جست‌وجوی استاد و پژوهشگر

Manar Amayriمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Explainable AI
• +۵ مورد دیگر

Dr. Manar Amayri is a researcher affiliated with the CIISE (College/Institution name not explicitly provided), focusing on machine learning, data science, and their applications in energy management systems. Her work emphasizes smart buildings, smart cities, and human-in-the-loop AI systems. She explores advanced clustering techniques, domain adaptation, and anomaly detection in smart environments. Research Interests: Machine Learning, Explainable AI, Energy Management Systems, Smart Buildings, Data Clustering, Domain Adaptation, and Anomaly Detection. Her studies address challenges in load forecasting, occupancy estimation, and energy disaggregation using deep learning, Bayesian methods, and probabilistic models. Publications highlight innovations in hybrid models (e.g., Transformer-BiLSTM networks), data imputation for electric vehicle charging, and unsupervised learning techniques for smart grid applications. Recent work emphasizes interpretability and robustness in AI systems. No scientific awards or grants were explicitly mentioned in the provided texts. No student advisees or lab affiliations were listed.

Honghao Fuمشاهده پروفایل

استادیار

• Quantum Computing
• Device-independent Quantum Cryptography
• Entanglement Verification
• +۳ مورد دیگر

Dr. Honghao Fu is an Assistant Professor at the Concordia Institute for Information Systems Engineering (Department of Information Systems Engineering), Concordia University. His primary research focuses on quantum computing, device-independent quantum cryptography, entanglement verification, and testing of untrusted devices. He actively seeks students for thesis supervision in these areas. His research interests emphasize foundational aspects of quantum information science, including protocols for quantum state purification, undecidability in quantum correlation problems, zero-knowledge proofs in multi-prover interactive systems, and practical implementations of device-independent cryptography using quantum entanglement. His work bridges theoretical computer science and experimental quantum physics, with applications in secure communication and computational complexity. Recent publications (2020–2025) highlight advancements in quantum state purification techniques, undecidability proofs for quantum correlations, low-latency quantum randomness expansion, and computational self-testing for quantum devices. His research often explores the intersection of quantum theory and cryptographic security, particularly under noisy or untrusted conditions. No scientific awards or grants are explicitly mentioned in the provided text. His advising focus aligns with his research areas in quantum cryptography and information systems engineering.

Tristan Glatardمشاهده پروفایل

دانشیار

• Neuroinformatics
• Reproducibility in Science
• Big Data Infrastructures
• +۴ مورد دیگر

Tristan Glatard is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University . He holds a Canada Research Chair (Tier II) on Big Data Infrastructures for Neuroinformatics . His work focuses on reproducible research methodologies, neuroimaging analysis pipelines, and open science frameworks. Key contributions include developing tools like NeuroCI for continuous integration of neuroimaging results and VIP (Virtual Imaging Platform) for collaborative research. Research interests span neuroinformatics , reproducibility in computational science , big data architectures , and machine learning applications in healthcare . His projects address challenges in analytical flexibility, software dependency management, and numerical stability in neuroimaging workflows. He actively promotes open data sharing through initiatives like the Neuroimaging Data Model (NIDM) and Brain Imaging Data Structure (BIDS). Recent work emphasizes cross-platform reproducibility, leveraging containerization (Docker/Guix) and cloud-based solutions. He collaborates with international teams to validate neuroimaging pipelines and benchmark computational tools for large-scale biomedical data analysis. Notably absent from the profile are explicit mentions of academic awards or named graduate students, though his work has significant impact on research methodologies. He is deeply involved in open science advocacy through platforms like Brainhack and the Montreal Neuroinformatics Ecosystem.

Maria Ntekoumeمشاهده پروفایل

استادیار

• Nonlinear dispersive PDEs
• Integrable systems

Maria Ntekoume serves as an Assistant Professor in the Department of Mathematics and Statistics at Concordia University, actively supervising thesis work for MA, MSc, and PhD students in Mathematics and Statistics programs. She is currently seeking new graduate students for research collaboration. Education Ph.D., University of California, Los Angeles (2020) B.Sc., University of Crete (2014) Research Focus : Dr. Ntekoume specializes in nonlinear dispersive partial differential equations and integrable systems, investigating wave propagation phenomena, soliton dynamics, and stability analysis in mathematical physics. Her theoretical work bridges abstract analysis with physical applications in fluid dynamics and quantum mechanics. Academic Supervision : She mentors graduate students through Concordia's Mathematics and Statistics programs, emphasizing rigorous analytical methods and computational techniques for solving complex differential equations. Prospective students with strong foundations in advanced calculus and mathematical modeling are encouraged to contact her directly.

Xuekui Zhangمشاهده پروفایل

دانشیار

• Bioinformatics
• Biostatistics
• Machine Learning
• +۴ مورد دیگر

Dr. Xuekui Zhang is an Associate Professor at the University of Victoria (UVic) and holds a Tier 2 Canada Research Chair in Biostatistics and Bioinformatics. He is also a Michael Smith Health Research BC Scholar. His primary affiliation is with the Department of Mathematics and Statistics, Faculty of Science at UVic, with adjunct roles at University of Manitoba and UBC's Centre for Heart Lung Innovation. He specializes in developing statistical methods and software tools for analyzing genomic data, clinical trials, and environmental datasets. **Education**: PhD in Statistics (UBC, 2011), postdoctoral training at Johns Hopkins University (2011–2013), and visiting scholarships at institutions like Fred Hutchinson Cancer Research Center.** **Research**: Focuses on bioinformatics, biostatistics, and machine learning applications in COPD, environmental monitoring, and precision medicine. Notable projects include the OPMS ocean pollution monitoring system and ADSP adaptive diagnostic testing strategy. His work integrates multi-omics data (scRNA-seq, scATAC-seq) and clinical trial design innovations. **Awards**: Recipient of the Canadian Journal of Statistics (CJS) Award (2022) for collaborative work on logistic regression modeling.** **Grants**: Leads or co-leads major projects funded by Genome Canada ($6.3M), NRC ($1.5M), and CIHR ($0.8M). Current initiatives include COPD biomarker discovery and clinical decision tools for shoulder replacements. **Students**: Supervises a dynamic team of 12+ PhD/MSc students and undergraduates, focusing on machine learning, environmental statistics, and genomic data analysis. Notable trainees include Yushan Hu (COPD research) and Haochen Ning (plant virology diagnostics). **Labs/Teams**: Coordinates a multidisciplinary group with expertise in statistical computing, collaborating with researchers in medicine, environmental science, and computer science.

David Burgessمشاهده پروفایل

دانشیار

• Organizational Modelling
• Educational Law
• University Administration
• +۴ مورد دیگر

Dr. David Burgess is an Associate Professor in the Department of Educational Administration at the University of Saskatchewan, currently on leave following service as Associate Dean. His research focuses on organizational theory, agent-based modelling (ABM), educational law, and university administration. He has co-authored foundational texts in educational administration and law, including widely used textbooks in Canadian teacher education programs. His international collaborations include partnerships with institutions in China, Spain, Bangladesh, and Jamaica, emphasizing organizational simulations and policy development. Educational Background: PhD, University of Saskatchewan (2008) MEd, University of Saskatchewan (2003) BEd (Distinction), University of New Brunswick (2006) BA (Honours), University of Saskatchewan (1997) Research Interests: Agent-based modelling of schools Computer algorithms for mentorship matching Statistical generation of synthetic school populations Artificial neural networks for policy support Bayesian network simulations Educational law and governance Professional Roles: Former Department Head and Graduate Chair Editor-in-Chief of SELU Research Review Journal Consultant for UN/NGLS and Jubilee Residences His recent work includes pro bono projects with Jamaican and Mexican educational ministries, and initiatives in Mozambique. He has received the Thomas B. Greenfield Dissertation Award (2009) and continues to supervise graduate students focusing on ABM and micro-economic analysis in education.

Divya Sharmaمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Medical AI
• +۲ مورد دیگر

Divya Sharma is Assistant Professor in York University's Mathematics and Statistics Department, with cross-appointments at University Health Network and University of Toronto. She leads the IMPACT-AI Lab developing deep learning models for healthcare applications including liver transplantation, sepsis management, and osteoarthritis. Her research integrates multimodal data (clinical, genomic, imaging) through novel neural network architectures. Recent CIHR-funded projects focus on AI for transplant decision-making and equity in liver allocation. Collaborations span Toronto General Hospital, Princess Margaret Cancer Centre, and Schroeder Arthritis Institute. Publications in Lancet Digital Health and Bioinformatics demonstrate innovations in: 1) Multimodal data fusion; 2) Longitudinal disease trajectory modeling; 3) Generative AI for medical data; 4) Interpretable clinical AI systems. Recent work includes reinforcement learning for sepsis therapy optimization and LLMs for mental health support.

Julie Zhouمشاهده پروفایل

استاد

• Robust Statistics
• Optimal Design of Experiments
• Fractional Factorial Design
• +۶ مورد دیگر

Dr. Julie Zhou is a Professor in the Department of Mathematics and Statistics at the University of Victoria. Her research focuses on robust statistics and optimal design of experiments, with applications in medical, natural, and social sciences. PhD in Statistics, University of Alberta B.Sc. and M.Eng. from Nanjing University and Nanjing University of Science and Technology Her research spans robust statistical procedures , optimal design theories , and statistical computing . Recent work includes robust designs for heteroscedastic errors, multi-response regression models, and computational tools using CVX and SeDuMi. Her 15 most recent publications highlight advancements in minimax D-optimal designs , Bayesian optimization , and biomedical applications . Collaborators include researchers from the University of Alberta, Simon Fraser University, and international institutions. She has supervised over 20 graduate students and serves on editorial boards of the Canadian Journal of Statistics and Statistical Papers . Her work emphasizes cost-effective experimental designs and accurate statistical inferences.

• Harmonic Analysis on Quantum Groups
• Operator Algebras
• Quantum Information Theory
• +۳ مورد دیگر

Jason Crann is an Associate Professor in the School of Mathematics and Statistics at Carleton University. He is affiliated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), where he contributes to the Joint Analysis Group and the interdisciplinary QUASAR Group, focusing on quantum security through algebra and representation theory. Additionally, he is an Affiliate Member of the Institute for Quantum Computing in Waterloo. His research spans operator algebras, harmonic analysis on quantum groups, and quantum information theory, with a strong emphasis on functional analysis and algebraic structures. Research interests include the interplay between quantum groups and harmonic analysis, operator modules, and applications to quantum information. His work explores topics such as quantum teleportation, state convertibility, and the mathematical foundations of quantum systems. Crann’s articles reflect a focus on advanced theoretical frameworks, including non-commutative theorems, quantum kinematical systems, and algebraic approaches to spacetime reconstruction. While no scientific awards are explicitly listed, his contributions to quantum information theory and operator algebras highlight significant academic engagement. Supervision opportunities are available for students interested in functional analysis and quantum information intersections. Collaborations extend to the QUASAR Group and the Institute for Quantum Computing, emphasizing interdisciplinary research in quantum security and algebraic methodologies.

• Graph Searching
• Graph Polynomials
• Network Reliability
• +۳ مورد دیگر

Danielle Cox is an Associate Professor in the Department of Mathematics at Mount Saint Vincent University (MSVU). Her research focuses on Graph Searching, Graph Polynomials, Network Reliability, and Mathematics Education. She holds a Ph.D. in Mathematics from Dalhousie University (2013), an M.Sc. (2008) from the same institution, and a B.Sc. Honours (2007) from MSVU. Dr. Cox teaches courses including calculus, statistics, graph theory, and programming. Her work spans theoretical contributions to graph theory and practical applications in network reliability, with publications in journals like Graphs and Combinatorics and Theoretical Computer Science . She explores topics such as domination polynomials, hyperopic cops-and-robbers games, and damage number analysis in graphs. No scientific awards are explicitly listed in the profile. Her research emphasizes foundational mathematical problems with real-world implications in network design and educational methodology. Dr. Cox’s advising and grants are not detailed in the provided text, though her academic contributions suggest active involvement in student mentorship and collaborative research. She is affiliated with MSVU’s Department of Mathematics and contributes to interdisciplinary initiatives in applied mathematics and education.

Kyran Cupidoمشاهده پروفایل

دانشیار

• Actuarial Science
• Spatial Statistics
• Data Science
• +۴ مورد دیگر

Kyran Cupido is an Associate Professor in the Department of Mathematics and Statistics at Saint Francis Xavier University. His work bridges spatial statistics with actuarial science, focusing on mortality risk, insurance modeling, and socioeconomic implications of geospatial patterns. PhD in Statistics, Arizona State University, 2020 MSc in Statistics, McMaster University, 2017 BSc in Mathematics and BEd in Intermediate/Senior Education, Brock University, 2015 Research interests center on integrating spatial statistics into actuarial domains. His publications analyze mortality trends, cybercrime patterns, emergency response systems, and social media dynamics through geospatial frameworks. Recent work includes natural hedging techniques for mortality risk and applications of machine learning to emergency logistics. Kyran Cupido has not been explicitly recognized for scientific awards in the provided texts. His academic contributions are documented through publications rather than student advisement details or laboratory affiliations.

Darien DeWolfمشاهده پروفایل

دانشیار

• Category Theory
• Partiality in Algebra
• Combinatorial Group Theory
• +۳ مورد دیگر

Darien DeWolf is an Associate Professor in the Department of Mathematics and Statistics at St. Francis Xavier University. He holds a Ph.D. in Mathematics and specializes in category theory, partiality models across algebra and computation, and combinatorial group theory. His research focuses on inverse semigroups, restriction categories, and their applications in geometry and topology. Research interests include: Primary: Categorical models of partiality, restriction monads, and double categories. Secondary: Commutation semigroups in finite groups, particularly metacyclic and dihedral groups. His work bridges pure mathematics and interdisciplinary applications, as seen in studies of Pythagorean triples and social media analysis (e.g., Instagram alcohol advertising). Recent publications span from quantum mechanics (Coulomb integrals) to public health (student drinking risk factors). No scientific awards or grants are explicitly documented here. He has not listed advisees or lab affiliations.

M Aydinمشاهده پروفایل

استاد آموزشی

• Computational Fluid Dynamics
• Automotive Aerodynamics
• Aerosol Transport
• +۲ مورد دیگر

Dr. M. Aydin is an Associate Teaching Professor at Ontario Tech University's Faculty of Engineering and Applied Science, specializing in the Department of Automotive and Mechatronics Engineering. With extensive teaching responsibilities, he instructs graduate courses (Fuel Cells and Hydrogen Systems, Advanced Turbomachinery, Automotive Aerodynamics) and undergraduate courses (Industrial Automation, Thermodynamics, Fluid Mechanics, and Dynamics). Education PhD in Mechanical Engineering, University of London, UK (1999) MRes in Fluid Dynamics, von Karman Institute for Fluid Dynamics, Belgium (1993) MSc in Nuclear Engineering, Istanbul Technical University, Turkey (1991) BSc in Mechanical Engineering, Istanbul Technical University, Turkey (1988) Research Focus Dr. Aydin's research integrates computational and experimental approaches in fluid dynamics and energy systems. His primary work explores automotive aerodynamics optimization, aerosol transport mechanisms, fuel cell efficiency, and constructal theory applications. Significant contributions include multiphase flow modeling in porous media, heat transfer enhancement through particle suspensions, and biomedical fluid dynamics related to respiratory systems. His publications demonstrate consistent focus on energy conversion efficiency, environmental fluid dynamics, and computational methods, with recent emphasis on sustainable energy applications and automotive engineering challenges. Awards 2022: Dr. Yulei (James) Yang Engineering Teaching Award 2020: Student Choice Teaching Award, Ontario Tech University No information about advised students, research grants, or laboratory affiliations was found in the source materials.

Shahram ShahbazPanahiمشاهده پروفایل

استاد

• Wireless communications
• Statistical signal processing
• Co-operative communications
• +۴ مورد دیگر

Dr. Shahram ShahbazPanahi is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, within the Faculty of Engineering and Applied Science. His research focuses on wireless communications, statistical signal processing, and smart antenna systems. He holds a PhD in Electrical Engineering from Sharif University of Technology (2001), and has held academic positions at institutions including McMaster University (Canada) and Razi University (Iran). He is a recipient of prestigious awards such as the Ontario Tech Junior Research Excellence Award and NSERC grants. His professional roles include Senior Area Editor for IEEE Signal Processing Letters and membership in IEEE technical committees. Education: PhD (Statistical Signal Processing), Sharif University of Technology, Tehran, Iran (2001) MSc (Signal Processing and Communication Systems), Sharif University of Technology, Tehran, Iran (1994) BSc (Communications Engineering), Sharif University of Technology, Tehran, Iran (1992) Research Interests: His work spans array processing, cooperative communications, dynamic spectrum access, and wireless communication systems. Recent trends in his publications emphasize massive MIMO systems, cell-free networks, and optimization of distributed beamforming techniques. His research bridges theoretical signal processing with practical wireless system design. Selected Awards: Early Researcher Award (2010) NSERC Discovery Grants (2006-2016) Outstanding Graduate Student Recognition Advising & Grants: He has led projects funded by NSERC, focusing on distributed MIMO and energy-efficient networks. His academic service includes editorial roles for IEEE journals and organizing conferences like CCECE 2008. His lab explores advanced signal processing for next-generation wireless systems.

Young Ki Shinمشاهده پروفایل

استاد

• Econometrics
• Causal Inference
• Quantile Regression
• +۳ مورد دیگر

Young Ki Shin is a Professor of Economics at McMaster University, where he has established himself as a leading researcher in econometrics and economic theory. His work bridges theoretical developments with practical applications in statistical methods for economic analysis, with significant contributions to quantile regression, causal inference, and economic modeling. His educational background includes a Ph.D. in Economics from the University of Rochester (2002-2007) and a B.A. from Seoul National University (1996-2001). Professor Shin's research focuses on several key areas: causal inference and identification , where he develops methods to determine cause-effect relationships; modern computational algorithms and inference , creating efficient statistical methods for large datasets; and applications of statistical decision theory to economic problems. His work often addresses computational challenges in applying econometric methods to massive datasets. His recent publications demonstrate a strong focus on quantile regression methods, with several papers addressing computational challenges in applying these techniques to large datasets. He has made significant contributions to generalized method of moments (GMM) estimation and developed innovative approaches to monotone signaling equilibria in matching markets. His research spans from theoretical econometrics to applied economic questions, including analyses of government spending multipliers during economic crises. As an educator, Professor Shin has taught a comprehensive range of econometrics courses at both undergraduate and graduate levels, including Econometrics I, Econometrics II, and Applied Econometrics, reflecting his research expertise in both theoretical foundations and practical applications of econometric methods.