جست‌وجوی استاد و پژوهشگر

Noman Mohammedمشاهده پروفایل

دانشیار

• Data Security
• Privacy-Preserving Techniques
• Healthcare Data Privacy
• +۷ مورد دیگر

Noman Mohammed is an Associate Professor of Computer Science at the University of Manitoba’s Faculty of Science, leading the Data Security & Privacy (DSP) laboratory. He specializes in privacy-preserving techniques for data sharing, addressing challenges in healthcare, genomic, and financial data. In 2020, he received the Terry G. Falconer Memorial Rh Institute Foundation Emerging Researcher Award for his contributions to bridging privacy and data utility gaps. His research focuses on balancing data accessibility and individual privacy through technical solutions like federated learning, differential privacy, and secure genomic data processing. He emphasizes integrating policy guidelines with advanced technologies to mitigate privacy risks from interconnected data sources. Notable achievements include developing toolkits for data anonymization and federated learning frameworks, as well as advancing methods to secure cloud-based data storage and analysis. His work aligns with societal needs for robust privacy mechanisms in an era of expanding personal data collection. Future objectives involve addressing privacy challenges in emerging technologies, such as heterogeneous data integration and scalable systems for personal data management. Despite his research focus, he notably avoids social media platforms.

Tianzheng Wangمشاهده پروفایل

دانشیار

• Database Systems
• Operating Systems
• Parallel Programming
• +۵ مورد دیگر

Tianzheng Wang is an Associate Professor and Director of the Dual-Degree and Partnerships Programs at the School of Computing Science, Simon Fraser University. His research focuses on database systems, transaction processing, parallel and distributed computing, and embedded systems. He holds a PhD in Computer Science from the University of Toronto (2017) and a BSc in Computing from Hong Kong Polytechnic University (2012). Research Interests: Database systems optimized for modern hardware, parallel programming, synchronization, and distributed architectures. His work emphasizes high-performance transaction processing and efficient indexing techniques, with applications in cloud and embedded systems. Awards: ACM SIGMOD Best Paper Award (2025), IEEE TCSC Early Career Award (2019), and multiple distinguished reviewing recognitions (SIGMOD/VLDB 2021-2024). His research has been integrated into systems like Amazon Redshift and DragonflyDB. Teaching: Leads courses such as CMPT 454 (Database Systems II), CMPT 300 (Operating Systems), and special topics in databases. Actively mentors graduate and undergraduate students in research projects. Labs & Collaborations: Heads the Data-Intensive Systems Lab, part of SFU's Data Science and Systems groups. Collaborates on tools like PiBench for persistent memory benchmarking and contributes to open-source projects like CoroBase and Tabular.

• Computer Architecture
• Interconnection Networks
• Approximate Computing
• +۵ مورد دیگر

Natalie Enright Jerger is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She holds the Canada Research Chair in Computer Architecture and serves as Director of the Division of Engineering Science (2023-2028). Previously, she was the Percy Edward Hart Professor (2016-2019). She received her B.S. in Computer Engineering from Purdue University (2002), and M.S./Ph.D. in Electrical Engineering from University of Wisconsin-Madison (2004/2008). Her research focuses on: Multi/many-core architectures and on-chip networks Cache coherence protocols and memory hierarchy optimization Approximate computing and sustainable systems Intermittent computing for energy-harvesting devices Hardware acceleration for machine learning Her publications demonstrate strong emphasis on networks-on-chip (NoC) innovations, including routing algorithms, deadlock handling, power-efficient designs, and topology optimizations. Recent work expands into approximate computing, mobile architectures, and ML-driven hardware design. Major Awards: Fellow of Engineering Institute of Canada (2023) McLean Senior Fellow (2019) IEEE Micro Top Picks (2016) ACM/IEEE Microarchitecture Hall of Fame (2015) Sloan Research Fellowship (2015) Canada Research Chair (current) Distinguished Scientist, ACM Fellow, IEEE She leads the NEJ research group and collaborates with industry partners including Intel, AMD, Qualcomm, and IBM. Her work is funded by NSERC, CFI, and industrial grants. She co-chaired ASPLOS 2023 and HPCA 2014, and actively promotes diversity through WICARCH and ACM initiatives.

Andrew Warfieldمشاهده پروفایل

استاد مدعو

• Computer Systems Software
• Virtualization
• Distributed Storage
• +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.

Ahmad Afsahiمشاهده پروفایل

استاد

• Parallel Processing
• High-Performance Computing
• Network-Based Computing
• +۵ مورد دیگر

Dr. Ahmad Afsahi is a Professor in the Department of Electrical and Computer Engineering at Queen's University, Canada. He leads the Parallel Processing Research Laboratory (PPRL) and chairs the Graduate Studies committee in ECE. His research focuses on parallel processing, high-performance computing (HPC), and network-based systems, with emphasis on communication runtime systems, accelerated computing, and deep learning infrastructure. Education: Ph.D. (Electrical Engineering, 2000) from University of Victoria; M.Sc. (Computer Engineering, Sharif University of Technology); B.Sc. (Computer Engineering, Shiraz University). Research interests include parallel programming models, MPI optimization, GPU-aware communication, network-aware algorithms, and power-efficient HPC systems. He is a Senior Member of IEEE, ACM member, and licensed Professional Engineer in Ontario. Key Awards: Canada Foundation for Innovation Award, Ontario Innovation Trust Award. Over 50 publications in top venues like SC, EuroMPI, IPDPS, and IEEE journals. Current teaching includes cluster computing and digital systems. Labs/Groups: PPRL, Queen's Collaborative Graduate Specialization in Computational Science and Engineering, Data, Analytics, and Computing (DAC) Research Group.

Kaiyang Liuمشاهده پروفایل

استادیار

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +۳ مورد دیگر

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

M. Tamer Özsuمشاهده پروفایل

استاد

• Data Science
• Database Systems
• Distributed Data Management
• +۳ مورد دیگر

M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.

Gabriel Wainerمشاهده پروفایل

استاد

• Modelling and Simulation Methodologies
• Discrete Event Systems
• Real-Time Modelling
• +۷ مورد دیگر

Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

• Energy Efficiency in Software
• Multicore Memory Systems
• Hardware/Software Co-Design
• +۳ مورد دیگر

Arrvindh Shriraman is an Associate Professor and Program Director of Software Systems at Simon Fraser University's School of Computing Science in Surrey. His research focuses on energy-efficient software, multicore memory systems, and optimizing hardware/software interfaces for parallel programming. He holds a Ph.D. (2010) and M.S. (2006) in Computer Science from the University of Rochester, and a B.Eng. (2004) from the University of Madras. He teaches courses on parallel programming and energy-conscious software design. His research interests include synchronization mechanisms for domain-specific architectures, cache optimization, and FPGA-based acceleration. He has contributed to frameworks like Mu-grind for HLS-generated RTL instrumentation and TAPAS for parallel accelerator generation. Notable projects include RANGE-BLOCKS for synchronization in domain-specific systems and TapeFlow for gradient computation in neural networks. His work emphasizes real-time verification of autonomous systems and safety-critical trajectory planning for underwater vehicles. Shriraman collaborates closely with the Tangent Lab, exploring cutting-edge solutions in hardware-software co-design and embedded systems. His teaching and research bridge theoretical computer science with applied engineering challenges, addressing scalability, efficiency, and safety in modern computing systems.

Alaa Alameldeenمشاهده پروفایل

دانشیار

• Computer Architecture
• Memory Systems
• Hardware Security
• +۵ مورد دیگر

Alaa Alameldeen is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), part of the Faculty of Applied Sciences. Previously, he worked as a Research Scientist at Intel Labs (2006–2020) and held an Adjunct Faculty position at Portland State University (2008–2018). He earned a PhD in Computer Sciences from the University of Wisconsin-Madison (2006), and earlier degrees from Alexandria University, Egypt. His research focuses on computer architecture, including memory systems (processing-in-memory, cache/memory compression, security), energy-efficient architectures, and hardware-software co-design for machine learning. He advises PhD and MSc students in these areas and teaches advanced computing science courses. Key contributions include innovations in memory hierarchies, cache compression techniques, and mitigating hardware vulnerabilities. His work has been published in top conferences (e.g., ISCA, MICRO, HPCA) and patented in areas like near-memory processing and error correction. Alameldeen currently leads a research group exploring secure and high-performance memory architectures. He has supervised multiple graduate students, with many progressing to roles at leading tech companies and academic institutions.

Sharan Vaswaniمشاهده پروفایل

استادیار

• Machine Learning
• Reinforcement Learning
• Stochastic Optimization
• +۳ مورد دیگر

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their application to modern machine learning, particularly reinforcement learning and generalization analysis. Education & Career: PhD in Computer Science (2015-2018), University of British Columbia (UBC), supervised by Laks Lakshmanan and Mark Schmidt. Postdoc (2019-2021) at Mila - Quebec AI Institute with Simon Lacoste-Julien, and University of Alberta with Csaba Szepesvári. MSc in Computer Science (2015), UBC, focusing on influence maximization in social networks. BS from Birla Institute of Technology and Science, Pilani (2012), followed by research engineering at Siemens Corporate Research. Research Interests: Algorithmic development for decision-making in uncertain environments (bandits, reinforcement learning). Stochastic optimization methods with provable guarantees. Generalization and theoretical foundations of machine learning models. Recent Trends in Publications: Focus on optimization algorithms (e.g., stochastic gradient methods, line search, momentum techniques) with theoretical analysis. Contributions to reinforcement learning, including policy gradient methods and constrained MDPs. Exploration of adaptive algorithms for continual learning and over-parameterized models. Awards: Best Paper Honorable Mention (AISTATS 2022). Best Paper Award (2nd IEEE International Conference on Parallel Distributed and Grid Computing 2012). Research Group: Leads a team at SFU focused on machine learning optimization and decision-making systems. Active in organizing workshops at NeurIPS and ICML on optimization and reinforcement learning theory.

Lata Narayananمشاهده پروفایل

استاد

• Distributed computing
• Graph algorithms
• Routing algorithms
• +۷ مورد دیگر

Dr. Lata Narayanan is a Professor in the Department of Computer Science and Software Engineering at Concordia University, Montreal, Canada. Her research spans theoretical and applied aspects of distributed systems, with a focus on algorithms for mobile agents, communication networks, and sensor networks. Department: Computer Science and Software Engineering University: Concordia University Research Interests Lata Narayanan specializes in algorithms for mobile robots and ad hoc networks , with expertise in routing on distributed networks , parallel algorithms , and social network analysis . Her work addresses challenges in sensor network optimization, barrier coverage, and time-energy tradeoffs for evacuation systems. Article Trends Her recent publications (2021-2025) emphasize game theory for network dynamics, cloud resource allocation , and temporal graph exploration . Key themes include strategic diversity, truck-drone delivery logistics, and energy-sharing protocols for mobile agents.

Andrew Rau-Chaplinمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +۱۴ مورد دیگر

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Trevor Brownمشاهده پروفایل

دانشیار

• Concurrent Data Structures
• Lock-Free Algorithms
• Transactional Memory
• +۴ مورد دیگر

Trevor Brown is an Associate Professor in the Computer Science department at the University of Waterloo, affiliated with the Cheriton School of Computer Science. He leads the Multicore Lab and specializes in concurrent data structures, non-blocking algorithms, and memory management. His research bridges theory and systems, focusing on practical implementations of lock-free trees, transactional memory, and techniques for non-uniform memory architectures. Education includes a PhD in Computer Science from the University of Toronto and a B.Sc. in Computer Science and Mathematics from York University. Research interests center on concurrent systems, with recent work exploring hardware-accelerated indexing, memory reclamation techniques, and performance anomalies in microbenchmarks. His publications demonstrate consistent innovation in parallel computing, with articles frequently appearing at top conferences like PPoPP, SPAA, and DISC. Sustainable energy research includes optimizing hybrid power systems and battery storage solutions. Awards include multiple best paper/artifact recognitions at SPAA and PPoPP, teaching excellence honors, and nominations for the Governor General’s Gold Medal. Extensive advising includes 13+ graduate students and PDFs, with research grants exceeding $965K from NSERC, Huawei, and CFI. He directs the Multicore Lab, developing open-source tools like SetBench for rigorous performance benchmarking.