Ouldooz Baghban Karimi is a Lecturer at Simon Fraser University's School of Computing Science, serving as Chair of the CS Diversity Committee and holding the Rajan Family Scholar title. She specializes in Networks and Systems, Data Security, and Cloud Computing Education. Her research emphasizes diversity and inclusion in computing curricula, open educational resources, and project-based learning methodologies. Education: PhD in Computer Science from Simon Fraser University (SFU), MSc in Computer Engineering from Iran University of Science and Technology, and BSc in Computer Engineering from University of Tehran. Research Interests: Her work focuses on integrating diversity initiatives into computing education, optimizing wireless networks, and advancing cloud computing curricula through vendor certifications. Key areas include curriculum design, admissions equity, and pedagogical strategies for inclusive education. Publications Trends: Recent articles highlight diversity in admissions processes, open educational resources, and project-based learning. Earlier work explores wireless network optimization, multicast protocols, and virtual MIMO systems. Awards: Rajan Family Scholar Teaching and Contributions: Teaches courses in Data Communications, Cloud Security, and special research projects. Actively involved in shaping computing education policies and fostering inclusive academic environments. Labs/Teams: Engaged with SFU's School of Computing Science research groups, focusing on educational technology and network systems innovation.
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Yagiz AksoyView profile
Assistant Professor
Yağız Aksoy is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Computational Photography Lab. His research focuses on inverse rendering and computational photography, aiming to enable 3D physical control over light and geometry in image editing and movie post-production. His work is funded by NSERC, CFI, BC government, Adobe Research, and Meta Reality Labs. Education: PhD in Computer Science, ETH Zurich (2019), supervised by Marc Pollefeys. Postdoctoral research at MIT CSAIL with Wojciech Matusik and Disney Research Zurich. MSc in Computer Engineering, Middle East Technical University (2013). Research Interests: His lab develops physically-based methods for relighting, intrinsic image decomposition, HDR reconstruction, and monocular depth estimation. Recent breakthroughs include Physically Controllable Relighting (SIGGRAPH 2025) and Colorful Diffuse Intrinsic Decomposition (SIGGRAPH Asia 2024, Best Paper Honorable Mention). Grants & Awards: Best Paper Award Honorable Mention at SIGGRAPH Asia 2024. NSERC Discovery Grant, CFI John R. Evans Leaders Fund, and BC Early Career Research Award. Industry partnerships with Adobe and Meta. Students & Lab: Supervises PhD/MSc students including Chris Careaga, Sebastian Dille, and Mahdi Miangoleh. The lab's Computational Photography Studio operates in a production environment, emphasizing real-world applicability. Active projects include RGB+NIR editing, dynamic scene rendering, and physically-aware compositing tools.
Huapeng WuView profile
Professor
Huapeng Wu is a Professor at the University of Windsor specializing in robotics, nuclear engineering, and control systems. His research focuses on advanced robotics for fusion reactor maintenance, remote handling systems, and intelligent control algorithms. He has secured significant funding, including a $383k grant for cybersecurity in autonomous vehicles (2020). Key projects include development of the CFETR remote handling system, deformation modeling of fusion manipulators, and EEG-based human-machine interfaces. Research interests span parallel mechanisms, cognitive load monitoring, and bionic robotics. Notable contributions include: Design of vacuum vessel assembly robots with anti-vibration features Development of neural network-based fault prediction frameworks Integration of digital twin technology for fusion maintenance systems His work combines mechanical engineering principles with AI-driven solutions for complex industrial challenges. Current efforts emphasize improving safety and precision in nuclear fusion environments through innovative robotic systems and predictive modeling.
Shengrong BuView profile
Associate Professor
Shengrong Bu is an Associate Professor at the Department of Engineering, Brock University, Canada. She holds a Ph.D. in Electrical and Computer Engineering from Carleton University and has held academic positions at the University of Glasgow and industrial roles at Huawei Technologies. Her research bridges smart grid communications, wireless network security, and deep reinforcement learning applications. Ph.D., Electrical & Computer Engineering, Carleton University MEng by Research, Electrical Engineering, University of Wollongong BEng, Mechanical Engineering & Automation, Huazhong University Research focuses on: Multi-vector energy microgrids and smart grid communications Deep reinforcement learning for network optimization Game theory applications in energy systems Big data analytics for grid resilience Articles show expertise in P2P energy trading, fog computing resource allocation, and wireless security in smart grid environments, with funding from EPSRC and NSERC. Awards include multiple IEEE best papers and prestigious research fellowships. She supervises Ph.D. and MSc students in energy systems research.
Septimiu Tim E. Salcudean is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, holding the C.A. Laszlo Chair in Biomedical Engineering and a Canada Research Chair. He joined UBC in 1989 after research at IBM Watson Research Center and fellowships in France. His research focuses on medical robotics, image-guided interventions, and ultrasound-based technologies for surgical guidance and cancer diagnosis. Key projects include real-time ultrasound elastography for prostate cancer imaging and augmented reality systems for robot-assisted surgery. Education: B.Eng (Hons.) and M.Eng from McGill University Ph.D. in Electrical Engineering from UC Berkeley Research Interests: Medical robotics, image-guided interventions, ultrasound elastography, teleoperation, and needle-tissue interaction modeling. His work bridges robotics, control systems, and biomedical applications, with a focus on improving surgical precision and cancer diagnosis. Publications: Over 100 peer-reviewed articles, including contributions to IEEE Transactions on Robotics , Medical Image Analysis , and Medical Engineering & Physics . Recent work emphasizes real-time tissue parameter estimation and brachytherapy guidance. Awards: Fellow of the Canadian Academy of Engineering Fellow of the IEEE NSERC Synergy Award (2009) UBC Killam Research Prize (2004) Advising & Grants: Supervised over 20 graduate students, co-authored patents on steerable needles and ultrasound guidance systems. Active in NSERC and NIH-funded projects, including collaborations with the British Columbia Cancer Agency. Labs: Leads the Robotics and Control Laboratory (RCL) with facilities in haptic interfaces, medical robotics, and ultrasound systems. Collaborates with industry partners like Ultrasonix Medical Corporation.
Chenkuan LiView profile
Professor
Dr. Chenkuan Li is a Professor in the Department of Mathematics and Computer Science at Brandon University, where he has been since 2002. His academic career includes roles at institutions such as the University of Alberta (Augustana), Cape Breton University, and the University of Lethbridge. He holds a Ph.D. in Mathematics from the University of Regina (1994) and has published extensively in areas like fractional calculus, distribution theory, and algorithm analysis. His research focuses on fractional differential equations, generalized integral transforms, and network communication algorithms, including studies on average-case messy broadcasting in network topologies. Research Interests: - Mathematics: Distribution theory, fractional calculus, nonlinear differential equations, generalized integral transforms. - Computer Science: Algorithm analysis, data networking, messy broadcasting in hypercubes and d-ary trees. Awards: NSERC Discovery Grant (2019–2025) Brandon University Senate Award for Excellence in Teaching (2016) Brandon University Senate Award for Excellence in Research (2015) 1994 Graduate Student Scholarly Research Award (University of Regina) Grants and Services: Recipient of multiple institutional and government grants, including NSERC and Brandon University funding. Roles in editorial boards, conference committees, and academic evaluations across institutions. Supervised numerous undergraduate research projects and advised on academic policies. Teaching: Courses span Mathematics (Calculus, Real Analysis) and Computer Science (Algorithms, Networking, Programming), with a focus on bridging theory and practical applications.
Ashraf AbdelbarView profile
Professor
Dr. Ashraf Abdelbar is a Professor in the Department of Mathematics and Computer Science at Brandon University. He holds a BSc in Computer Science from the American University in Cairo (1991), an MSc (1994), and a PhD (1996) in Computer Science from Clemson University, USA. His primary research focuses on artificial intelligence, optimization algorithms, and machine learning, with notable contributions to ant colony optimization, particle swarm optimization, and genetic algorithms. Research Interests: Development of swarm intelligence algorithms for optimization problems (e.g., ant colony optimization, particle swarm optimization). Applications of neural networks in classification, data reduction, and training methodologies. Integration of evolutionary computation techniques with classical optimization methods. Publications Trends: His work spans theoretical algorithm development and practical applications in domains like classification systems, recommendation systems, and combinatorial optimization. Recent trends include hybrid approaches (e.g., combining ant colony optimization with crossover operators) and parameter adaptation strategies for improved algorithm performance. Over 90 publications since 1996 reflect his sustained research output across conferences and journals in computational intelligence, swarm intelligence, and neural networks. Labs/Teams: While no specific lab names are mentioned, collaborations with researchers like Khalid M. Salama, Donald C. Wunsch, and others indicate active participation in interdisciplinary projects focusing on optimization, machine learning, and neural network applications.
Onaizah OnaizahView profile
Assistant Professor
Dr. Onaizah Onaizah is an Assistant Professor at McMaster University’s Department of Computing and Software and a cross-appointed member of the School of Biomedical Engineering and the Department of Mechanical Engineering. She leads the Hardware at the millimetre-scale for healthcare (HeART) Lab, focusing on developing miniature magnetic robots for biomedical applications. Her research spans robotics, magnetics, manufacturing, and clinical integration. BSc, University of Toronto (2013) MSc, Western University (2015) PhD, University of Toronto (2019) Dr. Onaizah’s work revolves around magnetic soft robots, emphasizing design, fabrication, and control for medical use. She explores programmable magnetization, real-time feedback systems, and material characterization to enhance precision in minimally invasive procedures. Her expertise includes mechatronics, vision sensing, and localization for microscale devices. Her recent publications highlight advancements in 3D-printed magnetic robots, autonomous endoscopy, and surgical tools. Key trends include the integration of machine learning for design optimization, closed-loop control for dynamic environments, and pandemic-adapted clinical protocols. Scientific recognition includes a $5.2M SSHRC and CFI grant (2024) for her research. She teaches graduate courses in microrobotics and medical robotics, emphasizing interdisciplinary collaboration between engineers, clinicians, and scientists. At the HeART Lab, she leads a multidisciplinary team of roboticists, engineers, and clinicians, addressing challenges in healthcare through hardware innovation. The lab focuses on tetherless devices, programmable materials, and integration into clinical workflows.
Justin LongoView profile
Associate Professor
Justin Longo is an Associate Professor at the Johnson Shoyama Graduate School of Public Policy (University of Regina campus) and Director of the Digital Governance Lab . His work bridges artificial intelligence , policy analytics , and digital governance , with a focus on the social implications of emerging technologies and transboundary environmental policy . He holds a PhD in Public Policy from the University of Victoria (2013). Key Roles : Cisco Research Chair in Digital Governance (2015-2020), Affiliate Member at the University of Victoria's School of Public Administration (2024-2025), and Fulbright Canada Visiting Scholar at UCLA Law's Institute for Technology, Law & Policy (2023). Research Themes : AI in public administration, digital policy tools, citizen science, and governance of Internet-of-Things (IoT) devices . His recent work includes generative AI for briefing note drafting and facial recognition regulation . His 15 most recent publications span AI governance (2025-2016), digital policy tools (2024), collaborative platforms (2018), and health policy analytics (2021). Notable awards include the Fulbright Canada Visiting Scholar Program (2023) and the Sylvia Ostry Prize in Public Policy (2011). Grants : Over CAD 1.2 million in competitive funding, including SSHRC, SHRF, and MacArthur Foundation support. Teaching : Courses in Digital Governance , Policy Analytics , and Strategic Management , with a focus on generative AI applications in public administration education. Service : Advisor to SSHRC, reviewer for journals like Canadian Public Administration , and organizer of national student case competitions.
Binyan XuView profile
Assistant Professor
Binyan Xu is an Assistant Professor at the University of Guelph, affiliated with the Department of Mechanical Engineering. Their research focuses on advanced control systems, fault detection, and autonomous mechatronics applications. Specializes in fault-tolerant control and performance recovery algorithms Investigates cooperative control in multi-agent systems Develops perception and navigation solutions for aerial vehicles Works on autonomous mechatronics systems with practical applications Professor Xu teaches undergraduate courses including ENGG*1100 Engineering and Design I and ENGG*4490 Sampled Data Control Design.
Nicola GuglielmiView profile
Professor
Nicola Guglielmi is a Professor of Numerical Analysis at the Gran Sasso Science Institute (GSSI) in L'Aquila, Italy, since February 2018. He currently serves as Deputy Rector of GSSI (2022–2026) and Director of the Division of Mathematics (2019–2025). Previously, he held positions at the University of L'Aquila, including Full Professor of Numerical Analysis (2006–2018). His education includes a Laurea from the University of Bologna (1991, summa cum laude) and a PhD in Computational Mathematics from the University of Padova (1996). His research focuses on numerical analysis, particularly delay differential equations, matrix theory applications, and stability analysis of dynamical systems. He has authored over 100 publications and developed the RADAR5 software for delay differential equations. Recent grants include the 2023 MIUR/PNRR project on geothermal energy modeling and the 2022 PRIN project on numerical methods for PDEs. Notable awards include the New Talent Award at SciCADE-99 (1999) and a plenary lecture at the UMI-23 conference (2023). He has supervised numerous PhD students, many now in academia and industry. Upcoming activities include visits to the Courant Institute (2025) and McGill University (2025). His work bridges theoretical and applied mathematics, with contributions to scientific computing, control theory, and data science.
Langis MichaudView profile
Professor
Dr. Langis Michaud is a Full Professor and Chair of Contactology at the Université de Montréal's École d'optométrie since 2001. He leads a 12-clinician team treating 5,000 patients annually, specializing in specialty contact lenses for corneal pathologies like keratoconus and dry eye. He served as Director of the École d'optométrie from 2020 to 2022. Dr. Michaud holds advanced degrees in optometry and public administration, with fellowships from the American Academy of Optometry and British Contact Lens Association. Education: Doctorat en Optométrie, Université de Montréal (1986) Maîtrise en Optique Physiologique, Université de Montréal (1998) Diplôme de 3ᵉ cycle en Santé Publique, École Nationale d'Administration Publique (2006) His research focuses on specialty contact lenses (scleral lenses, orthokeratology), Fabry disease ocular manifestations, and myopia control. Key projects include longitudinal studies on Fabry disease's ocular impact and clinical trials for myopia management. He has authored over 100 peer-reviewed articles and book chapters, earning recognition as one of the most cited authors on scleral lenses. Dr. Michaud is actively involved in media outreach as a regular contributor to Radio-Canada and La Conversation. His clinical expertise spans emergency eye care, contact lens fitting, and managing complex corneal cases. Grants & Projects: Leading a lab studying corneal diseases linked to peripheral neuropathy (2023–2029) Evaluating scleral lenses for dry eye management (2019–2023) Investigating oxygen permeability in orthokeratology (2022–2023) He supervises master's theses on topics like multifocal lens design, myopia control algorithms, and corneal biomechanics. His work bridges clinical practice and research, emphasizing patient-centered care and innovation in optometric science.
Hong-Chuan YangView profile
Professor
Prof. Hong-Chuan Yang is a Professor of Electrical and Computer Engineering at the University of Victoria, Canada. He holds a B.E. from Jilin University (1995), M.Sc. degrees in Applied & Computational Mathematics and Electrical Engineering, and a Ph.D. in Electrical Engineering from the University of Minnesota (2003). Previously, he worked as a research associate at the China Academy of Information and Communications Technology (1995–1998). His research focuses on wireless communications, particularly intelligent transmission for cyber-physical systems, with emphasis on UAV-enabled networks, FSO/RF hybrid systems, and reinforcement learning applications. Notable contributions include optimization of UAV trajectory design, energy-efficient IoT transmission, and secure satellite communication systems. His work spans Max-Min Fairness-Oriented UAV Navigation , Age of Information Minimization in NOMA-IoT , and FSO/RF Hybrid Systems for Satellite Links . Awards include the University of Minnesota Doctoral Dissertation Fellowship (2002–2003). He is a Senior IEEE Member and a registered Professional Engineer in BC. Key Research Themes: UAV Networks, FSO/RF Hybrid Systems, Reinforcement Learning, Satellite Communications Notable Achievements: 20+ publications since 2017, with focus on stochastic modeling, network optimization, and secure transmission Current projects address challenges in ultra-reliable low-latency communications (URLLC), energy-efficient WBANs, and AI-driven network design.
David ArmstrongView profile
Professor
David Armstrong is a Professor in the Department of Medicine at McMaster University, specializing in gastroenterology and nutritional science. His research focuses on Gastroesophageal Reflux Disease (GERD) , Inflammatory Bowel Disease (IBD) , Celiac Disease , and Home Parenteral Nutrition (HPN) . He has contributed to clinical guidelines and large-scale studies on digestive diseases. Key research themes include: Development of evidence-based guidelines for endoscopic and pharmacological interventions in gastroenterology Impact of nutrition on gastrointestinal health and disease management Quality improvement in healthcare delivery for chronic digestive conditions Health policy and workforce planning in gastroenterology Environmental sustainability in medical practices Recent publications highlight his work on antibiotic risks in chronic diseases, micronutrient deficiencies in gluten-free diets, and innovative therapies like teduglutide for short bowel syndrome. His studies often employ multicenter cohort analyses and meta-analytic techniques to address clinical and public health challenges. Armstrong actively collaborates with interdisciplinary teams and has advanced patient-centered approaches in nutrition therapy and gastrointestinal care.
Cheng-Zhi Anna HuangView profile
Associate Professor
Dr. Cheng-Zhi Anna Huang is currently a faculty member at the Massachusetts Institute of Technology (MIT) with a joint appointment between the Department of Electrical Engineering and Computer Science (EECS) and the Department of Music and Theater Arts (MTA), spanning both the College of Computing and School of Humanities, Arts, and Social Sciences. She simultaneously holds an adjunct associate professor position at the Université de Montréal's Department of Computer Science and Operations Research. Her academic background includes a PhD from Harvard University, a master's from the MIT Media Lab, and dual bachelor's degrees in music composition and computer science from the University of Southern California. Dr. Huang's research focuses on human-AI co-creation in music, with particular emphasis on: Generative AI models for music composition and performance Neural network interpretability for musical applications Interactive systems for real-time human-AI collaboration Reinforcement learning frameworks for creative expression Cross-cultural music modeling and computational musicology She pioneers novel approaches to musical interaction through machine learning, aiming to develop systems that extend how humans understand, learn, and create music. Her publication portfolio shows strong emphasis on generative models for music, particularly transformer architectures, with consistent output in top AI/ML venues since 2014. Recent work focuses on controllable music synthesis, human-AI co-creation frameworks, and performance modeling. The 14 most recent publications demonstrate progression from fundamental music representation research toward sophisticated interactive systems and evaluation frameworks. Awards and Honors: Canada CIFAR AI Chair (Mila) Outstanding Paper Award at NeurIPS Workshop CtrlGen (2021) First Prize in San Francisco Choral Artists New Voices Project (composition) Dr. Huang actively supervises graduate students, with recent master's advisees including Nithya Shikarpur (2024) working on human-AI co-creation for Hindustani music, and Yusong Wu (2023) researching controllable performance synthesis. She is currently recruiting postdoctoral researchers and PhD students for her MIT Music Technology laboratory, focusing on multi-agent reinforcement learning and human-AI interaction in musical contexts.
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