جستجوی استاد راهنمای دانشگاهی

Sujaya Maiyya is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. Prior to this, she completed a postdoc at Cornell University and earned her PhD from the University of California, Santa Barbara. Her research focuses on distributed systems, databases, and privacy/security, particularly in designing secure and efficient data management systems. She leads projects on oblivious databases, trusted execution environments (TEEs), and scalable privacy-preserving systems. Education: PhD in Computer Science, University of California, Santa Barbara (2018) MSc in Computer Science, University of California, Santa Barbara (2017) BE in Information Science, PESIT Bangalore (2014) Research Interests: Distributed systems, database privacy, oblivious datastores, genomics data security, and secure computation using TEEs. Her work emphasizes practical solutions for privacy-preserving storage and query processing, including tunable-privacy mechanisms and fault-tolerant ORAM systems. Awards and Grants: CFI/ORF Infrastructure Grant (2024-2029) NCC Research Awards (2024-2028) NSERC Discovery Grant (2023-2027) MIT EECS Rising Stars (2021) Teaching: Courses include CS348 (Introduction to Databases) and CS848 (Privacy Enhancing Data Systems). She emphasizes foundational concepts and system internals in database design and secure systems. Professional Service: Chair of Ontario Database Day (2024), PC member for SIGMOD, EDBT, VLDB, and ICDE. Frequent reviewer for journals like TKDE and DKE.

• Distributed Systems
• Database Systems
• Data Privacy
• +۳ مورد دیگر

Dr. Khurram Aziz is a Senior Instructor in the Faculty of Computer Science at Dalhousie University , Halifax, Canada. He is actively engaged in teaching and research, with a focus on optical networks, data center interconnects, and network performance modeling. Education: PhD in Electrical Engineering, Vienna University of Technology, Austria (2008) MSc in Electrical Engineering, National University of Singapore (2003) BSc (Hons) in Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan (1998) His research interests include optical packet and burst switched networks , optical interconnects for data centers , analytical modeling and simulation , and network routing and switching . He has contributed extensively to the design and performance evaluation of scalable optical switches and hybrid switching systems. The recent publications reflect a strong trend in data center optical networks , focusing on performance, blocking probability, signal degradation, and architectural classification. His work bridges theoretical modeling with practical simulation frameworks, such as CloudNetSim++ in OMNeT++, contributing to cloud and high-capacity network research. Dr. Aziz has no listed scientific awards in the provided text. He teaches several core computer science courses including CSCI 2141: Intro to Database Systems , CSCI 3171: Network Computing , CSCI 3132: Object Orientation and Generic Programming , and CSCI 3120: Operating Systems . There is no mention of graduate student supervision or external research grants. He has co-authored book chapters in major handbooks on data centers and switched systems. Dr. Aziz has not listed any formal lab or research team affiliations in the provided content.

• Optical Networks
• Data Center Networks
• Optical Interconnects
• +۷ مورد دیگر

Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.

• Databases
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Oana Balmau is an Assistant Professor in the School of Computer Science at McGill University, where she leads the Data-Intensive Storage and Computer Systems Laboratory (DISCS Lab). She also holds a status-only appointment at the University of Toronto and serves as a working group chair for MLPerf Storage. Her research focuses on creating storage infrastructure that enables fast and energy-conscious insights from data, with particular emphasis on storage and persistent memory technologies for machine learning, data science, and edge computing workloads. Dr. Balmau's research interests span computer systems, with specific focus on: Design and implementation of efficient key-value stores Storage systems for machine learning workloads Edge computing infrastructure Persistent memory technologies Performance optimization of data-intensive systems Her recent work has led to significant contributions in storage benchmarking through the MLPerf Storage benchmark and in edge computing frameworks. The MLPerf Storage benchmark has become an industry standard for evaluating storage performance in machine learning environments, while her work on hierarchical edge computing addresses security and performance challenges in distributed edge environments. Her publications show consistent high-impact contributions to top systems venues, with recent work focusing on processing-in-memory virtualization, stream processing reconfiguration, and efficient data preprocessing pipelines. Dr. Balmau has received numerous awards for her research, including: SEC 2024 Best Paper Award for "Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge" MLCommons Hero Award 2023 for leadership as MLPerf Storage working group chair ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award 2021 Honorable Mention CORE John Makepeace Bennett Award 2021 for the best Computer Science doctoral dissertation in Australia and New Zealand USENIX ATC 2019 Best Paper Award for "SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores" As an educator, Dr. Balmau teaches courses on advanced computer systems, operating systems, and principles of computer systems design at McGill University. She has served on program committees for top systems conferences including SOSP, SIGMOD, FAST, and EuroSys, and has co-organized workshops on resource-efficient machine learning and edge computing. She leads the DISCS Lab, which focuses on two main research directions: Systems for ML (including the MLPerf Storage benchmark) and Edge computing (including frameworks for fast and secure edge computing in hierarchical edge environments).

• Storage Systems
• Persistent Memory
• Key-Value Stores
• +۴ مورد دیگر

Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.

• Ancient Middle Eastern History
• Cuneiform Studies
• Astronomy and Astrology in Mesopotamia
• +۳ مورد دیگر

Jelle Hellings is an Assistant Professor in the Department of Computing and Software at McMaster University , Canada. His research focuses on high-performance large-scale data management systems with a strong theoretical and algorithmic component, including resilient systems (blockchains) , graph databases , and external-memory algorithms . He previously worked as a Postdoc Scholar at the University of California, Davis and earned his PhD from Hasselt University in Belgium. Education: Doctor of Sciences in Computer Science (2018), Hasselt University Master of Science in Computer Science and Engineering (2011), Eindhoven University of Technology His research interests include scalable resilient systems with Byzantine fault tolerance, database theory, graph query languages, constraints on graph data, and external-memory algorithms for large graph datasets. He has authored numerous high-impact publications on blockchain-based resilient systems, query optimization in graph databases, and theoretical advancements in relation algebra expressiveness. Hellings actively contributes to academic service through program committee memberships and tutorial organization, and he currently teaches courses on future resilient databases and foundational computer science topics.

• Resilient Systems
• High-Performance Data Management
• Database Theory
• +۱۰ مورد دیگر

Lawrence Goodridge is a Professor and Director of the Canadian Research Institute for Food Safety (CRIFS) at the University of Guelph's Ontario Agricultural College. He holds the Leung Family Professorship in Food Safety and leads research at the intersection of food safety, antibiotic resistance, and One Health principles. His work focuses on applying genomic technologies to study foodborne pathogens (E. coli, Salmonella, Listeria, Cronobacter) and leveraging wastewater surveillance for infectious disease outbreak prediction. Academic History: BSc Microbiology (University of Guelph, 1995), MSc Food Microbiology (2003), PhD Food Microbiology (2002), followed by post-doctoral training in Food Safety at the University of Georgia (2002). Joined CRIFS in 2003. Research Interests: Genomic analysis of pathogen emergence, wastewater-based epidemiology, bacteriophage applications, and consumer education strategies for food safety. His lab develops innovative methods for rapid pathogen detection in food systems and environmental samples. Articles Trends: Over 100 peer-reviewed publications emphasize genomic surveillance of foodborne pathogens and SARS-CoV-2, with a focus on wastewater sampling innovations. Recent work explores multi-modal data integration for public health forecasting and ethical data protection frameworks for surveillance programs. Awards: While no specific prizes are listed, his $50M+ research funding from Canadian/international sources underscores recognition of his impactful work. Grants support projects like phage-based sanitization and antimicrobial resistance tracking. Advising & Labs: Leads CRIFS laboratory operations and collaborates globally on food safety initiatives. His research has informed food industry guidelines and policy frameworks for mitigating pathogen risks in agricultural and environmental systems.

• Food Safety
• Antibiotic Resistance
• Foodborne Pathogens
• +۶ مورد دیگر

Charles Yang is a Professor of Linguistics and Computer Science at the University of Pennsylvania , where he also directs the Cognitive Science Program. His research integrates computational models with studies of language acquisition, processing, and evolution. Education: Ph.D. in Computer Science, MIT, 2000 Yang's work spans language acquisition , computational linguistics, and the evolution of cognition. He has authored The Price of Linguistic Productivity (2016), which received the Leonard Bloomfield Award from the LSA. Recent publications focus on large language models as cognitive models, the Chinese aspectual system , and statistical approaches to linguistic patterns. His 15 most recent articles (2025-2021) demonstrate a trajectory from computational models of language change to machine translation and multiword expression analysis . Yang has received significant funding from the National Science Foundation and the Guggenheim Foundation . He co-directs the Integrated Language Science and Technology group with John Trueswell and mentors students in linguistics, computer science, and psychology.

• Language Acquisition
• Computational Linguistics
• Cognitive Development
• +۴ مورد دیگر

Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۵ مورد دیگر

Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.

• Data Mining
• Database Systems
• Data Privacy
• +۳ مورد دیگر

Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.

• Database Systems
• Data Management
• Data Curation and Integration
• +۳ مورد دیگر

Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.

• Big Data Management and Analytics
• Database Systems
• Machine Learning
• +۳ مورد دیگر

Dr. Kamran Sedig serves as a Professor in the Department of Computer Science and the Faculty of Information and Media Studies at Western University, where he directs the Insight Lab. His research focuses on designing interactive technologies to enhance human cognitive activities involving data and information, including decision making, problem solving, and learning across domains like healthcare, finance, and scientific discovery. His academic credentials include: Ph.D. in Computer Science (Human-Computer Interaction) from The University of British Columbia under Prof. Maria Klawe, with dissertation nominated for the Governor General’s Gold Medal M.Sc. in Computer Science (Artificial Intelligence) from McGill University under Prof. Renato De Mori B.Sc. in Computer Engineering and Science from Concordia University as Valedictorian with The Most Great Distinction Sedig’s research synthesizes computer science, information science, cognition theory, and game studies to develop frameworks for interactive visual tools (IVTs). He investigates human-data interaction, visual reasoning, and interactivity design to support complex cognitive tasks like medical diagnosis, financial analysis, and scientific exploration. His human-centered approach emphasizes how computational tools and humans form coordinated cognitive systems for optimal task execution. Analysis of his recent publications reveals dominant trends in health informatics applications (drug safety analytics, electronic health records) and foundational work on human-information interaction frameworks. His visual analytics systems consistently bridge theoretical models with practical tools for ontology exploration, document triage, and explainable AI, demonstrating strong interdisciplinary collaboration across medical and computational domains. Key recognitions include: Governor General’s Gold Medal nomination for doctoral research Valedictorian honors at Concordia University As Insight Lab director, Sedig mentors graduate students through courses like Human-Computer Interaction, Information Visualization, and Design of Digital Cognitive Games. His teaching philosophy emphasizes how cognitive technologies mediate human thinking processes in professional and private contexts. While specific grant details aren’t provided, his lab’s sustained output in health analytics and visual interfaces indicates robust research funding. The Insight Lab operates as a collaborative hub for developing and evaluating IVTs, with current projects including VICTORIOUS for document scoping reviews and VISEMURE for multimorbidity analysis. Sedig’s team prioritizes empirical validation of how interaction design affects cognitive load and task efficiency in real-world data-intensive environments.

• Human-Computer Interaction
• Information Visualization
• Cognitive Technologies
• +۳ مورد دیگر

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

• Computer Systems Software
• Virtualization
• Distributed Storage
• +۳ مورد دیگر

Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.

• Hydro-geomorphology
• River Restoration
• Floodplain Management
• +۳ مورد دیگر