جست‌وجوی استاد و پژوهشگر

John McPheeمشاهده پروفایل

استاد

• Biomechanical Engineering
• Robotics
• Control Systems
• +۷ مورد دیگر

John McPhee is a Professor in the Department of Systems Design Engineering at the University of Waterloo, with a cross-appointment in other academic groups. His research focuses on biomechanical systems, robotics, control systems, and musculoskeletal modeling with applications in wearable devices, surgical simulation, and sports biomechanics. He leads the Motion Research Group, developing advanced technologies for exoskeletons, prostheses, and human motion analysis. Key research themes include optimizing implant positioning for joint replacements, energy-efficient exoskeleton design, and predictive multibody dynamics modeling. His work integrates machine learning with physics-based simulations for applications in healthcare, transportation, and sports engineering. Recent studies emphasize environment-aware control systems for wearable robots and data-driven approaches for muscle-torque estimation using sEMG signals. McPhee collaborates on interdisciplinary projects involving robotics, biomechanics, and computational modeling. His research outputs highlight advancements in exoskeleton safety standards, automated anatomical landmark detection, and optimal control strategies for autonomous systems. The work demonstrates a strong focus on real-world applications in rehabilitation, surgical planning, and performance enhancement.

William Melekمشاهده پروفایل

استاد

• Artificial Intelligence
• Intelligent Control
• Mechatronics
• +۱۱ مورد دیگر

William Melek is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He serves as Director of the Laboratory of Computational Intelligence and Automation and RoboHub. His research focuses on intelligent systems, modular robotics, autonomous vehicles, and bioinformatics. He holds a B.A.Sc. in Electrical & Computer Engineering (Zagazig University, 1994), M.Sc. and Ph.D. in Mechanical Engineering (University of Toronto, 1998/2002), and a Post-Doctorate in Computer Science (Ryerson University, 2004). Education: Bachelor's: B.A.Sc in Electrical & Computer Engineering, Zagazig University (1994) Master's: Mechanical Engineering, University of Toronto (1998) Doctorate: Mechanical Engineering, University of Toronto (2002) Post-Doctorate: Computer Science, Ryerson University (2004) His research interests span artificial intelligence, intelligent control, mechatronics, automotive design, and computational intelligence. Recent work includes autonomous vehicle navigation, predictive maintenance in manufacturing, and bioinformatics-driven clinical decision support systems. He teaches courses such as ME 360 (Control Systems), ME 780 (Mechatronics), and MTE 100 (Intro to Mechatronics). Articles from 2023–2025 highlight advancements in autonomous driving decision-making, modular robotics, and 5G-enabled teleoperation systems. His lab develops algorithms for urban autonomous systems and industrial automation. He currently oversees graduate student applications in mechatronics and robotics. Labs/Teams: Laboratory of Computational Intelligence and Automation, RoboHub.

Houari Sahraouiمشاهده پروفایل

استاد

• Software Engineering
• Automated Software Engineering
• Model-Driven Engineering
• +۵ مورد دیگر

Houari Sahraoui is a Full Professor and Vice Dean of Planning and Infrastructure at the Faculty of Arts and Sciences, University of Montreal, where he also previously served as Department Director from 2013 to 2017. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) and has been actively supervising graduate students and conducting research in software engineering since at least 2000. Dr. Sahraoui's research focuses on automated software engineering , with particular emphasis on model transformations learning from examples using evolutionary approaches. His work spans reverse engineering (comprehension) and reengineering (refactoring, migration to component-based software), utilizing static and dynamic analysis techniques. He also investigates visualization of large sets of multidimensional data for software comprehension and maintenance. His research integrates artificial intelligence approaches to enhance various phases of the software development lifecycle. His recent publications demonstrate a strong trend toward integrating large language models and artificial intelligence with traditional software engineering practices. The research spans code review automation, model-driven engineering, microservices identification, and digital twins for applications like vertical farming. Many publications focus on improving automation through techniques like parameter-efficient fine-tuning, knowledge distillation, and social diversity metrics. Dr. Sahraoui was named a Fellow of Automated Software Engineering in 2023, an honor recognizing his significant and sustained contributions to the ASE community, both scientifically and professionally. This prestigious title is awarded by the IEEE/ACM International Conference Steering Committee. Dr. Sahraoui has supervised over 50 Master's and PhD students throughout his career, with recent supervision focusing on AI-assisted software engineering, code review automation, and model-driven approaches. His research has been supported by multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), MITACS, and industry partners, with projects spanning from 2000 to projected completion in 2031. He leads the GEODES research group (Groupe de recherche sur les systèmes ouverts et distribués et l'expérimentation dans les logiciels) which focuses on open and distributed systems and software experimentation. His current projects include 'Improving automation and assistance for software engineering tasks with generative AI' (2025-2031) and work on digital twins for vertical farming.

• Astrophysics
• Star Formation
• Galaxy Evolution
• +۳ مورد دیگر

Angus Mok is an Assistant Professor, Teaching Stream at OCAD University's Faculty of Arts & Science since 2021. He teaches courses in astronomy, science, and statistics, including SCTM-2003: Astronomy-Scales & Structures, SCTM-2012: The Science of Colour, and SCTM-3001: Statistics. His research focuses on astrophysics, particularly star formation processes in nearby galaxies, combining observational data with theoretical models. Education: PhD in Astrophysics from McMaster University, followed by a postdoctoral position at the University of Toledo. Research interests include galaxy evolution, molecular cloud properties, star cluster demographics, and high-redshift analog studies. He actively contributes to major surveys like PHANGS (Physics at High Angular resolution in Nearby Galaxies), leveraging JWST and ALMA observations. Recent work emphasizes cluster formation efficiency, X-ray binary populations, and post-starburst galaxy dynamics. Publications span 2013–2024, with notable contributions to understanding star formation in mergers, globular cluster systems, and environmental impacts on galaxy evolution. Collaborations include JINGLE (dust/gas studies) and GEEC2 (galaxy group dynamics at z~1).

• Mechatronics
• Noise and Vibration Control
• Respiratory Devices
• +۷ مورد دیگر

Philippe Micheau is a Professor and Director of the Centre for Research in Acoustics-Signal-Human (CRASH-UdeS) at Université de Sherbrooke since 2020. His academic career spans multiple institutions including Université de Poitiers and Université de Bordeaux I, with development leaves in France. He holds expertise in mechanical engineering with a focus on mechatronics, noise and vibration control, and respiratory device development. Dr. Micheau earned his PhD in Mechanical Engineering from Université de Poitiers (1995), followed by postdoctoral studies at Université de Sherbrooke (1997). His educational background includes a DEA (1991) and Maîtrise in Electrical Engineering, Electronics, and Automation (1990), all from Université de Poitiers. His research interests center on mechatronics, noise and vibration control, respiratory devices, and modeling and simulation. Dr. Micheau leads significant projects including Inolivent on total liquid ventilation in collaboration with CHUS clinicians, active noise control for airplane engines with Safran Nacelles, and advanced propulsion research with Bombardier Recreational Products. His work bridges engineering principles with medical applications, particularly in developing innovative respiratory support technologies. The research trends in his recent publications demonstrate a strong interdisciplinary approach combining acoustics, biomedical engineering, and respiratory medicine. His work spans from fundamental acoustic source development to clinical applications of liquid ventilation, showing consistent focus on solving complex engineering problems with medical relevance. Bourses de chercheur invité de l'Université de Bordeaux (2018, 2011) 3rd prize of Poster-video presentation at CRIAQ Forum (2014) Certificate of Excellence to the CRIAQ Pioneers (2007) First Place Award at ASME IMECE (2014) First rank at Young Entrepreneur Initiative competition (2007) Meilleure présentation at ACC2001 (2001) Premier prix for CRIAQ 6.1 project (2011) Dr. Micheau has successfully secured substantial research funding as principal investigator from NSERC, FRQNT, MITACS, and industry partners including Safran Nacelles and Bombardier. His collaborative approach is evident in numerous international partnerships, including cotutelle arrangements with French universities and research institutes. Current projects include active control of aircraft engine noise and liquid ventilation for extreme prematurity support. As Director of CRASH-UdeS, he leads a multidisciplinary research team focusing on acoustics, signal processing, and human physiology applications. His laboratory work integrates mechanical engineering principles with medical device development, particularly in respiratory support systems and noise control technologies.

Alán Aspuru-Guzikمشاهده پروفایل

استاد

• Quantum Computing
• Theoretical Chemistry
• Machine Learning
• +۳ مورد دیگر

Alán Aspuru-Guzik is Professor of Chemistry and Computer Science at the University of Toronto, holding the Canada 150 Research Chair in Theoretical Chemistry, Canada CIFAR AI Chair at the Vector Institute, and Google Industrial Research Chair in Quantum Computing. He directs the Acceleration Consortium, a strategic initiative uniting industry, government, and academia for pre-competitive research on the 'lab of the future'. His educational background includes a PhD in Physical Chemistry from UC Berkeley and BSc in Chemistry from the National Autonomous University of Mexico. Research spans quantum information, chemistry, and machine learning—pioneering quantum algorithms for chemical simulations, investigating quantum coherence in photosynthesis, and accelerating discovery of organic semiconductors, photovoltaics, and batteries. Current focus centers on autonomous chemical laboratories integrating AI and robotics. Recent publications (2024-2025) reveal dominant trends: self-driving laboratories for materials synthesis, quantum computing applications in molecular simulation, and generative AI for metal-organic frameworks and drug discovery. Key methodologies include reinforcement learning for experimental design and vision-language models for reaction mining. Major awards include: Chemical Engineering Medal (2023) John C. Polanyi Award (2023) MIT TR35 (2010) Sloan Research Fellowship (2009) Canada 150 Research Chair (2018) As Director of the Acceleration Consortium, he leads multi-institutional efforts in autonomous research systems, supported by Canadian government chairs and Google funding. His editorial role at Digital Discovery and co-founding of Zapata AI (quantum software) and Kebotix (robotic labs) demonstrate commitment to transforming scientific discovery through AI-driven automation.

Parminder Singh Kangمشاهده پروفایل

دانشیار

• Supply Chain Management
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Parminder Singh Kang serves as Associate Professor in the Department of Decision Sciences at MacEwan University's School of Business, specializing in supply chain optimization through advanced analytics and machine learning. His cross-disciplinary research integrates people, process, and technology elements to solve complex industrial and service operations challenges. Education: PhD from De Montfort University M.Sc. from De Montfort University Dr. Kang's research centers on applying AI/ML, combinatorial optimization, and simulation modeling to enhance supply chain resilience and customer-centric service systems. His work bridges theoretical frameworks with industry applications, focusing on sustainable digitization, risk profiling, and process improvement through evolutionary algorithms and Lean/Six Sigma methodologies. Recent projects address circular economy integration and Service 4.0 transformation. His publication portfolio reveals strong trends in machine learning applications for supply chain risk analysis, text mining for competency mapping, and digital twin development for customer-centric operations. The research spans theoretical models like HI-TOP to practical case studies on pandemic disruptions and ERP implementation, demonstrating industry-relevant academic contributions. Scientific Awards: Best Paper Award, Administrative Sciences Association of Canada (2018) Horace Pops Medal Award, Wire Cable Conference (2015) Dr. Kang supervises senior student independent studies while teaching business intelligence, machine learning, and supply chain management courses at undergraduate, graduate, and MBA levels. His research is funded by NSERC, MITACS, Alberta Innovates, and industry partners including Fluor Canada, Apana Technologies, and the City of Calgary, supporting projects valued at over $2M annually. He leads collaborative research with Panjab University, CPA Canada, and TATA Steel Europe, focusing on supply chain digitization and process optimization. His work with UK-based SMEs and Canadian organizations drives practical implementation of analytics frameworks in real-world operational environments.