جست‌وجوی استاد و پژوهشگر

Mina Hoorfarمشاهده پروفایل

استاد

• Microfluidic Technologies
• Nanotechnology
• Sensing Materials
• +۷ مورد دیگر

Mina Hoorfar is the Dean of Engineering and Computer Science and a Professor in the Mechanical Engineering Department at the University of Victoria (UVic). She holds a BASc from the University of Tehran, MASc and PhD from the University of Toronto (U of T), and is a Professional Engineer (P.Eng). Her research focuses on advancing surface science and microfluidic technologies, with specialization in nanostructured sensors for gas monitoring, hydrogen detection in natural gas, wearable health diagnostics, and micro/nano-encapsulation for pharmaceutical applications. Her research areas include: Nanostructured microfluidic artificial olfaction for real-time gas monitoring Hydrogen monitoring in hydrogen-blended natural gas Breath analysis for health and safety applications Wearable microfluidic sensing for personal health Micro/nano-encapsulation in food and pharmaceuticals Current team members: 13 researchers. Lab spaces: Two specialized labs focusing on Advanced Sensors and Advanced Drug Delivery. The MiNa Lab collaborates with industry partners to develop innovative solutions in sensing technologies and biomedical engineering. Advising and grants: While specific grants are not listed, her work reflects significant industry and academic collaboration. The lab emphasizes high-throughput microfluidic synthesis, 3D bioprinting for neural models, and sensor integration with machine learning. Labs/Teams: The MiNa Lab at UVic is a leader in microfluidic sensor development, with dual lab spaces dedicated to advancing sensor technologies and drug delivery systems through cutting-edge research and industry partnerships.

Jasleenمشاهده پروفایل

مدرس

• Machine Learning
• Healthcare Technology
• Natural Language Processing
• +۴ مورد دیگر

Jasleen is a Lecturer at the Edwards School of Business, part of the University of Saskatchewan. Her work spans interdisciplinary research in machine learning, thermal imaging technologies, and Punjabi language studies. She has contributed to advancements in healthcare technology through studies on lung cancer treatment protocols and facial emotion recognition systems. Her research interests prominently feature applications of artificial intelligence in healthcare diagnostics, development of novel algorithms for material science analysis, and exploration of postcolonial narratives in South Asian literature. Notable work includes innovations in infrared thermography for non-destructive testing and quantum Fourier transform spectroscopy techniques. Jasleen's publications demonstrate a focus on both technical engineering solutions and humanities-based scholarship, reflecting her unique academic positioning at the intersection of business education and cutting-edge scientific inquiry. She has collaborated on projects ranging from spectral domain quantum interferometry to sociological studies of Punjabi immigrant mental health.

• Rehabilitation Engineering
• Human-Computer Interaction
• Health Informatics
• +۷ مورد دیگر

Aaron Tabor is an Assistant Professor at the University of New Brunswick . His research focuses on bridging Human-Computer Interaction (HCI) with Biomedical Engineering and Health Informatics to develop innovative rehabilitation technologies and wellness-oriented systems. Email: q4k8p@unb.ca Office: Room GE109A His work emphasizes inbodied interaction design , which integrates internal physiological processes into technology development. Key areas include breathing exercise systems for ADHD and chronic disease management, EMG feedback for spinal cord injury rehabilitation, and gamification in therapeutic interventions. Research Trends : The provided articles highlight his focus on three domains: (1) gait analysis using underfoot pressure sensors and deep learning, (2) respiratory therapy through biofeedback and idle games, and (3) inbodied interaction frameworks that leverage neuro-physiological pathways for self-tuning systems. These works often intersect machine learning , gamification , and non-invasive physiological monitoring .

April Khademiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. April Khademi serves as Canada Research Chair in AI for Medical Imaging and Associate Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University. She maintains dual appointments as Affiliate Scientist at Keenan Research Centre for Biomedical Science and Senior Scientist/Innovation Specialist at GE Healthcare's Pathology Innovation Centre of Excellence (PICOE). Education: PhD, University of Toronto (2012) MASc, Toronto Metropolitan University (2006) BEng, Toronto Metropolitan University (2004) Research Focus: Dr. Khademi pioneers AI-driven medical image analysis with emphasis on automated MRI biomarker detection for neurodegenerative diseases and digital pathology cancer characterization . Her work bridges machine learning with clinical applications in radiology, pathology, and neurology to transform diagnostic workflows through objective, rapid analysis systems that reduce human error. Publication Trends: Her recent research demonstrates leadership in large-scale collaborative medical imaging initiatives, particularly in dementia cohort harmonization and multiple sclerosis lesion segmentation. These works establish standardized AI frameworks for national clinical data integration while addressing critical challenges in color normalization, unsupervised proliferation indexing, and cross-institutional MRI analysis. Scientific Recognition: Google Canada and Anita Borg Scholar Award NSERC Alexander Graham Bell Doctoral Scholarship Governor General’s Gold Medal L'Oréal-UNESCO for Women in Science Scholarship Academic Leadership: As Editor for Frontiers in Biomedical Engineering and committee member for IEEE ICASSP 2021 and ICPR 2020, she shapes AI in digital pathology research. Her Canada Research Chair supports translation of algorithms into clinical practice through partnerships with Canadian Consortium on Neurodegeneration in Aging (CCNA) and St. Michael's Hospital. Research Infrastructure: She directs the Image Analysis in Medicine Lab (IAMLAB) while contributing to the Neuroimaging Platform of CCNA and the Institute for Biomedical Engineering, Science & Tech (iBEST), driving innovation from algorithm development to clinical implementation.

Sri Krishnanمشاهده پروفایل

استاد

• Biomedical Signal Analysis
• Wearables/Textile Computing
• Assistive Technology
• +۵ مورد دیگر

Dr. Sri Krishnan is a Professor and Dean at the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. With a focus on interdisciplinary research, he bridges electrical engineering, computer science, and biomedical applications. His work emphasizes mathematical signal analysis for healthcare solutions. PhD, University of Calgary (1999) MSc, University of Calgary (1996) BE, College of Engineering, Guindy, Anna University (1993) Research Interests: Dr. Krishnan specializes in biomedical signal analysis, affective computing, and compressive sensing. He develops wearable technologies and machine learning algorithms for applications in mental health, cardiovascular diagnostics, and neurological disorders. Scientific Contributions: His recent publications highlight advancements in fetal ECG separation, seizure detection via EMD dictionaries, acoustic scene classification using local binary patterns, compressive sensing in gait signals, and wavelet-based gait diagnostics. Fellow, Canadian Academy of Engineering Outstanding Canadian Biomedical Engineer Award Canada Research Chair in Biomedical Signal Analysis Sarwan Sahota Distinguished Scholar Award Laboratory: He leads the Signal Analysis Research Group, fostering innovation in time-frequency theory and non-stationary signal processing.

• Allergic Asthma
• Immunology
• Respiratory Diseases
• +۴ مورد دیگر

Professor Gail Gauvreau is a leading academic in the Department of Medicine at McMaster University , specializing in Allergic Asthma , Immunology , and Respiratory Diseases . Her research focuses on allergen-induced immune responses, cytokine signaling, and therapeutic interventions in asthma and atopic conditions. Key research areas: Eosinophil biology, IL-33/ST2 axis, TSLP pathway Recent work: SARS-CoV-2 receptor expression in asthmatics, anti-alarmin therapies, and inhaled vaccine development Major collaborations: Clinical trials with biologics (Benralizumab, Ligelizumab) and inhalation challenge studies Her 15 most recent publications (2025-2024) emphasize airway inflammation , immune cell regulation , and novel therapeutic approaches , with a focus on real-time clinical models and molecular biomarkers. Notably, her 2025 study on Benralizumab in atopic dermatitis and 2024 work on neuromedin-U in asthma highlight translational impact.

• Pervasive Computing
• Ambient Intelligence
• Internet of Things
• +۷ مورد دیگر

Bessam Abdulrazak is an Associate Professor at the University of Sherbrooke, Canada, where he has been serving since 2013. He is also the Director of the Intelligence AMbiante Lab (AMI-Lab) at the University of Sherbrooke since 2013, and an Active Researcher at the Centre de recherche sur le vieillissement since 2011. Prior to his current position, he served as an Assistant Professor at the University of Sherbrooke from 2007 to 2013. Dr. Abdulrazak earned his Doctorate in Computer Science from Université d'Évry Val d'Essonne in 2004, a Master's equivalent degree in Robotics from Université de Paris VI (P & M Curie) in 2000, and an Engineering degree in Electronics from Université des Sci et de la Tech Houari in 1997. His research focuses on pervasive computing, ambient intelligence, and the Internet of Things (IoT) with applications in healthcare and aging. Specifically, he works on adaptive systems, context-aware computing, activity recognition, and smart environments designed to support elderly individuals and those with special needs. His interdisciplinary research integrates computer science with gerontology to develop technologies that enhance quality of life and promote healthy aging. His work spans smart homes, intelligent environments, semantic modeling, and behavior recognition systems tailored for aging populations. His recent publications demonstrate a strong focus on IoT architectures for healthcare applications, discreet health monitoring systems, and context-aware adaptive technologies for elderly care. His work spans computer science, biomedical engineering, and healthcare, with particular emphasis on developing practical solutions that can be deployed in real-world settings. He has developed the AMI Platform, a reliable and scalable solution for IoT deployment that addresses system reliability through plug-and-play architecture. Our paper for Open Living Labs 2022 has been among the top Selected papers in the conference Dr. Abdulrazak has directed multiple research labs including the AMI-Lab and previously the DOMUS Lab (2009-2010). His research has involved numerous collaborations with healthcare institutions and has resulted in practical applications for aging in place and remote health monitoring. He has established strong international collaborations, including European projects such as PULSE, City4Age, and Silver, working with partners from France, Singapore, and other countries. His research program bridges computer science with healthcare needs, particularly focusing on how technology can support independent living and improve quality of life for elderly populations.

René Mayerمشاهده پروفایل

استاد

• Precision engineering
• Manufacturing
• Metrology
• +۷ مورد دیگر

René Mayer is a Full Professor at the Department of Mechanical Engineering, Polytechnique Montréal. With a B.Eng. (1983) and Ph.D. (1989) in mechanical engineering, he has dedicated his career to precision manufacturing, machine tool calibration, and metrology. He directs the Laboratoire de recherche en fabrication virtuelle (LRFV) and co-leads the Groupe de recherche en développement et fabrication des produits (GRDFP) . His research focuses on improving machine tool accuracy through rapid, automated measurement systems. Key areas include volumetric error compensation, thermal distortion modeling, and advanced calibration methods like SAMBA and R-test devices. Collaborations span aerospace, automotive, and biomedical industries, with international academic ties in Sweden, Poland, Germany, Switzerland, and France. Education : B.Eng. and Ph.D. in Mechanical Engineering Expertise : Precision engineering, robotics, artificial vision, finite element modeling Labs : Laboratoire de recherche en fabrication virtuelle, GRDFP Recent publications highlight machine learning applications for error prediction, thermal compensation, and uncertainty quantification in five-axis machining. Scientific honors include Fellowships with CIRP (2022) and IMechE (2012), recognizing his contributions to manufacturing science.

Benjamin De Leenerمشاهده پروفایل

دانشیار

• Pediatric Neuroimaging
• Medical Imaging
• Computer Vision
• +۷ مورد دیگر

Benjamin De Leener is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds the TransMedTech Research Chair in Pediatric Neuroimaging and serves as Co-director of the Neuroimaging Research Laboratory (NeuroPoly). His work bridges engineering and medical research through multiple institutional affiliations including the Sainte-Justine University Hospital Research Center, the Groupe de recherche en gestion et mondialisation de la technologie (GMT), and the Union Neuroscience et Intelligence Artificielle - Québec (UNIQUE). Dr. De Leener's research focuses on developing advanced technologies for pediatric neuroimaging, particularly open-source image analysis tools for brain and spinal cord MRI. His work integrates techniques from image segmentation and registration, neurodevelopmental templates and atlases, and machine learning. His research spans both biomedical technology and artificial intelligence applications, with particular emphasis on pediatric populations. His recent publications demonstrate a strong trajectory in developing computational methods for spinal cord and brain imaging, with increasing focus on pediatric applications and machine learning approaches. The research shows consistent growth in methodological sophistication, moving from basic image processing to advanced deep learning applications for medical imaging. Dr. De Leener actively supervises doctoral and master's students, with current projects focusing on neonatal brain imaging, spinal cord quantification, and advanced MRI techniques. His laboratory maintains strong collaborations between engineers, researchers, psychologists, radiologists, and physicians through its connections with the TransMedTech Institute and Sainte-Justine University Hospital.

Manaf Zargoushمشاهده پروفایل

دانشیار

• Health Policy
• Healthcare Operations Management
• Data Science
• +۷ مورد دیگر

Dr. Manaf Zargoush serves as Associate Professor of Health Policy & Management and Associate Dean at McMaster University's DeGroote School of Business. His academic appointments span health systems research, operations management, and data-driven healthcare policy. Education: Ph.D. in Healthcare Operations and Information Management, McGill University Ph.D. in Decision Science and Statistics, ESSEC Business School M.Phil. in Decision Sciences, ESSEC Business School M.Sc. in Socio-Economic Systems Engineering, Sharif University of Technology B.Sc. in Mechanical Engineering, Jundi-Shapoor University His research integrates data science and stochastic optimization to solve healthcare challenges, with focus areas including chronic disease management (hypertension/diabetes), aging research (disability trajectories, ALC), and causal analytics. Current projects leverage machine learning for personalized medication prescription, climate-resilient elderly care, and hospital discharge optimization. His work bridges operations research with clinical implementation through predictive-prescriptive analytics frameworks. Recent publications demonstrate strong trends in gerontechnology applications (wearable monitoring), health equity analysis (care fragmentation), and pandemic response systems . Key thematic clusters include: aging population health modeling, climate-health intersections, and AI-driven clinical decision support systems across 15 high-impact publications from 2023-2025. Teaching Portfolio: Decision Analysis for Healthcare (BUSADMIN BL718) Analytics and Decision Making in Healthcare (BUSADMIN C755) Operations Management (BUSADMIN O650) Research Issues in Health Management (BUSINESS C783) Dr. Zargoush actively leads interdisciplinary research through McMaster's health analytics initiatives, with current projects examining climate impacts on elderly care infrastructure and machine learning applications for hospital discharge optimization. His work connects mathematical modeling with real-world healthcare policy implementation.

• Ophthalmology
• Retinal Surgery
• Medical Imaging Analysis
• +۳ مورد دیگر

Renaud Duval is a Clinical Associate Professor in the Department of Ophthalmology at the University of Montreal's Faculty of Medicine. He directs the Ophthalmology Fellowship Program and leads retinal services at the Centre universitaire d’ophtalmologie (Hôpital Maisonneuve-Rosemont). His clinical practice integrates surgical innovation, medical education, and research supervision for medical students, residents, and fellows. Education: MD CM, McGill University (2006) Residency in Ophthalmology, Université de Montréal (2011) Fellowship in Vitreoretinal Surgery, Rush University/Illinois Retina Associates (2013) His research bridges clinical ophthalmology and technology development. Primary interests include: retinal pathophysiology (age-related macular degeneration, diabetic retinopathy), surgical innovation (new instrument design, vitrectomy techniques), AI integration for automated analysis of OCT/fundus images, and tele-ophthalmology frameworks for remote diagnostics. He co-authored Canadian tele-retina screening guidelines and develops deep learning models for lesion segmentation in retinal imaging. Publications emphasize translational applications: 65% focus on surgical outcomes (vitrectomy mechanics, intraocular pressure dynamics), 25% on AI/imaging diagnostics, and 10% on infectious/inflammatory retinopathies. Recent work demonstrates strong convergence of biomechanical modeling (ocular rigidity) and AI-driven image analysis. Awards & Honors: 6× Rhett Buckler Awards (ASRS Film Festival, 2012-2019) for innovative surgical videos ASRS Junior Achievement Award (2015) Dean’s Honour List (McGill 2006, UdeM 2011) Robert Forsyth/Francis Williams Prizes (McGill, 2006) He leads or co-investigates clinical trials (6+ as PI/co-I) and secured CIHR funding for a 5-year project (2022-2026) developing an OCT-controlled subretinal drug injection system for preclinical research. As director of fellowships, he oversees advanced ophthalmology training programs. Affiliations include the Vision Health Research Network (RRSV), emphasizing collaborative retinal research.

• Respiratory assistance in intensive care
• Nutrition in intensive care
• Computerization of diagnostic and therapeutic assistance
• +۳ مورد دیگر

Philippe Jouvet is a Clinical Professor in the Department of Pediatrics at Université de Montréal's Faculty of Medicine. He is affiliated with CHU Sainte-Justine, a leading pediatric healthcare institution in Montreal, Canada. His clinical and research activities focus on advanced technologies for critical care environments, particularly in respiratory assistance and diagnostic automation. Dr. Jouvet's research spans pediatric intensive care innovations, including machine learning applications for clinical decision-making, respiratory distress detection, and safety protocols for aerosol transmission. His work addresses key challenges in mechanical ventilation, hypoxemia triage, and artifact detection in physiological signals, with a strong emphasis on cross-national collaborative studies. His recent publications demonstrate expertise in integrating artificial intelligence with clinical workflows, evidenced by studies on LoRA adapters for LLMs, vision transformers for rPPG estimation, and hybrid neural networks for signal processing. His work also includes critical contributions to international guidelines for pediatric ventilator liberation and acute respiratory distress syndrome management.

• Biomedical Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal

Xin Tangمشاهده پروفایل

استادیار

• Explainable AI
• Interpretable Machine Learning
• Computational Biology
• +۷ مورد دیگر

Xin Tang is an Assistant Professor at the Michael Smith Laboratories and the Department of Computer Science in the Faculty of Science at the University of British Columbia. He leads the Tang Lab, which focuses on developing AI models to advance biological understanding at multiple scales and modalities. PhD in Engineering Sciences from Harvard University and the Broad Institute of MIT and Harvard Xin Tang's research spans computational cell biology, brain-computer interfaces, and in silico cellular digital twins. His work integrates explainable and interpretable AI with biological systems to address fundamental questions from molecular interactions to animal behaviors. Key areas include computational omics, multi-modality cell biology, spatio-temporal gene regulation, neuroengineering, and biological large language models. His lab develops autonomous AI approaches that serve as digital twins for biological systems, enabling in silico experiments that guide wet lab research. Analysis of Tang's recent publications reveals a strong focus on bridging AI and biology across multiple scales. His work spans from molecular and cellular levels (single-cell biology, multi-omics, spatial transcriptomics) to neural systems (brain-computer interfaces, neural activity tracking) and organ-level applications (cardiac interfaces). A consistent theme is the development of explainable and interpretable AI methods that provide mechanistic insights rather than just predictive power. His research has significant implications for understanding development, aging, and diseases like neurodegeneration. NSERC Discovery Grant (2025) Resource Allocation Competition of Digital Research Alliance of Canada (2025) Professor Tang actively supervises multiple graduate students, postdoctoral fellows, and undergraduate researchers across UBC's Computer Science, Bioinformatics, and Genome Science and Technology programs. His lab has received significant research funding including an NSERC Discovery Grant. He is committed to interdisciplinary collaboration and has established research partnerships with biologists, engineers, and clinicians to address complex biological questions related to neurodegenerative diseases, heart disease, and aging. The Tang Lab, located in the Michael Smith Laboratories at UBC, fosters a collaborative environment for researchers interested in AI for biology. The lab actively recruits dry-lab researchers with strong coding and machine learning backgrounds to work on projects spanning computational biology, neuro-inspired AI, explainable AI, biological LLMs, computational omics, and brain-computer interfaces. The lab has a remote work policy that allows flexible arrangements while maintaining strong collaborative ties.

Daniel Franklinمشاهده پروفایل

استادیار

• Biomedical Engineering
• Cardiovascular Engineering
• Optics
• +۷ مورد دیگر

Daniel Franklin is an Assistant Professor in the Institute of Biomedical Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Ted Rogers Chair in Cardiovascular Engineering at the Ted Rogers Centre for Heart Failure and is affiliated with the Translational Biology & Engineering Program (TBEP). Dr. Franklin's research focuses on the intersection of optics, engineering, and biology to develop next-generation medical technologies. His lab specializes in: Wearable health devices for advanced hemodynamic monitoring (blood pressure, cardiac output, systemic vascular resistance) Wireless implants for physiological monitoring and closed-loop stimuli/feedback Novel materials like liquid crystal for bioresorbable temperature sensors Applications in fundamental understanding of heart disease and commercial health telemetry Dr. Franklin's work has resulted in significant publications including research in Nature Biomedical Engineering, Science Advances, and Physiological Measurement, with findings highlighted by major media outlets including Forbes, Wired, and the World Economic Forum. His scientific achievements have been recognized with prestigious awards: Baxter Young Investigator Award Displaying Futures Award from Merck KGaA, Germany Connaught New Researcher Award from the University of Toronto Dr. Franklin actively mentors graduate students and has secured numerous competitive grants including NSERC Discovery Grants, CIHR Project Grants, and Canadian Foundation for Innovation awards. His lab partners with industry-leading semiconductor manufacturers and hospitals to develop and leverage new sensor technologies for clinical translation. The Franklin Research Lab (FRL) is a multidisciplinary team dedicated to exploring bioelectronic and biophotonic systems to shed light on unexplored aspects of human physiology. The lab's work combines fundamental science with practical applications for medical device commercialization.