Find Academic Supervisors

Mike KatchabawView profile

Associate Professor

• Game Design and Development
• Adaptive Game Systems
• Believable Agents and Characters
• +5 more

Dr. Mike Katchabaw is an Associate Professor in the Department of Computer Science at The University of Western Ontario. His research focuses on game design and development, including adaptive game systems, believable agents, algorithmic music composition, and networked game optimization. He holds a Ph.D. from Western (2002) and has been with the department since 2002. Teaching responsibilities include courses on open-source projects, software maintenance, game development, and game design. He is affiliated with the Digital Recreation, Entertainment, Art, and Media (DREAM) Group. Key research areas include psychosocial behavior modeling in NPCs, automated difficulty adjustment, and latency management in multiplayer games. Publications span over 30 refereed journal/conference papers, book chapters, and technical reports. Notable achievements include the Best Paper Award at GameOn 2011 and a patent for a flexible music composition engine. He has contributed to commercial games like 'To The Moon' and 'Animal Planet Vet Life' as a consultant/lead programmer.

Daniel Y ChenView profile

Lecturer

• Data Science Education
• Statistics
• Bioinformatics
• +3 more

Daniel Y Chen is a Lecturer in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. He holds a PhD Candidate status in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, focusing on data science education in the biomedical sciences. His expertise includes statistical computing, open source software development, and reproducible research practices. Education: PhD Candidate (in progress): Genetics, Bioinformatics & Computational Biology, Virginia Tech MSc: Relevant field (implied via career progression) Research Interests: Daniel's work bridges data science pedagogy and biomedical applications. He focuses on improving computational literacy among medical practitioners through accessible education resources, open source tools, and reproducible workflows. His technical contributions span R and Python ecosystems, including packages like grader and pyprojroot , and authoring Pandas for Everyone . He actively promotes best practices in scientific programming and collaborative software development. Grants & Contributions: Contributions to RStudio's gradethis and learnr packages Open source maintenance (e.g., Arch Linux AUR packages) Labs/Teams: Affiliated with UBC's Data Science for Biomedicine (DS4BIOMED) initiative and Virginia Tech's Social Decision Analytics Laboratory. Collaborates with RStudio and GitHub communities on education and tool development.

Alan HuView profile

Professor

• Formal Verification
• Algorithms
• Computer Architecture
• +3 more

Alan Hu is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on formal verification, algorithms, computer architecture, and electronic design automation. He teaches courses such as Intermediate Algorithm Design and Analysis (CPSC 320) and Introduction to Formal Verification and Analysis (CPSC 513). Dr. Hu has received notable awards, including the IEEE Council on Electronic Design Automation Outstanding Service Award and the IBM Faculty Award. His work emphasizes scalable verification techniques, SAT-based algorithms, and optimization in cloud computing and hardware systems. His research spans formal methods for hardware/software systems, including verification of embedded software, cache coherence, and network function virtualization. Notable contributions include advancements in SAT modulo theories, data race detection in heterogeneous systems, and cloud resource allocation frameworks like Cospot. Dr. Hu has been actively involved in teaching and curriculum development, consistently offering courses on algorithms, formal verification, and software design since 2000. His publications reflect a blend of theoretical foundations and practical applications in electronic design, cloud infrastructure, and verification tools. His scientific achievements include innovations in post-silicon validation, emulation-based coverage reduction, and formal analysis for debug trace optimization. He also contributes to the academic community through conference organization and editorial roles in formal verification and computer-aided design.

Jessie Galasso-CarbonnelView profile

Assistant Professor

• Software Reverse Engineering
• Knowledge Extraction and Representation
• Variability Management
• +3 more

Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. She holds a Ph.D. in Computer Science from the University of Montpellier (2018), followed by postdoctoral research at the University of Montréal (2020-2023) under IVADO funding. Her expertise lies in software reverse engineering, knowledge extraction, variability management, and AI-driven pipeline development. Education: Ph.D. in Computer Science (2018), University of Montpellier Postdoctoral Researcher (2020-2023), University of Montréal (Geodes group) Research Focus: Combines symbolic AI, software engineering, and machine learning to address challenges in model extraction, software product line analysis, and low-code solutions for AI pipelines. Current work emphasizes domain-specific AI pipeline automation and accessibility for non-experts. Professional Activities: PC member for IEEE SANER'25, ICSR'24, and MODELS'24 Co-chair of Poster Track (MODELS'24), Doctoral Symposium (SPLC'24), and MODEVAR workshops Invited speaker at OntarioTech, DiverSE (Rennes), and SmartFCA (Nancy) Grants & Funding: Holder of IVADO postdoctoral fellowship (2020) for ML/optimization research in source code recommendation. Labs & Collaborations: Works with Geodes research group and collaborates with institutions like ÉTS and École Polytechnique de Montréal on cross-disciplinary AI/SE projects.

Jialu ZhangView profile

Assistant Professor

• AI for Programming
• LLM-Powered Software Development
• Automated Debugging
• +4 more

Jialu Zhang is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. Her research focuses on AI-driven solutions for programming challenges, including LLM-powered software development, automated debugging, and program repair. She holds a PhD in Computer Science from Yale University (2023) and a BS in Electrical and Computer Engineering from Shanghai Jiao Tong University (2017, IEEE Honor Class). Her work emphasizes practical applications like Gmerge (merge conflict resolution), PyDex (automated code repair), and Clef (AI-assisted competitive programming). She collaborates with Microsoft Research’s RiSE and PROSE teams, exploring industry partnerships in AI-assisted software engineering. Recent projects include productizing Gmerge for Microsoft Edge and developing educational tools like PyDex for AI-driven tutoring systems. Zhang actively seeks students (Masters/PhD/interns) and industry collaborations to advance AI’s role in programming and software engineering. Her research bridges foundational AI capabilities with real-world software development challenges, emphasizing both technical innovation and educational impact.

L. Nicole WilsonView profile

Lecturer

• Computer Science
• Mathematics
• Programming
• +3 more

L. Nicole Wilson serves as a Lecturer in the Department of Mathematics & Computer Science at the University of Lethbridge, maintaining office C532 with contact via (403) 329-5115 or n.wilson@uleth.ca. Her teaching focuses on programming and mathematical concepts through structured office hours and the Coding Clinic initiative running 8:00am-6:00pm Monday-Thursday and Friday during 2025 summer sessions. Her professional interests span computer science education, statistics, and software engineering, demonstrated through original resources like normal curve images and unit circle diagrams. She actively promotes inclusive language in technology by updating course materials to eliminate discriminatory terms, guided by the University of Washington's IT Inclusive Language Guide as part of her documented antiracism commitment. No scientific awards or honors were identified in available materials. Student support occurs through booked appointments and the Coding Clinic's live debugging sessions. While specific graduate advisees aren't listed, her biography notes extensive student interaction through course instruction and booking systems. Her teaching methodology features a curated collection of rubber ducks for "rubber duck debugging" pedagogy, with named specimens like Snowy, Avery, and Millie Spectre documented in her office to help students verbalize code logic during problem-solving.

Andriy MiranskyyView profile

Associate Professor

• Applied Machine Learning
• Big Data
• Blockchain
• +4 more

Dr. Andriy Miranskyy is an Associate Professor in the Department of Computer Science at Toronto Metropolitan University. His research focuses on applied machine learning, quantum computing, cloud computing, and software engineering, with notable contributions to anomaly detection in cloud systems and quantum software engineering. He leads the AMiR Lab, exploring risk mitigation and software development challenges in emerging technologies. Education: Ph.D. in Computer Science from The University of Western Ontario (2011). Research Interests: He investigates quantum software engineering methodologies, cloud-native systems governance, and big data applications. His work bridges theoretical advancements with industrial-scale implementations, such as IBM Db2 quantum safety case studies and cloud monitoring tools like CloudHeatMap. He emphasizes practical solutions for flakiness detection in quantum programs and sustainable software development practices. Awards: Recognitions include the Rogers Cybersecure Catalyst Fellowship (2023-2024), IBM CAS Best Project (2021), and a Guinness World Record for pioneering a 3Pb data warehouse (IBM DB2 team). Teaching: Teaches courses like CPS 840 (Quantum Computing), CPS 847 (Software Tools for Startups), and CPS 731 (Software Engineering). Labs/Teams: Directs the AMiR Lab, focusing on risk-aware software engineering in quantum and cloud domains.

Paul GriesView profile

Teaching Professor

• Computer Science Education
• Programming Fundamentals
• Educational Technology
• +3 more

Paul Gries is a Teaching Professor in the Department of Computer Science at the University of Toronto, Faculty of Arts and Science. He specializes in computer science education, focusing on programming fundamentals and pedagogical tools. His work includes textbooks like Practical Programming and MOOCs on platforms like Coursera. Education: Paul holds a BA and MEng in Computer Science from Cornell University (Ithaca, New York) and pursued PhD studies at the University of Toronto before shifting his focus to teaching in 1999. He became a Lecturer in 1999 and was promoted to Senior Lecturer in 2004. Research and Teaching: His research interests center on improving teaching methods in computer science, particularly through multimedia and interactive learning tools. He has authored influential textbooks and developed courses that emphasize programming best practices and software development principles. Awards: Paul has received numerous teaching awards, including the OCUFA Teaching Award (2012), the University of Toronto President's Teaching Award (2011), and the Faculty of Arts and Science Outstanding Teaching Award (2006). He has also been recognized repeatedly by student unions for his instructional excellence. Labs/Teams: While no specific labs are mentioned, his contributions to educational technology and collaborative textbook development highlight his collaborative nature in pedagogical innovation.

Pengyu NieView profile

Assistant Professor

• Software Engineering
• Machine Learning
• Program Analysis
• +2 more

Dr. Pengyu Nie is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science. His research enhances developer productivity through machine learning techniques for software testing, code maintenance, and program analysis. Specific interests include execution-guided test completion, code-comment co-evolution, and multilingual programming systems. Current projects investigate LLM-based code editing in computational notebooks, multilingual code co-evolution, and test generation for exceptional behaviors. Research outputs include tools like pytest-inline for Python testing and Roosterize for Coq lemma suggestions. Awards include the Margarida Jacome Dissertation Award (2023) and ACM SIGSOFT Distinguished Paper Awards (2023, 2019). He leads the UW-SWaG research group and advises PhD/master's students on software engineering and ML projects.

Saba AlimadadiView profile

Assistant Professor

• Human-centred software engineering
• Program analysis
• Program comprehension
• +7 more

Saba Alimadadi is an Assistant Professor in the School of Computing Science at Simon Fraser University, located in Burnaby, BC, Canada. She holds a PhD from the University of British Columbia (2017). Her research focuses on software engineering, particularly program analysis, debugging, and testing of dynamic languages like JavaScript, TypeScript, and Python, aiming to enhance developer productivity through semi-automated techniques. She teaches courses such as CMPT 276 (Introduction to Software Engineering), CMPT 479 (Special Topics in Computing Systems), and CMPT 982 (Special Topics in Networks and Systems). Her research interests include human-centred software engineering, program comprehension, and web engineering. She actively contributes to the academic community, serving on program committees for ICSE, ASE, ISSTA, and organizing conferences like SPLASH/ISSTA and SCAM. Her work bridges theoretical insights with practical tools for improving software development practices. Dr. Alimadadi oversees research opportunities for SFU students, including USRA and work-study positions. She is affiliated with the university’s research groups and labs, though specific lab names are not explicitly mentioned. No scientific awards are cited in the provided materials.

Joseph WonsilView profile

Adjunct Assistant Professor

• Computational Reproducibility
• Data Provenance
• Geospatial Analysis
• +3 more

Joseph Wonsil is a PhD Candidate in Computer Science at The University of British Columbia (UBC), expected 202X, and concurrently serves as an Adjunct Assistant Professor at Madonna University. His research focuses on computational reproducibility and data provenance, specifically developing methods to enhance reproducibility accessibility for research programmers through multi-source provenance integration. He explores user interactions with provenance data and has contributed to provenance-based debuggers, nano-satellite systems, and geospatial public health analyses. Education includes an M.S. in Computer Science (UBC, 2021), and a B.A. in Computer Science, Environmental Science, and Geospatial Science from Carthage College (2019). His work spans interdisciplinary areas including environmental monitoring via satellite data and applying technology to theater. Teaching experience includes undergraduate courses in Geographic Information Systems (GIS) at Carthage College and Madonna University, alongside tutoring roles in computer science and geography departments during his undergraduate studies. Key contributions include advancing reproducibility frameworks for machine learning models and creating visualization tools for provenance comprehension. His work emphasizes practical applications of provenance systems in both academic and applied contexts.

• Software Engineering
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Abram Hindle is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. He holds a Ph.D. from the University of Waterloo (2010), an M.Sc. from the University of Victoria (2005), and a B.Sc. (Honours with distinction) from the University of Victoria (2003). His research focuses on evidence-based software development, leveraging techniques from data mining, machine learning, and empirical analysis. Hindle's research spans multiple domains including software repository mining, energy efficiency in software systems, and interdisciplinary applications like computer music and ECG analysis. His work integrates statistical analysis, NLP, and visualization to study software processes, maintenance, and metrics. His recent publications demonstrate a strong focus on healthcare applications of machine learning (particularly ECG-based diagnostics), software defect prediction, container orchestration, and energy-aware development practices. These reflect an ongoing commitment to empirical validation and real-world impact.

Gabriela NicolescuView profile

Professor

• Cybersecurity for Avionics
• Security by Design for Cyber-Physical Systems
• Advanced Programming Models for Avionic Multi and Many-Core Systems
• +11 more

Gabriela Nicolescu is a Professor at Polytechnique Montréal , affiliated with the Department of Computer Engineering and Software Engineering and the LAMA-WeST (Web, Semantics and Text) research group. Her work focuses on design methodologies , programming , and security for advanced systems integrating 3D SoCs , optical networks , and heterogeneous technologies . Education: B.Sc. and M.Sc., Politehnica Bucharest Ph.D., Institut National Polytechnique de Grenoble (2002), awarded Best Thesis in Microelectronics Research Interests include cybersecurity for avionics , security by design for cyber-physical systems , advanced programming models for multi-core systems , and modeling/simulation of heterogeneous systems . She has contributed to fields like optical networks on chip , real-time embedded systems , and edge computing security . Recent Publications highlight her leadership in all-optical neural networks , formal verification , and security for avionic systems . Her work spans machine learning-driven intrusion detection , photonic integrated circuits , and ontology-based cybersecurity frameworks . Scientific Awards: Best Thesis in Microelectronics, Institut National Polytechnique de Grenoble (2002) Student Supervision encompasses doctoral and master’s theses in areas such as multi-core RTOS , security in autonomous systems , AI-based penetration testing , and performance optimization in avionics . Current advisees include Felipe Magalhaes , Julien Carayol , and Kacem Khaled , while completed supervision includes A. S. Shahnejat Bushehri and Rami Zrelli . She is actively involved in collaborative research projects addressing national cybersecurity priorities and safer aircraft design , as highlighted in media outlets like The Globe and Mail and Science-Presse .

• Trustworthy Artificial Intelligence
• Software Verification and Validation
• Requirements Engineering
• +4 more

Prof. Lionel Briand is a distinguished academic and researcher in software engineering, holding the Tier 1 Canada Research Chair in Intelligent Software Dependability and Compliance at the University of Ottawa's School of Electrical Engineering and Computer Science (EECS Department). He is also the director of Lero, Ireland's Research Centre for Software, and affiliated with the Nanda Laboratory. His roles span technical leadership, research, and academia across institutions in seven countries. Educated with a PhD, Briand's research focuses on trustworthy AI, software verification/validation, requirements engineering, and regulatory compliance. He pioneered model-driven development and search-based techniques in software engineering. His work bridges academia and industry, collaborating with sectors like aerospace, automotive, and finance. Key research trends in his articles include AI-driven testing methodologies, regulatory compliance tools (e.g., GDPR), safety-critical systems, and metamorphic testing for autonomous systems. His recent work emphasizes large language models (LLMs) for consensus-based test generation and safety monitoring of AI agents. Awards: IEEE/ACM Fellowships, Harlan Mills Award, ACM SIGSOFT Research Award Grants: ERC Advanced Grant, Canada Research Chairs Tools: CompAI (GDPR compliance), TEASMA (DNN testing), Smarla (safety monitoring) Labs/Teams: Leads Lero and collaborates with interdisciplinary teams at the University of Luxembourg's SnT center and Fraunhofer Institute. His work emphasizes synergies between Canadian and Irish research ecosystems.

Zhen Ming JiangView profile

Associate Professor

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +5 more

Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at the Department of Electrical Engineering & Computer Science, York University, Canada. He earned his Ph.D. (2013) from Queen's University and MMath/BMath degrees from the University of Waterloo. Research Focus: Software Engineering for AI, Performance Engineering, Logging Practices, and Software Visualizations. Education: Ph.D. in Computer Science, Queen's University MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research explores engineering rigor in AI-powered applications, performance optimization in foundation model-driven systems, and efficiency improvements in large-scale software. Recent work analyzes logging practices, code cloning in blockchain, and AIOps models. He has received prestigious awards including the NSERC Discovery Accelerator Supplements (2020) and multiple Best Paper Awards at ICST, ICSE, and MSR. He served on program committees for ICSE, ICSME, and ICPE, and reviewed for top journals like IEEE Transactions on Software Engineering.