جستجوی استاد راهنمای دانشگاهی

Joel Zylberbergمشاهده پروفایل

دانشیار

• Visual Cortex
• Retina
• Synaptic Plasticity
• +۴ مورد دیگر

Joel Zylberberg is an Associate Professor in the Department of Biology at York University, holding a Canada Research Chair (Tier 2). His research focuses on understanding how the brain encodes sensory information, particularly in the visual cortex and retina, and translating this knowledge into advancements in machine learning and prosthetics. His work integrates computational neuroscience, theoretical physics, and artificial intelligence to develop technologies like camera-to-brain translators and next-generation retinal prosthetics. Education details are not explicitly listed, but his research spans interdisciplinary areas including Biophysical neural adaptation mechanisms Machine learning algorithm optimization Synaptic plasticity dynamics Visual information processing His recent articles emphasize bridging neuroscience and AI, exploring topics like neural network pruning, retinal computation models, and sleep-stage classification for medical applications. While no specific grants or awards are listed, his Canada Research Chair position highlights his recognized expertise.

Sébastien LEMIEUXمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Cancer Research
• +۵ مورد دیگر

Sébastien Lemieux is an Associate Professor in the Department of Biochemistry and Molecular Medicine at the Faculty of Medicine, Université de Montréal, and holds a joint appointment in the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences. His research focuses on developing bioinformatics algorithms for analyzing high-throughput biological data, particularly in cancer genomics and precision medicine. He earned his PhD in Computer Science (2002), M.Sc. in Computer Science (1999), and B.Sc. in Microbiology (1998), all from Université de Montréal. He leads the Functional and Structural Bioinformatics Research Unit at the Institut de recherche en immunologie et en cancérologie (IRIC), directing the bioinformatics platform. His work integrates transcriptomics, proteomics, and high-throughput screening to address challenges in cancer research. Notable projects include developing AI-driven tools for drug discovery and analyzing genomic data in acute myeloid leukemia (AML). Dr. Lemieux has supervised over 20 graduate students and postdocs, focusing on computational methods for cancer biology. He has secured funding from major agencies like the Canadian Institutes of Health Research (CIHR), Genome Canada, and Fonds de recherche du Québec (FRQS). Key collaborations include work on personalized immunotherapy and chemogenomic tools for AML treatment. His research outputs span bioinformatics algorithm development, cancer biomarker discovery, and translational applications in clinical oncology. Current projects emphasize integrating multi-omics data to identify therapeutic targets and improve treatment outcomes for high-risk cancers.

Nicolas Le Rouxمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Nicolas Le Roux is an Associate Professor at the Université de Montréal, affiliated with the Department of Computer Science and Operations Research within the Faculty of Arts and Science. He is also an adjunct faculty member at McGill University and part of the Mila – Quebec Institute of Artificial Intelligence. His research focuses on machine learning, optimization, and statistical modeling, particularly in supervised learning and reinforcement learning from an optimization perspective. He has contributed to foundational work on policy gradient methods and surrogate functions in reinforcement learning. Recently, he explores model adaptation, modular architectures, and decentralized AI systems. Le Roux is a co-recipient of the 2018 Lagrange Prize and a CIFAR AI Chair. His work bridges technical advancements with organizational practices, emphasizing team well-being and inclusive research environments. He has advised three PhD students (Alan Chan, Reyhane Askari Hemmat, and Valentin Thomas) on topics like generative models and optimization in AI agents. Though currently on a career break, he remains active in research and maintains collaborations through Mila. Key research themes include optimizing policy functions over parameters, modular neural architectures, and ethical AI governance. His recent papers address operator-based policy gradients, decision-aware actor-critic algorithms, and adapter routing for cross-task generalization. Le Roux’s interdisciplinary approach integrates technical innovation with societal impact, advocating for equitable and sustainable AI development.

Pierre McKenzieمشاهده پروفایل

دانشیار

• Finite Automata
• Boolean Circuit
• Formal Language
• +۳ مورد دیگر

Pierre McKenzie is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal, part of the Faculty of Arts and Sciences. He is a member of the LITQ (Laboratoire d'informatique théorique et quantique). His research focuses on computational complexity theory, finite automata, Boolean circuits, formal languages, and logic. He has advised numerous graduate and master's students, including Hugo Côté, Nathan Grosshans, and Michael Blondin. His work has been supported by grants from the Natural Sciences and Engineering Research Council of Canada (CRSNG), particularly in projects like 'Lower bounds and derandomizations for branching programs' (2018–2026). Research Interests: Computational complexity hierarchy separations Branching programs and circuit lower bounds Automata theory and its connections to circuit complexity Algorithmic and complexity aspects of counter systems Key Projects: Lower bounds and derandomizations for branching programs (Lead researcher, 2018–2026) THE COMPUTATIONAL COMPLEXITY OF POLYNOMIAL TIME PROBLEMS (Lead researcher, 2012–2019) Labs/Teams: Active member of LITQ, contributing to theoretical computer science research.

• Machine Learning
• Probabilistic Modeling
• Unsupervised Learning
• +۳ مورد دیگر

Maxwell Libbrecht is an Associate Professor and Michael Smith Health Research Scholar at the School of Computing Science, Simon Fraser University. His research focuses on machine learning methods for genomics, particularly applying probabilistic graphical models, deep neural networks, and optimization techniques to analyze genomic datasets like DNA sequences, epigenetic data (ChIP-seq, ATAC-seq), and single-cell data (flow cytometry, single-cell genomics). He holds a PhD in Computer Science from the University of Washington (2016) and a BSc from Stanford University (2011). His research interests include machine learning, probabilistic modeling, unsupervised learning, submodular optimization, genomics, and gene regulation. Teaching interests span machine learning, probability and statistics, data structures, and discrete mathematics. His group actively recruits students at all levels (undergraduate, masters, PhD) and collaborates with SFU's Libbrecht Lab . Key research contributions include methods like CANDI for genomic data imputation, VSS-Hi-C for chromatin contact analysis, and Segway for chromatin state segmentation. His work has been recognized with the Michael Smith Health Research Scholar award and Best Paper Award.

Longhai Liمشاهده پروفایل

استاد

• Statistical Machine Learning
• Model Diagnostics
• Bioinformatics
• +۳ مورد دیگر

Prof. Longhai Li is a Professor at the University of Saskatchewan, specializing in statistical machine learning and model diagnostics. His research focuses on developing methods for analyzing high-throughput and complex data, particularly in bioinformatics and medical research. He holds a Ph.D. from the University of Toronto and a B.Sc. from the University of Science and Technology of China. Prof. Li teaches diverse statistics courses, including introductory and applied statistics, computational methods, and probability theory. His teaching emphasizes practical tools like R and R Markdown, and he involves undergraduates in research projects, such as developing real-time disease tracking websites and statistical packages. His research has been funded by agencies like NSERC, CFI, and MITACS. Notable projects include statistical methodologies for microbiome data, geospatial AI for wheat production analysis, and predictive modeling for high-dimensional datasets. He has developed R packages like HTLR for Bayesian logistic regression. Prof. Li has supervised 3 postdoctoral fellows, 23 graduate students, and 14 undergraduates. His work appears in journals such as the American Statistician and BMC Medical Research Methodology.

Zissis Poulosمشاهده پروفایل

استادیار

• Machine Learning
• Financial Engineering
• Risk Management
• +۶ مورد دیگر

Zissis Poulos is an Assistant Professor at the School of Information Technology within the Faculty of Liberal Arts & Professional Studies at York University. He holds a PhD and MASc in Electrical and Computer Engineering from the University of Toronto, obtained in 2018 and 2014 respectively. Previously, he served as a postdoctoral fellow at the Joseph L. Rotman School of Management, University of Toronto. His research focuses on machine learning applications in financial risk management, derivatives hedging, volatility modeling, natural language processing for financial analysis, and decentralized finance technologies. He co-founded Tartan AI, a startup developing energy-efficient deep learning solutions. **Education:** - PhD in Electrical and Computer Engineering, University of Toronto (2018) - MASc in Electrical and Computer Engineering, University of Toronto (2014) **Research Interests:** Zissis' research spans machine learning for derivatives hedging and risk management, natural language processing (NLP) applied to financial soft information analysis, generative models for financial data synthesis, and blockchain-based decentralized finance systems. His work emphasizes volatility surface modeling, reinforcement learning for financial decision-making, and distributed ledger technologies. **Recent Research Trends:** His publications highlight advancements in reinforcement learning for hedging strategies, volatility modeling via variational autoencoders, and text-based analysis of monetary policy uncertainty. He also explores synthetic data applications in regulatory frameworks and decentralized blockchain oracles for trustless systems. **Professional Activities:** Zissis co-founded Tartan AI, which focuses on optimizing deep learning compute and compression for energy efficiency. His work bridges academic research with industry applications in fintech and blockchain solutions.

Ning Zhangمشاهده پروفایل

استاد

• Edge Computing
• Internet of Vehicles (IoV)
• Cybersecurity
• +۷ مورد دیگر

Prof. Ning Zhang is a tenured Professor in the Department of Electrical and Computer Engineering at the University of Windsor's Faculty of Engineering. He holds a Tier 2 Canada Research Chair in Edge Computing and the Internet of Vehicles (since 2022) and has been recognized as a 2024 Clarivate Highly Cited Researcher. His research focuses on edge computing, vehicular networks, cybersecurity, federated learning, and 6G systems. He is affiliated with the Centre for Engineering Innovation, where cutting-edge labs were recently established under his leadership. Dr. Zhang's academic achievements include membership in the Royal Society of Canada's College of New Scholars (2024) and the IEEE Rising Star Award (2021). His work bridges theoretical innovations with practical applications in autonomous systems, digital twin technology, and metaverse infrastructure. He advises students like Pegah Mansourian and Mina Zamanirafe, whose research on automotive cybersecurity won international acclaim. His lab explores edge computing optimizations for low-light video analytics, federated learning frameworks, and secure vehicular communication systems. Key Research Themes: V2X security, digital twins, 6G edge infrastructures, AI-driven network optimization Lab Affiliations: Centre for Engineering Innovation Grants: Canada Research Chairs Program funding, NSERC grants (implied via research outputs)

Saeed Sametمشاهده پروفایل

استاد

• Cybersecurity
• Privacy-Preserving Data Mining
• Federated Learning
• +۵ مورد دیگر

Dr. Saeed Samet is a Professor in the School of Computer Science at the University of Windsor. His research focuses on cybersecurity, privacy-preserving data mining, federated learning, blockchain technology, and healthcare informatics. He has led projects such as the privacy-preserving personal health record system (P3HR) and the decentralized electronic health records (DEHR) model leveraging blockchain. Samet actively engages in interdisciplinary collaborations, including developing frameworks like SEERa for community prediction and RNBFT for scalable Byzantine consensus. He advises students like Saghi Khani on topics such as social isolation detection algorithms. Notably, his work addresses adversarial attacks in machine learning and federated learning security. Samet has contributed to initiatives like the International Collegiate Programming Contest and the WE-Spark Health Institute grants. Education While formal academic credentials are not explicitly detailed in the text, Samet holds a professorship, indicating advanced qualifications in computer science or related fields. His extensive publications suggest doctoral-level expertise. Research Interests Samet’s research spans cybersecurity mechanisms, privacy-preserving methodologies (e.g., homomorphic encryption, federated learning), blockchain applications in healthcare, and machine learning robustness. Recent work emphasizes scalable consensus algorithms (RNBFT), adversarial defense in federated learning, and frameworks for decentralized data evaluation. His projects often integrate real-world challenges, such as combating fake news via generative AI and enhancing pandemic response through blockchain-based patient tracking. Awards & Grants Recipient of a WE-Spark Health Institute grant for innovative research in Windsor-Essex (2021). His work on social isolation algorithms (2020) and privacy-preserving statistical analysis (2019) underscores sustained funding support for impactful projects. Advising & Grants Advises Saghi Khani on machine learning algorithms for social isolation detection. Collaborates on grants involving interdisciplinary teams, such as the $287,000 WE-Spark Health Institute award. His research often bridges academia and industry, addressing practical challenges in healthcare and cybersecurity. Labs & Teams Leads initiatives in the School of Computer Science’s research groups, focusing on privacy-preserving systems and distributed learning. Collaborates with organizations like the WE-Spark Health Institute and the University of Windsor’s interdisciplinary teams on projects such as the DEHR blockchain model and federated learning frameworks.

Sherif Saadمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning
• Blockchain Technology
• +۵ مورد دیگر

Sherif Saad is a Professor in the Department of Computer Science at the University of Windsor. His research focuses on cybersecurity, machine learning, blockchain technology, and adversarial defense mechanisms. He has contributed to projects involving fake news detection, threat intelligence systems, and privacy-preserving frameworks in vehicular networks and decentralized systems. Recent research highlights include developing novel approaches to federated learning for privacy, ensemble defense strategies against adversarial attacks, and hybrid machine learning techniques for robotics and medical imaging. His work bridges theoretical advancements with practical applications in cyber-physical systems and autonomous vehicle technologies. Key areas of exploration include improving blockchain-based frameworks for supply chain logistics, analyzing adversarial robustness in NLP models, and enhancing anomaly detection in drone systems. His research often integrates evolutionary algorithms and genetic techniques for optimization problems in automated machine learning. While no formal awards are listed, his publications demonstrate significant contributions to cybersecurity, AI ethics, and interdisciplinary applications of machine learning. Collaborative efforts include international research with institutions like South Korea’s Ewha Womans University on autonomous vehicle comfort optimization.

Simon X. Yangمشاهده پروفایل

استاد

• Robotics
• Intelligent Systems
• Control Systems
• +۹ مورد دیگر

Simon X. Yang is a Professor and Head of the Advanced Robotics and Intelligent Systems Laboratory at the University of Guelph, School of Engineering. He holds a Ph.D. in Electrical and Computer Engineering from the University of Alberta and is a Fellow of the Canadian Academy of Engineering. His expertise spans robotics, intelligent systems, control systems, sensors, and bio-inspired intelligence. Dr. Yang has authored numerous publications and serves as Editor-in-Chief/Associate Editor for international journals, as well as a grant panel member for NSERC and CIHR. Prof. Yang’s research focuses on real-time sensing, robotic teleoperation, neural networks, fuzzy systems, and applications in agriculture, transportation, and environmental monitoring. He has pioneered bio-inspired algorithms for path planning, multi-robot systems, and industrial automation. His work includes innovations in underwater robotics, drone coordination, and smart agriculture technologies. Dr. Yang teaches graduate-level courses on advanced control systems, soft computing, and robotics, as well as undergraduate courses in neuro-fuzzy systems and engineering design. He actively participates in organizing international conferences and has developed the PIGRGB-Weight dataset for livestock monitoring. Professional Highlights: Editor-in-Chief roles, NSERC grant panel, and leadership in robotics research Labs: Advanced Robotics and Intelligent Systems Lab Awards: Fellow of Canadian Academy of Engineering His lab’s innovations include a biomimetic gecko-inspired robot for microgravity environments and a bionic ray robot with high motion performance. Current projects address challenges in smart farming, infrastructure health monitoring, and autonomous systems safety.

Andrew Hamilton-Wrightمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Visualization
• +۶ مورد دیگر

Dr. Andrew Hamilton-Wright is an Associate Professor at the School of Computer Science, University of Guelph, part of the College of Engineering and Physical Sciences. He is a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI) and has been at the university since 2017. He holds a PhD from the University of Waterloo (2006) and previously worked as a Postdoctoral Researcher at Queen’s University and an Associate Professor at Mount Allison University. His research focuses on machine learning, association mining, and data visualization to support decision-making in healthcare. Key areas include electrophysiological data analysis for disease characterization, fatigue and pain prediction using biophysical signals, and the development of robust software tools for visualizing complex data. He has pioneered work on EMG simulators and models for validating techniques in neuromuscular research. Notable awards include an NSERC Discovery Grant (2021) and an NSERC Engage Grant (2018). His work has been featured in media coverage, such as CTV News’ report on an iron-tracking app for genetic condition management. He is also affiliated with the OneHealth Institute and editorial boards in scientific journals. His grants and collaborations include NSERC-funded projects and partnerships with industry. He advises on ergonomic and postural data analysis, with applications in occupational health and safety. His lab focuses on integrating machine learning with biomedical systems to improve clinical decision-making.

• Biomedical Signal Processing
• Unobtrusive Sensing
• Medical Devices
• +۵ مورد دیگر

Esteban J. Pino is an Associate Professor at Universidad de Concepcion, Chile, actively engaged in research at the intersection of biomedical engineering and healthcare technology. His work focuses on developing innovative medical devices and signal processing techniques for clinical applications. His research interests span biomedical signal processing , unobtrusive sensing , and medical device development , with particular emphasis on respiratory monitoring, cardiac diagnostics, and pediatric health interventions. Utilizing advanced computational models and machine learning, his work bridges engineering solutions with real-world medical challenges. The recent publications highlight a strong trend in portable diagnostic systems , neural signal interpretation , and digital health frameworks for chronic disease management and early childhood development. These contributions reflect a multidisciplinary approach combining engineering, neuroscience, and clinical medicine. Dr. Pino serves as an Associate Editor for Connected Health in Frontiers in Digital Health , contributing to the peer-review and editorial process in his domain. He collaborates extensively across international research networks, as evidenced by co-authorship with researchers from diverse institutions. Though no formal advisees are listed, his editorial role and publication record suggest active mentorship and leadership in the academic community. His work is centered on practical, deployable healthcare technologies, particularly in low-resource or home-based settings, aiming to improve diagnostic accessibility and patient outcomes.

Jinan Fiaidhiمشاهده پروفایل

استاد

• Thick Data Analytics
• Deep Learning
• Few Shots Learning
• +۱۲ مورد دیگر

Dr. Jinan Fiaidhi is a Professor of Computer Science at Lakehead University, Canada since 2001. She served as Graduate Coordinator for the MSc and PhD programs in Computer Science and Biotechnology. She holds adjunct positions at the University of Western Ontario. Her academic journey includes degrees from Essex University (PgD, 1983) and Brunel University (PhD, 1986). She has held academic roles at institutions like Sultan Qaboos University and Philadelphia University prior to Lakehead. Her research focuses on Thick Data Analytics , Deep Learning , and Collaborative Learning , with applications in healthcare, medical imaging, and AI-driven diagnostics. She is a Professional Engineer (PEng) in Ontario and a Senior Member of IEEE. She chairs the IEEE Special Interest Group on Big and Thick Data for eHealth and founded the International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) as its Emeritus Editor-in-Chief. Her research is funded by NSERC and MITACS grants. Key projects include developing frameworks like QL4POMR for problem-oriented medical records and applying thick data analytics to Crohn’s disease, ulcerative colitis, and cataract severity analysis. Education: PhD in Computer Science, Brunel University (1986) PgD in Computer Science, Essex University (1983) Awards: None explicitly listed, but holds professional designations: PEng, IEEE Senior Member, ISP (CIPS), and MBCS (Chartered Computing). Grants: NSERC, MITACS (specific projects unspecified). Labs/Teams: Leads IEEE eHealth initiatives and collaborates on projects involving thick data analytics in healthcare interoperability and extreme automation.

Zipeng Liuمشاهده پروفایل

استادیار

• Information Visualization
• Human-Computer Interaction
• Data Analysis
• +۳ مورد دیگر

Zipeng Liu is an Assistant Professor at the College of Software, Beihang University (China). His research focuses on information visualization, human-computer interaction, and data analysis, emphasizing design of communication-focused visualizations and their integration with computational methods. He holds a PhD from the University of British Columbia (UBC, Canada) and a BSc from Peking University (PKU, China). Prior roles include research at Adobe Research and teaching at UBC. Education: PhD in Computer Science (UBC, 2015-2021), BSc in Computer Science (PKU, 2011-2015) Research interests include developing new metrics for graph readability (e.g., Sprawlter metric), visualizing GNN embeddings, and segmenting user interaction logs in image editing. His work bridges theoretical visualization principles with practical applications in data-driven problem-solving. Selected awards include Best Paper Honorable Mention at PacificVis 2020. He has taught courses on information visualization and served as a teaching assistant for computer systems and bioinformatics courses at UBC.