جست‌وجوی استاد و پژوهشگر

Mark Lawfordمشاهده پروفایل

استاد

• Software engineering
• Safety critical systems
• Digital & Smart Systems
• +۳ مورد دیگر

Mark Lawford is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. His expertise spans software engineering, safety critical systems, and model-based development, with a focus on digital & smart systems and transportation research clusters. Contact: Email: lawford@mcmaster.ca Website: www.cas.mcmaster.ca/~lawford His research explores automotive systems , formal verification , and model-driven engineering , particularly addressing challenges in automotive electrification , centralized E/E architectures , and safety assurance cases . Recent publications emphasize test case generation , change impact analysis , and model transformation tools for Simulink, SysML, and AUTOSAR. Key trends in his work include autonomous vehicle safety , real-time control systems , and integration of formal methods with industrial practices across automotive, biomedical, and nuclear domains.

• Nuclear Engineering
• Reactor Safety
• Licensing and Regulatory Compliance
• +۷ مورد دیگر

Victor Snell is an Industry Professor in Engineering Physics at McMaster University with 50 years of experience in nuclear reactor safety, licensing, and computer code validation. His work focuses on CANDU reactor technology, severe accident management, and regulatory compliance for advanced nuclear systems. Research interests span Nuclear Engineering , Reactor Safety , Licensing Procedures , and Thermal Hydraulics . His recent publications analyze power reactor reactivity coefficients , severe accident programs , and computer code validation in nuclear contexts. Key trends in his 15 most recent articles (2000-2020) include reactor design safety ( ACR-1000 ), licensing harmonization across international markets, computer simulation for thermalhydraulic phenomena, and PIRT methodology for accident analysis. Collaborative efforts with institutions like UNENE and CNS are recurrent themes. As an instructor, he taught the course Nuclear Reactor Safety Design - UN 803 in 2019 and 2022. His career includes extensive work with the Canadian Nuclear Society and Atomic Energy of Canada Limited.

• Software Engineering
• Web Applications
• Formal Methods
• +۳ مورد دیگر

May Haidar is an Assistant Professor in the Department of Electrical Engineering & Computer Science at York University. Her work bridges theoretical and applied domains, focusing on software engineering, web applications, and formal verification techniques. Education: Ph.D. in Computer Science (Université de Montréal, 2008), M.S. (Concordia University), B.S. (American University of Beirut) Current Role: Teaching Object-Oriented Programming, Advanced Programming, and Web Development courses Her research explores formal methods in software engineering, particularly temporal logics and run-time verification for web applications. She has contributed to hybrid analysis techniques for composite web services and anomaly detection frameworks. Publications span formal verification, web application security, and embedded systems design, with a focus on integrating temporal logic extensions into validation processes. While no explicit awards are listed, her work has been presented at global conferences, including events in Dubai, Cambridge, and Las Vegas.

Rachida Dssouliمشاهده پروفایل

استاد

• Software Engineering
• Quality Assurance
• Service Engineering
• +۷ مورد دیگر

Rachida Dssouli is a Professor at the Concordia Institute for Information Systems Engineering (Concordia University). Her research focuses on advanced software engineering methodologies, quality assurance systems, and distributed computing frameworks. She specializes in model-based testing, federated learning optimization, and big data quality management. Her work integrates formal verification techniques with modern machine learning approaches to address challenges in edge computing, IoT, and safety-critical systems. Key research areas include: Development of hybrid swarm intelligence algorithms for optimizing large language model deployment in edge-cloud environments Design of reinforcement learning frameworks for robotics motion planning and IoT device scheduling Creation of interpretable machine learning tools for fault detection in software systems Establishment of holistic big data quality frameworks for continuous monitoring and unstructured data analysis Formal verification methods for avionics systems using multi-agent models Her recent work demonstrates trends toward AI-driven solutions for testing methodologies (e.g., SHAP-Driven fault detection) and edge-cloud integration (e.g., MIMO-based computation offloading optimization). The 2025 publications highlight advancements in federated learning and trust-aware IoT scheduling. Earlier works (2018-2020) emphasize foundational contributions to cloud trust models, big data quality metrics, and safety-critical system testing. Her research also addresses emerging technologies for developing countries through frameworks like neurodegenerative disease monitoring systems and mobile application requirements engineering. She has contributed to service-oriented architectures for healthcare systems and cloud-based resource orchestration strategies.

Jinqiu Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Automated Program Repair
• Software Testing
• +۴ مورد دیگر

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on software reliability, automated program repair, software testing, and quality assurance of machine learning systems, particularly in autonomous vehicles. He holds a PhD and MASc from the University of Waterloo and a B.Eng. from Nanjing University. Yang has been a tenure-track faculty member at Concordia since 2018, following research roles at IBM Watson and IBM CAS. Research Interests: Automated Program Repair Software Testing Machine Learning Systems Text Analytics of Software Artifacts Mining Software Repositories Autonomous Systems Quality Assurance Key Contributions: His recent work includes detecting concept drifts in ML systems (ICSE-25) and investigating social bias in LLM-generated code (AAAI-25). He leads the O-RISA Lab and has authored over 50 peer-reviewed publications, including distinguished papers at MSR-2018. Yang currently holds grants such as the NOVA FRQNT-NSERC Program (2024-2027) and the NSERC Discovery Grant (2019-2025). Service & Awards: Editorial Board Member of the Empirical Software Engineering Journal (EMSE) and active PC member in top venues like ICSE and FSE. Recipient of the IBM CAS Fellowship and ACM SIGSOFT Distinguished Paper Award. Teaching & Students: He mentors students in Master's and PhD programs, with funding available. His lab focuses on cutting-edge topics like secure code generation and autonomous vehicle reliability.

Shahrear Iqbalمشاهده پروفایل

دانشیار مدعو

• Smart Systems Security and Privacy
• Critical Infrastructure Security
• Secure Software Engineering
• +۳ مورد دیگر

Shahrear Iqbal is an Adjunct Associate Professor at Queen's University and a Cyber Security Researcher at the National Research Council (NRC) Canada . He holds a PhD in Cybersecurity (2017) and MSc in Combinatorial Optimization (2011) from Queen's University, along with a BSc in Computer Science and Engineering (2008) from Bangladesh University of Engineering and Technology. Research Focus: Security and Privacy of Smart Systems, including in-vehicle security, self-aware operating systems, IoT-cloud security, and AI-driven cybersecurity. Teaching: Previously taught CISC490: Cybersecurity at Queen's University. Key Projects: Droid Mood Swing (DMS) for context-aware Android security policies Securing ECU Communications in connected vehicles FCFraud for user-side click-fraud detection

Nandini Dendukuriمشاهده پروفایل

دانشیار

• Biostatistics
• Diagnostic Test Accuracy
• Bayesian Inference
• +۵ مورد دیگر

Nandini Dendukuri, PhD is a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the 5252 de Maisonneuve site. She holds dual affiliations with the Infectious Diseases and Immunity in Global Health Program and the Centre for Outcomes Research and Evaluation (CORE). Academically, she serves as an Associate Professor in the Department of Medicine, Faculty of Medicine and Health Sciences at McGill University, specifically within the Division of Clinical Epidemiology at the MUHC. Dr. Dendukuri's research program centers on the development of advanced statistical methodologies for diagnostic test accuracy evaluation in the absence of perfect reference standards. Her work spans Bayesian inference, latent class modeling, and meta-analysis techniques specifically designed for diagnostic accuracy studies. She has made seminal contributions to addressing methodological challenges such as conditional dependence between diagnostic tests, verification bias, and limitations of composite reference standards. Her research bridges theoretical statistical methodology with practical applications in infectious disease diagnostics, particularly tuberculosis where perfect reference tests are often unavailable. An analysis of Dr. Dendukuri's extensive publication record reveals consistent innovation in diagnostic test methodology with increasing sophistication in statistical modeling approaches. Her recent work (2024-2025) demonstrates expansion into diverse application areas including cardiovascular disease, emerging infectious diseases like COVID-19, and novel diagnostic technologies, while maintaining her core methodological focus. Her publications show particular strength in applying latent class models to complex diagnostic scenarios across multiple disease domains. Dr. Dendukuri actively collaborates with researchers across multiple disciplines, particularly in epidemiology, infectious diseases, and technology assessment. Her work with hospital-based technology assessment units has directly contributed to translating scientific evidence into healthcare policy. She maintains strong connections between statistical theory and practical healthcare applications, frequently developing software implementations (particularly rjags programs) of her methodological innovations to facilitate adoption by the research community.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Pengyu Nieمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۲ مورد دیگر

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 Alimadadiمشاهده پروفایل

استادیار

• Human-centred software engineering
• Program analysis
• Program comprehension
• +۷ مورد دیگر

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.

Michael W. Godfreyمشاهده پروفایل

استاد

• Software Engineering
• Program Comprehension
• Mining Software Repositories
• +۵ مورد دیگر

Affiliations & Roles Michael W. Godfrey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo . He holds the David R. Cheriton Faculty Fellowship and has served as an associate director of Cornell's M.Eng. program. His roles include: General Chair for ICPC 2025 (IEEE Program Comprehension) Member of steering committees for ICSME, MSR, SCAM, and SWAN Course coordinator for CS138/CS246 and instructor for advanced topics courses Research Focuses on software evolution , program comprehension , and mining software repositories . His work addresses challenges in code clone analysis, developer productivity, and empirical software engineering. Notable contributions include: Advocating for intentional cloning as valid design practice Pioneering studies on code review quality and anomaly detection Developing tools like JavaDUCK (educational project) and mel (model extraction) Awards & Recognition Recipient of: Best Paper Awards at WCRE 2006, 2011, 2013 Most Influential Paper Award at SANER 2016 Outstanding Reviewer Awards (ICSME 2019/2020) Service & Outreach Active in: Program committee roles for ICSE, ICSM, MSR, and 30+ conferences University service: Undergraduate Recruitment Committee (2016–present) Industry collaborations with CWI (Amsterdam), Sun Microsystems, and automotive software teams

• Software Engineering
• Formal Methods
• Model-Driven Engineering
• +۳ مورد دیگر

Chen-Wei Wang is an Associate Professor (Teaching Stream) in the Electrical Engineering & Computer Science (EECS) department at York University's Lassonde School of Engineering. He holds a DPhil in Computer Science from the University of Oxford, specializing in Software Engineering. His career includes post-doctoral research at McMaster Centre for Software Certification and York University Software Engineering Lab, focusing on certification of safety-critical systems. He previously taught at SUNY Korea and joined York University in 2017, where he coordinates experiential labs and emphasizes pedagogical innovation. He became an Eng. L. (Engineering Licensee) in 2020 and received tenure in 2022. Education: B.A. (summa cum laude) in Computer Science from York University (2006); DPhil in Computer Science from Oxford (2011). Research interests span formal verification of software systems, model-driven engineering, and computing education. Notable collaborations include work with Ontario Power Generations and Systemware Innovation. Research focuses on automated verification, real-time systems, and pedagogical tools. His publications emphasize educational technology integration and formal methods in software engineering. He actively participates in tennis and has a passion for music, having played violin and recently resumed piano lessons. Teaching contributions include foundational courses in programming, software design, and mission-critical systems. Students praise his clear explanations and high standards, despite rigorous assessments. He advocates for quality learning environments and has developed innovative teaching materials using drawing tablets and video capture.

Akramul Azimمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Software
• Safety-Critical Systems
• +۵ مورد دیگر

Dr. Akramul Azim is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His research focuses on real-time systems, embedded software, safety-critical systems, IoT, software verification/validation, and applied machine learning. He leads the Real-Time Embedded Software Research (RTEMSOFT) Lab and has contributed to over 50 peer-reviewed publications. His work spans cybersecurity for automotive systems, edge computing optimization, and AI-driven software testing. Education: PhD (Electrical and Computer Engineering), University of Waterloo (2014) MSc (Computer Science and Engineering) (2009) BSc (Computer Science and Information Technology) (2006) Research Interests: Dr. Azim explores the intersection of real-time systems and machine learning, particularly in embedded environments. Key areas include: Autonomous vehicle safety Edge/IoT security Optimized task scheduling ML-enhanced software testing FPGA vulnerability mitigation Recent Research Trends: His publications (2023-2025) highlight advancements in LLM-based code optimization, lock contention mitigation in Java, energy-efficient vehicular networks, and safety-critical AI applications. Notable contributions include frameworks for situation-aware adaptation in ITS and reinforcement learning for edge task scheduling. Labs/Teams: Directs the RTEMSOFT Lab, collaborating on projects like AD-CGAN for anomaly detection and ESOps agile pipeline development.

Gabriela Nicolescuمشاهده پروفایل

استاد

• Cybersecurity for Avionics
• Security by Design for Cyber-Physical Systems
• Advanced Programming Models for Avionic Multi and Many-Core Systems
• +۱۱ مورد دیگر

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
• +۴ مورد دیگر

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.