جست‌وجوی استاد و پژوهشگر

Andrew J. Parkمشاهده پروفایل

استاد

• Interactive Data Analytics
• Visual Analytics
• Computational Criminology
• +۵ مورد دیگر

Andrew J. Park is a faculty member in the Computing Science department at Trinity Western University’s Faculty of Natural & Applied Sciences. His research integrates interactive data analytics, visualization, and agent-based modeling to address complex social issues in criminology, cybersecurity, and public safety. Education : PhD in Interactive Arts and Technology (Simon Fraser University, 2008), MSc in Computer Science (Loyola University Chicago, 1998), BSc in Mathematics and Physics (Sogang University, 1993 and 1991). Dr. Park specializes in 3D virtual environments for simulating criminal and crowd behaviors, leveraging AI, machine learning, and social network analysis on big data (crime statistics, Darkweb forums, AIS streams). His work informs counter-terrorism, fire egress planning, and urban security strategies. Recent publications focus on agent-based simulations for crowd control (2021) and social network analysis of illegal fishing (2020), alongside awards such as the MITACS-Accelerate Stipend (2010-2011) and multiple internal research grants (2011-2019). He teaches courses in programming, data structures, and virtual reality. Scientific Awards : Research Training Recognition Fund (2019, 2018), TRU World Internal Fund (2015), Research Enhancement Fund (2011-2012), MITACS-Accelerate Stipend (2010-2011), President’s PhD Research Stipend (2006), and Graduate Fellowships (2004, 2005).

Yigit Aydedeمشاهده پروفایل

استاد

• Machine Learning
• Health Analytics
• Econometrics
• +۵ مورد دیگر

Yigit Aydede is a Professor of Economics at Saint Mary’s University’s Sobey School of Business, serving as Department Chair since 2021 and holding the Sobey Professorship in Economics (2021–2026). He specializes in machine learning applications in social and health policies, chronic disease surveillance, and econometrics. He earned his PhD in Economics from the University of Delaware in 2006. His research bridges econometrics with modern AI, focusing on unconventional data sources and public policy. Key contributions include co-founding the Research Portal on Machine Learning for Social and Health Policies (MLPortal), organizing workshops and summer schools on AI applications, and authoring books like Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R (2023). He also co-founded Novastorms.ai, an AI-driven startup providing real estate and public safety insights. His recent work includes analyzing regional drivers of influenza-like illness in Nova Scotia using dominance analysis and leading a project in the Nova Scotia COVID-19 Research Coalition. Teaching focuses on data analytics, machine learning, and AI at both undergraduate and graduate levels, including courses in the Master of Business Analytics program. Awards include the Sobey Professorship and appointments as an Affiliate Scientist at Nova Scotia Health Innovation Hub (2024).

Afshin Rahimiمشاهده پروفایل

استاد

• Aerospace Engineering
• Control Systems
• Machine Learning
• +۳ مورد دیگر

Dr. Afshin Rahimi is a Professor of Engineering at the University of Windsor's Faculty of Engineering, specializing in aerospace systems, machine learning applications, and fault diagnosis. His research focuses on enhancing reliability in satellite systems, renewable energy infrastructure, and smart manufacturing through advanced control systems and AI-driven solutions. He has won prestigious awards including gold medals at the Seoul International Invention Fair (2022) and the International Competition for Inventors (2021) for his work on helicopter engine diagnostics and satellite fault detection. Dr. Rahimi's research interests include: Machine learning for fault detection/prognosis in aerospace and energy systems Control systems design for satellites, UAVs, and offshore wind turbines Optimization of energy efficiency in cloud data centers and renewable systems Smart manufacturing applications using computer vision and object detection His recent work emphasizes applying AI to solve challenges in: Real-time satellite fault diagnosis using hybrid frameworks PV-integrated robotic systems for sustainable energy management Thermal regulation in smart buildings via LSTM networks Data scarcity mitigation in space systems using GANs Dr. Rahimi collaborates with industry partners and student teams like the WinSAT satellite design group. His research bridges theoretical advancements with practical implementation in critical infrastructure sectors.

Alejandro Vital Sotoمشاهده پروفایل

استادیار

• Operations Research
• Manufacturing Systems
• Production Scheduling
• +۳ مورد دیگر

Dr. Alejandro Vital Soto is an Assistant Professor of Operations Research & Management Science at Cape Breton University. His expertise spans production scheduling, lot-sizing, process planning, and decision support systems, with a focus on reconfigurable and flexible manufacturing systems. He holds a Ph.D. from the University of Windsor (Canada) and degrees from the University of Puebla and the University of the Americas Puebla (Mexico). His research emphasizes operational resilience, energy-aware scheduling, and supply chain sustainability, with practical applications in SME collaboration and digital twin implementations. Dr. Vital’s work bridges theoretical models (e.g., mixed-integer programming) with real-world challenges like pandemic-driven disruptions and perishable goods logistics. His recent contributions address dual-resource scheduling, bi-level optimization for hierarchical systems, and automated process planning in smart manufacturing. Education: Ph.D. in Industrial and Manufacturing Systems Engineering, University of Windsor, Canada M.Sc. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico B.Sc. in Industrial Engineering, University of Puebla (BUAP), Mexico Research Focus: Optimization of production systems under uncertainty Integration of scheduling and process planning Supply chain resilience and disruption recovery Adoption of digital twins and smart manufacturing technologies Energy-efficient and sustainable manufacturing practices Awards & Recognition: No specific awards mentioned in the provided materials. Grants & Collaborations: Collaborations with SMEs in Ontario on decision support systems; research supported by projects addressing supply chain resilience and pandemic impact analysis. Labs/Teams: Engaged in CBU’s Financial & Information Management department, contributing to interdisciplinary research in operations research and industrial engineering.

Jun Chenمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Wireless Communications
• +۲ مورد دیگر

Dr. Jun Chen is Professor and former Barber-Gennum Chair in Information Technology at McMaster University's Faculty of Engineering. His research spans information theory, machine learning, and communications systems. Core research areas: Information theory and coding Machine learning for signal processing Wireless communication systems Multimedia compression Publications address fundamental limits of rate-distortion-perception tradeoffs and computational methods for fluid dynamics. Research shows strong mathematical foundations with applications in communications and engineering. Honors include Early Researcher Award, IBM Faculty Award, and JSPS Invitational Fellowship. Current projects examine quantum information constraints and learned data compression.

Olivier Bahnمشاهده پروفایل

استاد

• Operational research
• Mathematical models in economy
• Mathematical models in energy
• +۲ مورد دیگر

Olivier Bahn is a Professor in the Department of Decision Sciences at HEC Montréal. He holds roles such as Director of the Group for Research in Decision Analysis (GERAD), Co-Director of the e3 Hub: Expertise in Energy and Environment, and Member of the Institut EDDEC. His education includes a Diplôme d'ingénieur from the Institut d'Informatique d'Entreprise (CNAM, Paris) and a Doctorat en sciences économiques et sociales from the Université de Genève. His research focuses on operational research, mathematical models in economy/energy/environment, and climate change. Notable publications from the last five years explore hydrogen's role in energy models, decarbonization pathways, and smart grid optimization. He has supervised numerous PhD and Master’s theses, including work on energy efficiency, EV charging integration, and carbon-negative materials. Bahn’s expertise is reflected in his leadership roles across institutions and his contributions to energy policy analysis. His projects often involve techno-economic assessments and stakeholder-driven modeling for sustainable transitions. Current activities include directing GERAD and advancing Canada’s net-zero strategies through collaborative frameworks.

Hatem Ben Ameurمشاهده پروفایل

استاد

• Financial engineering
• Stochastic processes
• Stochastic simulation
• +۴ مورد دیگر

Hatem Ben Ameur is a Full Professor in the Department of Decision Sciences at HEC Montréal, Canada, and a member of the Group for Research in Decision Analysis (GERAD). He holds a Ph.D. in Financial Engineering from HEC Montréal, an Engineer degree in statistics and economics from ENSAE Paris, and a Master’s in Mathematics from Tunis University. His research interests focus on financial engineering, stochastic processes, and risk measurement, with applications in option pricing, bankruptcy prediction, and dynamic programming. Recent publications emphasize structural models for corporate securities valuation and the integration of machine learning techniques in financial analysis. Professor Ben Ameur has supervised 3 Master’s theses and 5 project supervisions, collaborating with students on topics like neural networks in bankruptcy prediction and Monte Carlo simulation for option valuation. His teaching includes courses on business analytics, applied mathematics, and Monte Carlo methods. He is actively involved in GERAD, contributing to interdisciplinary research in decision sciences and quantitative finance. His work bridges theoretical advancements with practical applications in financial markets and risk management.

Sonia Hassiniمشاهده پروفایل

استادیار

• Resilient Infrastructure Systems
• Water Security Under Climate Change
• Stormwater Management
• +۳ مورد دیگر

Dr. Sonia Hassini is an Assistant Professor in Civil Engineering at McMaster University's Faculty of Engineering. Her expertise lies in resilient infrastructure systems and water security under climate change. She specializes in analytical probabilistic stormwater management models and quantification of uncertainties related to land-use and climate impacts on water runoff. Dr. Hassini holds a BEng from École Nationale d’Ingénieurs de Tunis , an MEng from the University of Waterloo, and a PhD from McMaster University. Her research focuses on sustainable urban drainage systems, probabilistic modeling of infrastructure resilience, and climate adaptation strategies for water resources. Her recent work includes studies on Markov chain models for flood forecasting, dam failure risk prediction, and LID system resilience quantification. She teaches CIVENG 3M03: Municipal Hydraulics , focusing on water distribution and wastewater systems analysis. Dr. Hassini collaborates with multidisciplinary teams on projects involving GIS-based hydrological analysis and satellite remote sensing for groundwater assessment. She actively advises graduate students in resilient infrastructure and water security domains.

Kyle Biggarمشاهده پروفایل

دانشیار

• Functional Proteomics
• Protein Post-Translational Modifications
• Lysine Methylation
• +۶ مورد دیگر

Kyle Biggar is an Associate Professor at the Department of Biology, Faculty of Science, Carleton University. His research bridges functional proteomics, post-translational modifications (particularly lysine methylation), and their roles in cancer and hypoxia. He leads a multidisciplinary lab with expertise in biochemical techniques, peptide array synthesis, and computational modeling of enzyme-substrate interactions. Education: B.Sc. (St. Francis Xavier), Ph.D. (Carleton) Research Themes: Regulation of protein interaction networks by lysine methylation, hypoxia signaling, therapeutic peptide design, and environmental stress adaptation Collaborations: Co-supervises students with Dr. James Green, partners with NuvoBio for cancer therapeutics His publications focus on enzyme specificity, methyllysine proteomics, and stress-responsive microRNA mechanisms. The lab trains students in advanced cellular and biochemical methods, with alumni contributing to fields like neurodegenerative disease and DNA repair. Recent work includes AI-driven protein interaction prediction, exploring lysine PTM competition in cancer, and developing KDM5C/SETD6 inhibitors. The lab also investigates mTOR-IGFBP-1 signaling in intrauterine growth restriction and employs peptide arrays for drug discovery. Lab Members & Students: Current: Francois Charih (NSERC PGS-D), Ruofan Wang, Mullen Boutler, Mitch Allen, Ella De Nicola, Johanna Enright Alumni: Dr. Hemanta Adhikary (KMT/KDM inhibitors), Viktoria Xing (methyl-binding domains), Ryan Collins (MLL4 substrates)

James J. Cheethamمشاهده پروفایل

دانشیار

• Membrane Biochemistry
• Protein-Lipid Interactions
• Synaptic Vesicle Dynamics
• +۴ مورد دیگر

James J. Cheetham is an Associate Professor in the Department of Biology at Carleton University within the Faculty of Science. He holds a B.Sc. and Ph.D. from McMaster University and serves as an Undergraduate Co-op Advisor. His research bridges membrane biochemistry, computational biology, and biophysics. Education: B.Sc. (McMaster University) Ph.D. (McMaster University) Research Interests: Protein-lipid interactions in synaptic vesicle regulation Computational modeling of presynaptic nerve terminals Biophysical techniques for membrane analysis Application of machine learning to protein interaction prediction Research Trends: His publications span molecular neuroscience, computational modeling, and bioinformatics. Recent work focuses on antimicrobial mechanisms and fungal membrane disruption, while earlier studies emphasize synaptic protein dynamics and algorithm design for biological simulations. Advising & Techniques: He employs liposomes, atomic force microscopy, and fluorescence spectroscopy alongside bioinformatics tools like PIPE. Collaborations include neuroscience (e.g., synapsin studies) and computational science (e.g., DEVS models).

Puyan Mojabiمشاهده پروفایل

استاد

• Applied Electromagnetics
• Antennas and Propagation
• Electromagnetic Inverse Problems
• +۵ مورد دیگر

Puyan Mojabi is a Professor and a Canada Research Chair (Tier 2) in the Department of Electrical and Computer Engineering at the University of Manitoba within the Price Faculty of Engineering. His research integrates electromagnetic inverse problems with metasurface design for applications in wireless communication and non-destructive testing. Education BSc Electrical Engineering, University of Tehran (2002) MSc Electrical Engineering, Iran University of Science and Technology (2004) PhD Electrical Engineering, University of Manitoba (2010) His work focuses on two key themes: systematic metasurface design for next-generation wireless systems and metasurface-enhanced electromagnetic imaging for cost-effective non-destructive testing. Articles demonstrate expertise in deep learning integration , implicit solvers , and phaseless inversion techniques . Scientific Awards University of Manitoba Falconer Emerging Researcher Award University of Manitoba Excellence in Teaching Award (x2) University of Manitoba Graduate Students Association Teaching Award International Union of Radio Science (URSI) Young Scientist Award (x3) Mojabi actively seeks graduate students in applied electromagnetics and contributes to professional communities as an Early Career Representative of URSI and Chair of the IEEE Winnipeg Waves Chapter.

• Environmental Radioactivity
• Machine Learning
• Muon Physics
• +۵ مورد دیگر

Dr. Trevor J. Stocki is a Researcher at the Radiation Protection Bureau of Health Canada, specializing in environmental radioactivity and nuclear nonproliferation. His work integrates machine learning, muon physics, and Monte Carlo techniques to address global radiation safety challenges. He collaborates with international bodies like the IAEA and contributes to the Canadian CRIPT team for cosmic ray inspection technologies. Education: B.Sc. (Honours Physics, University of Alberta, 1991); Ph.D. (Physics, University of British Columbia, early 2000s). Postdoctoral research at Simon Fraser University focused on laser trapping of radioactive atoms. Research Interests: Machine learning applications for distinguishing nuclear explosion signatures from anthropogenic radioxenon sources Muon tomography and detection of special materials via muonic X-ray spectroscopy Environmental modeling of radioactive plumes using Monte Carlo simulations Development of flash ADC systems for high-purity germanium detectors in muon projects Recent projects include leading an IAEA working group on controlled nuclear releases and developing algorithms for cosmic ray inspection systems. He actively seeks M.Sc. students to advance muon-based detection technologies. Collaborations: Canadian CRIPT team, TRIUMF, and global CTBT monitoring networks.

Peter D. Minevمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Analysis
• Fluid-Structure Interaction
• +۴ مورد دیگر

Peter D. Minev is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada. His research specializes in computational fluid dynamics, numerical methods for partial differential equations (PDEs), and large-scale simulations for scientific and engineering problems. He develops advanced algorithms for incompressible Navier-Stokes equations, fluid-structure interaction, multiphase flows, and magnetohydrodynamics (MHD). Education: MSc and PhD from Sofia University, Bulgaria. Research Focus: Minev's work spans: High-order time-stepping schemes and splitting methods for complex PDEs Fictitious domain approaches for fluid-structure interaction Parallel algorithms for supercomputing applications Modeling of multiphysics phenomena (e.g., porous media flows, phase-field equations) He has created benchmark results for 3D lid-driven cavity flows and simulations of particle sedimentation/bubble dynamics. Publications: His recent articles (2014–2021) focus on: Efficient splitting schemes for stress/vorticity formulations High-order adaptive time integration Algorithms for spherical/heterogeneous geometries Applications in fuel cells, geophysics, and biomechanics A consistent theme is minimizing computational complexity while maintaining accuracy.

David Hardistyمشاهده پروفایل

دانشیار

• Behavioral Science
• Consumer Behavior
• Sustainability
• +۴ مورد دیگر

Dr. David Hardisty is an Associate Professor and SMEV Chair of Marketing & Behavioral Science at the Sauder School of Business , University of British Columbia. His research focuses on consumer behavior in sustainability and financial decision-making, with notable work on behavioral nudges, intertemporal choices, and environmental policy. He co-founded the Decision Insights for Business and Society (DIBS) group and the Interdisciplinary Biodiversity Solutions (IBioS) research cluster. In a recent seminar, he discussed a longitudinal study on household laundry behaviors, revealing that environmental benefit nudges and multiple behavior requests yield stronger energy efficiency outcomes. His research integrates behavioral science with real-world applications, addressing climate change mitigation and consumer psychology challenges. Key research themes include: Behavioral interventions for sustainable choices Temporal decision-making and patience Marketing strategies for environmental and financial contexts Cross-cultural mental accounting His work spans academic and applied domains, with studies on charity dynamics, gig economy biases, and policy design. He has collaborated on projects like parking compliance signs and energy label effectiveness, emphasizing practical solutions to global challenges.

James McLellanمشاهده پروفایل

استاد

• Mathematical Modelling
• Process Control
• Chemical Engineering Design
• +۶ مورد دیگر

James McLellan is a Professor in the Department of Chemical Engineering at Queen's University, affiliated with Smith Engineering. He holds cross-appointments in the Department of Mathematics and Statistics and the School of Drama and Music within the Faculty of Arts and Science. His research focuses on dynamic modeling, control, and simulation of chemical processes, emphasizing nonlinear systems and stochastic models. McLellan received his B.Sc. in Mathematics and Engineering (Process Control Option) from Queen's University (1981), M.A.Sc. in Chemical Engineering from the University of Waterloo (1983), and Ph.D. in Chemical Engineering from Queen's University (1990). He has been Department Head since 2006 and Academic Director of the Dunin-Deshpande Queen's Innovation Centre. His research interests span stochastic modeling, molecular design, computational models, and chemical engineering design. Key areas include extremum-seeking control for distributed systems, bioscrubber modeling for toxic gas treatment, and optimization of bioreactors. He has contributed to advancements in process control for industrial applications like polyethylene reactors and bioenergy systems. McLellan's work integrates mathematical rigor with engineering practice, addressing challenges in environmental biotechnology, energy systems, and nonlinear control. His publications emphasize system dynamics, parameter estimation, and controller design for complex processes. He currently teaches ENIN 400: Innovation Design Sprint and has advised on grants related to wastewater treatment, bioreactor optimization, and sensor development. His collaborative projects include cross-disciplinary initiatives in innovation and entrepreneurship through Queen's Innovation Centre.