Find Academic Supervisors

Jim BywaterView profile

Assistant Professor

• Artificial Intelligence in Education
• Educational Technology Design
• Mathematics Education
• +3 more

Dr. Jim Bywater is an Assistant Professor in the Department of Learning, Technology, and Leadership Education at James Madison University's College of Education. His work focuses on developing AI-driven tools to enhance teaching practices in K-12 settings, particularly in mathematics education. He holds a Ph.D. in Instructional Technology from the University of Virginia, an M.A. in Adult Education from Canterbury Christ Church University College (UK), and degrees in Physics from the University of Oxford. Education Background: Ph.D. Instructional Technology, University of Virginia M.A. Adult Education, Canterbury Christ Church University College B.A. and MPhys Physics, University of Oxford Research highlights include the DiSCS algorithm for segmenting categorical sequences and the Teacher Responding Tool that improves teacher feedback quality. He collaborates on AI-based dialogue simulators to train teachers in questioning techniques. His projects emphasize student-centered pedagogies, equity in education, and leveraging technology for deeper conceptual understanding. Labs/Projects: Leads the Mathematical Discourse Teaching Simulator and Teacher Responding Tool initiatives. Active in open-source development, including the DiSCS algorithm repository .

Rocco PietriniView profile

Researcher

• Artificial Intelligence
• Computer Vision
• Retail Technology
• +3 more

Prof. Rocco Pietrini is a Researcher at the Department of Information Engineering, Università Politecnica delle Marche. His work focuses on AI-driven systems for retail, fashion, agriculture, and digital humanities. He specializes in computer vision, machine learning, and ethical AI applications. Pietrini leads projects like OutfitAI (fashion recommendation systems), Shelf Management (retail automation), and Social4Fashion (social media trend forecasting). His research bridges technical innovation with societal impact, including ethical AI frameworks and legacy code modernization. Roles: Researcher in Information Engineering Affiliations: Dipartimento di Ingegneria dell'Informazione, UNIVPM Key research interests include AI ethics, precision agriculture, and multimodal data integration. His recent studies span smart retail systems, environmental monitoring via YOLO-based buoy detection, and Fortran-to-Python code conversion using LLMs. Pietrini collaborates on EU-funded projects like GREEN PATH (space planning) and Edge-AI for aquaculture. Publications emphasize practical AI applications, from consumer behavior analysis to sustainable water management systems. He maintains an active presence in academic communities through the IRIS repository and teaching at the School of Engineering.

Gerald BalekakiView profile

Teaching Professor

• Computer Science
• Databases
• High-Performance Computing
• +3 more

Gerald Balekaki is an Assistant Teaching Professor of Computer Science at Illinois Institute of Technology (Illinois Tech). He holds a PhD in Computer Science from the University of Cape Town, specializing in computational radio astronomy and scientific database modeling. His academic journey includes an MSc in Data Communication and Software Engineering from Makerere University, Uganda, and a Bachelor of Statistics with specialization in Statistical Computing, also from Makerere University. Professor Balekaki's research focuses on designing scalable databases for managing large scientific datasets, with emphasis on real-time support and advanced analytics. His work integrates intelligent models into very large databases (VLDBs), particularly in the context of radio astronomy data from the MeerKAT telescope. He explores query optimization in specialized database systems and high-performance computing architectures. He actively teaches core computer science courses at Illinois Tech, including object-oriented programming, data mining, database organization, and accelerated introductory courses. His GitHub repository showcases contributions to database systems development, including a sample MySQL database implementation. Affiliations: Illinois Tech, ACM, IEEE, IITPSA, ISOC Research Areas: Databases, High-Performance Computing, Computer Networks Teaching: CS 115, CS 116, CS 201, CS 422, CS 425, CS 525

• Information Technology
• Telecommunications
• Social Media
• +3 more

Charles Steinfield is a Professor at Michigan State University, USA, with a career spanning over three decades in Information Systems and Human-Computer Interaction. His work focuses on enterprise social media, virtual teams, and ICT adoption in rural and organizational contexts. He has collaborated extensively with researchers like Nicole B. Ellison, Robert E. Kraut, and Susan Wyche. Key research themes: Digital Transformation, Social Capital, Telecommunications Notable collaborations: Wietske van Osch, Yanjie Zhao, Cliff Lampe His 2013-2022 publications examine boundary spanning in social media, while 2024+ work addresses knowledge graphs, FAIR data principles, and LLM applications in scholarly infrastructure. Steinfield has contributed to understanding digital divide issues through studies in Kenya and Malawi.

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.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +5 more

**Ahmed Hassan** is a **Professor** and **Canada Research Chair (Tier 2) in Software Analytics** at the **School of Computing**, **Queen's University**, affiliated with the **Faculty of Arts and Science**. He holds the **NSERC/RIM Industrial Research Chair in Software Engineering for Ultra Large Scale Systems**. His research focuses on software systems, data analytics, and mining software repositories (MSR), leading the **Software Analysis and Intelligence Lab (SAIL)**. **Education**: PhD (2005), MMath (2001), BMath (1999) in Computer Science from the University of Waterloo, Canada. **Research Interests**: Software evolution, performance engineering, distributed systems debugging, cybersecurity, and AI-driven software solutions. SAIL explores tools for maintaining large-scale software systems, improving developer productivity, and integrating AI into software workflows. **Awards & Recognition**: ACM and IEEE Fellowships, patents in multiple jurisdictions, and leadership roles in MSR and ICSE conferences. Industrial experience includes RIM/BlackBerry, IBM Research, and Nortel Networks. **Current Projects**: Focus on foundation model-powered software (FMware), AIOps, and ethical AI applications. Teaching courses like CISC322: Software Architecture.

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.

Hui ChenView profile

Associate Professor

• Software Engineering
• Wireless Networks
• Wireless Sensor Networks
• +4 more

Hui Chen is an Associate Professor in the Department of Computer and Information Science at Brooklyn College, City University of New York, and a member of the doctoral faculty in the CUNY Computer Science Ph.D. program. His research integrates software engineering, wireless networks, and system security. Education: B.E. (1993), M.S. (1996), M.S. (2003), Ph.D. (2007) in Computer Science and Geophysics. Chen's research spans modeling and analytics of software and systems , wireless sensor networks, network security, and computer science education . His lab focuses on accountable systems and predictive models for developer behavior. Recent publications emphasize just-in-time defect prediction , internet censorship detection , and wireless sensor applications . Awards include multiple PSC-CUNY grants and an NSF award for secure programming education innovations. Scientific Awards: PSC-CUNY Award #67751-00 55 (2024-2025) NSF #2235976 (2023-2026) Additional PSC-CUNY grants (2018-2024) Chen serves on IEEE/ACM technical program committees and advises students in his MASS lab , which emphasizes industry collaboration and hands-on research in software/hardware security domains.

Johannes WachsView profile

Associate Professor

• Network Science
• Data Science
• Complexity Science
• +14 more

Johannes Wachs is an Associate Professor at the Institute of Data Analytics and Information Science, Corvinus University of Budapest, and a Research Fellow at the Centre for Economic and Regional Studies. He is affiliated with the Complexity Science Hub Vienna, where he has been a faculty member since April 2020. His interdisciplinary research bridges data science, network science, and complexity to study digital economies, open source software, and societal challenges. PhD in Network Science, Central European University (2019) MS in Applied Mathematics, Central European University (2012) BS in Mathematics and Economics, Tulane University (2009) His research focuses on the application of network and data science to understand social, economic, and technical systems. Key interests include open source software ecosystems, AI’s impact on knowledge sharing, corruption detection, urban inequality, and digital innovation. He uses large-scale digital trace data to model complex behaviors in online communities, software development, and public policy. His recent publications reveal a strong trend in analyzing digital platforms such as GitHub and Stack Overflow, studying brain drain in tech, and assessing climate and health risks in Austria. His work combines network modeling, machine learning, and empirical analysis to uncover patterns in human behavior and systemic risk. IMF Anti-Corruption Challenge Winner (2020) Principal Investigator, CRISP Project (2021–2024), funded by FFG Grants from Hungarian Research Funding Agency (OTKA), City of Vienna, and WU Projects Johannes Wachs actively supervises PhD and Master’s students, including Hannah Schuster and Brigi Németh. He has taught courses in computational social science, social networks, and data mining at institutions including RWTH Aachen, CEU, and WU Vienna. He is launching a new MSc in Social Data Science at Corvinus in 2025. He leads the CRISP project, which builds semantic data pools for real-time crisis response and intervention, integrating heterogeneous data sources for impact forecasting and policy transparency.

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +4 more

Fabio Palomba is an Associate Professor at the Department of Computer Science , University of Salerno, Italy. He earned a European PhD in Management & Information Technology (2017), funded by University of Salerno and University of Molise, under advisor Prof. Andrea De Lucia. His research spans software maintenance and evolution , empirical software engineering , and ML systems quality . Recipient of IEEE Computer Society Best PhD Thesis Award (2017) Multiple Distinguished Paper Awards from ACM/SIGSOFT and IEEE/TCSE Recipient of prestigious SNSF Ambizione grant (2019) and IEEE Rising Star Award (2023) His work investigates fairness-aware practices in ML , technical debt in AI systems , and LLM applications in software engineering . Recent studies focus on automated requirements generation via RECOVER, quantum software engineering , and socio-technical community smells in ML-enabled systems, with empirical analyses across large datasets. Key editorial roles include Elsevier's Information and Software Technology Journal (2022-), Springer's Empirical Software Engineering Journal (2021-), and IEEE Transactions on Software Engineering (2020-). He has served as program co-chair for SANER 2024 , ICPC 2021 , and multiple conference tracks. 16 Distinguished Reviewer Awards for his refereeing work Co-authored 80+ journal papers , 100+ conference papers , and advised 300+ theses

Shane McIntoshView profile

Associate Professor

• Computer Science
• Software Engineering
• Release Engineering
• +5 more

Shane McIntosh is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He leads the Software Repository Excavation and Build Engineering Labs (Software REBELs), focusing on empirical studies of historical data in large-scale software development. Research: Specializes in release engineering (assembling, verifying, and delivering software releases) and software quality (deriving guidelines for reliable systems). Teaching: Has taught courses like ECSE 321 (Introduction to Software Engineering), ECSE 611 (Software Analytics), and ECSE 437 (Software Delivery). Service: Organized key conferences (e.g., ICSE 2022, PROMISE 2021–2022) and served as Senior Associate Editor for the Journal of Systems and Software (2019–Present). Labs: Leads the Software REBELs lab, emphasizing software repository analysis and build engineering.

Juan Carlos FarahView profile

Researcher

• Educational Technology
• Artificial Intelligence in Education
• Learning Analytics
• +4 more

Juan Carlos Farah is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), holding dual appointments in the Fondation Bertarelli Chair in Neuroprosthétique Cognitive (School of Life Sciences/SV) and the SCI-STI-DG group (School of Engineering/STI). His work bridges neuroscience, artificial intelligence, and educational technology, focusing on innovative applications of AI in learning environments and neuroprosthetics research. Research Interests: Farah’s research spans educational chatbot design, AI-enhanced learning analytics, neuroprosthetic systems, and the ethical integration of technology in education. He has pioneered frameworks for task-oriented conversational agents, blockchain-based learning trace repositories, and gamified computational thinking tools. Key Projects: He contributed to the Graasp Desktop initiative for underconnected African schools and developed the TRACE model for educational chatbots. His work on code review notebooks and bot-mediated software engineering education has influenced pedagogical practices globally. Awards & Recognition: No specific awards listed, but his publications reflect high-impact contributions to IEEE, ACM, and Elsevier journals/conferences. Active in global initiatives like UNESCO’s Unequal World Conference on education equity. Technical Expertise: Proficient in Python, JavaScript, and AI toolkits. Specializes in building scalable educational platforms, learning analytics pipelines, and neuroimaging analysis for cognitive studies.

Nuwan IndikaView profile

Assistant Professor

• Econometrics
• Big Data Analytics
• Health Data Science
• +3 more

Nuwan Indika is an Assistant Professor of Business Analytics at the Department of Business Analytics, College of Business, Loyola University New Orleans. He holds a Ph.D. in Economics from Kansas State University (2019), an MA in Economics from Western Illinois University (2013), and a BA in Economics from the University of Colombo, Sri Lanka (2009). A Fulbright Scholar and econometrician, his research spans econometrics, big data, health data science, and biomedical text processing. Education: Ph.D. (Economics, Kansas State University, 2019); MA (Economics, Western Illinois University, 2013); BA (Economics, University of Colombo, 2009) Research Interests: Focus on econometric modeling of complex health data (human and veterinary medicine), biomedical text processing, and soda industry vertical merger analysis. Engages in large-scale data mining and computational epidemiology. Collaborations: Research associate with 1DATA Consortium (Kansas State University) and sports analytics collaborations with Prof. Sanders at Syracuse University. Scientific Awards: Recognized as a Fulbright Scholar. Collaborative work spans oncology, pharmacokinetics, and behavioral economics. Consulting: Analytics consultant for Plan International, World Bank, and GTZ projects.

• Uncertain Data Management
• MapReduce Algorithms
• Social Choice Theory
• +5 more

Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.