Large-scale Software Reliability Professors and Supervisors in Canada | ApplySharif

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Linyi LiView profile

Assistant Professor

Simon Fraser UniversityCanada

Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.

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Nick KoudasView profile

Professor

University of TorontoCanada

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.

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Zhen Ming JiangView profile

Associate Professor

York UniversityCanada

Zhen Ming (Jack) Jiang is an Associate Professor and York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems at York University's Department of Electrical Engineering and Computer Science. His research bridges software engineering, artificial intelligence, and computer systems with significant industrial impact. Dr. Jiang earned his Ph.D. from Queen's University's School of Computing and MMath/BMath degrees from the University of Waterloo's David R. Cheriton School of Computer Science. During his doctoral studies, he collaborated with BlackBerry's Performance Engineering team, developing tools now used daily to monitor commercial software systems. His research focuses on engineering rigor for AI-powered applications , software engineering evolution in the Generative AI era , and performance optimization of large-scale systems . Key areas include software performance engineering, mining software repositories, debugging distributed systems, source code analysis, and software visualization. His work combines empirical studies with practical tool development. Recent publications reveal strong trends in applying AI to software engineering challenges, particularly in machine learning systems reliability, blockchain efficiency, and AIOps solutions. His research consistently emphasizes empirical validation using real-world systems and industrial case studies. Scientific recognition includes: York Research Chair (Tier II) in Software Engineering for Foundation Model-Powered Systems NSERC Discovery Accelerator Supplements (DAS), 2020 Best Paper Award at ICST 2016 IEEE Software Best SEIP Paper at ICSE 2015 Ph.D. Research Achievement Award at Queen's University Multiple best paper awards at WCRE, MSR, and ICSE Dr. Jiang actively supervises graduate students and has secured competitive research funding including NSERC grants. His service includes program committee roles for top conferences (ICSE, ASE, ICSME) and editorial work for leading journals (TSE, TOSEM, EMSE). He leads research initiatives focused on foundation model-powered systems, collaborating with industry partners on performance monitoring and debugging solutions for large-scale distributed environments.

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Ridha KhedriView profile

Professor

McMaster UniversityCanada

Ridha Khedri is a Professor in the Department of Computing and Software at McMaster University . His research spans formal methods in software engineering, cybersecurity, information security ontology, network segmentation, and covert channels analysis. Full Professor since 2000 Contact: khedri@mcmaster.ca Research Interests : Prof. Khedri develops algebraic frameworks for software security, with recent work on network segmentation , ontology engineering , and covert channel detection . His interdisciplinary efforts include hybrid machine learning-ontology models for environmental predictions (e.g., river ice breakup) and digital twin healthcare systems . Article Trends : His 15 most recent works (2016-2025) focus on network security , knowledge representation , and formal verification . Notable trends include automated security testing , ontology modularization , and multi-context reasoning systems . Teaching : He has taught courses like Software Design (CAS 703), Discrete Mathematics (SFWRENG 2DM3), and Algebraic Methods in Software Engineering (CAS 738) since 2017.

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Diala NaboulsiView profile

Professor

École de Technologie SupérieureCanada

Diala Naboulsi is a Professor at the École de technologie supérieure (ÉTS) in the Department of Software Engineering and IT. Her research focuses on mobile networks, wireless systems, and cybersecurity, with a strong emphasis on machine learning applications in network optimization. She holds an M.Eng. from the Lebanese University and M.Sc. and Ph.D. degrees from INSA Lyon. Research Units: Summit Tech Research Chair, LASI Lab, Imagin Lab Expertise: Network virtualization, resource allocation, mobility management, UAV-based computing Her work spans resilience in wireless backhaul networks, energy-efficient frameworks in RAN slicing, and federated learning for privacy-aware traffic forecasting. She has advised numerous doctoral students, including Ahmed Abdelmoaty, Hnin Pann Phyu, and Philippe Lavoie. Key contributions include deep reinforcement learning approaches for network topology optimization and UAV-assisted MEC systems for Industry 5.0. Recent publications highlight advancements in 6G networks, network slicing, and edge computing. Her research aligns with strategic initiatives in sustainable and secure communication systems.

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Elisa BaniassadView profile

Teaching Professor

University of British ColumbiaCanada

Elisa Baniassad is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. She specializes in software engineering education and has received numerous teaching accolades including the UBC Killam Teaching Prize and CS-Can/INFO-CAN Excellence in Teaching Award. Her courses focus on software construction, engineering principles, and advanced software design. Dr. Baniassad has taught CPSC 310 (Introduction to Software Engineering), CPSC 210 (Software Construction), and CPSC 410 (Advanced Software Engineering) across multiple terms since 2000. Her research interests span educational methodologies in software engineering, aspect-oriented programming, and the design of effective learning tools. Notable contributions include studies on team dynamics in software development, automated assessment techniques, and pedagogical frameworks for large-scale programming courses. She has authored over 50 peer-reviewed articles on topics ranging from mutation analysis in student tests to the efficacy of online learning environments. Awards include recognition for teaching excellence at UBC and contributions to computer science education. Her work emphasizes practical applications of software engineering principles in academic settings, with a focus on fostering student mastery through innovative assessment strategies and feedback mechanisms.

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Ayşe BaşarView profile

Professor

Toronto Metropolitan UniversityCanada

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.

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Patrick HaydenView profile

Research Fellow

McGill UniversityCanada

Patrick Hayden is a Research Fellow in the Department of Physics at Stanford University specializing in Quantum Information Science, focusing on communication protocols for large-scale quantum information processing including noise-resistant quantum state transmission and cryptographic protection methods. Education: PhD in Physics from Oxford University BSc in Mathematics and Physics from McGill University His research expertise spans quantum information theory with emphasis on: Reliable quantum communication through noisy media Quantum encryption against unauthorized manipulation Minimizing communication in multi-party quantum processing Publications demonstrate foundational contributions to quantum cryptography and information theory, particularly in uncertainty relations and quantum commitment protocols. Awards: Outstanding Young Computer Science Researcher Prize, Canadian Association of Computer Science (2011) Sloan Research Fellowship (2007) No information is available regarding students advised or research grants secured by Hayden. He is affiliated with Stanford's Quantum Information Science research group, building on early high-school work at Quantum Software Systems which presaged his current research focus.

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Hedayat ZarkoobView profile

Researcher

University of British ColumbiaCanada

Hedayat Zarkoob is a Researcher in the Department of Computer Science at the University of British Columbia (UBC), where he completed his PhD under Prof. Kevin Leyton-Brown. His work focuses on AI applications in education, particularly in peer grading systems, active learning, and large-scale classroom engagement. He is actively involved in teaching within UBC's Master of Data Science (MDS) program and the Computer Science department, leading courses on privacy, algorithms, communication, and societal impacts of technology. Education: PhD in Computer Science, UBC (supervised by Kevin Leyton-Brown) MSc in Computer Science, Simon Fraser University (supervised by Andrei Bulatov) Research Interests: Zarkoob's research bridges AI and education, with emphasis on scalable assessment tools (e.g., Mechanical TA 2), classroom engagement platforms (Agora), and AI-driven conference review systems. His work combines algorithm design, educational technology, and human-AI collaboration. Recent Projects: Agora, an open-source tool for student participation in large courses; Mechanical TA 2, a peer-grading system with algorithmic support; and AAAI 2021 workflow innovations. Teaching Roles: Upcoming courses include DSCI 541, 553, 542, 512, and CPSC 430. He has co-taught CPSC 430 (Computers and Society) since 2023.

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Israat HaqueView profile

Associate Professor

Dalhousie UniversityCanada

Dr. Israat Haque is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, where she leads the Programmable and Intelligent Networking (PINet) research group. She also holds an adjunct professorship in the Department of Computing Science at the University of Alberta. PhD in Computer Science, University of Alberta NSERC Postdoctoral Fellow, University of California, Riverside MSc in Computer Science, Concordia University Her research focuses on developing high-performance, secure, and dependable distributed and emerging networking systems, with key interests in Software-Defined Networking (SDN), Cyber-physical Systems (CPS), Internet of Things (IoT), 5G/6G technologies, and AI/ML applications in networking. She applies data-driven approaches to solve real-world problems in network programmability, stream processing, and edge/cloud computing. The 15 most recent publications highlight a strong trend in in-network computing, IoT security, AI-driven network reliability, and stream processing. Her work spans from theoretical surveys to practical system implementations, often leveraging programmable data planes (P4), machine learning, and hardware acceleration to address challenges in performance, security, and fairness in distributed systems. Dr. Haque has received numerous prestigious recognitions: IEEE/ACM N2Women Rising Star Award (2021) ACM FAccT Best Paper Award (2023) IEEE WICE Outstanding Mentoring Award (2025) University of Alberta Alumni Honour Award (2024) Digital Nova Scotia Thinking Forward Award (2022) Intel Fast Forward Initiative Winner (2022) President’s Research Excellence Award, Dalhousie (2021) She actively mentors PhD and MSc students and leads externally funded research projects, including a Canada First Research Excellence Fund (CFREF)-supported initiative on Securing Smart Environments. She has served on editorial boards of IEEE Transactions on Vehicular Technology and IEEE Communications Magazine, and on program committees of top conferences such as IEEE ICNP, IEEE NetSoft, and ACM SIGCOMM. Her lab, PINet, fosters innovation in networking systems and produces high-impact research with real-world applicability. Dr. Haque leads the PINet research group, which includes current PhD and MSc students working on cutting-edge topics like post-quantum cryptography, network programmability for security, and large ML model security. The group collaborates with institutions such as the University of Alberta, Laval University, and Concordia, and has strong industry ties with Intel, Meta, Amazon, and Facebook.

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Sihang LiuView profile

Assistant Professor

University of WaterlooCanada

Dr. Sihang Liu is an Assistant Professor in the School of Computer Science at the University of Waterloo. Prior to this role, he served as a visiting faculty member at SystemsResearch@Google. He holds a PhD from the University of Virginia, where his research was supported by the Google Fellowship Award. His academic career has been marked by contributions to computer architecture, systems, and persistent memory technologies. Dr. Liu's research interests span computer architecture, systems, and cybersecurity, with a focus on persistent memory systems, energy-efficient computing, and data center optimization. His work bridges hardware-software co-design and explores challenges in scalable computing, fault tolerance, and sustainable AI systems. His recent publications highlight advancements in adaptive memory tiering (HybridTier), carbon intensity forecasting (EnsembleCI), and securing persistent memory (Side-Channel Attacks on Optane). These contributions underscore his commitment to addressing critical issues in modern computing systems' performance, security, and environmental impact. Awards: Google Fellowship Award (PhD), NVMW Memorable Paper Award Finalist (2019), 2019 MICRO Top Picks Honorable Mention Professional Service: Program committees for ISCA, MICRO, ASPLOS; Artifact evaluation roles in leading conferences As an advisor, Dr. Liu mentors a diverse group of students, including current PhD candidates Henry Tian and Desen Sun, and numerous undergraduates. His lab focuses on innovation in sustainable computing and resilient systems design.

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Animesh AnimeshView profile

Associate Professor

McGill UniversityCanada

Animesh Animesh is an Associate Professor and Area Coordinator of Information Systems at McGill University's Desautels Faculty of Management. His research examines the economic and strategic impacts of digital technologies, with focus areas including online advertising, social media analytics, platform ecosystems, and sustainable business models. Animesh's work combines econometric analysis with large-scale data to investigate how digital innovations transform business practices. Recent studies explore AI hallucination detection, open government data economics, and sustainability communication strategies. His research on software patents has influenced understanding of innovation management in competitive IT markets. Animesh maintains industry partnerships through the McGill Dobson Centre for Entrepreneurship.

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Olena SyrotkinaView profile

Professor

University of WindsorCanada

Dr. Olena Syrotkina is a Professor at the University of Windsor's School of Computer Science. She holds a Ph.D. in Mathematical Simulation from Ukrainian State University of Science and Technologies and an M.Sc. in Computer Science from Dnipro University of Technology. Her research focuses on SCADA diagnostics, big data analytics, discrete mathematics, and machine learning algorithms. Research Interests: Her work spans computational methods for large-scale data analysis, including machine learning applications in industrial systems and theoretical frameworks for data optimization. Primary areas include: SCADA system reliability and diagnostics Big data processing and resource optimization Machine learning-driven predictive modeling Research Trends: Her recent publications emphasize mathematical optimization in big data contexts, with themes like quantum computing applications, fault detection in complex systems, and e-commerce analytics. Over 80% of her 2020-2024 publications involve scalable algorithms for industrial data. Honors & Advising: No awards are listed. She currently mentors graduate students but no named advisees were specified. Laboratory & Teams: Leads research in computational methods at UWindsor, collaborating with industrial partners on SCADA and data infrastructure projects.

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Miguel F. AnjosView profile

Professor

University of MontrealCanada

Miguel F. Anjos is a Professor in the School of Mathematics at the University of Edinburgh, where he serves as Deputy Head of School and Head of the research theme Data and Decisions. He also holds an Associate Professor position at Polytechnique Montréal in the Department of Mathematics and Industrial Engineering. Additionally, he chairs the Mathematical Optimization Society (2023-2025) and serves as Vice-President (International Activities) of INFORMS. His research focuses on mathematical optimization to solve large-scale discrete nonlinear optimization problems in engineering applications, particularly in conic optimization for facility layout problems and optimal power flows in electricity grids. His work addresses critical challenges in smart grid development, including efficient network utilization, renewable energy integration, energy storage, and customer participation in grid operations. He has extensive industry experience working with National Grid ESO, Hydro-Québec, Schneider Electric, Rio Tinto, and EDF. His publications reveal a strong focus on optimization methods applied to energy systems, with recent work spanning bilevel optimization, robust optimization with uncertainty quantification, electric vehicle infrastructure planning, and carbon credit markets. His research consistently bridges theoretical optimization with practical engineering applications. Canada Research Chair in Nonlinear Discrete Optimization in Engineering (2011-2016) NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair in Smart Grid Optimization (2016-2019) INRIA International Chair for Optimizing Smart Grids (2016-2021) Winner of MOPTA 2020 and 2024 competitions Queen Elizabeth II Diamond Jubilee Medal (2012) Fellow of the Humboldt Foundation, EUROPT, and Canadian Academy of Engineering Professor Anjos has supervised numerous PhD students and postdoctoral fellows, with graduates securing positions at academic institutions including University of Tennessee, Western Ontario University, and industry roles at Amazon, CAISO, and major consultancies. He maintains three benchmark datasets (QAPLIB, FLPLIB, and the Jones Benchmark) as a service to the optimization community.

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Peter LelievreView profile

Associate Professor

Mount Allison UniversityCanada

Peter Lelievre is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on advancing geophysical inversion technology to improve subsurface imaging for applications in flood risk management, resource exploration, and environmental monitoring. He emphasizes collaborative, interdisciplinary approaches combining mathematics, computer science, and geophysics. His work addresses computational challenges in geophysical inversion, including unstructured mesh modeling, machine learning integration, and uncertainty quantification. He leads a research group prioritizing diversity and innovation, with current projects involving geophysical imaging of agricultural dykes and seafloor massive sulphide deposits. Recent publications span magnetic surface inversion, transient electromagnetic data analysis, and gravity modeling of crustal systems. Lelievre collaborates on initiatives like the AARMS-CRG Numerical Solution of Geophysical Inverse Problems, fostering discussion on optimization methods and joint inversion strategies. His team includes postdoctoral fellows, undergraduate researchers, and technical staff, with notable contributions to software development for geophysical analysis.

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