جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Communication and Information Technologies
• Healthcare Quality
• +۶ مورد دیگر

Farid Al Zoubi is a Research Fellow at the School of Electrical Engineering and Computer Science, affiliated with the METRICS Lab. He holds a PhD in Electrical Engineering and Computer Science from the same institution (2020–2024), under the supervision of Dr. Pascal Fallavollita. His research focuses on integrating artificial intelligence, machine learning, and prescriptive analytics to optimize healthcare delivery systems, particularly in high-volume surgical environments like operating rooms. His work emphasizes improving surgical team efficiency, reducing delays in arthroplasty procedures, and enhancing healthcare quality through data-driven decision-making frameworks. Key research interests include Human-Computer Interaction, Communication Technologies, Healthcare Analytics, and the application of AI/ML in surgical workflows. He has published in journals such as Frontiers in Digital Health , Canadian Journal of Surgery , and International Orthopaedics , addressing topics like AI-driven prescriptive models, surgical workflow optimization, and team coordination in healthcare settings. Al Zoubi’s research bridges engineering and medicine, aiming to transform healthcare operations through innovative analytical systems. His current work explores real-world deployment of AI recommendations in orthopedic surgery and the use of multidisciplinary strategies to improve surgical efficiency. He is based in Ottawa, Canada, and collaborates with interdisciplinary teams to advance healthcare technology. Future directions include expanding AI applications in prescriptive analytics for healthcare systems, developing benchmarking tools for surgical teams, and advancing machine learning models tailored to healthcare operational challenges.

• Artificial Intelligence
• Multi-Agent Systems
• Preference Elicitation
• +۶ مورد دیگر

Scott Buffett is an Adjunct Professor at the Faculty of Computer Science, University of New Brunswick, and an Associate Research Officer at the National Research Council Canada (NRC), working in the Learning and Collaborative Technologies Group under the Information and Communications Technology Portfolio. He holds a PhD in Computer Science from the University of New Brunswick (UNB). His primary research focus is on artificial intelligence, particularly multi-agent systems, preference elicitation, workflow/process mining, and social commerce. He has developed systems like OmniBid, which demonstrates mechanism design for automated negotiations that balance individual utility and societal welfare. His work extends to privacy in e-commerce, data mining, and machine learning. Education: PhD (UNB), MSc in Automated Theorem Proving. Current teaching includes MBA courses in Production and Operations Management and Social Network Analysis. Past courses include Decision-Theoretic Agents and FORTRAN Programming. He supervises graduate students in preference modeling, negotiation systems, and workflow analytics. His research has been published extensively in conferences like CAI, ICEC, and journals like Electronic Commerce Research and Applications. He is active in collaborative technologies and data-driven process optimization. Research Interests: Multi-Agent Systems: Negotiation mechanisms, utility theory, and societal welfare optimization. Preference Elicitation: Techniques for extracting user preferences with minimal intrusion, including Bayesian methods and clustering. Workflow Mining: Dynamic process modeling for real-time guidance in industries like manufacturing and energy. Social Commerce: Analyzing social network effects on commercial interactions using network analysis techniques. Privacy and Data Analytics: Frameworks for privacy compliance in collaborative environments and energy management systems. Publications: Over 30 peer-reviewed articles, including work on automated negotiation, process mining theory, and preference network adaptation. Recent focus on dynamic process composition and socially aware commerce systems.

Maryam Shafiei Alavijehمشاهده پروفایل

استاد

• Non-Destructive Evaluation
• Ultrasonic Testing
• Machine Learning
• +۳ مورد دیگر

Maryam Shafiei Alavijeh is a Professor at the University of Windsor, specializing in Non-Destructive Evaluation (NDE) and advanced materials research. She collaborates closely with Prof. Roman Maev and students like Vlad Tusinean on projects integrating ultrasonic testing, machine learning, and artificial intelligence for industrial applications. Their work focuses on improving pipeline safety, composite material analysis, and defect detection in critical infrastructure. Her research interests span non-destructive evaluation techniques, ultrasonic testing methodologies, and the application of AI in material science. Key projects include developing automated flaw detection systems for polyethylene gas pipes, optimizing chord transducer performance, and leveraging deep learning models for NDE 4.0 frameworks. In 2022, Prof. Shafiei Alavijeh and her team received recognition from the Canadian Institute for Non-Destructive Evaluation for their innovative contributions to NDE methodologies. Her work bridges academic research with industry needs, emphasizing practical solutions for structural integrity assessment and material characterization.

Yves Lucetمشاهده پروفایل

استاد

• Computational Mathematics
• Optimization
• Convex Analysis
• +۶ مورد دیگر

Yves Lucet is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD from the University of Toulouse. His research spans computational mathematics, optimization, and convex analysis, with a focus on algorithm development for computer-aided convex analysis and road design optimization. He leads the development of the CCA (Computational Convex Analysis) toolbox under Scilab, emphasizing hybrid symbolic-numerical algorithms. His work includes visualizing set-valued operators using Virtual Reality (VR) and collaborating on large-scale road design projects funded by NSERC, aiming to minimize construction costs while meeting safety and environmental constraints. Lucet advises graduate students and contributes to interdisciplinary teams involving mathematics, statistics, and engineering. His research also explores applications in computational geometry, image processing, and network communication. Education: PhD in Mathematics from the University of Toulouse. Research Interests: Computational Convex Analysis Algorithm Design for Optimization VR Visualization of Mathematical Operators Multi-objective Road Design Optimization Data-Driven Process Monitoring Key Projects: Development of the CCA toolbox, road design optimization funded by NSERC, and reinforcement learning approaches for prescriptive process monitoring. His work bridges mathematics and computer science, with applications in engineering and transportation.

Jasmin Jelovicaمشاهده پروفایل

دانشیار

• Structural Analysis
• Structural Optimization
• Computational Methods
• +۶ مورد دیگر

Dr. Jasmin Jelovica is an Associate Professor in the Department of Civil Engineering and Mechanical Engineering at the University of British Columbia (UBC), with a joint appointment since 2017. He holds a D.Sc. (Tech.) from Aalto University and an M.Sc. in Naval Architecture from the University of Rijeka. His research focuses on computational methods for structural analysis and optimization, emphasizing lightweight structures and mechanical response analysis. He leads the Structural Efficiency Laboratory, exploring advanced materials, welding techniques, and machine learning applications in structural design. Research Interests: Structural optimization, computational methods, advanced materials, marine vessels design, machine learning, and welding technologies. His work spans structural mechanics, vibration analysis, and multi-objective optimization frameworks. Awards: NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels (2020). Lab & Collaborations: The Structural Efficiency Laboratory at UBC focuses on innovative structural components, topology optimization, and advanced welding processes. Research is supported by collaborations with Civil and Mechanical Engineering departments. Advising & Grants: Advises graduate students in structural analysis and optimization projects. Active in conferences such as MARSTRUCT and OMAE, publishing on topics like neural network-assisted constraint handling and underwater acoustics.

Steven Weijsمشاهده پروفایل

دانشیار

• Water Resources Management
• Mountain Hydrology
• Catchment Hydrology
• +۴ مورد دیگر

Dr. Steven Weijs is an Associate Professor and Associate Head (Graduate Programs) in the Department of Civil Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. He leads research in water resources management, hydrology, and information theory applications in environmental systems. His work explores optimizing information flows from observations to decision-making in complex water systems. Education: PhD (2011) and M.Sc. (2004) in Water Resources Management from TU Delft, Netherlands. Postdoctoral Research: AXA Research Fund fellowship (2011–2013) at EPFL, Switzerland, followed by roles in environmental fluid mechanics and cryospheric science groups. Research Interests: Mountain hydrology, catchment dynamics, flood forecasting, uncertainty quantification, and information theory-based monitoring strategies. He teaches CIVL 418 Engineering Hydrology and contributes to UBC’s Master of Engineering Leadership in Integrated Water Management. Publications focus on sensor networks, data-driven hydrology, flood analysis, and environmental decision-making. Notable work includes novel methods for streamflow prediction in ungauged basins and optimal monitoring network design. Awards: Leading Scholar (Green College, 2016–2018), AXA Research Fund Postdoctoral Fellowship. He collaborates on projects like WeSenseIt, a citizen observatory initiative, and develops low-cost sensing technologies for hydrological monitoring. His research bridges theoretical frameworks with practical applications in water resource management.

• Mathematical Modeling of Electrochemical Systems
• Battery Technology
• Computational Fluid Dynamics
• +۷ مورد دیگر

Jamie Foster McMaster is an Adjunct Associate Professor in the Department of Mathematics and Statistics at McMaster University. His scholarly activity integrates mathematical modeling, computational physics, and interdisciplinary applications in energy storage, biomedical engineering, and astrophysical phenomena. He has collaborated extensively on studies related to lithium-ion battery optimization, perovskite solar cells, and respiratory disease research. Key Research Areas: Mathematical modeling of electrochemical systems, computational fluid dynamics, astrophysical jets, and biomedical applications. Notable Contributions: Development of fast solvers for battery models, studies on adherence behavior in asthma patients, and experimental-validation frameworks for energy materials. Recent Publications: Focused on lithium-ion battery degradation, espresso brewing dynamics, and perovskite solar cell performance optimization. While no explicit awards or student advisement information is available in the provided data, his extensive publication record across diverse fields highlights his interdisciplinary expertise. His work bridges theoretical mathematics with practical applications in renewable energy and healthcare diagnostics.

Habib Hamamمشاهده پروفایل

استاد

• Optics
• Biomedical Engineering
• Wireless Communication
• +۷ مورد دیگر

Habib Hamam is a Professor in the Department of Electrical Engineering at the University of Moncton's Faculty of Engineering. His research spans optics, biomedical engineering, wireless communication, and AI applications. Specializations: Diffractive elements, optical interconnections, biomedical engineering, hybrid fiber/wireless systems, and AI-driven optimization. Contact: Email habib.hamam@umoncton.ca | Phone (506) 858-4762 Recent publications highlight his focus on AI integration across healthcare, energy systems, and education, combined with blockchain for security and transparent data management. Key subdomains include medical imaging, renewable energy optimization, and IoT scalability. Notable research trends involve deep learning for diagnostics (e.g., brain tumors, diabetic retinopathy), blockchain applications in smart grids and fisheries, and quantum-inspired algorithms for energy forecasting.

Xiaoliang Jinمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Machining of Composite Materials
• Additive Manufacturing
• +۵ مورد دیگر

Xiaoliang Jin is an Associate Professor and Canada Research Chair in Advanced Manufacturing within the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science. His research focuses on advanced manufacturing processes for next-generation materials and systems. His academic background includes: B.Sc. and M.Sc. from Beihang University Ph.D. from the University of British Columbia Dr. Jin's research spans cutting-edge manufacturing domains with emphasis on machining of composite materials (particularly carbon fiber reinforced polymers), hybrid additive-subtractive manufacturing , and advanced machining mechanics . His work integrates analytical modeling , experimental validation , and computational techniques to solve complex challenges in material processing, surface integrity, and process dynamics. Current investigations include vibration-assisted machining, micro-scale manufacturing, and real-time process monitoring systems. Analysis of his recent publications (2022-2025) reveals strong methodological trends: increasing application of machine learning for predictive modeling (especially in tool wear prediction), sophisticated residual stress characterization in additive manufacturing, and novel analytical frameworks for chip formation mechanics . His work bridges fundamental mechanics with industrial applications across aerospace and advanced materials sectors. His scientific recognition includes: Canada Research Chair in Advanced Manufacturing As a Canada Research Chair holder, Dr. Jin directs a significant research program securing substantial funding for advanced manufacturing innovation. He mentors graduate students in experimental and computational manufacturing research, with projects often involving industry partnerships to address real-world production challenges. His group develops specialized instrumentation for process monitoring and control. He leads the Advanced Manufacturing Processes (AMP) Laboratory (amp.mech.ubc.ca), which features capabilities for precision machining of composites and metals, vibration-assisted systems, hybrid additive-subtractive platforms, and dynamic process analysis. The lab supports fundamental research while maintaining strong connections to industrial manufacturing applications.

Jiang Hanshengمشاهده پروفایل

استادیار

• Data-Driven Decision-Making
• Operations Management
• Statistics
• +۳ مورد دیگر

Jiang Hansheng is an Assistant Professor in Operations Management and Statistics at the Joseph L. Rotman School of Management , University of Toronto. Her research integrates methodologies from statistics, optimization, and machine learning to advance data-driven decision-making in business contexts. Education : Ph.D. (2023) in Industrial Engineering and Operations Research, University of California, Berkeley M.S. in Industrial Engineering and Operations Research, University of California, Berkeley B.S. (2017) in Mathematics, University of Science and Technology of China Research Focus : Jiang’s work centers on creating robust algorithms and frameworks for data-driven decision-making. Key areas include supervised/unsupervised learning, optimization techniques, and their applications in manufacturing, service operations, and business analytics. Scientific Awards : Berkeley Fellowship IISA Best Student Paper Award (Theory and Methodology category) Finalist in the MSOM Data-Driven Research Challenge Supervision & Collaboration : Currently open to supervising Masters and PhD students, as well as engaging in collaborative and industry projects.

Mohammad Masudur Rahmanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Software Engineering
• Software Maintenance and Evolution
• +۳ مورد دیگر

Mohammad Masudur Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research focuses on intelligent automation of software maintenance and evolution , combining Artificial Intelligence (AI) and Software Engineering (SE) to address challenges in bug detection, diagnosis, and reproducibility. He leads the RAISE Lab , which aligns with Dalhousie’s strategic goals in Advanced AI & Digital Innovation , particularly Sustainable Software Innovation and Sustainable AI . Dr. Rahman earned his PhD in Computer Science/Software Engineering from the University of Saskatchewan (2019), advised by Prof. Dr. Chanchal Roy, and completed a postdoc at Polytechnique Montreal under Prof. Dr. Foutse Khomh. He has published 50+ papers in top venues like ICSE , ESEC/FSE , ASE , and TOSEM , with research funded by NSERC Discovery Grant, Mitacs Accelerate International, and Dalhousie Startup Fund. His work investigates the challenges of software bugs, crashes, vulnerabilities, and technical debt , particularly in AI-driven systems like Large Language Models and Deep Learning frameworks. He develops tools to automate bug diagnosis, leveraging code structures and neural machine translation. Recent articles analyze deep learning bug reproducibility , fault diagnosis in attention models , and code smell impacts . Scientific Awards : Governor General's Gold Medal, U of S Doctoral Thesis Award, Dalhousie Belong Research Fellowship, President Gold Medal (Bangladesh). Grants : $475K+ (PI) and $4.3M+ (Co-PI) from NSERC, Mitacs, Climate Action Fund, and Dalhousie.

Daniel Chungمشاهده پروفایل

دانشیار

• Building Science
• Hygrothermal Analysis
• Building Envelopes
• +۵ مورد دیگر

Dr. Daniel Chung is an Associate Professor of Building Science at the University of Toronto's John H. Daniels Faculty of Architecture, Landscape, and Design. A registered architect and professional engineer with over 20 years of experience, he focuses on improving building performance and resilience through hygrothermal envelope analysis and material testing. His educational background spans architecture, civil engineering, and environmental design from Yale, Drexel, Princeton, and the University of Pennsylvania. Ph.D., Architectural Engineering, Drexel University (2019) M.Arch., Yale University (2006) M.S.E., Civil & Environmental Engineering, Princeton University (2000) B.A. & B.S.E., University of Pennsylvania (1998) Chung's research examines building envelope performance under climate change, emphasizing stochastic modeling, degradation analysis, and biogenic material properties. His lab investigates moisture dynamics in retrofitted assemblies and develops simulation tools like HAM-Tools. Current projects include NSERC-funded retrofit solutions, equitable low-carbon housing initiatives, and Canada Mortgage and Housing Corporation collaborations. Dr. Chung teaches advanced building envelope systems, architectural design studios, environmental systems, and materials/construction courses. He actively mentors graduate students in physical sciences, building physics, and engineering disciplines.

Alex Hernandez-Garciaمشاهده پروفایل

استادیار

• Machine Learning
• Climate Science
• Health Sciences
• +۴ مورد دیگر

Prof. Alex Hernandez-Garcia is an Assistant Professor at the Université de Montréal's Department of Computer Science and Operational Research (DIRO) and a member of Mila, the Quebec AI Institute. He holds a PhD from the University of Osnabrück and completed postdoctoral research with Yoshua Bengio and David Rolnick at Mila. His research focuses on machine learning for scientific discovery, particularly in climate science, materials design, and health applications. He is a proponent of open science and equity initiatives, serving on Mila's Equity, Diversity, and Inclusion Committee and co-organizing the 'Against Military AI' reading group. Educations: B.Sc. and M.Sc. in Audiovisual Systems Engineering and Communication/Multimedia from Universidad Carlos III de Madrid (2014). PhD in Cognitive Science and Deep Learning from the University of Osnabrück (2020). Postdoctoral Fellow at Mila (2020–2022). Research interests include generative models (e.g., GFlowNets), active learning, and their applications to climate modeling, drug discovery, and sustainable materials. Notable projects include ThisClimateDoesNotExist.com and collaborations on electrocatalyst design. He teaches courses on machine learning and advises initiatives like the AI4Good Lab. Key Contributions: Development of GFlowNets for scientific exploration. Advocacy for open science and anti-military AI. Co-lead of Mila's Sustainability Committee. Awards: Marie Sklodowska-Curie ITN NexGenVis Fellowship (PhD). Current projects emphasize ethical AI, climate action, and interdisciplinary collaborations with domain scientists.

Homayoun Najjaranمشاهده پروفایل

استاد

• Autonomous Vehicles and Robots
• Control Systems
• Artificial Intelligence
• +۷ مورد دیگر

Homayoun Najjaran is a Professor of Mechanical Engineering and Electrical and Computer Engineering at the University of Victoria (UVic), cross-listed between departments. He founded the Advanced Control and Intelligent Systems (ACIS) laboratory, focusing on AI, robotics, and control systems. Dr. Najjaran earned his Ph.D. from the University of Toronto in 2002 and holds a Professional Engineer (P.Eng.) license in British Columbia. As a Fellow of the Canadian Society for Mechanical Engineering (CSME), he leads research in autonomous systems, industrial automation, and smart manufacturing. His work integrates machine learning, computer vision, and MEMS technologies to enhance system reliability and safety. Research interests span control systems, robotics, AI-driven manufacturing, and wearable technologies. Notable projects include vacuum bagging leakage prediction in composite manufacturing and sim-to-real domain adaptation for robotics. He collaborates with industry partners in aerospace, healthcare, and automation, emphasizing practical applications. Dr. Najjaran also serves as President of Advanced Engineering Solutions Inc., providing technical consulting services. Awards & Recognition Fellow of the Canadian Society for Mechanical Engineering (CSME) Professional Engineer (P.Eng.) in British Columbia Lab & Collaborations The ACIS Lab develops AI-driven solutions for industrial automation, human-robot collaboration, and digital twins. Partnerships include aerospace, automotive, and healthcare sectors.

Amir Ardestani-Jaafariمشاهده پروفایل

دانشیار

• Game theory
• Optimal order
• Mathematical modeling
• +۷ مورد دیگر

Amir Ardestani-Jaafari is an Associate Professor at the Faculty of Management, University of British Columbia, specializing in operations research and optimization under uncertainty. His research bridges theoretical advances in robust optimization with practical applications in healthcare operations and supply chain management. His educational background includes: Ph.D. in Management Science from HEC Montreal (2012-2016) under Erick Delage Postdoctoral fellowship at McGill University (2019-2021) M.Sc. and B.Sc. in Industrial Engineering from Tehran Polytechnic Ardestani-Jaafari's research focuses on developing advanced optimization techniques for decision-making under uncertainty, with particular emphasis on healthcare operations. His work combines mathematical modeling, simulation, and algorithm development to address complex operational challenges where traditional optimization methods fail. He has made significant contributions to robust optimization frameworks that can handle unpredictable scenarios in facility location, resource allocation, and appointment scheduling systems. His recent work demonstrates strong trends in applying optimization techniques to healthcare delivery systems, particularly in home healthcare facility location and telemedicine appointment optimization, showing how mathematical approaches can improve healthcare access and equity. Notable awards include: Principal's Research Chair in Data-Driven Operations Management (2023-2028) Multiple Golden Apple teaching awards recognizing his dedication to student learning Finalist for educational resource excellence award (2025) GERAD's Best Thesis award (2017) Ardestani-Jaafari has supervised 13 graduate and undergraduate students across various research projects. He is actively involved with the UBC Optimization Center (COCANA), Materials and Manufacturing Research Institute (MMRI), and GERAD Research Center, where he collaborates on interdisciplinary projects addressing real-world operational challenges. His current research explores how robust optimization can protect networks against unpredictable events, from forest fires threatening transportation networks to hospitals planning optimal routes for home healthcare delivery.