates EU projects on low-code engineering platforms. Supervises doctoral research in model-driven technologies and software quality.

Evgeny KharlamovView profile

Research Fellow

• Semantic Technologies
• Data Integration
• Big Data
• +3 more

Evgeny Kharlamov is a Senior Research Fellow at the Department of Computer Science, University of Oxford. His research focuses on semantic technologies, particularly addressing challenges in data integration and Big Data. He leads the EU-funded Optique project on scalable end-user access to Big Data, and has contributed to foundational data management projects like Webdam and ACSI. Education: PhD in Computer Science (2011) from the Free University of Bozen-Bolzano (FUB), with research conducted also at Télécom ParisTech. European M.Sc. in Computer Science (2006) from Dresden University of Technology and FUB. Alumni of Novosibirsk State University (Russia). Research interests include ontology evolution, knowledge representation, query answering over complex data models, and probabilistic data management. His work spans conferences like VLDB, ICDT, KR, and journals such as TODS and JCSS. Grants include EU projects Optique, Webdam, and ACSI. Collaborations with institutions like Inria Saclay (France), Edinburgh University, and Télécom ParisTech.

• Ecology
• Curriculum and Pedagogy
• Electrical Engineering
• +4 more

Dr. Jessica Rivera Villicana is a Research Fellow in Computational and Geospatial Data Science at RMIT University's School of Global, Urban and Social Studies (GUSS). Her work integrates interdisciplinary approaches across ecology, education technology, and biomedical engineering. She leads projects like liveability scorecards analyzing urban inequities in Australian local government areas (LGAs), using geospatial and machine learning methodologies. Her research also focuses on EEG-based seizure detection, contributing to both technical advancements and clinical professional insights. Collaborating with global teams, she explores AI applications in healthcare (e.g., caregiver support systems) and cybersecurity (e.g., MLGuard framework). Key research areas include: Urban policy analysis through geospatial data modeling Machine learning interpretability in medical diagnostics Embodied conversational agents for caregiving AI safety and security in critical systems Behavioral modeling in interactive narratives Her recent work highlights include: 2025 Burwood & Canada Bay LGA liveability reports 2023 seizure detection survey with medical professionals MLGuard cybersecurity tool for ML models Dr. Villicana holds an ORCID identifier (0000-0003-1955-3310) and is based at RMIT's City Campus in Melbourne, Australia.

• Real-time Systems
• Operating Systems
• Information Theory
• +3 more

Dr. Carlos A. Rincon C. is an Instructional Assistant Professor in the Computer Science Department at the University of Houston. His research focuses on real-time systems, operating systems, information theory applications to scheduling, and storage systems. He holds a PhD in Computer Science from the University of Houston and has industry experience in software development prior to his academic career. Current research projects include optimizing probabilistic compression algorithms based on symbol position, developing machine learning models for disk failure prediction in heterogeneous environments, and applying information theory principles to real-time multiprocessor scheduling. His work bridges theoretical computer science with practical system optimization challenges. Dr. Rincon teaches core computer science courses including Operating Systems (COSC 3360) and Introduction to Programming (COSC 1437), with learning objectives centered on foundational concepts in system design, process management, memory management, and object-oriented programming principles. As Director of the ConocoPhillips Computer Science Learning Center, he oversees academic support programs and resources for computer science students. His pedagogical approach emphasizes algorithmic thinking and practical problem-solving skills development.