جست‌وجوی استاد و پژوهشگر

Khalil El-Khatibمشاهده پروفایل

استاد

• Smart Cities
• Cybersecurity
• Big Data Analytics
• +۶ مورد دیگر

Dr. Khalil El-Khatib is a Professor and Director of the Institute for Cyber Security and Resilient Systems at Ontario Tech University's Faculty of Business and Information Technology. His expertise spans cybersecurity, smart cities, big data analytics, and wireless network security. He holds a PhD from the University of Ottawa, following a Master's from McGill University and a Bachelor's from the American University of Beirut. Dr. El-Khatib's research focuses on smart communities, security in wireless sensor networks, smart grid security, cloud computing, and e-health. His work integrates technical and societal dimensions to enhance resilience in critical infrastructure. He leads initiatives in cyber resilience, emphasizing proactive strategies against evolving threats. His academic career includes prior roles at the University of Western Ontario and extensive contributions to supply chain resilience, disruption management, and optimization methodologies. His research often bridges theoretical models with real-world applications, such as urban planning for smart cities and sustainable transportation systems. As Director of the Institute for Cyber Security and Resilient Systems, he oversees multidisciplinary projects addressing cybersecurity challenges in interconnected systems. His lab collaborates with industry partners to develop innovative solutions for privacy, data protection, and resilient networks.

Josh Loweمشاهده پروفایل

عضو هیئت علمی

• Computer Networking
• IT Security
• Information Technology Education
• +۲ مورد دیگر

Josh Lowe is an Associate Teaching Professor specializing in Advanced Computer Networking within the Bachelor of Information Technology program at Ontario Tech University. He serves as Lead Instructor for the Cisco Networking Academy and oversees the university's Networking Lab. His expertise spans IT infrastructure, network security, and experiential learning design. He holds an Honours Diploma in Computer Systems Technology from Durham College and a Bachelor of Information Technology (Honours) from Ontario Tech University, complemented by certifications such as CCNA, CCNP (R&S, SEC), CCSP, CCAI, and JNCIA. His industry experience includes roles at Cisco Systems Inc., Bell Canada, and eHealth Ontario. Lowe’s teaching has earned multiple awards, emphasizing practical skills in networking and cybersecurity. His work focuses on bridging theoretical knowledge with real-world applications through lab-based learning and industry partnerships. He actively contributes to curriculum development and maintains advanced networking facilities for student training.

Faisal Qureshiمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Camera Networks
• +۵ مورد دیگر

Dr. Faisal Qureshi is a Professor of Computer Science in the Faculty of Science at Ontario Tech University, where he leads the Visual Computing Lab. He holds a concurrent role as Guest Professor at Mid Sweden University. His research focuses on visual sensor networks, computer vision, and deep learning, with applications in smart camera networks, video surveillance, and hyperspectral imaging. He earned his PhD in Computer Science from the University of Toronto (2007), following degrees from Punjab University (BSc 1993) and Quaid-e-Azam University (MSc Electronics 1995). Key research areas include multicamera tracking, graphics for vision, and neural networks for computer vision. He has pioneered work on self-organizing visual sensor networks and developed computational models for autonomous visual perception. His teaching includes courses on computer graphics, algorithms, and advanced computer vision. He has authored over 100 peer-reviewed publications, including a Best Paper Award at CRV 2017 for optical flow estimation research. Dr. Qureshi’s lab develops innovative solutions for video summarization, optical flow estimation, and activity analysis. His work bridges theoretical computer science with real-world applications in robotics, aerospace, and environmental monitoring. Recent projects explore adversarial machine learning for satellite communications security and hyperspectral image compression using neural representations.

Stephen Finbowمشاهده پروفایل

استاد

• Graph Theory
• Discrete Mathematics
• Combinatorics
• +۶ مورد دیگر

Stephen Finbow is a Professor in the Department of Math & Statistics at St. Francis Xavier University. His academic career focuses on advancing combinatorial mathematics with particular emphasis on graph theory concepts and processes. Dr. Finbow's research program centers on discrete time processes in graph theory, with principal focus on the firefighter problem (modeling fire containment and virus spreading through networks), eternal domination (representing mobile resource allocation and emergency response systems), and the watchmen's walk. His work also investigates graph colourings and fundamental bounds on domination and irredundance parameters. He seeks to balance innovative problem-solving with classical theoretical approaches in combinatorics. His research has significant real-world applications in network security, information dissemination modeling, and resource allocation optimization. Dr. Finbow examines complexity results, asymptotic behavior, and optimal network design within these dynamic graph processes, contributing to both theoretical understanding and practical implementations of graph theory concepts.

Khalid Abdel Hafeezمشاهده پروفایل

عضو هیئت علمی

• Computer Networks and Security
• Artificial Intelligence and Machine Learning
• Computer Science
• +۱ مورد دیگر

Khalid Abdel Hafeez holds the position of Associate Teaching Professor at the Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology. He is an IEEE Senior Member and serves as Vice Chair of IEEE-VT. His research focuses on Computer Networks and Security, and Artificial Intelligence and Machine Learning. Education: PhD (Electrical and Computer Engineering), Ryerson University, 2012 MASc (Computer Networks-Security), Ryerson University, 2008 B.Eng (Electrical and Computer Engineering), JUST-Jordan, 1989 His academic contributions span teaching core courses such as Computer Networks, Computer and Software Security, Artificial Intelligence, Database Management Systems, Microprocessors and Computer Architecture, and Operating Systems. His research interests emphasize advancing cybersecurity solutions and AI-driven systems in engineering contexts. Awards include recognition as an IEEE Senior Member and leadership roles in IEEE-VT committees. His work bridges theoretical research with practical engineering applications, reflecting a commitment to both academic rigor and industry relevance.

Moshe Schwartzمشاهده پروفایل

استاد

• Coding Theory
• Storage Systems
• Digital Sequences
• +۴ مورد دیگر

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

Michel Grignonمشاهده پروفایل

استاد

• Health Economics
• Aging
• Healthcare Systems
• +۷ مورد دیگر

Michel Grignon is a Professor of Economics and Health Economist at McMaster University , with appointments in both the Department of Economics and Department of Health, Aging and Society. His research focuses on reconciling efficiency and equity in healthcare systems, particularly through resource allocation strategies for aging populations. He collaborates with institutions like Dalhousie University and has contributed to policy discussions on universal healthcare and aging populations. Education: Master's Equivalent from École Nationale de la Statistique et de l'Administration Économique (ENSAE), PhD in Economic History from École des Hautes Études en Sciences Sociales (EHESS) Research Interests: Grignon specializes in aging populations, health economics, equity in healthcare systems, social determinants of health, and policy analysis. His work examines chronic disease management, health insurance models, and international comparisons of healthcare efficiency. Recent Publications: His 2025-2023 work demonstrates trends in analyzing social equity through longitudinal studies, pandemic impacts on aging populations, housing insecurity effects on mortality, and economic evaluations of palliative care programs. Scientific Awards: Fellow, Canadian Academy of Health Sciences Advising & Grants: While no specific students are listed, his 40+ years of publications show sustained grant funding and collaborative research across disciplines.

• Stochastic Modeling
• Actuarial Science
• Catastrophe Risk Modeling
• +۵ مورد دیگر

Petar Jevtic is an Adjunct Assistant Professor in the Department of Mathematics and Statistics at McMaster University. His research focuses on stochastic modeling, actuarial science, catastrophe risk modeling, and cybersecurity. He has contributed significantly to mortality modeling, cyber risk quantification, and disaster impact analysis. His work often integrates interdisciplinary methods from statistics, economics, and computer science. Research interests include spatial mortality analysis, percolation theory applications in network vulnerability, and blockchain security. Notable projects involve quantifying social hardship from disasters like Hurricane Maria in Puerto Rico and developing frameworks for cyber risk assessment in critical infrastructure. Publications span topics such as mortality hedging strategies, loss distribution models for cyber attacks, and software tools for Petri net analysis. Collaborations include work with Traian Pirvu, Paul McNicholas, and Hector Antonio Paez. No grants or awards are explicitly mentioned in the provided texts. Advising and lab affiliations remain unspecified.

Benzhong Zhaoمشاهده پروفایل

دانشیار

• Climate Change Mitigation
• Renewable Energy
• Water Security
• +۹ مورد دیگر

Benzhong (Robin) Zhao is an Associate Professor in the Department of Civil Engineering at McMaster University. He holds a BASc from the University of Waterloo and SM/PhD degrees from MIT. His research focuses on multiphase flow in porous media, with applications to climate change mitigation (e.g., geological CO2 storage) and adaptation (e.g., water security under climate change). He teaches courses such as CIV ENG 708 (Computational & Experimental Methods for Flow in Porous Materials) and CIVENG 2O04 (Fluid Mechanics). His lab employs advanced imaging techniques (microscopy, synchrotron X-ray, neutron imaging) to study subsurface energy systems and hydrologic processes. Recent work includes developing frameworks for gigaton-scale CO2 storage in Alberta oil sands and evaluating mask effectiveness for source control during pandemics. Advising includes 1-2 funded graduate students annually and undergraduate researchers. Notable alumni include postdocs (e.g., Daniel Hallack, Tian Lan) and PhD graduates (e.g., Mahtab Masouminia, Ashkan Irannezhad). The Zhao Lab collaborates internationally, hosting scholars like Prof. Ruben Juanes (MIT), and emphasizes interdisciplinary research spanning energy, environment, and micro-nano systems.

• Computer Science Education
• Numerical Analysis
• Network Security
• +۳ مورد دیگر

Dr. Ron Meleshko is a Sessional Instructor in the Department of Computer Science at MacEwan University. He holds a PhD, M.Sc., and B.Sc. (Hons.) in Computing Science from the University of Alberta. His research interests span introductory computer science pedagogy, numerical methods, and computer/network security. He has expertise in numerical analysis, stability of numerical methods, and first-year programming languages. Education: PhD in Computing Science (University of Alberta) M.Sc. in Computing Science (University of Alberta) B.Sc. (Hons.) in Computing Science (University of Alberta) Dr. Meleshko's career includes roles at Myrias Research (parallel algorithms and supercomputer marketing) and a biochemistry lab (IT infrastructure and security). At MacEwan, he focuses on introductory course design and IT management. Recent work emphasizes computer/network security. No scientific awards or grants are explicitly listed. No advised students or lab affiliations are mentioned.

Bin Changمشاهده پروفایل

دانشیار

• Corporate Payout Policy
• Corporate Governance
• Cash Policy
• +۷ مورد دیگر

Dr. Bin Chang is an Associate Professor of Finance at the Faculty of Business and Information Technology, Ontario Tech University. She holds a PhD from the University of Toronto and has prior experience as a credit derivatives risk analyst at CIBC, an instructor at the University of Toronto, and a foreign exchange trader at the Bank of China. Her research focuses on corporate payout policy, corporate governance, cash policy, innovation, entrepreneurship, pension systems, and climate change. She has secured multiple SSHRC grants, including a 2021 Insight Development Grant as Principal Investigator. Her work bridges theoretical frameworks with practical applications in financial policy and supply chain resilience. Dr. Chang contributes to interdisciplinary initiatives within the faculty, emphasizing data-driven decision-making and sustainable business practices. Education: PhD in Finance, University of Toronto MA in Finance, Queen's University MA, LLB, and BA from Wuhan University, China Research Interests: Dr. Chang’s research explores corporate financial policies, governance structures, and the impact of external factors like climate change on financial decision-making. She employs computational methods to analyze supply chain resilience, risk management, and sustainable infrastructure planning (e.g., EV charging stations). Her work often addresses global challenges, such as balancing economic growth with environmental stewardship. Grants & Collaborations: As Principal Investigator, she led a 2021 SSHRC grant examining corporate governance’s role in cash policies. Co-investigations include studies on dividend policies and financial crisis impacts. Her collaborations emphasize cross-disciplinary approaches to solving real-world business and environmental challenges. Labs/Teams: While primarily affiliated with the Finance department, her research aligns with the faculty’s broader initiatives in business analytics and AI, particularly in optimizing supply chain and financial systems.

Omar Abdul Wahabمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Internet of Things
• +۳ مورد دیگر

Omar Abdul Wahab is an Assistant Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He holds a PhD in Information Systems Engineering from Concordia University (2017) and completed postdoctoral research at École de technologie supérieure (2017-2018) in collaboration with Rogers and Ericsson. PhD: Concordia University, Montréal, Canada Master's: Lebanese American University, Beirut, Lebanon Bachelor's: Lebanese University, Tripoli, Lebanon His research focuses on Cybersecurity , Artificial Intelligence , and Internet of Things , with expertise in cloud computing security , federated learning , and game-theoretic security models . He has developed frameworks for threat intelligence coalitions , IoT intrusion detection , and trust-aware client selection in distributed systems. His recent publications emphasize federated learning security , IoT anomaly detection , and trust modeling in collaborative environments. Current supervised students include PhD candidates Sarhad Arisdakessian and Osama Wehbi, and Master's student Ranim Rahali.

Samira Keivanpourمشاهده پروفایل

دانشیار

• Circular Economy
• End-of-Life Product Management
• Sustainable Supply Chains
• +۷ مورد دیگر

Dr. Samira Keivanpour is an Associate Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal. She holds a Ph.D. in Industrial Engineering from Laval University, an MBA in Operation Management from Iran, and a B.Sc. in Electrical Engineering from Iran. Prior to joining Polytechnique Montréal, she was a faculty member at Thompson Rivers University and completed her postdoctoral fellowship at Laval University's Department of Mechanical Engineering (2015-2017). Her research focuses on Circular Economy & End-of-Life Product Management, Sustainable & Circular Supply Chains, Human-Robot Collaboration & Smart Automation in Logistics, and AI for Sustainable Operations. Her work bridges theoretical frameworks with practical applications in sustainable manufacturing and logistics. Her recent publications demonstrate a strong trend toward integrating artificial intelligence, particularly fuzzy logic and reinforcement learning approaches, with sustainable operations management. Her work spans multiple domains including circular supply chains, human-robot collaboration, and sustainable manufacturing systems, with particular emphasis on aerospace applications and end-of-life product management. Dr. Keivanpour actively supervises graduate students, with eight completed theses (one Ph.D. and seven Master's) between 2021-2024, focusing on sustainable manufacturing, circular economy, and smart logistics applications. She holds significant research affiliations as a Co-investigator with the Safran Industrial Chair on Multifunctional Passive Acoustic Treatments for Turbofan Composite Structures (TAPPIS) and the Institutional Research Chair on Additive Manufacturing technologies (FACMO 2), and is a member of both the Research Group in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT).

• Probability Theory
• Random Matrix Theory
• Stochastic Processes
• +۵ مورد دیگر

Elliot Paquette is an Associate Professor in the Department of Mathematics and Statistics at McGill University, located in Burnside Hall. His research bridges probability theory, stochastic processes, and high-dimensional statistics, with a focus on applications in machine learning optimization and random matrix theory. He holds a prominent position within the Faculty of Science, contributing to both theoretical and applied mathematical research. While explicit educational details are not provided, his work suggests expertise in advanced mathematical disciplines. His research interests include stochastic gradient descent dynamics, random matrix ensembles, and the interplay between topology and data analysis. Notable contributions address high-dimensional optimization challenges, such as generalization properties of SGD and the behavior of neural scaling laws. His recent publications (2023–2025) explore topics like the extremal landscape of random ensembles, phase transitions in computational models, and the theoretical foundations of differential privacy in regression. These works often combine rigorous probabilistic methods with computational insights. No awards or grants are listed in the provided profile. His advising and team collaborations remain unspecified, though his research often intersects with machine learning and computational statistics communities. He is affiliated with McGill’s Mathematics and Statistics department and contributes to interdisciplinary research through his position in the Faculty of Science.

• Robust Statistics
• Machine Learning
• Biostatistics
• +۳ مورد دیگر

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.