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اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Ana L. Maneraمشاهده پروفایل

پژوهشگر ارشد

McGill UniversityCanada

Medical image analysis
Machine learning and pattern recognition
Biomarkers
+۱ مورد دیگر

Ana L. Manera is a Post-Doctoral Fellow at McGill University's Image Processing lab and a cognitive neurology/neuropsychiatry fellow at McGill University Health Centre, specializing in brain imaging biomarkers for neurodegenerative disorders including Alzheimer's Disease and Frontotemporal Dementia. Her educational background includes: MD degree from the University of Buenos Aires, Argentina Neurology residency at the Italian Hospital of Buenos Aires Specialized training in cognitive and behavioral disorders Her research interests focus on: Medical image analysis techniques for neurological disorders Machine learning applications in pattern recognition Biomarker development for dementia diagnosis Differential diagnosis methodologies for neurodegenerative conditions Analysis of her publication record (2018-2021) reveals consistent emphasis on MRI-based structural analysis, particularly ventricular morphology and cortical atrophy patterns, to differentiate behavioral variant Frontotemporal Dementia from other dementias. Key contributions include computational tools like MNI-FTD Templates and CerebrA for standardized image processing, alongside machine learning algorithms for diagnostic classification. No scientific awards were mentioned in the source material. The provided information does not reference student advising responsibilities or externally funded research grants. She operates within McGill University's Image Processing lab environment, maintaining active collaborations with researchers including Mahsa Dadar, D. Louis Collins, and Simon Ducharme across multiple neuroimaging projects.

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Molly Babelمشاهده پروفایل

استاد

University of British ColumbiaCanada

Dr. Molly Babel is a Professor of Linguistics at the University of British Columbia, where she directs the Speech In Context Lab (SpeeCon). Her research examines speech perception, processing, and production within the broader context of spoken language use, including social and performative elements such as gender identity and accent variation. Her work spans sociolinguistics, phonetics, and psycholinguistics with core interests in bilingual speech (particularly Cantonese-English), phonetic variation, voice evaluation, and perceptual learning. She investigates how social context, syntactic factors, language environment, and talker variability influence speech processing, often focusing on gender identity and crosslinguistic phenomena in early bilinguals. Analysis of her recent publications (2023-2025) reveals dominant trends in bilingual voice learning, Cantonese-English phonetic mergers, perceptual adaptation to pronunciation variation, and critical examinations of essentialist language ideologies. Her research integrates acoustic analysis, perception experiments, and computational modeling to explore speech intelligibility, voice recall, and cognitive representation of spoken language. The Speech in Context Lab, which she leads, studies spoken communication through a broad lens of context—including social, linguistic, and environmental factors—to unravel how language is cognitively represented. The lab's work emphasizes practical applications in speech technology, language education, and understanding social biases in voice perception, using the acorn metaphor to represent speech as a dense information package requiring careful analysis.

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Louis-Martin Rousseauمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Louis-Martin Rousseau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds the Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG) and manages the Combinatorial Optimization and Reasoning in Artificial Intelligence Laboratory (CORAIL). He is also a member of the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Institute for Data Valorization (IVADO). B.Sc., Ph.D. from Université de Montréal His research focuses on hybridizing classical operations research techniques with constraint programming (AI methods) for applications in transport logistics, schedule optimization, and healthcare resource management. He has pioneered work in healthcare analytics, particularly in liver transplantation survival prediction (CASE method), radiation therapy treatment planning, and pandemic triage protocols. Recent publications highlight his work in deep reinforcement learning for inventory control, dual bound generation in constraint programming, and dynamic homecare scheduling. Keywords include Healthcare Analytics , Machine Learning , Combinatorial Optimization , and Logistics . Scientific awards include the 2019 Outstanding Achievement Award from the IEOM Society International. He supervises 5 doctoral, 2 master's, and 2 postdoctoral researchers, with a total of 23 PhD and 30 master's theses completed under his guidance since 2003. His lab (CORAIL) collaborates with CIRRELT and IVADO on problems involving synchronized vehicle routing, stochastic programming, and machine learning integration. Media appearances discuss healthcare system reforms, including private mini-hospitals and the Santé Québec agency's impact on patient access.

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Mohsen Sadatsafaviمشاهده پروفایل

دانشیار

University of British ColumbiaCanada

Mohsen Sadatsafavi is an Associate Professor at The University of British Columbia's Faculty of Pharmaceutical Sciences, where he serves as Associate Director of Research for the Collaboration for Outcomes Research and Evaluation. He holds additional affiliations with the Centre for Heart Lung Innovation, the Division of Respiratory Medicine at UBC, and the Centre for Clinical Epidemiology and Evaluation. As principal investigator of the Respiratory Evaluation Sciences Program (RESP), he leads a multidisciplinary research team focused on quantitative approaches to respiratory disease management. Dr. Sadatsafavi's research interests center on predictive analytics, clinical prediction modeling, and epidemiological modeling with a focus on chronic respiratory diseases, particularly asthma and COPD. His work employs a variety of quantitative techniques to project outcomes of medical decisions at both the individual patient level and population level. His lab specializes in decision analysis, clinical prediction modeling, health economics, and outcomes research in respiratory medicine. Analysis of Dr. Sadatsafavi's recent publications reveals a strong focus on precision medicine approaches for respiratory diseases, with particular emphasis on prediction modeling for asthma and COPD exacerbations, health economic evaluations, and methodological advancements in clinical prediction. His work increasingly incorporates advanced statistical techniques, machine learning approaches, and real-world evidence to address critical questions in respiratory medicine. Scientific Awards: New Investigator Award from the Canadian Institutes of Health Research (CIHR) (2015-2020) Scholar Award from the Michael Smith Foundation for Health Research (MSFHR) (2015-2020) Bisby Fellowship Prize from the Canadian Institutes of Health Research (CIHR) (2009-2012) Early Research Leaders Initiative (ERLI) Award (2015-2018) Dr. Sadatsafavi has successfully secured multiple research grants including funding from the Canadian Foundation for Innovation (CFI) for the PRogrammable Interface for Statistical & Simulation Models (PRISM) project, and several Canadian Institutes of Health Research (CIHR) grants focused on prediction tools for COPD exacerbations and respiratory evaluation sciences. His research group, the Respiratory Evaluation Sciences Program (RESP), mentors graduate students including PhD candidates like Amin Adibi who completed a dissertation on race and spirometry. The RESP lab maintains active collaborations through the Peer Models Network and works closely with clinical partners across respiratory medicine. Their research infrastructure includes specialized software tools like iPRESS for implementing prediction models in clinical practice. The team regularly contributes to major clinical practice guidelines, including the American Thoracic Society Clinical Practice Guideline on Pharmacologic Management of COPD.

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Shangpeng Sunمشاهده پروفایل

استادیار

McGill UniversityCanada

Shangpeng Sun is an Assistant Professor in the Department of Bioresource Engineering at McGill University . His research focuses on the development and adoption of innovative sensing technologies and computational methodologies for next-generation smart agriculture, addressing challenges related to sustainable food production under global environmental changes. Specialization : Smart agriculture, sustainable resource management, bioproducts, and computational methods Fields : Agriculture, animal production, biomass engineering His work emphasizes soil and water sustainable management , precision crop/livestock systems , and bioenergy production , leveraging technologies like hyperspectral imaging, 3D reconstruction, and deep learning for automated plant phenotyping, microbial contamination detection, and robotic harvesting. Recent publications highlight advancements in: 3D imaging for root architecture analysis Salient object detection in orchard robotics Foodborne pathogen classification Generative growth modeling under variable conditions Multi-scale weed segmentation frameworks Contact: shangpeng.sun@mcgill.ca

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Matthias Kleinمشاهده پروفایل

استادیار

McGill UniversityCanada

Matthias Klein serves as an Assistant Professor in the Department of Animal Science at McGill University's Macdonald Campus, operating from the Macdonald-Stewart Building (Room MS1-086). His academic role focuses on integrating advanced computational methods with agricultural and nutritional sciences to address global food system challenges. His research spans sustainable crop-livestock systems, food security, human nutrition, and One Health frameworks, with specialized expertise in metabolomics, NMR spectroscopy, and AI-driven analysis. Key interests include dairy science applications, antibiotic resistance dynamics in fermented foods, and machine learning for biomarker discovery, reflecting a commitment to translating analytical innovations into practical food safety and nutritional solutions. Analysis of his publication trajectory (2018-2024) reveals consistent methodological innovation in metabolomics data processing (e.g., mrbin R package development) and interdisciplinary exploration of microbial safety, insect physiology, and nutritional impacts. His work bridges food science, microbiology, and computational biology to tackle issues like antibiotic resistome transmission and metabolic health markers. No scientific awards were documented in the source materials. Information regarding student supervision, grant funding, laboratory facilities, or research teams was not provided in the available documentation.

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Charles L. Bérubéمشاهده پروفایل

استادیار

Polytechnic School of MontrealCanada

Charles L. Bérubé is an Assistant Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal since 2021. His research couples applied geophysics, mineral physics and machine learning to advance mineral exploration and resource characterisation. Education: B.Sc. in Physics, Université de Montréal Ph.D. in Mineral Engineering, Polytechnique Montréal (2018) Research Interests: Dr. Bérubé focuses on electromagnetic geophysics , induced polarization and machine-learning-assisted inversion . Key themes include: Spectral Induced Polarization (SIP) tomography and modelling Neural-network-based rock-physics inversion Mineral prospectivity mapping with variational autoencoders Quantitative mineralogy and computer vision for outcrop analysis Uncertainty quantification in geophysical imaging Publications Overview: His recent work (2022-2024) demonstrates a clear pivot toward data-driven and AI-enhanced geoscience , integrating Bayesian inversion, generative models and deep learning with traditional exploration geophysics. Earlier studies (2017-2020) concentrated on petrophysical characterisation of the world-class Canadian Malartic gold deposit and non-destructive evaluation of concrete degradation. Graduate Supervision & Funding: Dr. Bérubé has supervised one completed M.Sc. thesis (Benzetta, 2024) on graphite geoelectrics. In May 2023 he received a research grant from the Fonds de recherche du Québec as part of a campus-wide allocation exceeding CAD 1.57 million. Contact: Local B-632, Department of Civil, Geological and Mining Engineering Tel: +1 (514) 340-4711 ext. 4991 | Email: charles.berube@polymtl.ca

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Olivier Henryمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Olivier Henry is a Full Professor in the Department of Chemical Engineering at Polytechnique Montréal , with a focus on biochemical engineering and bioprocess development . His research explores the production of recombinant proteins , viral vectors , and vaccine manufacturing , particularly through animal cell culture and metabolic engineering of CHO and HEK293 cells . He has published extensively on glycosylation analysis , SPR biosensors , and bioprocess optimization . Research Interests include: Development and control of CHO cell bioprocesses Viral vector and vaccine production Glycosylation characterization of therapeutic proteins Surface plasmon resonance (SPR) for protein interaction analysis Metabolic modeling and process intensification Bioprocess monitoring and soft sensor development Recent Trends in his 75 publications emphasize: Integration of machine learning in bioprocess control Advancements in SPR-based assays for monoclonal antibody quality Optimization of fed-batch and perfusion cultures for vaccine antigens Metabolic engineering of mammalian cell lines to reduce byproduct accumulation Standardization of glycosylation analysis for therapeutic proteins Supervision : Mentored 5 professional master’s and 1 DESS students, with 11 Ph.D. and 6 master’s theses completed.

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Philippe Pasquierمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Philippe Pasquier is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. He holds the NSERC Industrial Chair in Geothermal Energy and leads the Geothermal and Hydrogeology Research Group. His research integrates spectral methods, artificial intelligence, and stochastic modeling to advance geothermal system design, with experimental validation at the Geothermal Research Unit (URG). Education & Teaching: Teaches Applied Hydrogeology (GLQ2601) and Low-Temperature Geothermal Energy (GML6113). Recognized with multiple teaching awards, including Best Professor in Civil/Geological/Mining Engineering (2011-2015). Research Focus: Specializes in low-temperature geothermal systems (open/closed loops, standing column wells), thermal response testing, inverse problems in hydrogeology, and energy-efficient building integration. His work aims to reduce costs and promote geothermal adoption through advanced computational tools. Publications: Recent articles (2022-2025) emphasize machine learning applications for geothermal modeling, thermal plume dynamics, and system optimization, reflecting a trend toward AI-driven sustainable energy solutions. Awards & Leadership: NSERC Industrial Chair in Geothermal Energy Best Professor Awards (2010-2015) Head, Geothermal Research Unit (URG) Students & Grants: Supervised 7 doctoral and 11 master's students. Secured NSERC Alliance grants for large-scale geothermal deployment in Quebec institutions. Tools & Outreach: Developed open-source software TRT-SInterp for thermal response test analysis. Frequently featured in media (Le Devoir, La Presse) advocating geothermal solutions.

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Tallulah Andrewsمشاهده پروفایل

استادیار

McMaster UniversityCanada

Dr. Tallulah Andrews is an Assistant Professor in the Department of Biochemistry at the University of Western Ontario, where she leads the Andrews Computational Biology Lab. Her research focuses on developing computational methods for analyzing single-cell and spatial transcriptomics data to understand tissue structure and immune context in disease. Dr. Andrews earned her Ph.D. from the University of Oxford, where she used systems biology approaches to study rare genetic diseases. She completed postdoctoral training at both the University Health Network Research in Toronto and the Wellcome Trust Sanger Institute in the UK. She is a long-term member of the Human Cell Atlas initiative. Her research interests center on computational biology and bioinformatics approaches to single-cell and spatial transcriptomics. She develops methods for analyzing single-cell RNA sequencing data, integrating imaging with spatial expression data, and understanding immunological and metabolic dysfunction in diseases. Her work has particular applications in liver diseases, atherosclerosis, and other complex disorders. Analysis of her recent publications reveals a strong focus on methodological development for single-cell analysis, with significant contributions to quality control (EmptyDrops), feature selection (M3Drop), and standardized analysis protocols. Her work bridges computational methodology with biological applications, particularly in liver disease and immune dysfunction. Dr. Andrews supervises a diverse group of students including PhD, MSc, and undergraduate researchers working on projects related to spatial transcriptomics, atherosclerosis analysis, and cell-cell interaction inference. Her collaborative approach is evident through partnerships with research groups at UHN, Northwestern University, Robarts Research Institute, and clinical teams in Toronto. The Andrews lab provides opportunities for students interested in computational biology, bioinformatics, and machine learning applications to biomedical problems. Current projects include developing machine learning models for spatial transcriptomics data, building analysis pipelines for disease-specific scRNAseq data, and integrating multiple omics datasets to understand disease mechanisms.

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Matt Davisonمشاهده پروفایل

استاد

Western UniversityCanada

Matthew Davison is a distinguished academic serving as Dean of Science at the University of Western Ontario. Holding a PhD from the same institution, his career spans multiple disciplines including financial mathematics, stochastic control, and renewable energy systems. As a Professor in the Department of Statistical and Actuarial Sciences, he leads research initiatives at the intersection of mathematics and finance. Key research areas include commodity finance, stochastic optimal control, and financial wellness Supervises graduate students in mathematical finance and data science Active in cross-departmental collaborations with Engineering and Energy Systems His scholarly work examines: Energy production optimization with production constraints Renewable energy project valuation with real options Robust portfolio construction in stochastic environments Mathematical properties of mean-reverting processes Applications of Hidden Markov Models in financial contexts Recent publications demonstrate a strong focus on energy systems, risk management, and financial optimization. He has contributed to advancements in: Commodity pricing under uncertainty Energy storage arbitrage strategies Stochastic process inference with change points Financial applications of Laplace transforms Smart meter data analysis with privacy preservation As Dean of Science, he oversees one of Western University's largest faculties while maintaining an active research program.

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Tianlong Liuمشاهده پروفایل

استادیار

Western UniversityCanada

Tianlong (Taylor) Liu is an Assistant Professor in the Department of Chemical and Biochemical Engineering at Western University. His academic journey includes a Ph.D. in Civil Engineering from the University of British Columbia (2020), an M.A.Sc. from South China University of Technology, and a B.Eng. from Qilu University of Technology. Research Focus AI-Enhanced Process Control and Optimization Machine Learning for Industrial Data Analysis Physics-Informed Machine Learning (PIML) Large Language Models (LLMs) in Engineering His publications span machine learning , environmental engineering , and chemical process optimization , with recent work on AI-driven conductive polymer development, water quality prediction, and industrial wastewater treatment. He actively collaborates with industry partners across engineering disciplines. Education Ph.D., Civil Engineering, University of British Columbia (2020) M.A.Sc., Chemical Engineering, South China University of Technology B.Eng., Chemical Engineering, Qilu University of Technology

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Bruno Agardمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Bruno Agard is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal , specializing in industrial engineering, data mining, and Industry 4.0 applications. He serves as Director of the Data Intelligence Laboratory (LID) and holds memberships in multiple research centers including the Poly-Industries 4.0 Laboratory, IVADO, CIRRELT, and CIRODD. His academic career spans over two decades with progressive roles from Assistant to Full Professor since 2014. Ph.D. in Industrial Engineering (2002), National Polytechnic Institute of Grenoble DEA in Industrial Engineering (1999), National School of Industrial Engineering, Grenoble Aggregation in Mechanical Engineering (1998), École Normale Supérieure de Cachan Professor Agard's research focuses on data mining applications for engineering challenges across manufacturing, logistics, transportation, and agriculture. Key areas include product family design, modular systems, delayed differentiation, and spatiotemporal data analysis. Recent publications highlight AI-driven energy consumption modeling for electric buses, sensor failure detection using variational autoencoders, and optimization of agri-food processes through data intelligence. His work demonstrates strong industry collaboration with partners like Air Liquide, Bridgestone, and La Milanaise across 221 publications. Current projects involve real-time data processing for mining equipment, predictive maintenance analytics, and smart card data analysis for urban mobility patterns. 13 Ph.D. students supervised 29 Master's students mentored Current courses: Facilities Planning (IND3303), Industrial Data Mining (IND6212) As Director of LID, he leads interdisciplinary research integrating machine learning with industrial systems, with special emphasis on sustainable development and operational efficiency. His methodological contributions include innovative approaches to product design, process optimization, and knowledge extraction from complex datasets.

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Apurva Narayanمشاهده پروفایل

استادیار

Western UniversityCanada

Dr. Apurva Narayan is an Assistant Professor at Western University, with joint appointments in the Department of Computer Science and the Department of Electrical & Computer Engineering. He leads the Intelligent Data Science Lab (IDSL), which trains students across Western University, the University of British Columbia (UBC), and the University of Waterloo (UW). He also holds affiliate and adjunct positions at UBC and UW, respectively. Primary Institution: Western University Secondary Affiliations: UBC (Affiliate Assistant Professor), UW (Adjunct Assistant Professor) Research Focus: Dr. Narayan specializes in machine learning, data science, and trustworthy AI systems. His work addresses safety and security challenges in AI-enabled systems, including: Reachability analysis for neural-network controlled systems Neural network attack detection (Trojan and adversarial) Defense mechanisms against AI vulnerabilities Reinforcement learning applications The IDSL lab bridges intelligent systems, data science, and decision-making under uncertainty. Dr. Narayan's research interests also extend to earth observation, exploration technologies, and remote health monitoring.

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Charles de Lannoyمشاهده پروفایل

دانشیار

McMaster UniversityCanada

Charles de Lannoy is an Associate Professor in the Chemical Engineering department at McMaster University . His research focuses on Membrane Technology , Environmental Remediation , and Electrochemical Processes for water treatment and CO2 capture. Recent publications highlight advancements in membrane fouling prediction , electrically conductive membranes , and CO2 removal via electrochemical systems . His work spans both theoretical and applied domains, including mask filtration efficiency during the pandemic. He teaches graduate and undergraduate courses such as Advanced Mass Transfer , Particle Electrokinetics , and Mass Transfer and Stagewise Operations , with a consistent focus on chemical engineering fundamentals since 2017.

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