Software Reverse Engineering Professors and Supervisors in Canada

Harold Warehamمشاهده پروفایل

استاد

Memorial University of NewfoundlandCanada

Dr. Harold Wareham is a tenured Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN), Faculty of Science. He holds a Ph.D. from the University of Victoria and has been at MUN since 1999, advancing through roles including Assistant Professor, Associate Professor, and Full Professor. His research focuses on computational complexity theory applied to cognitive science, robotics, and computational biology. He has served on numerous academic committees and international conference program committees. Education: B.Sc. (Computer Science) and B.A. (Linguistics/Mathematics) from MUN, followed by an M.Sc. in Computational Biology and a Ph.D. in Parameterized Complexity in Computational Phonology from the University of Victoria. Research Interests Computational complexity of cognitive processes Algorithm design for robotics and swarm control Parameterized complexity in biological systems Machine learning interpretability Recent Work Trends Recent publications emphasize applying complexity theory to AI interpretability (e.g., neural network reverse-engineering) and cognitive science (segmentation processes). His robotics research explores algorithmic foundations for swarm construction tasks. He also investigates computational challenges in software reconfiguration and modularization. Awards 2017 Best Professor Award (MUN CS Graduate Society) Teaching Fellow at Radboud University (2014–) F. A. Aldrich Alumni Graduate Scholarship (1989) Grants & Advising Has supervised numerous courses across undergraduate and graduate levels, including AI topics, computational biology, and theoretical computer science. Active in grant review roles for NSERC and international funding bodies. Served on MUN committees for promotion, tenure, and graduate studies. Labs & Collaborations Associated with the MUN CS Theory Group and the Bio-inspired Robotics (BOTS) Lab. Collaborates internationally on projects like the Dagstuhl Complexity Workshops and cognitive science initiatives.

پروفایل رسمی دانشگاه

Moshe Schwartzمشاهده پروفایل

استاد

McMaster UniversityCanada

Dr. Moshe Schwartz is a Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on error-correcting codes for improving digital communication and storage systems, particularly in storage technologies and their application in distributed systems. He specializes in coding theory, storage systems, and digital sequences, with a focus on resolving conflicts between storage density, reliability, and energy efficiency. He teaches courses such as 'Introduction to Digital Sequences' and 'Coding Theory,' emphasizing both theoretical foundations and practical applications. His work integrates hardware and software solutions to address challenges in non-volatile memory fragility and data center reliability. Research interests include: Coding theory for DNA storage and bioinformatics applications Error-correcting codes for tandem duplication and substitution errors Network coding and distributed storage systems Algebraic coding and combinatorial optimization Awards: Won the Best Paper Award at the DRCN Conference (2024) for research on covert communication via error-correcting codes. His work bridges theoretical advancements and real-world applications in storage and communication technologies. Dr. Schwartz collaborates on interdisciplinary projects involving genomic data integrity and secure network protocols. His lab focuses on advancing storage efficiency through graph-based coding and asymptotic rate optimization.

پروفایل رسمی دانشگاه

Giuliano Antoniolمشاهده پروفایل

دانشیار

Polytechnic School of MontrealCanada

Giuliano Antoniol is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He is a member of the Institute for Data Valorization (IVADO) and has established himself as a prominent researcher in software engineering, with recognition as one of the world's most productive software engineering researchers and among the top 2% most cited researchers in his field. Professor Antoniol's research spans several key areas in software engineering, with a particular focus on software evolution and maintenance, reverse engineering, static code analysis, and empirical software studies. His work frequently addresses practical challenges in software development, including software quality assurance, technical debt identification, and the application of machine learning techniques to software engineering problems. His research group has produced significant contributions to understanding code smells, identifier naming practices, and the impact of programming language features on software quality. His recent publications reveal a strong trend toward the intersection of traditional software engineering with artificial intelligence and machine learning. Many of his 2023-2025 publications focus on the challenges of testing and verifying machine learning systems, analyzing bugs in AI-generated code, and applying search-based techniques to complex software engineering problems. His work demonstrates a consistent empirical approach, with numerous studies analyzing real-world software systems and developer practices. Ranked among the world's most productive software engineering researchers (2021) Among the top 2% most cited researchers in his field (2021) Award for Most Influential Article of the Decade in Software Engineering (2021) Professor Antoniol has supervised an impressive number of graduate students, with 14 completed PhD theses and 18 Master's theses to his credit. His students have explored diverse topics including software testing, technical debt, machine learning applications in software engineering, and code quality analysis. His research has been supported by numerous grants that have enabled extensive empirical studies and the development of innovative software engineering tools and techniques.

پروفایل رسمی دانشگاه

Stéphane Sotèg Soméمشاهده پروفایل

دانشیار

University of OttawaCanada

Stéphane Sotèg Somé serves as an Associate Professor and SEG Co-op Coordinator at the School of Electrical Engineering and Computer Science within the Faculty of Engineering at the University of Ottawa. His office is located in room STE 5033, with contact available via phone at 613 562-5800 ext. 6714 or email at ssome@uOttawa.ca. His research focuses on critical software engineering domains: Use case based requirement engineering for systematic system specification Reverse engineering for software analysis and reconstruction These specialties address foundational challenges in software development lifecycle management, emphasizing practical methodologies for requirements capture and legacy system comprehension through structured use-case modeling and reverse-engineering techniques.

پروفایل رسمی دانشگاه

John Aycockمشاهده پروفایل

دانشیار

University of CalgaryCanada

John Aycock is an Associate Professor in the Department of Computer Science at the University of Calgary. His research focuses on interdisciplinary studies of retro computer games, binary reverse engineering with applications to computer security, and analysis of malicious software such as viruses, spam, and spyware. He has authored two books and numerous research papers in these areas. Dr. Aycock has taught specialized courses on topics like malware analysis and computer security. His work bridges historical computing creativity with modern cybersecurity challenges. His professional activities include contributions to academic publishing and curriculum development in computer science security domains. No awards or grants are explicitly listed in the provided materials. While no formal student advisee list is available, his research areas suggest active involvement in mentoring related projects. He maintains an academic presence at the University of Calgary without indication of part-time status or retirement.

پروفایل رسمی دانشگاه

Jessie Galasso-Carbonnelمشاهده پروفایل

استادیار

McGill UniversityCanada

Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. Her research focuses on software reverse engineering, knowledge extraction, variability management, and software repository mining. She holds a Ph.D. in Computer Science from the University of Montpellier (2018) and completed postdoctoral research at the French National Institute for Sustainable Development (IRD) and the University of Montréal's Geodes group. She is currently co-chairing several international conferences and workshops, including the Poster Track at MODELS’24 and the Doctoral Symposium at SPLC’24. Her academic background includes: Ph.D. in Computer Science, University of Montpellier (2018) Postdoctoral Researcher at IRD (2019–2020) and University of Montréal (2020–2023) Recipient of IVADO Postdoctoral Research Funding (2020) Research interests span: Software product line engineering Formal concept analysis AI-driven code recommendation systems Domain-specific language design Reproducible software development practices Recent contributions include advancements in: Automated repair of semantic errors in model transformations Social diversity metrics for collaborative problem-solving Low-code development frameworks for AI pipelines She actively contributes to the academic community through roles such as PC member for SANER’25 and co-chair of multiple international workshops. Her work intersects software engineering, AI, and formal methods to advance scalable and accessible software development practices.

پروفایل رسمی دانشگاه

Houari Sahraouiمشاهده پروفایل

استاد

University of MontrealCanada

Houari Sahraoui is a Full Professor and Vice Dean of Planning and Infrastructure at the Faculty of Arts and Sciences, University of Montreal, where he also previously served as Department Director from 2013 to 2017. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) and has been actively supervising graduate students and conducting research in software engineering since at least 2000. Dr. Sahraoui's research focuses on automated software engineering , with particular emphasis on model transformations learning from examples using evolutionary approaches. His work spans reverse engineering (comprehension) and reengineering (refactoring, migration to component-based software), utilizing static and dynamic analysis techniques. He also investigates visualization of large sets of multidimensional data for software comprehension and maintenance. His research integrates artificial intelligence approaches to enhance various phases of the software development lifecycle. His recent publications demonstrate a strong trend toward integrating large language models and artificial intelligence with traditional software engineering practices. The research spans code review automation, model-driven engineering, microservices identification, and digital twins for applications like vertical farming. Many publications focus on improving automation through techniques like parameter-efficient fine-tuning, knowledge distillation, and social diversity metrics. Dr. Sahraoui was named a Fellow of Automated Software Engineering in 2023, an honor recognizing his significant and sustained contributions to the ASE community, both scientifically and professionally. This prestigious title is awarded by the IEEE/ACM International Conference Steering Committee. Dr. Sahraoui has supervised over 50 Master's and PhD students throughout his career, with recent supervision focusing on AI-assisted software engineering, code review automation, and model-driven approaches. His research has been supported by multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), MITACS, and industry partners, with projects spanning from 2000 to projected completion in 2031. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) which focuses on open and distributed systems and software experimentation. His current projects include 'Improving automation and assistance for software engineering tasks with generative AI' (2025-2031) and work on digital twins for vertical farming.

پروفایل رسمی دانشگاه

William N. Sumnerمشاهده پروفایل

دانشیار

Simon Fraser UniversityCanada

William N. Sumner is an Associate Professor and Associate Director, Academic and Administration in the School of Computing Science at Simon Fraser University. He holds a Ph.D. in Computer Science from Purdue University (2013) and a B.Sc. in Computer Science from Hope College (2005). His research focuses on automated debugging tools, concurrency, parallelism, program analysis, and software quality. He teaches courses such as Software Development Methods, Software Testing, and Automated Software Analysis. Sumner actively recruits graduate and undergraduate students for research in improving software development processes through automation. His work emphasizes reducing repetitive tasks in software development using techniques like test case reduction and program analysis. Education: Ph.D., Computer Science, Purdue University, 2013 B.Sc., Computer Science, Hope College, 2005 Research Interests: Sumner’s research addresses challenges in software development automation, including automated debugging, test suite assessment, and program hardening. He develops tools like Pardis to optimize test case reduction and improve software reliability. His work intersects with concurrency, parallelism, and program transformation to enhance developer productivity and software quality. Recent Contributions: Recent publications include advancements in type batched program reduction (2023) and leveraging models for test case reduction in software repositories (2021). His Pardis framework (2019) improves test case reduction efficiency by addressing priority inversion in structured inputs. Teaching & Engagement: Sumner teaches courses such as CMPT 373 (Software Development Methods), CMPT 473 (Software Testing), and CMPT 479 (Automated Software Analysis). He emphasizes hands-on projects, such as the Geotagged Meeting Planner in CMPT 276, focusing on teamwork, agile methodologies, and software engineering best practices.

پروفایل رسمی دانشگاه

Mahmut Parlarمشاهده پروفایل

استاد

McMaster UniversityCanada

Dr. Mahmut Parlar is a Distinguished University Professor and Professor of Management Science at McMaster University's DeGroote School of Business, where he holds a Business Research Chair (2014-2025). He also serves as an Adjunct Professor at Lingnan University, Hong Kong (2024-2027). His research focuses on stochastic modeling, dynamic optimization, and game theory applications in supply chain management, supported by continuous NSERC funding since 1981. His primary research areas include: Stochastic inventory and queueing systems Dynamic pricing and contract design Game-theoretic supply chain coordination Optimization in healthcare and retail operations Analysis of his 15 most recent publications (2020-2025) reveals strong emphasis on: supply chain incentive design, dynamic pricing strategies, operational risk management, and retail/e-commerce optimization. Key methodologies include differential games, stochastic modeling, and empirical validation. Major scientific recognitions: CORS Award of Merit (2018) Stanford's Top 2% Scientists (2023) President's Award for Graduate Supervision Excellence (2013) DeGroote Research Excellence Award (2006) Dr. S. J. Basu Teaching Award (2014) He has supervised numerous graduate students and authored the book Interactive Operations Research with Maple (2000). NSERC grants have continuously funded his research program for over four decades.

پروفایل رسمی دانشگاه

Ettore Merloمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Ettore Merlo is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads research in cybersecurity, artificial intelligence, and software systems. He holds an M.Sc. from the University of Turin and a Ph.D. from McGill University. His affiliations include membership in the Institute for Data Valorization (IVADO), focusing on data science and AI innovation. His research integrates software engineering with AI, emphasizing: Software artifact analysis (static/dynamic/symbolic) AI-driven security solutions for clone detection, malware analysis, and access control Fairness and robustness in machine learning systems Evolutionary analysis of software vulnerabilities Recent publications (2022-2025) demonstrate a strong focus on ethical AI, including bias mitigation in neural networks, automated anomaly detection, and certification of safety-critical ML systems. His work frequently applies graph neural networks, unsupervised learning, and formal verification methods to industrial and cybersecurity challenges. Professor Merlo has supervised 25 graduate students (10 PhD, 15 Master's), with projects ranging from avionics software to phishing kit analysis. While no scientific awards are listed, his extensive publication record includes 136 works spanning journals, conferences, and technical reports. Collaborations include partnerships with industrial telecommunication firms and international academia. No dedicated lab is specified, but his research aligns with Polytechnique Montréal's 'New Frontiers in Information and Communications Technologies' center.

پروفایل رسمی دانشگاه

Uday Venkatadriمشاهده پروفایل

استاد

Dalhousie UniversityCanada

Dr. Uday Venkatadri is a Full Professor and Department Head in the Department of Industrial Engineering at Dalhousie University's Faculty of Engineering, where he has taught since 2001. His industrial experience includes roles as Lead Architect at Baan and Research Associate at Université Laval. His research bridges facilities planning, supply chain optimization, and Industry 5.0 technologies, utilizing Mixed Integer Linear Programming as a core methodology. Research interests span: Hyperconnected logistics systems Production planning under uncertainty Healthcare supply chain resilience Sustainable operations AI/ML applications in industrial systems His publication trends show strong focus on: Optimization under uncertainty (35% of recent works) Sustainable supply chains (25%) Healthcare operations (20%) Maintenance systems (15%) Industry 5.0 applications (5%) Notable scientific awards: 2021 IISE Teaching Award (Logistics Division) Dalhousie Teaching Excellence Awards (2016, 2004) Purdue University Fellowships (1991-1993, 1987-1990) IIE Master's Thesis Award (1988) Advising and grants: Supervises graduate students in supply chain optimization and production systems Research funded through industry partnerships and institutional grants Developed 15+ graduate courses including Advanced Facilities Planning Laboratory involvement includes leading computational research groups focused on logistics optimization and sustainable operations, though no dedicated physical lab is specified.

پروفایل رسمی دانشگاه