Find Academic Supervisors

• Process Engineering
• Pharmaceutical Engineering
• Chemometrics
• +4 more

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
• +6 more

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 ZhaoView profile

Professor

• Design and Manufacturing
• Additive Manufacturing
• Sustainable Manufacturing
• +5 more

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 DavisonView profile

Professor

• Financial Mathematics
• Commodity Finance
• Financial Wellness
• +9 more

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 LiuView profile

Assistant Professor

• Artificial Intelligence in Engineering
• Machine Learning Applications
• Process Control and Optimization
• +6 more

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 AgardView profile

Professor

• Industrial Engineering
• Data Mining
• Artificial Intelligence
• +8 more

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 SrivastavaView profile

Adjunct Professor

• Applied Conservation Science
• Ecosystems and Climate Change
• Technology and Innovation
• +4 more

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 ThompsonView profile

Professor

• Polymer Processing
• Rotational Molding
• Twin-Screw Extrusion
• +5 more

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) .

• Nuclear Engineering
• Thermal Hydraulics
• Neutron Radiography
• +5 more

Glenn Harvel is an Adjunct Associate Professor in the Department of Engineering Physics at McMaster University. His research spans nuclear engineering, thermal hydraulics, neutron radiography, and supercritical fluid dynamics, with a focus on reactor safety, decommissioning, and advanced cooling systems. Research Interests: His work integrates experimental and computational approaches to address challenges in nuclear technology. Key areas include: Thermal-hydraulic behavior of supercritical fluids in reactor cores Neutron radiography for non-invasive flow measurement Electrohydrodynamic applications in plasma-based systems Machine learning for nuclear maintenance optimization Small modular reactor (SMR) deployment for remote communities Publication Trends: Recent articles emphasize data-driven methods for nuclear maintenance, SMR feasibility studies, and decontamination tool development. Earlier work focused on experimental characterization of heat transfer fluids and CFD validation for supercritical water systems. Labs and Teams: Implied collaboration through conference papers and multidisciplinary topics (e.g., plasma physics, materials science, AI), though no specific labs or teams are named.

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Traffic Flow Modeling
• +3 more

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).

• Software Engineering
• Blockchain Technology
• Model-Driven Development
• +5 more

Issam Al-Azzoni is an Adjunct Assistant Professor at the Computing and Software school of McMaster University . His work spans software engineering, blockchain technology, and model-driven approaches. Research Focus: Blockchain applications, smart contracts, and model-driven development Key Contributions: Automated contract generation, cybersecurity frameworks, and network optimization Dr. Al-Azzoni’s research interests include: Blockchain-based systems and smart contracts Model-driven engineering for software verification AI integration in network planning and testing Key trends in publications include blockchain for access control, deep learning in graph transformation systems, and model-driven solutions for cybersecurity and wireless networks. His work bridges formal methods with practical applications in distributed systems and AI. Advising and grants information was not explicitly mentioned in the provided texts.

• Optimization under uncertainty
• Data-driven optimization techniques
• Business analytics
• +7 more

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

• Respiratory Medicine
• Critical Care
• Exercise Physiology
• +3 more

Graham Jones is an Associate Professor in the Department of Medicine at McMaster University, specializing in respiratory and critical care medicine. His research integrates exercise physiology, environmental health, and clinical immunology, with a career spanning over four decades of continuous publication. Research Focus: Primary interests include cardiopulmonary interactions during exercise, environmental pollutant effects on respiratory/cardiovascular systems, critical care ventilation strategies, and inflammatory pathways in airway diseases. Recent work examines COVID-19 complications, sepsis management, and population-level exercise capacity. Publication Trends: His 56 publications demonstrate consistent focus on respiratory mechanics across clinical, environmental, and physiological contexts. Recent articles (2013-2024) show increased emphasis on critical care applications, pollution impacts, and large-scale clinical studies, while earlier foundational work established mechanisms of ozone toxicity and exercise-induced bronchoconstriction. Collaborations: Maintains extensive research networks, notably with Kieran Killian (18 co-publications) and Paul O'Byrne (15 co-publications), spanning critical care, exercise science, and immunology disciplines.

• Software Verification
• Cyber-Physical Systems
• Formal Methods
• +6 more

Claudio Menghi serves as an Adjunct Assistant Professor in the Department of Computing and Software within McMaster University's Faculty of Engineering. His academic profile demonstrates a strong focus on bridging theoretical formal methods with practical software engineering challenges, particularly in safety-critical cyber-physical systems. Dr. Menghi's research spans multiple interconnected domains in software engineering and formal verification. His primary interests include model-based testing techniques for cyber-physical systems, assurance case development for safety-critical applications, and the practical application of formal methods in industrial settings. He has made significant contributions to trace checking methodologies that bridge the cyber-physical gap in verification processes. His work frequently addresses automotive systems, robotics, and medical devices, with numerous publications demonstrating his expertise in requirements validation, model checking, and specification patterns for complex systems. Dr. Menghi has been actively involved in the ARCH-COMP verification competition, contributing to the advancement of hybrid systems verification techniques through multiple category reports. His recent scholarly output shows an evolving research trajectory that now intersects with artificial intelligence, particularly exploring how large language models can enhance traditional software engineering tasks like change impact assessment. This represents a natural progression of his longstanding commitment to making verification techniques more accessible and practical for industrial applications. Dr. Menghi's teaching portfolio includes courses in software development and safe software-intensive medical devices, reflecting his dual expertise in foundational software engineering principles and specialized safety-critical applications.

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +7 more

Nedialko Nedialkov is a Professor in the Department of Computing and Software at McMaster University, Faculty of Engineering. His research centers on differential-algebraic equations (DAEs) , interval methods , and computational science , with a focus on rigorous numerical solutions and structural analysis. Contact: nedialk@mcmaster.ca Teaching: Courses include Scientific Computing, Advanced Topics in Computing and Software, and Principles of Programming. Research Highlights: Nedialkov has developed symbolic-numeric methods for DAEs, improved verified ODE integration, and created tools like DAETS for structural analysis. His work spans applications in hybrid systems , control theory , and biomedical computing . Publications Trends: Over the past 15 years, his scholarship emphasizes DAEs, validated numerical methods, and computational modeling. Key subfields include Port-Hamiltonian systems , block triangularization , interval constraints , and automatic differentiation . Labs & Teams: Collaborates with McMaster Experts and contributes to open-source projects for validated numerical computing.