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.
Lianying Zhao is an Associate Professor in the School of Computer Science at Carleton University and serves as a Director of the Carleton Computer Security Lab (CCSL). His research focuses on low-level platform security, including hardware, firmware, hypervisor, and operating systems, with an emphasis on trusted computing, authentication, privacy preservation, and security metrics. He leads the CCSL research group, collaborating with interdisciplinary teams to address critical security challenges in IoT, cloud systems, and web applications. Education: Not explicitly listed in provided texts. Roles: CCSL Director, Research Supervisor, and Graduate Program Advisor. Dr. Zhao’s work spans hardware security improvements, firmware vulnerability analysis, and user-centric security metrics. Recent research highlights include studies on router configuration habits, tracker detection in web browsers, and CVSS score discrepancies. He has supervised numerous graduate students in cybersecurity domains, contributing to over 30 peer-reviewed publications since 2013. His lab affiliations include CCSL and CISL, where he collaborates on projects such as secure deletion frameworks, TLS validation vulnerabilities, and hybrid decision-making models for cloud security. Current research also explores cross-regional login throttling mechanisms and AI-driven vulnerability detection in embedded systems.
Linan Chen is an Associate Professor in the Department of Mathematics and Statistics at McGill University since 2014, following a postdoctoral position at the same institution (2011–2014). He holds a Ph.D. from MIT (2011, supervised by Daniel Stroock) and a B.Sc. from Tsinghua University (2006). His research focuses on probability theory and its intersections with analysis and geometry, including partial differential equations, functional analysis, Gaussian measures, and random geometry. He is affiliated with the Probability Lab of the Centre de Recherches Mathématiques (CRM) and the CNRS-Unite Mixte Internationale (CNRS-UMI) since 2014. Chen teaches advanced probability courses such as Honours Probability (Math 356) and Advanced Probability Theory I/II (Math 587/589), alongside special topics courses like Topics in Geometry and Topology (Math 599). He has advised students including Leila Sloman, Reinhold Willcox, Ulysse Blau, and Olivier Nadeau-Chamard through independent study programs. His research explores cutting-edge topics in probability, including Gaussian free fields, degenerate diffusion equations, and asymptotic properties of geometric stochastic structures. Recent work addresses high-dimensional phenomena, stochastic processes in geometry, and applications in mathematical physics. Chen’s contributions span theoretical advancements and methodological innovations, with publications in journals such as the Journal of Theoretical Probability, Annales Henri Poincaré, and SIAM Journal on Mathematical Analysis.
Daniel Roy is a Full Professor at the University of Toronto, holding cross-appointments in the Department of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and the Department of Computer and Mathematical Sciences at UTSC. He is also a Canada CIFAR AI Chair and Research Director at the Vector Institute, reflecting his leadership in AI and machine learning research. His educational background includes a PhD, MEng, and BSc in Computer Science from MIT, where his doctoral work earned the MIT EECS Sprowls Award. Prior to joining Toronto, he was a Newton International Fellow at the Royal Society and a Research Fellow at Emmanuel College, University of Cambridge. His research centers on foundational principles in machine learning, statistics, and probabilistic reasoning. Key interests include statistical learning theory, Bayesian nonparametrics, probabilistic programming, and information-theoretic generalization. His work bridges theoretical computer science, mathematical logic, and applied probability. His recent publications, appearing in ICML, NeurIPS, COLT, and JMLR, reflect a strong focus on theoretical advances in generalization, online learning, and stochastic optimization. Themes include minimax rates, conditional mutual information, and the role of data in PAC-Bayes bounds. His group has made foundational contributions to probabilistic programming, including work on Church and the computability of conditional probability. NSERC Discovery Accelerator Supplement Ontario Early Researcher Award Google Faculty Research Award Newton International Fellowship MIT EECS Sprowls Award Daniel Roy advises numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. His group actively collaborates with leading researchers in machine learning and statistics. He is also an Action Editor for the Journal of Machine Learning Research and Transactions of Machine Learning Research, underscoring his role in shaping the field. He leads a vibrant research group focused on theoretical machine learning and probabilistic modeling, and maintains active collaborations with institutions such as MIT, Cambridge, and the Vector Institute. He is also the founder and maintainer of the probabilistic-programming.org wiki, a key resource in the community.
Maria Rogers is an Associate Professor in the Counseling Psychology program at Carleton University, holding the prestigious Tier II Canada Research Chair in Child and Youth Well-being. She concurrently serves as an Adjunct Professor in the Faculty of Education at the University of Ottawa, where her institutional email is maintained. Her clinical credentials include licensure as a child and adolescent psychologist in Ontario and Quebec, enabling direct practice alongside academic work. Professor Rogers' research program centers on interpersonal dynamics in child development, with three interconnected pillars: (1) relational health of children with neurodevelopmental disorders (particularly ADHD) and its academic implications, (2) chronic school absenteeism and its mental health correlates, and (3) Indigenous children's educational well-being through community partnerships. Her work consistently examines how parent-child and teacher-student relationships mediate learning outcomes, especially during crisis periods like the COVID-19 pandemic. Analysis of her recent publications reveals evolving methodological sophistication, transitioning from single-institution studies to national longitudinal designs. Key emerging trends include intersectional analysis of ADHD symptomatology across gender identities, integration of physical activity metrics into mental health models, and development of virtual single-session interventions for parental support. Her work increasingly incorporates Indigenous research methodologies through community-led partnerships. Scientific Recognition: Tier II Canada Research Chair in Child and Youth Well-being As co-founder of the Canadian Partnership for School Attendance (CPSA), Rogers leads a transdisciplinary research collective involving school boards, mental health agencies, and Indigenous communities. Her lab develops evidence-based frameworks for addressing school absenteeism, with recent focus on pandemic recovery strategies and culturally responsive interventions for Indigenous youth. Current projects examine the effectiveness of mindfulness adaptations for neurodiverse learners and longitudinal tracking of mental health trajectories following school reintegration.
Meng Xu is an Assistant Professor in the Cheriton School of Computer Science at the University of Waterloo, Canada. He is affiliated with the Cryptography, Security, and Privacy (CrySP) group and the Cybersecurity and Privacy Institute (CPI). His research focuses on system and software security, emphasizing secure-by-design languages (e.g., Rust, Move), automated program analysis, and runtime defense techniques. Education : Ph.D., Computer Science (2020), Georgia Institute of Technology B.Eng. and B.Business (First Class Honors), Nanyang Technological University (2014) Research Interests : Secure-by-design languages Automated security analysis (fuzzing, symbolic execution) Runtime defense mechanisms (moving target defense, secure hardware) Key Awards : EAPLS Best Paper Award (2022) USENIX Security Distinguished Paper Award (2018) Grants & Funding : BlackBerry Research Grant (CAD $200,000) Amazon Research Award (USD $60,000) NSERC Discovery Grant (CAD $170,000) Labs & Collaborations : CrySP (Cryptography, Security, and Privacy Group) Cybersecurity and Privacy Institute (CPI)
Christopher Hearty is a Professor in the Department of Physics & Astronomy at the University of British Columbia (UBC), Faculty of Science, and serves as an IPP (Institute of Particle Physics) Principal Research Scientist. His office is located in Hennings 268 with laboratory space at TRIUMF/Hennings 222, where he conducts cutting-edge experimental particle physics research using major international facilities. Hearty earned his B.Sc. in Mathematics and Physics from Simon Fraser University (1982), followed by a Ph.D. in Physics from the University of Washington (1987). He completed postdoctoral research at Lawrence Berkeley National Laboratory from 1987 to 1994 before joining UBC. B.Sc., Mathematics and Physics, Simon Fraser University, 1982 Ph.D., Physics, University of Washington, 1987 Postdoctoral Researcher, Lawrence Berkeley National Laboratory, 1987-1994 His research program focuses on direct searches for physics beyond the Standard Model through e+e- collisions, with particular emphasis on dark sector phenomena including dark photons, axion-like particles, and strongly interacting dark matter. As a key contributor to the Belle II experiment, he develops advanced calorimeter calibration techniques, reconstruction algorithms, and trigger systems while mentoring students in machine learning applications for large-scale data analysis. His work bridges theoretical phenomenology with experimental verification in the search for new fundamental particles. Recent publications demonstrate a concentrated effort on dark sector exploration at Belle II, featuring innovative approaches like graph neural networks for photon reconstruction and sophisticated analysis of displaced vertices. The research spans both visible and invisible decay channels, significantly advancing constraints on dark matter models while establishing Belle II's sensitivity to elusive particles through precision measurements of e+e- collision data. Hearty's scientific recognition includes: APS Fellow (2015) Breakthrough Prize in Fundamental Physics (2016) as part of the T2K collaboration He actively supervises graduate students on thesis projects spanning dark photon searches, axion-like particle detection, and detector development, while serving on UBC's teaching peer review committee and as LHCb chief reviewer for CERN's LHCC committee. His mentorship provides students with hands-on experience in international collaborations, detector instrumentation, and advanced data analysis techniques. Based at TRIUMF Canada's particle accelerator centre and UBC's Department of Physics & Astronomy, Hearty leads a research group within the global Belle II collaboration. His team contributes to multiple detector subsystems including calorimetry and tracking systems, while developing novel analysis frameworks for new physics signatures in high-energy collision data.
Yizhou Zhang is an Assistant Professor in the Department of Computer Science at the University of Waterloo. He holds a PhD and MS from Cornell University (2019 and 2016) and a BS from Shanghai Jiao Tong University (2012). His research focuses on programming languages, including design, implementation, and theory, with emphasis on formal methods, compiler optimization, and probabilistic programming. Education: PhD, Cornell University, 2019 MS, Cornell University, 2016 BS, Shanghai Jiao Tong University, 2012 Research interests span programming language theory, compiler construction, and formal verification. His work explores topics like certified compilers, effect handlers, and probabilistic program analysis. Recent publications emphasize formal models for memoization, nested family polymorphism, and bidirectional control flow. His publications reflect contributions to probabilistic programming semantics, compiler optimization techniques, and type systems. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. Zhang’s research often intersects with formal methods and practical compiler implementation challenges.
Prof. Lionel C. Briand is a leading academic in software engineering and trustworthy AI, holding appointments at the University of Ottawa (EECS Department, Nanda Laboratory) and the University of Limerick (Lero - National Software Research Centre). He serves as Director of Lero and Scientific Director of the SnT software verification lab in Luxembourg. His research focuses on software testing, model-driven engineering, AI-driven quality assurance, and regulatory compliance. He has held the Canada Research Chair (Tier 1) since 2003 and led major institutions like Simula Research Laboratory (Norway) and Fraunhofer Institute (Germany). Education & Career: Full Professor at Carleton University (2008–2012) Head of Software Quality Engineering at Fraunhofer IESE (2000–2008) Research Scientist at NASA Software Engineering Lab (1990s) Research Interests: His work spans secure AI systems, automated legal compliance (e.g., GDPR), metamorphic testing, search-based software engineering, and safety-critical systems. He emphasizes practical applications, collaborating with industry partners globally. Awards & Recognition: IEEE Fellow (2010), ACM Fellow (2020) Harlan Mills Award (2012), ERC Advanced Grant (2016) Fellowships from Royal Society of Canada (2023) and Academia Europaea (2025) Grants & Labs: PEARL grant from Luxembourg FNR for SnT lab ERC Advanced Grant for software testing research Leadership roles in Lero and Nanda Lab Publications: Over 500+ papers on testing methodologies, AI ethics, and regulatory compliance. Notable tools include CompAI (GDPR compliance) and Teasma (DNN test adequacy).
Andrew Morton, PhD, PEng, is a Continuing Lecturer in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a BSc in Computer Science (Guelph, 1993), MSc in Computer Science (Guelph, 1996), and a PhD in Computer Engineering (Waterloo, 2005). Education: BSc (Guelph, 1993) Major: Computer Science, Minor: Chemistry MSc (Guelph, 1996) Computer Science PhD (Waterloo, 2005) Computer Engineering His research focuses on the software/hardware boundary, including embedded systems, hardware acceleration, and real-time operating systems. Key areas include FPGA placement, real-time scheduling, and system-on-chip design. Teaching responsibilities span courses such as CS 137 (Programming Principles), CS 450 (Computer Architecture), and ECE 252 (Systems Programming and Concurrency). He has advised multiple students on topics including MPSoC scheduling and dynamically reconfigurable systems.
Dr. Matt Amy is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), holding the Canada Research Chair in Quantum Computing. His research focuses on quantum compilers, programming languages, and formal verification of quantum programs. He also explores quantum circuit optimization and models of quantum computation. Education: PhD in Computer Science (University of Waterloo, 2019), M.Math in Quantum Information (2013), and B.Math in Computer Science (2011), all from the University of Waterloo. Research Interests: Quantum compilers and languages, circuit optimization, formal verification, and quantum computation models. His work bridges theoretical foundations with practical implementations, emphasizing efficient quantum software development. Recent research trends include advancing quantum compilation techniques, exploring NP-hard optimization problems in quantum circuits, and developing formal methods for quantum program analysis. His work on symbolic synthesis and equational theories for quantum circuits demonstrates a focus on foundational algorithmic challenges. Scientific Awards: Canada Research Chair (2025–present) Advising and Grants: While no current advisees are listed, his research is supported by grants focused on quantum computing and formal methods. He collaborates with industry through SFU’s School of Computing Science. Labs and Teams: Involved with the Tangent Lab, a research group exploring quantum algorithms and software systems at SFU.
Marsha Chechik is a Professor in the Department of Computer Science at the Faculty of Arts and Science, University of Toronto. She previously served as Department Chair from 2019-2022 and as Acting Dean in the Faculty of Information from July-December 2022. Her academic career spans numerous research contributions and leadership roles within the software engineering community. Professor Chechik's primary research interests focus on software engineering with emphasis on formal methods to enhance software quality. Her work encompasses scalable automated verification techniques including model-checking and theorem-proving, formal specification languages, verification of protocols, non-classical logics, and reasoning under inconsistency. She has made significant contributions to model management, software product lines, safety and security assurance, and automotive safety systems. Her research bridges theoretical foundations with practical applications, particularly in managing uncertainty in software models and developing techniques for automotive safety verification. Her recent publications demonstrate a strong focus on model management and transformations, software product lines and variability analysis, safety and security assurance cases, and semantic analysis of software evolution. The integration of formal methods with practical software engineering challenges, especially in safety-critical domains like automotive systems, represents a consistent theme throughout her work. Professor Chechik has been recognized with multiple prestigious awards including a Best Paper Award at RE'12, a SIGSOFT Distinguished Paper Award at ICSE'12, a Best Student Paper Award at CASCON'07, and a Distinguished Paper Award at ICSE'07, highlighting the impact and quality of her research contributions. She actively supervises graduate students and has successfully guided numerous Ph.D. candidates to completion. Her group has produced graduates who predominantly pursue research careers in both academic institutions and industrial research labs. She currently leads several funded projects including the Automotive Safety project (in collaboration with General Motors) and the Software Evolution project, focusing on practical applications of her research interests. Professor Chechik leads the Software Engineering Lab at the University of Toronto, where innovative projects like Matchmakers (a serious game for software engineering) are developed. Her collaborative network extends across institutions, with notable partnerships including Julia Rubin at the University of British Columbia, demonstrating her commitment to interdisciplinary research and academic collaboration.
Elaina Hyde is an Associate Professor in the Department of Physics and Astronomy at York University, serving as Director of the York Allan I. Carswell Observatory. She is affiliated with the Faculty of Science and eligible to supervise graduate students in the Physics and Astronomy program. Her research focuses on galactic archaeology, galaxy formation, and data science for astrophysics, leveraging cloud computing and machine learning. She has contributed to major initiatives like the GALAH survey and studies of the Sagittarius stream. Her work combines observational astronomy with technical leadership in telescope operations and public outreach. Hyde is also a certified Google Cloud Trainer and Engineer, integrating industry-level data science practices into academic and educational contexts. Education & Professional Background : While specific educational details are not listed, her roles indicate advanced expertise in astrophysics and data science. She has held technical and leadership positions in telescope operations and academic observatories. Research Interests : Hyde's work bridges computational and experimental astrophysics, emphasizing: Galactic archaeology via chemical and kinematic analysis of stellar populations Data-driven approaches to galaxy formation modeling Development of automated spectroscopic pipelines (e.g., GALAH survey) Machine learning applications for spectral classification and dimensionality reduction (e.g., t-SNE techniques) Astronomy education through public telescope access and interdisciplinary training Publications Overview : Her recent work focuses on the GALAH survey's chemical and kinematic inventory of the solar neighborhood, Sagittarius stream dynamics, and machine learning-enhanced spectral analysis. Key themes include stellar abundance trends in open clusters, tidal debris identification, and multi-survey data integration with Gaia. Labs & Teams : Leads the York Allan I. Carswell Observatory, fostering observational astronomy research and public engagement. Collaborates with global telescope networks and industry partners in cloud computing.
Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal. He leads the Software PErformance, Analysis, and Reliability (SPEAR) lab, focusing on improving software quality through log analysis, AIOps, and mining software repositories. His research collaborates with companies like Microsoft, BlackBerry, and Ericsson. Education: PhD, MSc, and BSc in Computer Science from Queen's University and the University of British Columbia. Awards include the Gina Cody Research Award (2021) and recognition as one of the world's most active software engineering researchers (JSS study). Research interests include software testing, DevOps, and leveraging LLMs for SE tasks. Recent work emphasizes log parsing with LLMs (e.g., LibreLog) and fault localization. Graduates from his lab hold academic positions at institutions like York University and DePaul University. Teaching includes courses on software verification, testing, and process management. Active in program committees for ICSE, FSE, and MSR. Over 50 publications in top venues like TSE, ICSE, and FSE.
Souradeep Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering within the Faculty of Applied Science at the University of British Columbia (UBC), joining in Fall 2024 after postdoctoral research at the PRECISE center, University of Pennsylvania. His academic credentials include a PhD in Electrical and Computer Engineering from the University of Colorado Boulder and a BE in Instrumentation and Electronics Engineering from Jadavpur University, India. Education: PhD, Electrical and Computer Engineering, University of Colorado Boulder BE, Instrumentation and Electronics Engineering, Jadavpur University, India Dr. Dutta's research centers on artificial intelligence with emphasis on reinforcement learning, cyber-physical systems, and formal methods. He investigates fundamental challenges in efficient data-driven learning and assurance techniques for learned models, targeting applications in robotics and medical devices. His work bridges theoretical guarantees with practical implementations for safe human-machine knowledge transfer. Analysis of his 15 most recent publications reveals dominant trends in robustness verification for learning-enabled systems, memory-based adaptation techniques, and distribution shift handling. His research consistently addresses safety-critical applications, particularly in medical diagnostics (e.g., ECG analysis, acne grading) and autonomous control systems, while maintaining strong theoretical foundations in formal methods. Awards: Recognition at top-tier conferences including ICLR, CORL, HSCC, ICCPS, ICAPS, NFM, L4DC, CHASE, and ADHS Dr. Dutta actively seeks graduate students for Fall 2025 and welcomes interdisciplinary collaborations. He serves on program committees for AAAI, ICCPS, ICML, and NeurIPS, and is available for undergraduate research supervision. His advising philosophy emphasizes safe and efficient transfer of human expertise to machine systems. He leads a research group at UBC focused on developing verifiable AI frameworks for cyber-physical applications, with current projects spanning medical device assurance and adaptive robotics control systems.
