جست‌وجوی استاد و پژوهشگر

Marin Litoiuمشاهده پروفایل

استاد

• Cloud Computing
• Self-Adaptive Systems
• DevOps
• +۶ مورد دیگر

About Marin Litoiu is a Professor at York University, holding dual affiliations in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering and the School of Information Technology in the Faculty of Liberal Arts and Professional Studies. He is a Fellow of the Canadian Academy of Engineering and a recipient of the 2020 IBM Faculty of the Year Award. His research focuses on cloud computing, self-adaptive systems, DevOps, IoT, and machine learning-driven performance engineering. Research & Awards Litoiu leads the Dependable Internet-of-Things Applications (DITA) program, funded by NSERC, and co-founded Bitnobi Inc., acquired by Myant. His notable awards include the CASCON 2019 Most Influential Paper Award and Best Paper Awards at multiple conferences. His work emphasizes practical applications of adaptive systems, cybersecurity, and smart infrastructure integration. Grants & Projects NSERC CREATE Program: $1.65M for the DITA program (2018) York Innovation, TIAP, NSERC, and OCI-funded Bitnobi incubation Leadership in multiple CASCON workshops on cloud computing and AIOps Labs & Teams Litoiu’s lab has produced impactful startups like Bitnobi and pioneered research in self-driving systems, edge computing, and AI-driven operations. His team collaborates with industry partners like IBM and explores cutting-edge topics such as LLMs in performance optimization and fault detection.

• Data Systems for AI
• Retrieval-Augmented Generation (RAG)
• Database Systems
• +۴ مورد دیگر

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.

Mohammad Hamdaqaمشاهده پروفایل

دانشیار

• Software Engineering
• Model-Based Software Engineering
• Cloud Computing
• +۷ مورد دیگر

Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.

Kenneth Kentمشاهده پروفایل

استاد

• Hardware/Software Co-Design
• Virtual Machines
• Reconfigurable Computing
• +۵ مورد دیگر

Dr. Kenneth Kent is a Professor in the Department of Computer Science at the University of New Brunswick (UNB), where he has served for 14 years. He is the Director of the Information Technology Centre (ITC) and heads the Reconfigurable Computing Group. He also serves as Director of the IBM Centre for Advanced Studies - Atlantic and holds an Honorary Professorship at Hochschule Bonn-Rhein-Sieg. His research focuses on hardware/software co-design, reconfigurable computing, virtual machines, and embedded systems. Dr. Kent earned his PhD and Master of Science in Computer Science from the University of Victoria. His work has led to over 100 refereed publications and the supervision of 70+ graduate students. He co-founded WEnTech Solutions Inc., a software firm addressing waste-to-energy optimization. His awards include the IBM Faculty Fellow of the Year and Project of the Year (as Principal Investigator) for contributions to the J9 Java Virtual Machine. His articles span FPGA acceleration, compiler optimization, cloud storage security, and IoT intrusion detection. Recent work emphasizes energy-efficient Node.js systems and advancements in CAD tools like VTR 9 for FPGA architecture. Dr. Kent’s advising and grants include leading the IBM CAS Atlantic and directing industry-academia collaborations. He has pioneered technologies such as the Eclipse OpenJ9 JVM and the CephArmor storage interface, balancing academic research with commercial innovation. He leads the Reconfigurable Computing Group at UNB and collaborates with the Institute for Visual Computing in Germany. His research bridges theoretical computing and practical applications, with a focus on scalable systems and embedded technologies.

Ali José Mashtizadehمشاهده پروفایل

دانشیار

• Operating Systems
• Distributed Systems
• Storage Systems

Ali José Mashtizadeh is an Associate Professor at the Cheriton School of Computer Science, University of Waterloo. His research focuses on operating systems, distributed systems, and storage, with expertise in system reliability, network optimization, and concurrent programming. Education: Ph.D., Computer Science, Stanford University (2017) M.S., Computer Science, Stanford University (2017) M.Eng., Electrical Engineering and Computer Science, MIT (2007) B.S., Electrical Engineering, MIT (2006) His research centers on designing scalable and reliable systems, with recent publications exploring TCP network frameworks, in-memory data persistence, and microsecond-scale scheduling. Key themes include optimizing tail latency, neutralization-based memory reclamation, and fault-tolerant distributed services. His articles consistently demonstrate innovations in low-latency networking, operating system architecture, and cloud infrastructure, with recent emphasis on serverless benchmarks and processor customization. No scientific awards or advising relationships are detailed in the provided materials.

Aastha Mehtaمشاهده پروفایل

استادیار

• Systems Security
• Data Privacy
• Operating Systems
• +۳ مورد دیگر

Aastha Mehta is a Tenure-Track Assistant Professor in the Computer Science Department at the University of British Columbia (UBC), affiliated with the Systopia Lab and the Security and Privacy Group. Her research focuses on systems security, data privacy, and distributed systems, particularly in cloud and edge platforms. She teaches courses such as CPSC 538M (Systems Security) and CPSC 317 (Introduction to Computer Networking). Research Interests: Her work spans systems security, network privacy, ICS security, and privacy-preserving technologies. Key projects include compliance tools for serverless applications (Growlithe), network side-channel mitigation (NetShaper), and epidemic risk mitigation systems leveraging Bluetooth beacons. Received NCC Funding for ICS security research (2024) Faculty Teaching Awards (UBC) Advising: Currently supervises PhD and MSc students like Yayu Wang, Angela DeMarco, and Satvik Vemuganti. Collaborates with researchers from MPI-SWS, UofT, and UBC ECE on security and privacy projects. Service: Serves on program committees for CCS 2025, ASPLOS 2025, and Oakland S&P. Organized mentoring programs at SOSP and OSI.

Lizhi Liaoمشاهده پروفایل

استادیار

• Software Engineering
• DevOps
• Performance Testing
• +۴ مورد دیگر

Lizhi Liao is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN). He received his Ph.D. in Electrical and Computer Engineering from the University of Waterloo under Dr. Weiyi Shang. His research focuses on software performance engineering, DevOps practices, and AI system quality, particularly addressing performance regression detection and optimization in fast-paced development environments. Education: Ph.D., Electrical and Computer Engineering, University of Waterloo (supervisor: Dr. Weiyi Shang). Previous roles include Research Intern at ERA Environmental Management Solutions (2019–2024) and Software Developer Intern at Nakisa (2019). Research interests include software performance engineering, web GUI testing, and AI-driven performance analysis. His work emphasizes bridging architectural models with performance data to improve testing efficiency. Recent awards include the ACM SIGSOFT Distinguished Paper Award (2025) and multiple doctoral awards from the University of Waterloo. Teaching includes AI-related courses at MUN and guest lectures on software architecture and testing at the University of Waterloo. He has served as a program committee member for conferences like ICSE, FSE, and ESEC, and co-chaired workshops such as LTB 2025. He is actively involved in the Standard Performance Evaluation Corporation (SPEC) Research Group, focusing on DevOps performance standards. Liao collaborates with industry partners to apply performance analytics in real-world systems, such as database-centric applications and serverless architectures. His presentations span global venues including ICSE, FSE, and the Consortium for Software Engineering Research.

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +۵ مورد دیگر

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

Mohammad Shahradمشاهده پروفایل

استادیار

• Cloud Computing
• Sustainability
• Large-Scale Computing Systems
• +۳ مورد دیگر

Mohammad Shahrad is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC) and an Associate Faculty Member of UBC's Computer Science Department. He leads the UBC Cloud Infrastructure Research for Reliability, Usability, and Sustainability (CIRRUS) Lab. His research focuses on sustainable and efficient large-scale computing systems, particularly in cloud resource management and serverless systems. Education: Ph.D. in Electrical Engineering, Princeton University (2020) M.A. in Electrical Engineering, Princeton University (2016) B.Sc. in Electrical Engineering, Sharif University of Technology, Tehran (2014) Research Interests: Shahrad's work emphasizes energy-efficient cloud computing, serverless architecture optimization, and sustainability in distributed systems. His projects address challenges like carbon footprint reduction, resource allocation, and cold-start delays in serverless platforms. He employs interdisciplinary approaches, combining system design, algorithm development, and empirical analysis to tackle real-world cloud infrastructure problems. Recent Contributions: His research trends include carbon-aware scheduling, geospatial shifting for sustainability, and developer-centric compliance tools for serverless applications. These efforts aim to balance performance, cost, and environmental impact in modern cloud ecosystems. Scientific Awards: Community Award at USENIX ATC '20 for Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider Advising & Grants: As CIRRUS Lab lead, Shahrad mentors researchers in cloud infrastructure and sustainability. While specific grant details are not listed, his work reflects substantial industry collaboration (e.g., Microsoft Research, Azure). Labs/Teams: The CIRRUS Lab focuses on creating sustainable cloud solutions through open-source frameworks like OpenPiton and collaborative industry partnerships.

Samer Al-Kiswanyمشاهده پروفایل

دانشیار

• Data Center Optimization
• Cloud Computing
• Serverless Computing
• +۳ مورد دیگر

Samer Al-Kiswany is an Associate Professor in the Department of Computer Science at the University of Waterloo. His research focuses on optimizing modern data center and cloud architectures, leveraging advancements in networking, storage, and processing to enhance system efficiency and performance. He holds a Ph.D. (2013) and M.Sc. (2008) from the University of British Columbia and a B.Sc. (2003) from Jordan University of Science and Technology. Research Interests: Dr. Al-Kiswany’s work emphasizes cloud-native paradigms (serverless, disaggregated architectures), data deduplication, network-accelerated scheduling, and fault-resilient systems. His projects include developing software stacks for efficient cloud applications and benchmarking frameworks for edge and serverless computing. Publications: His recent work spans innovations in data deduplication (e.g., VectorCDC, SeqCDC), network-accelerated scheduling (Draconis, Falcon), and fault tolerance (CASPR). Over 30 publications demonstrate expertise in storage systems, distributed computing, and networking. Awards: No awards explicitly listed in provided texts. Grants & Advising: No student advisees or grant details provided. His research is supported through university and collaborative projects. Labs/Teams: Involved in cloud systems and distributed computing research groups at the University of Waterloo.

• Software Engineering
• Performance Testing
• Machine Learning Applications in Software Engineering
• +۵ مورد دیگر

Weiyi (Ian) Shang is an Associate Professor at the University of Waterloo, affiliated with the Department of Electrical and Computer Engineering within the Faculty of Engineering. His research focuses on software engineering, performance testing, and machine learning applications in software systems. He leads the Software Engineering and System Engineering Lab, emphasizing practical solutions for logging, performance optimization, and automated testing. Key research areas include log analysis (privacy leakage detection, log summarization, and logging strategies), performance monitoring (regression detection, workload modeling), API evolution (migration techniques, workaround analysis), and automated code generation (LLMs in bug decomposition, AI code evaluation). His work bridges theoretical advancements with industrial applications, particularly in web systems and mobile app ecosystems. Publications span empirical studies, novel algorithms (e.g., DELA for error detection, CoMSA for configuration testing), and tools like LogAssist and Log4Perf. His research consistently addresses challenges in developer productivity, system reliability, and security across diverse domains like federated learning and DevOps practices. Notable contributions include improving log management through topic models, enhancing performance testing efficiency via microbenchmark optimization, and analyzing privacy risks in mobile app logs. Ongoing work explores AI-driven code evaluation and generalizable code embeddings for software tasks. Shang’s lab collaborates with industry on real-world systems, as seen in case studies involving serverless applications and database-centric systems. His research often involves empirical studies and tool development to bridge gaps between academic research and practical software engineering challenges.

Zadia Codabuxمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Technical Debt
• Software Metrics
• +۸ مورد دیگر

Zadia Codabux is an Associate Professor at the University of Saskatchewan , Department of Computer Science. Her work focuses on Empirical Software Engineering , Technical Debt , and Software Security . Education: Ph.D. in Computer Science, Mississippi State University (USA) M.S. in Computer Science, University of Mauritius Her research explores technical debt in code reviews, predictive analytics for vulnerability detection, and the intersection of software metrics with security. Recent work includes serverless smell detection tools and studies on LLM impacts on programming platforms. Key publications span conferences like FSE , ESEM , and MSR , with trends showing increasing focus on Android security , code review civility , and developer support systems . Scientific Awards: NSERC Discovery Grant (PI 2021-2026, 2026-2027) IEEE Computer Society TCSE Distinguished Paper Award (SANER'22) SSHRC Insight Grant (collaborator 2024-2025) She supervises PhD and MSc students in software quality research and contributes to equity initiatives like CRA-WP and Grace Hopper Celebration.

Hong Zhangمشاهده پروفایل

استادیار

• Large-scale data analytics
• Distributed ML training and serving systems
• Application and network scheduling
• +۵ مورد دیگر

Hong Zhang is an Assistant Professor at the University of Waterloo, specializing in large-scale data analytics, distributed machine learning systems, and cloud computing. His research focuses on advancing robust and efficient methods for modern AI systems, including topics like adversarial robustness, zero-knowledge proofs for deep learning, and scalable inference techniques for large language models. He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology and dual bachelor's and master's degrees in Electronics and Communications Engineering from Huazhong University of Science and Technology. Education: Ph.D., Computer Science and Engineering, Hong Kong University of Science and Technology (2019) B.Sc. and M.Sc., Electronics and Communications Engineering, Huazhong University of Science and Technology, China (2013) Research Interests: Hong's work bridges theoretical foundations and practical applications in distributed ML systems, data center networking, and secure AI. Key areas include optimizing large-scale training/serve pipelines, mitigating adversarial vulnerabilities, and developing privacy-preserving techniques like zero-knowledge proofs for deep learning. His contributions span both algorithmic innovations and real-world system implementations. Publications Trends: Recent work emphasizes scalable inference acceleration (e.g., Eagle series), trustworthy AI (e.g., generative model guidelines), and cryptographic safeguards for ML (e.g., zkdl/zkllm frameworks). He frequently explores intersections between security, efficiency, and scalability in modern AI systems. Awards/Grants: No specific awards or grants mentioned in available texts. Advising & Labs: While student names aren't listed, his research group likely focuses on distributed systems and secure ML. Active collaborations likely involve cloud computing and adversarial machine learning domains.