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Robert HunterView profile

Assistant Professor

• Medical Diagnostics
• Nanotechnology
• Biomedical Engineering
• +3 more

Robert Hunter is an Assistant Professor at the University of Waterloo, specializing in the development of advanced biosensors and analytical platforms for medical diagnostics and biomedical applications. His research focuses on integrating nanotechnology and surface-enhanced Raman scattering (SERS) for the early detection of diseases such as ovarian cancer, bacterial infections, and viral pathogens. He has pioneered work on exosome-based biosensors, light-triggered antimicrobial surfaces, and optofluidic diagnostic systems. Key research interests include the application of machine learning algorithms to biomedical spectral data, the design of electrochemical sensors for on-farm livestock health monitoring, and the use of C. elegans as a model organism for drug discovery. His interdisciplinary approach bridges engineering, chemistry, and biology to address challenges in precision medicine and rapid diagnostics. As a Full-time Faculty member, Dr. Hunter contributes to teaching and research in cutting-edge technologies, with a focus on translating laboratory innovations into practical clinical and agricultural solutions.

Igor IvkovicView profile

Professor

• Information Systems
• Software Engineering
• Knowledge Engineering
• +3 more

Igor Ivkovic was a Professor in the Department of Systems Design Engineering at the University of Waterloo, Canada. His work focused on integrating theory and practice in complex information systems, emphasizing system modeling, process modeling, and data modeling to bridge technical and business stakeholder needs. He taught courses such as Data Structures and Algorithms, Algorithms and Data Structures, and Digital Systems in recent years. Education: Holds a Doctorate in Electrical and Computer Engineering (2011), along with advanced certificates in university teaching (Certificate in University Teaching, 2011) and instructional skills (ISW, 2015). Earlier degrees include a Master of Mathematics in Computer Science (2003) and a Bachelor of Mathematics in Operations Research (2001), both from the University of Waterloo. Research Interests: Specialized in Information Systems, Software Engineering, and Knowledge Engineering. His work addressed challenges in model synchronization, software evolution, and architecture recovery, leveraging formal methods and model-driven approaches to enhance system consistency and interoperability. Notable Contributions: Authored influential papers on model synchronization for software evolution (2011) and improving the Gnutella protocol (2001), the latter earning a prize-winning award. He also pioneered educational innovations like Design Days Boot Camps (2017–2021), integrating remote learning and augmented reality into engineering education. Awards: Recognized for his 2001 research on the Gnutella protocol, which won a prestigious award through the LimeWire contest.

• CMOS Integrated Circuits
• Biosensors
• Imagers (Visible/IR/X-ray)
• +3 more

Dr. Peter Levine is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, where he also serves as a First Year Academic Advisor. His research focuses on developing CMOS integrated circuits that bridge biology and digital systems, with applications in biosensors and advanced imaging. He leads the Silicon Bioelectronics (SiBio) Laboratory and has been recognized with awards including the Teledyne DALSA Componentware/CAD Award and the IEEE Best Paper Award. Education: Ph.D. (Columbia University, 2009), M.Eng. (McGill University, 2004), B.Eng. (McGill University, 2002). Research interests include CMOS analog/mixed-signal ICs, electrochemical biosensors, and visible/IR/X-ray imagers. Recent work emphasizes monolithic amorphous-selenium/CMOS integration for high-resolution biomedical imaging and photon-counting detectors. His team innovates in sensor design, material integration, and signal processing. Teaching includes courses like ECE 340 (Electronic Circuits 2) and ECE 636 (Advanced Analog Integrated Circuits). He actively supervises graduate students and holds Sole-Supervisory Privilege Status (SSPS). Awards: 2016 Teledyne DALSA Award, 2015 IEEE MWSCAS Best Paper, 2005 Intel Ph.D. Fellowship. His lab collaborates on projects involving CMOS-based biosensors, X-ray detectors, and lab-on-a-chip systems. Over 20 patents and 40+ publications reflect his contributions to semiconductor-based biomedical engineering and electronics innovation.

Benjamin EzeView profile

Adjunct Professor

• Big Data Analytics
• Cloud Data Warehousing
• Health Data Analytics and Integration
• +8 more

Benjamin Eze is a part-time and adjunct professor at the School of Engineering Design and Teaching Innovation within the University of Ottawa . He holds a PhD in Digital Transformation and Innovation, an MSc in Electronic Business Technologies, and a BSc in Computer Engineering. Professionally, he serves as Lead Architect at Next Pathway Inc. and Business Intelligence Partner with Champlain Ontario Health at Home. Education: PhD – Digital Transformation and Innovation (University of Ottawa) MSc – Electronic Business Technologies (University of Ottawa) BSc – Computer Engineering Benjamin specializes in Big Data Analytics , Healthcare Data Privacy , Cloud Computing , and AI in Software Engineering . His work bridges technology and healthcare, focusing on secure data integration and digital transformation. Scientific Awards: Holds two patents in Canada and the United States Benjamin's research spans healthcare data governance, cloud infrastructure (DevOps, DevSecOps), and privacy-preserving technologies. His publications emphasize systematic approaches to data anonymization, cloud-based healthcare monitoring, and ethical AI frameworks. He teaches in upskilling programs at the University of Ottawa’s Professional Development Institute, FxInnovation, and CENGN CloudCampus DevOps programs, while collaborating with organizations like Cisco, Montfort Hospital, and Accreditation Canada.

Herbert H TsangView profile

Professor

• Bioinformatics
• Computational Intelligence
• Machine Learning
• +7 more

Herbert H. Tsang is a Full Professor in Computing Science & Mathematics at Trinity Western University (TWU), British Columbia, Canada. He also serves as an Adjunct Professor at Simon Fraser University's School of Interactive Arts and Technology. His roles include Director of the Applied Research Lab, Undergraduate Research Academy, and Academy of STEAM at TWU. Dr. Tsang holds a PhD and completed an NSERC Postdoctoral Fellowship at the University of Calgary. His research focuses on computational intelligence, bioinformatics, mobile computing, and health technology applications. He has received notable awards, including the 2018 International E-Learning Award for Mobile Learning and the 2019 CNIE Partnership & Collaboration Award. His work spans grants from NSERC, SSHRC, CIHR, and private foundations. Active in professional societies, he is a Senior Member of IEEE and ACM. Recent projects include mobile application development and interdisciplinary research collaborations. Education: PhD in Engineering/Computer Science (exact program unspecified), NSERC Postdoctoral Fellow (2009–2011) Research Highlights: Phishing detection frameworks, HIV transmission modeling, and mobile learning applications. Grants: Funding from NSERC, SSHRC, CIHR, M.J. Murdock Charitable Trust, and Mitacs. Media & Outreach: Featured in articles on educational technology, including high school coding initiatives and SFU app development projects. Awards Overview: 2019 CNIE Innovation Award 2018 IELA Mobile Learning Award IEEE/ACM Review Service Contributor Advising & Mentorship: Supervises undergraduate/graduate research assistants. Former roles include engineering projects with the Canadian Space Agency and mentorship for Engineers and Geoscientists BC. Labs & Initiatives: Leads TWU's Applied Research Lab, fostering interdisciplinary projects in bioinformatics, computational creativity, and system design. Oversees the Undergraduate Research Academy, promoting student research engagement.

Wu LinView profile

Research Fellow

• Machine Learning
• Numerical Optimization
• Approximate Inference
• +3 more

Wu Lin is a Research Fellow specializing in computational aspects of differential, geometric, and algebraic structures in machine learning. Their research focuses on geometric methods for numerical optimization and approximate inference, particularly natural-gradient (NG) descent techniques. They investigate adaptive optimization methods, structured optimization, and Bayesian approaches in deep learning. Their work emphasizes practical and numerically-stable algorithms, including memory-efficient implementations and second-order methods. Research interests include exploiting hidden structures and symmetries in ML models, with emphasis on optimization challenges and statistical inference. Key contributions include foundational papers on structured NG descent, Riemannian gradient methods, and variational inference techniques. Their blog series provides accessible introductions to natural-gradient concepts. Publications span top venues like ICML and AI&Stats, addressing topics from curvature learning in neural networks to scalable Bayesian methods. While no institutional affiliation is explicitly stated in the provided text, their work is associated with Toronto, Canada. No awards or grant information is provided here.

• single-molecule biophysics
• intrinsically-disordered proteins
• GPCRs
• +4 more

Claudiu Gradinaru is an Interim Chair and Professor in the Department of Chemical and Physical Sciences at the University of Toronto Mississauga, part of the Faculty of Arts & Science. He holds a Ph.D. in Physics from VU University Amsterdam and has conducted postdoctoral research at Leiden University and Sandia National Laboratories. His research focuses on single-molecule biophysics, intrinsically disordered proteins (IDPs), and G protein-coupled receptors (GPCRs), leveraging advanced microscopy and computational techniques. Education: B.Sc. in Physics (University of Bucharest, 1995), Ph.D. in Physics (VU University Amsterdam, 2001). Roles: Interim Chair, Principal Investigator of the Gradinaru Biophysics Lab, and educator in molecular biophysics, optics, and classical mechanics. Research Interests: His lab employs ultrasensitive optical techniques (e.g., single-molecule fluorescence spectroscopy) to study conformational dynamics of IDPs and GPCRs in live cells. Key areas include: Structural ensembles of IDPs using NMR, SAXS, and smFRET. GPCR oligomerization and signaling dynamics. Development of computational methods for ensemble modeling. Advising: Gradinaru mentors numerous graduate and undergraduate students, including PhD candidates Xiaohan Zhou (GPCR dynamics) and Maria Shemeteva (GPCR signaling), and MSc students Aiyan Brown (smFRET analysis) and Xinge Liu (IDP complexes). His lab has produced over 40 publications since 2001, with recent work featured in Biophysical Journal , Journal of the American Chemical Society , and Nucleic Acids Research . Labs & Instruments: The Gradinaru Lab houses custom-built microscopes for multiparameter single-molecule fluorescence, fluorescence correlation spectroscopy, and TIRF microscopy. Collaborations span institutions in Canada, the U.S., the U.K., and Australia.

Nozomu YachieView profile

Professor

• Genome Editing
• Cell Engineering
• Synthetic Biology
• +2 more

Professor Nozomu Yachie directs research at UBC's School of Biomedical Engineering, focusing on cellular/molecular engineering and computational biology. His lab develops DNA event-recording technologies to trace molecular dynamics using genome editing and high-performance computing. Key innovations include CloneSelect for retrospective cell isolation and computational tools like FRACTAL for large-scale lineage reconstruction.

Orland HoeberView profile

Professor

• Interactive Information Retrieval
• Information Visualization
• Human-Computer Interaction
• +3 more

Dr. Orland Hoeber is a Professor and Head of the Department of Computer Science at the University of Regina. He holds a Ph.D. in Computer Science from the University of Regina, and his research focuses on developing visual and interactive tools for information exploration in digital libraries and mobile search environments. His work bridges information retrieval, human-computer interaction, and data visualization to support complex search tasks. Education: Ph.D. Computer Science, University of Regina (2007) M.Sc. Computer Science, University of Saskatchewan B.Sc. Mathematics, University of Saskatchewan Dr. Hoeber's research examines how people interact with information systems, with particular emphasis on visual interfaces that support exploratory search behaviors. His lab investigates novel approaches to representing search results, understanding mobile search interruptions, and analyzing social media data for sport analytics. Recent work explores cross-device search patterns in academic contexts and interface designs that facilitate serendipitous discovery. His publications demonstrate a consistent focus on human-centered approaches to information system design, with recent work examining multimodal evaluation techniques and search behavior in digital humanities. Article keywords frequently include information retrieval, visualization, and interactive systems, reflecting his interdisciplinary approach. Dr. Hoeber actively mentors graduate students and postdoctoral fellows in his research group, supervising projects on exploratory search behavior, information visualization, and user interface evaluation. He has served as Associate Head (Graduate) and currently leads the Department of Computer Science.

Samira SadaouiView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +6 more

Dr. Samira Sadaoui is a Full Professor at the University of Regina's Department of Computer Science. She holds a PhD and MSc in Computer Science from the University of Nancy I, France. Her research is supported by NSERC and MITACS grants, focusing on artificial intelligence applications in real-world environments. Dr. Sadaoui's research spans: Adaptive machine learning systems for evolving data streams Fraud detection algorithms in financial and auction systems Evolutionary optimization methods for feature selection Dimensionality reduction techniques for high-dimensional data Reinforcement learning approaches for combinatorial problems Her recent publications demonstrate strong emphasis on: Concept drift detection in noisy environments (2022-2024) Evolutionary computation for feature engineering (2021-2023) Real-world applications in fraud detection and service monitoring (2017-2022) Teaching responsibilities include: CS115: Object-Oriented Design CS230: Fundamentals of Computer Science III CS372: Software Engineering Methodology CS476: Software Development Project CS872: Advanced Software Engineering

Natalia NikolovaView profile

Professor

• Microwave and millimeter-wave imaging and detection
• Inverse problems
• Computational electrodynamics
• +6 more

Natalia K. Nikolova is a Professor and Canada Research Chair (Tier 2) in High-frequency Electromagnetics at the McMaster University Faculty of Engineering, Department of Electrical and Computer Engineering. Author of Introduction to Microwave Imaging (Cambridge University Press, 2017) Develops cognitive radar systems for concealed weapon detection and microwave scanners for breast cancer screening Commercialization with Patriot One Technologies Ltd. Education: Dipl. Eng. (Radioelectronics), Technical University of Varna, Bulgaria (1989) Ph.D. (Electrical Engineering), University of Electro-Communications, Tokyo, Japan (1997) NSERC Postdoctoral Fellowship (1998-1999) Research Focus: Microwave and millimeter-wave imaging systems for biomedical and security applications through computational electromagnetism and inverse scattering analysis. Her Electromagnetic Vision Laboratory (founded 1999) develops high-speed sensor arrays and computer-aided design tools. Scientific Contributions: Elevated to IEEE Fellow (2011) for sensitivity analysis methods Delivered 50+ international lectures as Distinguished Microwave Lecturer (2010-2013) Holder of 7 patents in microwave imaging technology

Kara Brisson-BoivinView profile

Academic

• Digital Citizenship
• Digital Equity
• Digital Well-Being
• +5 more

Kara Brisson-Boivin is an Adjunct Research Professor at Carleton University's Department of Sociology and Anthropology and serves as Director of Research at MediaSmarts, Canada’s national center for digital media literacy. With a PhD in Sociology (2016) from Carleton, her work bridges academia and public policy. She leads MediaSmarts’ research on youth digital experiences, digital equity, and online harms, focusing on topics like algorithmic transparency, privacy, and online hate. Her research informs government bodies, UN initiatives, and industry stakeholders. Key research initiatives include the decades-long Young Canadians in a Wireless World study (tracking 20,000+ participants) and projects addressing technology-facilitated violence and digital literacy strategies for survivors. She collaborates with federal agencies, NGOs, and academic partners on funded projects totaling millions in grants. Her advisory roles include supervising graduate students and directing MediaSmarts’ knowledge mobilization efforts through parliamentary testimony, media op-eds, and international policy input. Bringing together community-driven methodologies and policy advocacy, Brisson-Boivin emphasizes digital resilience and equity. Her current projects focus on youth engagement in platform accountability, privacy consent frameworks, and national digital literacy strategies. She holds adjunct status at Carleton while maintaining active roles in shaping Canada’s digital citizenship landscape.

Yuejian ChenView profile

Assistant Professor

• Dynamic Modelling
• Vibration Signal Processing
• Artificial Intelligence
• +5 more

Dr. Yuejian Chen is an Assistant Professor in the Department of Mechanical Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on dynamic modelling, vibration signal processing, artificial intelligence, and prognosis/health management for complex systems like railway vehicles, wind turbines, and steam turbines. He holds a PhD from the University of Alberta (2020) and was a postdoctoral fellow there until 2021. Prior to joining the University of Manitoba in 2024, he served as an Assistant Professor at Tongji University's Institute of Rail Transit in China from 2021 to 2024. His research integrates digital twins and advanced health management methodologies to ensure safe and cost-effective operation of mechanical systems. Key areas include non-stationary vibration analysis, fault detection via AI (e.g., LSTM networks), and physics-informed machine learning. He has authored/co-authored over 60 publications and serves on editorial boards for journals such as IEEE Sensors Journal , IEEE Transactions on Instrumentation and Measurement , and Smart and Resilient Transportation . Dr. Chen's work bridges theoretical advancements with practical applications, emphasizing railway transportation and energy systems. His recent publications (2021–2025) highlight contributions to gearbox fault detection under variable speed conditions, multi-camera metro monitoring systems, and data-driven load identification strategies. His research trends emphasize fusion of physical principles with AI for robust diagnostics and health management across mechanical systems. He is actively recruiting graduate students interested in dynamic systems, signal processing, and AI applications in mechanical engineering. His research group collaborates with industry partners to address real-world challenges in machinery safety and efficiency.

Qingjin PengView profile

Professor

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +5 more

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

Masoud AsadzadehView profile

Associate Professor

• Water Resources Engineering
• Integrated Water Management Modelling
• Watershed Modelling
• +4 more

Dr. Masoud Asadzadeh is an Associate Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on water resources engineering, hydrologic modeling, and optimization techniques applied to environmental systems. He holds a Ph.D. from the University of Waterloo and has extensive certifications in teaching and learning. His educational background includes degrees from Isfahan University of Technology (B.Sc.), Amirkabir University of Technology (M.Sc.), and the University of Waterloo (Ph.D.). Additional certifications include a Teaching and Learning Certificate (2019) and a Fundamentals of University Teaching Certificate (2011). Research Interests: His work emphasizes water resources systems analysis using optimization, sensitivity, and uncertainty tools. Specific areas include hydrologic modeling of nutrients/water quality, reservoir operation optimization, and algorithm development for multi-objective problems. Recent projects involve climate change impacts on hydropower systems, isotope tracer-based calibration, and integrated water management. Publications: His recent work spans advancements in multi-objective optimization, hydrologic model calibration, and climate change impact studies. Notable contributions include frameworks for model comparison (Nelson-MiP project) and innovative calibration techniques. Advising/Grants: While currently without graduate openings, his past work includes mentoring students on optimization and modeling projects. His research is supported by grants focusing on environmental systems analysis and water resource sustainability. Labs/Teams: Active in interdisciplinary teams addressing large-scale hydrological challenges, particularly in Canadian prairie and northern Manitoba basins.