Dr. Mohammad Zulkernine is a Full Professor and Canada Research Chair in Cyber-Physical System Security at Queen’s University’s School of Computing (Faculty of Arts and Science). He leads the Queen’s Reliable Software Technology (QRST) research group and directs the Queen’s Centre for Security & Privacy. His research focuses on secure software systems for cyber-physical systems, including autonomous vehicles, IoT, and cloud computing. Dr. Zulkernine holds a cross-appointment in Electrical and Computer Engineering and is a licensed Professional Engineer in Ontario. Education: BSc (Bangladesh BUET), MEng (Japan), PhD (University of Waterloo). He joined Queen’s in 2003 and has held sabbaticals at University of Trento, Italy and Irdeto Canada. He has published over 250 papers, led 35+ research projects, and supervised 120+ students. Awards include Canada Research Chairs (Tier I and II), Queen’s Excellence in Graduate Supervision Award, and Distinguished Supervision Award from the School of Computing. Research Interests: Cyber-Physical System Security, Software Reliability, IoT Security, Vehicle Networks, Secure Software Engineering, and Cybersecurity Risk Assessment. Active industry collaborations include EU-funded projects and Canada-Africa initiatives.
Magdy M. A. Salama is a Professor and University Research Chair at the University of Waterloo's Department of Electrical and Computer Engineering, Faculty of Engineering. He holds a P.Eng. license and is a Fellow of the IEEE. His research spans Energy Systems (Power Quality, Smart Grids, Renewable Energy) and Biomedical Engineering (Medical Imaging, Sleep Analysis). He has authored/co-authored over 460 publications and supervised numerous graduate students. Education: PhD (University of Waterloo), M.Sc. and B.Sc. (Cairo University). Awards include the IEEE Fellow distinction, University Research Chair, and multiple teaching/research awards from the University of Waterloo. Research trends in his articles focus on Smart Grid resiliency, renewable integration, cyber-physical security, and biomedical applications of AI. Notable projects include voltage sag mitigation, EV fleet electrification, and blockchain-based energy trading platforms. Scientific Awards: IEEE Fellow, University Research Chair, Teaching Excellence Award (2000) Grants/Consultation: Extensive industry and institutional collaborations on power systems and biomedical tech. Labs/Teams: Active in High Voltage Lab, Smart Grids Research Group, and Medical Image Processing Lab.
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.
Deepa Kundur is the Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. She earned her BASc, MASc, and PhD in Electrical and Computer Engineering from the same institution in 1993, 1995, and 1999, respectively. Current roles: IEEE Spectrum Advisory Board Conference leadership: General Chair of 2018 GlobalSIP Symposium, TPC Co-Chair for IEEE SmartGridComm 2018, among others Her research focuses on cybersecurity , signal processing , and complex dynamical networks , particularly in smart grid applications. She has authored over 200 publications and pioneered techniques for detecting false data injection attacks, enhancing grid resilience, and integrating machine learning into power systems. Her recent work spans quantum learning for grid security , LLM-based mental health prediction , and resilient control systems . She has received 14 best paper recognitions, including IEEE SmartGridComm (2015) and IEEE INFOCOM Workshop (2008). Fellowships: IEEE Fellow (2015), Canadian Academy of Engineering Fellow (2016), Massey College Senior Fellow (2019) Teaching awards: Tenneco Meritorious Teaching Award (2005), Gordon Slemon Teaching of Design Award (2002) Early career honors: NSERC Scholarships (PGS A/B), Canada Scholarship She leads the Kundur Research Group , developing models for cyber-physical systems in smart grids and autonomous vehicle networks. Her team explores reinforcement learning for grid defense , transmissibility-based fault detection , and privacy-preserving smart grid analytics .
Dr. Saiedeh Razavi is an Associate Professor and the inaugural Chair in Heavy Construction at McMaster University's Department of Civil Engineering, directing the McMaster Institute for Transportation and Logistics (MITL). She holds a multidisciplinary background with degrees in Computer Engineering (B.Sc., Sharif University), Artificial Intelligence (M.Sc., Iran), and Civil Engineering (Ph.D., Waterloo). Her research focuses on smart infrastructure, connected mobility, and construction safety, funded by NSERC and the Ontario Ministry of Transportation. Key areas include transforming construction management through AI, autonomous vehicles, and smart work zones. Education: B.Sc. (Sharif), M.Sc. (Iran), Ph.D. (Waterloo) Research Interests: Smart cities, connected vehicle systems, data fusion, risk analysis, and sustainable logistics Leadership Roles: Director of MITL, Associate Chair (Research), and lead of national/international multidisciplinary projects Her work bridges academia, government, and industry to enhance mobility and safety. Notable grants include NSERC funding for transformative transportation systems. Awards include teaching excellence and innovation in team-based projects. Grants & Projects: NSERC, Ontario Ministry of Transportation, and industry collaborations Labs/Teams: MITL, CPS-based construction safety initiatives, and autonomous vehicle research groups
Ikjot Saini is a Professor at the University of Windsor’s Faculty of Engineering, co-leading the SHIELD Automotive Cybersecurity Centre of Excellence, Canada’s first organization addressing threats in connected transportation. Her research focuses on automotive cybersecurity, vehicular networks, and privacy-preserving technologies. She has supervised doctoral students Shiva Nejati and Kunj Dhonde, and contributed to courses in the University’s Continuing Education program, specializing in cybersecurity education for professionals. Her work includes pioneering studies on blockchain-based security for connected autonomous vehicles (CAVs), machine learning-driven intrusion detection systems, and privacy-enhancing mechanisms like pseudonym-changing strategies. She has been recognized with the K.W. Michael Siu Award from the APMA Institute for Automotive Cybersecurity (2020). Saini’s research bridges theoretical advancements with real-world applications, ensuring vehicles and infrastructure remain secure against evolving cyber threats. Her contributions span academic publications, industry partnerships, and policy recommendations, positioning her as a leader in vehicular cybersecurity. Ongoing projects emphasize eco-efficiency in cybersecurity solutions and adversarial modeling for privacy evaluation.
Yazan Otoum is a Part-Time Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa and concurrently an Assistant Professor in the School of Computer Science and Technology at Algoma University . A licensed Professional Engineer in Ontario, he is internationally recognized for his interdisciplinary work at the intersection of cybersecurity, artificial intelligence, and the Internet of Things . Education Ph.D. in Electrical and Computer Engineering, University of Ottawa (September 2022) M.Sc. in Network Engineering and Management, DePaul University (December 2009) Research Interests Dr. Otoum’s research program is dedicated to securing the rapidly expanding IoT ecosystem. His core themes include: Scalable meta-learning models that adapt to evolving threats in resource-constrained IoT devices. Federated and transfer learning to enable privacy-preserving, collaborative intrusion detection across heterogeneous networks. Healthcare IoT (IoMT) security, ensuring safe and trustworthy medical devices and data streams. Smart-city infrastructures , where AI-driven security safeguards critical urban services. His recent work leverages large language models (LLMs) , blockchain , and differential privacy to push the boundaries of next-generation cyber-defence mechanisms. Publication Trends Across 23 peer-reviewed works (2017-2025), a clear evolution is evident: early studies established foundational deep-learning intrusion detection frameworks (DL-IDS), followed by federated and transfer-learning paradigms tailored for IoT and IoMT. The latest 2024-2025 publications integrate cutting-edge generative AI and blockchain techniques, highlighting a shift toward holistic, scalable, and privacy-preserving security ecosystems for IoT, Internet of Vehicles, and healthcare domains. Professional Recognition & Service Licensed Professional Engineer (P.Eng), Ontario Certifications: CEH, CCNA, CHFI, ISO 27001 Lead Implementer Peer reviewer for IEEE, ACM, and Elsevier journals Invited speaker and mentor in cybersecurity education initiatives Teaching & Mentorship Dr. Otoum currently teaches Data Science and Data Structures and Algorithms at the University of Ottawa. His office hours are held Mondays 11:30 AM–1:30 PM in SITE room 4075. While specific student advisees are not listed, he is actively engaged in mentoring emerging researchers and practitioners in secure AI and IoT systems. Labs & Teams Operating at the intersection of academia and industry, Dr. Otoum collaborates with multidisciplinary teams spanning embedded systems, AI laboratories, and healthcare technology partners, fostering innovation that transitions seamlessly from theory to real-world deployment.
Maha Ben Ali is an Associate Professor at the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. She is also an Adjunct Professor at Université Laval and a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Laboratoire Poly-Industries 4.0 . Education: Bachelor’s in Industrial Engineering, École Nationale des Ingénieurs de Tunis M.Sc. in Industrial Systems Engineering, École Nationale des Ingénieurs de Tunis M.Sc./Ph.D. in Mechanical Engineering (Industrial concentration), Université Laval Research Interests focus on Industrial Engineering , with emphasis on Demand Management , Supply Chain Optimization , Simulation Modeling , Industry 4.0 , and Data Valorization . Her work combines Operations Research with Machine Learning to solve complex production and logistics challenges, particularly in softwood lumber and bioethanol supply chains . Publications (34 total) include studies on reinforcement learning for demand-driven systems, hybrid recommendation systems in B2B contexts, and symbiotic bioethanol network design . She has presented at conferences like the Winter Simulation Conference and IISE Annual Conference . Scientific Awards: Best paper – CIGI-QUALITA-MOSIM 2023 David Martell Student Paper Prize in Forestry – CORS 2018 NSERC Alexander Graham Bell Canada Graduate Scholarship FRQNT Doctoral scholarship Fast-tracking scholarship Teaching includes courses like Production and Inventory Planning , Simulation of Production Systems , and Logistic Networks . She has supervised 9 completed Master’s theses on topics ranging from multi-project scheduling to CO₂ emission reduction in freight transportation .
Kuljeet Kaur is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. Her research is conducted through the LACIME (Communications and Microelectronic Integration Laboratory), a renowned research unit focusing on communications and microelectronic integration. She maintains an active research program with numerous publications and student supervision activities. Professor Kaur's research spans multiple interconnected domains focused on next-generation computing and communication systems. Her primary research axes include Sensors, Networks and Connectivity; Intelligent and Autonomous Systems; and Software Systems, Multimedia and Cybersecurity. Within these broad areas, she specializes in Cloud Computing, Edge/Fog Computing, Internet of Things (IoT), Cybersecurity, Privacy, Federated Learning, and Energy Management. Her work bridges theoretical foundations with practical implementations in intelligent transportation systems, healthcare applications, and smart grid technologies. Analysis of Professor Kaur's recent publications reveals a strong focus on security and privacy challenges in emerging computing paradigms. A significant portion of her work addresses federated learning approaches that maintain data privacy while enabling collaborative AI model training. Her research also demonstrates expertise in edge computing architectures, particularly for IoT applications, with emphasis on energy efficiency and security. The publications show consistent interdisciplinary collaboration across computer science, electrical engineering, and transportation domains. Professor Kaur actively supervises multiple graduate students at various levels. Her supervision portfolio includes doctoral candidates working on topics like decentralized AI networks and secure federated learning, as well as master's students focusing on edge AI for IoT applications, sensor drift compensation, and zero trust architecture for IoT. She also guides project students working on practical implementations of AI for smart grid optimization and secure IoT protocols. Her research is conducted within the LACIME laboratory, which brings together researchers working on everything from micro- and nanofabrication processes to communication protocols and signal processing. The lab provides a transdisciplinary environment where Professor Kaur's work on cyber-physical systems and secure communications benefits from complementary expertise in integrated circuit design and microsystems.
Dr. Aida Haghighi is an Assistant Professor at the School of Occupational and Public Health, Toronto Metropolitan University. She holds a PhD from Polytechnique Montréal, with prior industrial experience at the National Gas Company. Her research focuses on occupational health and safety (OHS) management systems, Industry 4.0 integration, and risk mitigation strategies. She teaches courses like OHS 718: Systems Management and OHS 811: OHSE Management Systems. Education: BSc, MSc, PhD in Industrial Engineering from Iran and Canada. Professional Engineer (PEng) designation. Research highlights include developing assessment tools to prevent machine safeguard bypassing and analyzing Industry 4.0's impact on worker safety. Research Interests: Industrial safety systems, OHS risk management, integrated management systems, and continual improvement. Current projects explore emerging technologies in OHS frameworks and gender disparities in workplace health. Awards include the Best Doctoral Thesis (2020, Polytechnique Montréal) and NSERC-funded research abroad. She advises MSc students in Occupational and Public Health starting 2025-2026. Teaching and Grants: Active in curriculum development for OHS education. Collaborates on projects funded by industry partnerships and governmental grants focusing on safety innovation.
Behrouz Far is a Professor at the University of Calgary’s Schulich School of Engineering, Department of Electrical and Software Engineering. He holds a PhD in Artificial Intelligence from Chiba University, Japan (1990) and degrees from the University of Teheran including a B.S. in Electrical Engineering (1983) and M.S. in Electrical Engineering (1986). His research focuses on AI applications in medical imaging, software engineering, transportation systems, and data mining. He has contributed to advancements in fundus image analysis, deep learning models for disease detection, and intelligent traffic management systems. Dr. Far has received notable awards such as the 2017 SSE Achievement Award and the AITF-AMA Tier-2 Chair in Smart Multimodal Transportation Systems (2013). His work bridges theoretical AI with practical healthcare solutions, including tools like LETTA for traffic management systems and methodologies for detecting ocular lesions using CNNs. He teaches courses on software testing, reliability engineering, and agent-based systems. His publications highlight contributions to medical diagnostics (e.g., choroidal nevi classification), transportation optimization (e.g., real-time traffic signal control), and machine learning explainability. Collaborative research includes projects on biopotentiostat biosensors for SARS-CoV-2 detection and data mining for cancer patient stratification.
Jorg Liebeherr is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, holding the Nortel Chair of Network Architecture and Services. His research focuses on computer networks , particularly network calculus , self-organizing networks , protocol design , and traffic scheduling . Education: Diplom-Informatiker (with distinction), University of Erlangen (Germany), 1988 PhD, Computer Science, Georgia Institute of Technology, 1991 His recent work includes low-cost LoRa mesh networks for environmental sensing and mathematical frameworks for traffic control in 5G and IoT systems. Publications span journals like IEEE Internet of Things Journal and conferences such as IEEE Infocom and ACM Sigmetrics . Scientific Awards: IEEE Fellow (2008) Outstanding Service Award, IEEE ComSoc TC on Computer Communications (2006) ACM Sigmetrics Best Student Paper Award (2005) NSF CAREER Award (1996) Advising and Grants: Supervised 15+ theses (MASc/PhD) and secured grants from NSF, Virginia Engineering Foundation, and industry partners. Labs: Leads the Network Research Lab and HyperCast projects, an open-source platform for application-layer internetworking.
Professor Omar Alam is an Associate Professor in the Department of Computer Science at Trent University. His research focuses on software engineering, particularly Model-Driven Software Engineering, Aspect-Oriented Modelling, and Mining Software Repositories. Prior to Trent, he earned his PhD at McGill University, specializing in Model-Driven Engineering. He has received notable awards such as the ACM SIGSOFT Distinguished Paper Award (2019) and the Merit Award for Excellence in Research (2022). His teaching includes courses like Software Specification (COIS 3030), Software Design and Modeling (COIS 2240), and Software Architecture (COIS 3040). Key grants include the NSERC Discovery Grant (2017-2024) and past fellowships from NSERC and FRQNT. His work spans collaborative modeling, automated grading systems, and empirical studies on developer practices. Research often intersects software development methodologies, tool integration, and socio-technical aspects of computing. His recent publications explore topics like developer workplace communication, transit schedule deviations using real-time data, and three-way domain-specific model differencing. He actively contributes to journals like SoSyM and conferences such as SLE and ICSM. His teaching excellence is recognized through student awards and nominations, reflecting his commitment to pedagogy in computer science education.
