Distributed Data Management Professors and Supervisors in Canada | ApplySharif

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استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۲۱-۷۳۵ از ۹۷۳ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Fatemeh Khoda Parastمشاهده پروفایل

استادیار

University of New BrunswickCanada

Fatemeh Khoda Parast is an Assistant Professor in the Department of Computer Science at the University of New Brunswick (UNB). She previously served as an Assistant Professor at the University of Guelph from September 2023 to September 2024. Her PhD in Computer Science from UNB (2019–2023) focused on the security of large-scale distributed storage systems. She currently researches transforming cyber threat intelligence into rapid defense mechanisms. Education: PhD in Computer Science, University of New Brunswick (2019–2023) Her research interests span cybersecurity, cyber threat intelligence, digital forensics, and cloud storage security. She explores cryptographic solutions for distributed systems and AI-driven threat detection. Recent work includes CephArmor (secure Ceph storage interfaces) and neural network-based RSA cryptanalysis. Her publications address cutting-edge topics like AI in threat intelligence automation and privacy-preserving machine learning for distributed data. No scientific awards are explicitly mentioned. No advising or grant details are provided. Her work contributes to advancing secure cloud infrastructure and cryptographic protocols.

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Yuhao Luمشاهده پروفایل

استادیار

University of ManitobaCanada

Yuhao Lu is an Assistant Professor in the Department of Landscape Architecture at the University of Manitoba, located at Room 201H, John A. Russell Building on the Fort Garry Campus. His research focuses on geospatial analytics, earth observation, cartographic design, and urban planning. He explores how maps and mapping processes influence settlement design, urban forestry, and environmental justice. Education: PhD in Urban Planning (University of British Columbia), BSc in Forestry (University of British Columbia and Nanjing Forestry University). Prior to this role, he worked at the Future Cities Laboratory (ETH Zürich) and elementslab (UBC), contributing to projects across neighborhood, regional, and global scales. Research Interests: Empirical and projective dimensions of cartography Participatory mapping methodologies Social/environmental justice through spatial representation Urban greenspace equity and dynamics Scenario-based urban modeling Critical cartography education He directs the Future Elements Studio (festudio.ca), a research lab advancing innovative spatial practices. His work bridges technical geospatial analysis with socially-engaged design processes, emphasizing map-making's role in shaping urban futures. Key contributions include developing the 'urban greenness score' metric and advancing methodologies for integrating energy-economy models with urban systems analysis. His interdisciplinary approach addresses both physical and socio-cultural dimensions of urban environments.

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Ba M. Chuمشاهده پروفایل

استاد

Carleton UniversityCanada

Ba M. Chu is a Full Professor in the Department of Economics at Carleton University, affiliated with the Faculty of Public and Global Affairs. He holds degrees from Hanoi and London, including a Ph.D. in Economics. His expertise spans optimal asset allocation, risk management, dependence modeling, and asymptotic theory. B.A. (Hanoi) M.Sc., M.Phil., Ph.D. (London) Research focuses on econometric methodologies such as VaR estimation via large deviations, copula modeling, goodness-of-fit testing with L-moments, and non-linear dependence measures. Key contributions include pandemic forecasting models and foundational work on copula recovery techniques. His recent publications address topics ranging from COVID-19 economic impacts to panel data analysis and financial risk modeling. Dr. Chu speaks Vietnamese and Mandarin fluently alongside English. His work integrates theoretical econometrics with practical applications in finance and public policy, emphasizing robust statistical frameworks for complex economic systems.

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Cheng Liمشاهده پروفایل

استاد

Memorial University of NewfoundlandCanada

Cheng Li is a Full Professor and Department Head of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds adjunct professorships at Harbin Institute of Technology and Xi'an University of Post and Telecommunications. His expertise spans wireless communications, underwater networks, and sensor systems. He has held academic roles since 1999, including leadership in Nokia Telecommunications and Baosteel. Education: B.Eng. (Hons.) and M.Eng. from Harbin Institute of Technology, followed by a PhD at Memorial University. His research focuses on energy-efficient wireless networks, underwater localization, and cooperative communication protocols. Research highlights include innovations in underwater acoustic sensor networks, vehicular ad-hoc networks, and integrated satellite-terrestrial systems. He has received 8 notable awards, including the Best Paper Award at IEEE ICC’2010 and the David Dunsiger Award. His work bridges academic and industrial applications, with contributions to standards like NECEC and IEEE conferences. He advises on network security, IoT, and multi-agent systems, with over 30 peer-reviewed publications since 2001.

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Ray Gosineمشاهده پروفایل

استاد

Memorial University of NewfoundlandCanada

Ray Gosine is a Professor at Memorial University of Newfoundland, holding the J.I. Clark Chair in Intelligent Systems. He specializes in robotics, machine vision, and industrial automation, particularly in natural resource industries. His research also addresses socio-economic impacts of technology and public policy reviews. Gosine has held significant administrative roles, including Dean of Engineering and Vice-President (Research) at Memorial University. He currently serves on boards such as the Canadian Engineering Accreditation Board and ACENET. Notable contributions include leading a public review of hydraulic fracturing in Newfoundland and collaborative research on digitalization in mining and oil/gas sectors. Education: B.Eng. in Electrical Engineering, Memorial University PhD in Robotics, University of Cambridge Research Interests: Automation in natural resources, socio-economic impacts, public policy, and university intellectual property Administrative Roles: Vice-President (Research), Memorial University (2008–2010, 2014–2015, 2016–2017) Dean of Engineering and Applied Science (2003–2008) Awards: Fellow of Canada Academy of Engineering Petro-Canada Young Innovator Award Current Projects: Visiting Scholar at U of Toronto's Innovation Policy Lab (2017 sabbatical) Research on automation in Canada's extractive industries

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Sara Rouhaniمشاهده پروفایل

استادیار

University of ManitobaCanada

Sara Rouhani is an Assistant Professor in the Department of Computer Science at the University of Manitoba. She is affiliated with the Faculty of Science and leads the Trustworthy Computing and Distributed Technologies Laboratory . Her research focuses on developing secure and trustworthy infrastructure using blockchain, federated learning, and distributed technologies. Her work addresses challenges in data management across healthcare, smart grids, agriculture, and supply chain sectors, emphasizing privacy and trust. She also explores blockchain interoperability to enhance scalability and security of decentralized systems. No specific awards or grants are listed in the provided information. Her research lab focuses on advancing decentralized application infrastructure through cross-blockchain integration and secure data management solutions.

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Parimala Thulasiramanمشاهده پروفایل

استاد

University of ManitobaCanada

Parimala Thulasiraman is a Professor in the Department of Computer Science at the University of Manitoba, affiliated with the Faculty of Science. Her research focuses on high-performance computing, graph analytics, and network science, leveraging bio-inspired algorithms and machine learning. She leads the IDEAS (InterDisciplinary Evolving Algorithmic Science) Lab, which develops innovative optimization techniques for complex systems and scalable algorithms for multicore architectures. Her work spans applications in finance, genomics, and vehicular networks, supported by grants from NSERC, Research Manitoba, and MITACS. She holds senior membership in the ACM and IEEE and has been recognized with the University of Manitoba Teaching Recognition Award. Research interests emphasize evolutionary computation, parallel algorithms, and their real-world applications. Recent projects include GPU-accelerated multi-objective optimization, blockchain-based VANET security, and genomic data analysis using parallel suffix tree methods. Her publications explore cutting-edge topics like coevolutionary systems, ant brood clustering for portfolio management, and knowledge distillation in sentiment analysis. Grants & Awards: NSERC, Research Manitoba, MITACS ACCELERATE; Teaching Recognition Award Labs/Teams: IDEAS Lab (InterDisciplinary Evolving Algorithmic Sciences) Advising and grant activities involve interdisciplinary collaborations, with a focus on training the next generation of computational scientists. Current research trends include post-cloud computing models (dew-blockcloud systems), AI bot optimization, and blockchain applications in finance and transportation.

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Jacky Baltesمشاهده پروفایل

استاد مدعو

University of ManitobaCanada

Jacky Baltes is an Adjunct Professor in the Department of Computer Science at the University of Manitoba, within the Faculty of Science. His primary research focuses on Robotics, Artificial Intelligence, Machine Learning, and Distributed Processing, with notable contributions to autonomous robot navigation, humanoid robotics, and computer vision systems. He has actively participated in international competitions like RoboCup and HuroCup, developing innovative solutions such as the Tao-Pie-Pie humanoid robot and the Doraemon video server. His work emphasizes practical applications, including rescue robotics, path planning in dynamic environments, and educational robotics workshops for children. Baltes has led projects like the Keystone Fire Brigade and the All Botz team, showcasing his expertise in multi-agent systems and robotic competitions. Publications highlight advancements in gait design for humanoid robots, stabilization using gyroscopes, and architectures for intelligent mobile robots. His research bridges theoretical AI principles with real-world robotic implementations, particularly in vision processing and control systems.

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Amy Hurfordمشاهده پروفایل

استاد

Memorial University of NewfoundlandCanada

Amy Hurford is a Professor jointly appointed in the Departments of Biology and Mathematics and Statistics at Memorial University of Newfoundland and Labrador. She holds a Ph.D. in Mathematics & Statistics from Queen's University (2011), an MSc in Biological Sciences from the University of Alberta (2005), and completed postdoctoral training at The Fields Institute and University of Oxford. Her research integrates mathematical modeling with ecological and epidemiological systems, focusing on: Theoretical ecology and evolutionary epidemiology Climate change impacts on species distributions and disease spread Public health modeling for infectious diseases Pandemic preparedness for small jurisdictions Recent publications demonstrate strong emphasis on COVID-19 modeling, climate-disease interactions, pollinator ecology, and methodological innovations in epidemiological modeling. Her work informs public health policy and has been featured in WHO reports and major media outlets. She received the President’s Award for Distinguished Teaching (2023) and serves as senior editor at the Bulletin of Mathematical Biology. She leads the Canadian small jurisdictions working group developing modeling frameworks for public health. Current PhD/MSc students: Giuseppe Pasqualino, Francis Anokye, Joseph Baafi, Laura Bruce, Joshua Mack, Miranda Wallace. Graduated advisees include 14 researchers now working in academia and industry. Research affiliations: Senior editor at Bulletin of Mathematical Biology; Organizer of AARMS-EIDM Summer School in Modelling Infectious Diseases.

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Junfeng Wenمشاهده پروفایل

استادیار

Carleton UniversityCanada

Junfeng Wen is an Assistant Professor at the School of Computer Science, Carleton University. He holds a Ph.D. in Statistical Machine Learning from the University of Alberta, where he worked with Prof. Dale Schuurmans and Prof. Russ Greiner. His research focuses on foundational machine learning algorithms, including reinforcement learning, transfer learning, federated learning, and domain adaptation. He teaches courses such as Reinforcement Learning and Machine Learning at Carleton University. Education: Ph.D. in Statistical Machine Learning (2013–2020), University of Alberta M.Sc. in Computing Science (2011–2013), University of Alberta Bachelor of Engineering (with Honors, 2007–2011), College of Computer Science and Technology, CKC College, Zhejiang University Research Interests: Dr. Wen’s work emphasizes advancing theoretical understanding and practical applications of machine learning. Key areas include federated learning frameworks for privacy preservation, reinforcement learning for decision-making systems, and multi-source domain adaptation techniques. His research bridges algorithmic innovation with real-world challenges in distributed systems and biological modeling. Publications: His recent articles explore federated learning optimization, decentralized collaboration strategies, and reinforcement learning applications in behavioral studies. Notable contributions include domain aggregation networks for cross-domain adaptation and proxy model-sharing techniques in decentralized learning. The work spans theoretical advancements and practical implementations, often addressing scalability and privacy concerns in distributed environments. Professional Contributions: Conference reviewer for NeurIPS, ICML, ICLR, AAAI, IJCAI, and ACML Journal reviewer for JMLR, Machine Learning Journal (Editorial Board member), and TPAMI Labs and Projects: He contributes to open-source projects like Domain Aggregation Networks (DARN) for multi-source domain adaptation and Frank-Wolfe for Covariate Shift . These repositories reflect his commitment to reproducible research and community engagement.

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Omair Shafiqمشاهده پروفایل

دانشیار

Carleton UniversityCanada

Omair Shafiq is an Associate Professor at the School of Computer Science, Carleton University. His research focuses on advanced topics in natural language processing (NLP), machine learning, and their applications in knowledge graphs, blockchain, and network security. He leads projects that integrate large language models (LLMs) with structured data to enhance information retrieval and decision-making systems. His work spans areas like explainable AI, adversarial robustness, and real-time traffic prediction. Shafiq is affiliated with Herzberg Laboratories and contributes to interdisciplinary research, including smart contracts, encrypted traffic classification, and IoT security. Education details are not explicitly listed in the provided text. His research interests emphasize practical solutions for challenges in AI ethics, data privacy (e.g., GDPR compliance), and scalable analytics for big data applications. He has developed frameworks like ECSGen/iZen for NLP tasks and CARD-B for encrypted traffic classification. His recent publications (2022–2025) highlight innovations in ensemble learning, adversarial attacks defense, and blockchain-based solutions for secure transactions. Shafiq’s work often bridges theory and practice, addressing real-world problems such as cybersecurity in vehicular networks, stock market prediction using deep learning, and user behavior analysis on social platforms. His contributions include tools like RevDet for event detection in news feeds and HybLoc for indoor localization. Though no specific awards are mentioned, his prolific publication record reflects sustained academic impact.

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Jean-Pierre Corriveauمشاهده پروفایل

دانشیار

Carleton UniversityCanada

Jean-Pierre Corriveau is an Associate Professor and Graduate Director at the School of Computer Science, Carleton University. He holds a Ph.D. in Natural Language Processing from the University of Toronto (1991), an M.C.S. from the University of Ottawa (1984), and a B.Sc. from the University of Ottawa. His research focuses on Object-Oriented Testing (scenario testing, test data generation) and Text Understanding (contextualized and generative comprehension), with recent emphasis on behavior-driven development and acceptance testing of systems with complex scenarios. Dr. Corriveau has extensive industry experience, including roles at Nortel where he contributed to the TELOS project, leading to the ObjecTime startup and subsequent IBM Rational Software Architect tools. His work bridges academia and industry, addressing challenges in software engineering, cybersecurity, and natural language processing. Notable contributions include frameworks for MapReduce optimization, secure localization in wireless sensor networks, and privacy-preserving data anonymization techniques. His publications span topics like AI detection in text, federated learning for NER, intrusion detection systems, and resilient network algorithms. He has advised on graduate studies as Director and contributed to teaching strategies for millennial students through innovative virtual learning environments in platforms like Second Life. His research trends reflect a blend of theoretical advancements in computational linguistics and practical software engineering solutions, with a recurring focus on system reliability, distributed computing, and privacy-preserving technologies. Grants and collaborations are implied through his tool development and industry partnerships, though specific grant details aren’t explicitly listed. Labs/teams associated with his work include the ObjecTime initiative and Carleton’s distributed systems research groups, though no dedicated lab names are mentioned. Future directions emphasize scalable testing methodologies and ethical AI integration in text analysis.

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Andy Adlerمشاهده پروفایل

استاد

Carleton UniversityCanada

Andy Adler is a Professor and Canada Research Chair (Tier II) in Biomedical Engineering at Carleton University's Faculty of Engineering and Design, Department of Systems and Computer Engineering. He specializes in Medical Image Processing, Electrical Impedance Tomography (EIT), and Biometrics. His research focuses on improving lung function monitoring, physiological signal analysis, and biometric system design. Adler teaches advanced courses like SYSC 4203 (Biomedical Instrumentation) and BIOM 5202 (Medical Image Processing), emphasizing hands-on laboratory work and project-based learning. His scientific contributions include over 80 peer-reviewed articles, with recent work on EIT applications in critical care, equine medicine, and pulmonary hypertension. Notable achievements include developing novel EIT techniques for lung ventilation/perfusion analysis and advancing biometric encryption standards. He advises graduate students in EIT system design and biomedical instrumentation, and collaborates with industry on medical device innovation. His lab, equipped with advanced imaging and signal acquisition systems, supports interdisciplinary research in bioinstrumentation and medical physics.

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Alan Davoustمشاهده پروفایل

استاد

Carleton UniversityCanada

Alan Davoust is a Professor at the University of Quebec in Outaouais (UQO) since December 2018. He holds a Ph.D. from Carleton University (2015), specializing in Decentralized Social Data Sharing, and has held postdoctoral positions at the University of Edinburgh (2016–2018) and Carleton University (2016). His academic focus spans distributed systems, multi-agent systems, and social machines, with a strong emphasis on ethical interactions and decentralized collaboration. Education: Ph.D., Carleton University, 2015 M.A.Sc., Carleton University, 2009 Engineer Degree (ENSIMAG), Grenoble INP, 2005 Research interests include distributed data management, game theory applications, and the design of social machines. His work explores mechanisms to enhance collaborative systems through decentralized architectures and ethical considerations. Notable projects include the Sociogrammes initiative, which aims to democratize social machine creation, and studies on social contracts to improve non-cooperative game outcomes. Teaching responsibilities include introductory programming courses in Java and courses on distributed artificial intelligence, focusing on multi-agent systems and social choice theory. He actively collaborates with researchers globally and mentors students in these areas.

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Betty Liمشاهده پروفایل

استاد مدعو

Carleton UniversityCanada

Dr. Betty Li is an Adjunct Professor in the Department of Systems and Computer Engineering at Carleton University and a researcher at the Human Health Therapeutics Research Center. She holds a Ph.D. in Biomedical Engineering from the University of Western Ontario (2018), with prior degrees from Western Ontario (Master of Engineering Science, 2013) and the University of Toronto (Bachelor of Applied Science in Nanoengineering, 2011). Her research focuses on developing microphysiological systems, such as organ-on-chip models, using stem cells, 3D bioprinting, and biomaterials to advance preclinical drug discovery. Key research areas include biomaterials, biomechanics, and microfluidic design. Her recent publications (2023–2025) emphasize 6G networks, non-terrestrial networks (NTN), and AI-driven solutions for wireless communication challenges. She explores topics like federated learning in NTN, hemispherical antenna arrays for HAPS, and energy-efficient satellite IoT systems. Her work bridges biomedical engineering and telecommunications, addressing challenges in both fields. No scientific awards are explicitly mentioned in the text. She advises no listed students but collaborates on projects involving robotic aerial base stations and spectral efficiency optimization. Her lab work integrates interdisciplinary approaches for in vitro tissue modeling and sustainable network architectures.

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