جست‌وجوی استاد و پژوهشگر

Shangpeng Sunمشاهده پروفایل

استادیار

• Smart Agriculture
• Sustainable Resource Management
• Bioproducts and Bioenergy
• +۲ مورد دیگر

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

Pierre Dutilleulمشاهده پروفایل

استاد

• Environment
• Ecology
• Agriculture
• +۷ مورد دیگر

Pierre Dutilleul is a Professor at McGill University's Macdonald Campus, associated with the McGill School of Environment and the Department of Mathematics and Statistics. His research focuses on spatio-temporal heterogeneity, plant and soil imaging, and fractal analysis of natural structures. Current affiliations: Department of Plant Science, McGill School of Environment Specializations: CT scanning for root and wood imaging, multifractal analysis, soil aggregate dynamics Research interests span environmental sustainability , soil science , and plant production , with recent work on 3D root phenotyping, biochar characterization, and space-time modeling of agricultural and seismic events. Articles demonstrate expertise in applied statistics and computational methods for ecological and agricultural systems. Key methodological contributions include: Development of R packages for spatio-temporal analysis Innovative CT scanning applications in plant and soil research Statistical tests for separable variance-covariance structures

Charles L. Bérubéمشاهده پروفایل

استادیار

• Applied geophysics
• Mineral physics
• Mathematical modeling
• +۷ مورد دیگر

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

Philippe Pasquierمشاهده پروفایل

استاد

• Geothermal Energy
• Hydrogeology
• Mathematical Modeling
• +۴ مورد دیگر

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.

• Clinical Biochemistry
• Analytical Methods Development
• Pharmacovigilance
• +۴ مورد دیگر

Robert Robitaille serves as a Clinical Lecturer affiliated with the Biochemistry and Molecular Medicine program, based at the Biochemistry Laboratory of Rimouski Regional Hospital under CISSS du Bas-St-Laurent in Quebec. His dual role bridges clinical laboratory practice and academic instruction in clinical biochemistry, with direct patient care implications through hospital-based diagnostics. His research centers on Clinical Biochemistry with specialized focus on analytical method development for pharmacovigilance applications and quality assurance systems in clinical laboratories. Key domains include therapeutic drug monitoring validation, biomarker discovery for organ transplantation, and implementation of advanced techniques like tandem mass spectrometry. His work consistently addresses practical challenges in laboratory standardization and accreditation processes. Analysis of his 2006-2020 publications reveals strong thematic continuity in optimizing clinical laboratory practices. Recurring emphases include inter-laboratory reproducibility studies (e.g., metal ion measurements), point-of-care diagnostic development (e.g., SPR biosensors), and context-specific pharmacokinetic modeling for critically ill populations. His research demonstrates translational impact through practical equation development for urinary stone risk assessment and validation frameworks for biosimilar monitoring. Scientific Awards: No awards or fellowships were documented in the source material. Academic advising activities are not specified in available records, with no student names listed. Grant funding details remain undisclosed, though his multi-institutional collaborations—evident in publications spanning nephrology, pharmacology, and infectious disease—suggest participation in team-based research initiatives. Dr. Robitaille operates within the OPTILAB Cluster Bas-St-Laurent-Gaspésie network at Rimouski Regional Hospital's Biochemistry Laboratory (150 avenue Rouleau). This infrastructure supports his method validation work and quality assurance protocols, positioning him within Quebec's integrated clinical laboratory ecosystem serving the Bas-St-Laurent region.

• Process Engineering
• Pharmaceutical Engineering
• Chemometrics
• +۴ مورد دیگر

Mark-John Bruwer serves as an Adjunct Assistant Professor in the Department of Chemical Engineering at McMaster University, specializing in chemometrics and multivariate statistical methods for industrial process optimization. His work bridges academic research with practical applications across pharmaceutical, bioprocessing, food, and steel industries. Research interests prominently feature Process Engineering , Pharmaceutical Engineering , and Chemometrics , with methodological expertise in latent variable modeling, principal component analysis, and model predictive control. Key contributions include pharmaceutical process scale-up (roller compaction, tablet manufacturing), bioprocess modeling for cell culture prediction, steel industry carbon endpoint forecasting, and food texture analysis through sensory-mechanical data fusion. Analysis of his 15 publications (2006-2016) reveals consistent application of multivariate techniques to quality assurance and process control challenges. His most recent work focuses on raw material specification frameworks for risk minimization in pharmaceutical manufacturing, while earlier research established foundational methods for latent variable MPC in batch processes and EIT regularization in biomedical imaging. Profile metrics indicate scholarly impact through references in policy documents and patents, alongside significant Mendeley readership, though no formal scientific awards are documented. No information is available regarding student supervision, research grants, or laboratory affiliations.

• Epidemiology
• Public Health
• Antibiotic Resistance
• +۶ مورد دیگر

Élise Fortin is an Adjunct Professor at Université de Montréal, affiliated with the National Institute of Public Health of Quebec (INSPQ) under the Immunization, Nosocomial Infections and Antibiotic Resistance Unit. Her work focuses on epidemiological surveillance of infectious diseases, antimicrobial use, and healthcare-associated infections. Bacteriology and Virology research themes Specializes in antibiotic resistance and nosocomial infection prevention Active in public health policy and intervention impact assessments Her recent publications analyze trends in Clostridioides difficile infections, antimicrobial resistance modeling, and healthcare-associated urinary infections. She has contributed to provincial surveillance frameworks and policy-oriented research in infection control.

Yaoyao Zhaoمشاهده پروفایل

استاد

• Design and Manufacturing
• Additive Manufacturing
• Sustainable Manufacturing
• +۵ مورد دیگر

Yaoyao Zhao is a Professor in the Department of Mechanical Engineering at McGill University's Faculty of Engineering, holding the prestigious William Dawson Scholar designation (2019-2022). She teaches core courses including MECH 290 (Design Graphics), MECH 360 (Principles of Manufacturing), and MECH 560 (Eco-design and Product Life Cycle Assessment). Her educational background includes a Ph.D. and M.Eng. from the University of Auckland, and a B.Eng. from Beijing Institute of Technology. Research interests span three interconnected themes: Sustainable Manufacturing (developing sustainability analysis methods and material databases), Additive Manufacturing (exploring new design methods and materials), and Industry 4.0 (adapting AI/ML tools for cognitive manufacturing). Her work integrates biomechanics and materials science with primary focus on design and manufacturing informatics. Professor Zhao directs the Additive Design and Manufacturing Laboratory (ADML), which develops specialized software including INTRALATTICE Pro, SimuLattice, ConsolidDesign, and Automated Candidate Detection tools. Her 15 most recent publications (2018-2020) demonstrate expertise in additive manufacturing process modeling, lattice structure design, sustainable product development, and AI-driven manufacturing solutions, with significant contributions to titanium alloy processing, high-entropy materials, and biomedical applications. ASME Journal of Mechanical Design (JMD) 2019 Associate Editor Award William Dawson Scholar (2019-2022) Design Society Reviewers’ Favourite paper award (2019) ASME DMLC Best Paper Award (2018) MAME Professor of the Year Award (2014) Her research program secures substantial funding for advancing sustainable manufacturing practices and intelligent design systems, with particular emphasis on translating theoretical models into industrial applications through the ADML Lab's proprietary software ecosystem. Current projects focus on AI adaptation for cognitive manufacturing and end-of-life product reuse analysis.

Matt Davisonمشاهده پروفایل

استاد

• Financial Mathematics
• Commodity Finance
• Financial Wellness
• +۹ مورد دیگر

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.

Tianlong Liuمشاهده پروفایل

استادیار

• Artificial Intelligence in Engineering
• Machine Learning Applications
• Process Control and Optimization
• +۶ مورد دیگر

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

Bruno Agardمشاهده پروفایل

استاد

• Industrial Engineering
• Data Mining
• Artificial Intelligence
• +۸ مورد دیگر

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.

Vivek Srivastavaمشاهده پروفایل

استاد مدعو

• Applied Conservation Science
• Ecosystems and Climate Change
• Technology and Innovation
• +۴ مورد دیگر

Vivek Srivastava is an Adjunct Professor in the Department of Forest and Conservation Sciences at the University of British Columbia (UBC) Faculty of Forestry , with affiliations to the BC Ministry of Forests and Agriculture and Agri-Food Canada (AAFC) . His work integrates ecological modeling, data science, and climate change adaptation to address forest and agricultural pest invasions. PhD in Ecological Modelling and Applied Machine Learning from UBC Master’s in Remote Sensing and GIS Bachelor’s in Forestry Vivek specializes in invasion ecology , pest risk modeling , and climate change impacts on eruptive insect pests . His research develops advanced spatial-temporal tools for forecasting forest and agricultural pest spread, focusing on mitigating ecological and economic damage. Key methodologies include species distribution models (SDMs) , machine learning , and real-time monitoring systems . Recent publications highlight his expertise in dynamic outbreak modeling , ensemble SDMs , and economic impact assessments for invasive pests like the mountain pine beetle and aphids. His work addresses challenges in geopolitical-level invasion tracking , biomechanical dispersal analysis , and climate uncertainty in pest forecasting . Vivek’s applied approach bridges forest science and data-driven decision support systems , collaborating with government agencies to safeguard ecosystems and agricultural industries through tools like the BC Bark Beetle Portal and interactive dashboards .

Michael Thompsonمشاهده پروفایل

استاد

• Polymer Processing
• Rotational Molding
• Twin-Screw Extrusion
• +۵ مورد دیگر

Dr. Michael Thompson is a Professor in the Department of Chemical Engineering at McMaster University . His research focuses on advanced polymer processing techniques including twin-screw extrusion , rotational molding , and solvent-free emulsification , with applications in bioplastics , nanostructured materials , and dry powder vaccine formulation . Develops data-driven control systems for polymer manufacturing Investigates environmental stress cracking resistance in polyethylene Designs acoustic monitoring solutions for granulation processes His scholarly work intersects materials science and biopharmaceutical engineering , demonstrated through publications in journals like Industrial & Engineering Chemistry Research , Carbohydrate Polymers , and Vaccine . He teaches courses including Advances in Polymeric Materials (CHEMENG 774) and Advanced Concepts of Polymer Extrusion (CHEMENG 773) .

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Traffic Flow Modeling
• +۳ مورد دیگر

Hao Yang is an Associate Professor in the Department of Civil Engineering at McMaster University, specializing in Intelligent Transportation Systems and Connected Vehicle Technologies . His research focuses on optimizing traffic flow, enhancing eco-driving strategies, and developing centralized control systems for urban mobility. Key areas: Traffic Flow Modeling, V2X Communication, Spatiotemporal Data Analysis Scientific Contributions : Recent publications analyze deep reinforcement learning for vehicle merging, macroscopic fundamental diagrams for congestion prediction, and multi-algorithmic ensemble models for traffic speed forecasting. His work spans hybrid physics-ML approaches and real-time traffic management systems. Teaching : Instructs courses including Pavement Materials and Design (CIVENG 4G04/6G04), Surveying for Transportation (CIVENG 2A03), and Travel Demand Analysis (CIVENG 711).

• Optimization under uncertainty
• Data-driven optimization techniques
• Business analytics
• +۷ مورد دیگر

Kai Huang is a Professor of Operations Management at the DeGroote School of Business , McMaster University . He specializes in optimization under uncertainty and data-driven optimization techniques with applications in business analytics , supply chain management , and humanitarian logistics . His work addresses challenges in electric/autonomous vehicles , food supply chain safety , and disaster response logistics . Teaches courses: Optimization Under Uncertainty , Supply Chain Management , Simulation for Business Analytics Research interests span stochastic optimization , AI integration , and sustainable supply chains Recent scholarly work includes: 2025: Four articles on reverse supply chains , green coordination , public-private collaborations , and robust inventory systems 2024: Six articles covering EV charging networks , AI-powered chatbots , and healthcare recovery modeling 2023: Eight publications on carbon reduction , VRP disruptions , and hematopoietic supply chains