جست‌وجوی استاد و پژوهشگر

Ihab Ilyasمشاهده پروفایل

استاد

• Data Management
• Data Cleaning
• Machine Learning for Data Quality
• +۶ مورد دیگر

Professor Ihab Ilyas is a leading figure in data management and machine learning at the Cheriton School of Computer Science , University of Waterloo , where he holds the Thomson Reuters Research Chair in Data Quality . He is currently on leave from the university, serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc . His research focuses on data cleaning , large-scale data integration , knowledge graphs , and machine learning applications in data quality . He has pioneered systems like HoloClean and Saga , with commercial impacts through co-founded startups Inductiv (acquired by Apple) and Tamr . PhD in Computer Science from Purdue University Co-founder of Inductiv (acquired by Apple) Co-founder of Tamr Research Interests include: Probabilistic and uncertain data management Machine learning for data quality and enrichment Big data systems and information extraction Knowledge graph construction and optimization Scientific Awards and Recognitions include: Fellow of the Royal Society of Canada (2024) C.C. Gotlieb Computer Award (2024) IEEE Fellow (2021) ACM Fellow (2020) Cheriton Faculty Fellowship (2013-2016) Ontario Early Researcher Award

Khuzaima Daudjeeمشاهده پروفایل

استاد

• Distributed Systems
• Database Management
• Cloud Computing
• +۳ مورد دیگر

Khuzaima Daudjee is a Professor and David R. Cheriton Faculty Fellow in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on systems-oriented problems at the intersection of systems and data management, particularly building large-scale systems, storage infrastructure in the cloud, and modern hardware applications. He leads projects in distributed database systems, elastic scaling, and resource optimization. His recent work includes Caerus (geo-replicated transactions), Tiresias (predictive storage), and MorphoSys (automatic physical design metamorphosis). Daudjee has chaired major conferences including ICDE 2026 and serves on editorial boards for VLDB, SIGMOD, and IEEE TKDE journals. His awards include ACM Distinguished Scientist and multiple best paper awards. Educational initiatives include developing distributed systems teaching materials and supervising graduate students across database and distributed systems domains. His industry collaborations involve cloud infrastructure optimization and scalable data processing frameworks.

• Databases
• Data Mining
• Machine Learning
• +۳ 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.

Florian Kerschbaumمشاهده پروفایل

استاد

• Data security and privacy
• Confidentiality and integrity in data science
• Applied cryptography
• +۴ مورد دیگر

Florian Kerschbaum is a Professor and NSERC/RBC Industrial Research Chair in Data Security at the Cheriton School of Computer Science, University of Waterloo. His research focuses on data security and privacy, applied cryptography, and confidentiality in data science. Research interests span data collection/preparation management, secure multi-party computation, homomorphic encryption, differential privacy, and machine learning robustness/privacy. His work develops cryptographic solutions for practical data management challenges in distributed systems.

Zhenman Fangمشاهده پروفایل

دانشیار

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +۸ مورد دیگر

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

• Data Integration
• Data Management
• Data Preparation
• +۵ مورد دیگر

Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).

Jelle Hellingsمشاهده پروفایل

استادیار

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

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۵ 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.

• Data Integration
• Data Lakes
• Schema Mapping
• +۲ مورد دیگر

Renée J. Miller is Professor and Canada Excellence Research Chair in Data Intelligence at the University of Waterloo. A Fellow of the Royal Society of Canada and ACM, her research transforms how organizations manage and derive value from heterogeneous data sources. Professor Miller pioneered foundational work in schema mapping and data exchange recognized by the ICDT Test-of-Time Award. Her current research develops frameworks for semantic data discovery in data lakes, including the SANTOS system for relationship-based table search and Gen-T for table reclamation. She leads international collaborations advancing data management practices through tools like iBench for metadata generation and DIALITE for open data integration. Her CERC position establishes Canada's leadership in next-generation data intelligence systems.

Ke Wangمشاهده پروفایل

استاد

• Data Mining
• Database Systems
• Data Privacy
• +۳ 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.

Miao Zhengjieمشاهده پروفایل

استادیار

• Database Systems
• Data Management
• Data Curation and Integration
• +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.

Keval Voraمشاهده پروفایل

دانشیار

• Parallel & Distributed Computing
• Irregular Big Data Processing
• High Performance Computing

Keval Vora is an Associate Professor at the School of Computing Science, Simon Fraser University. His research focuses on scalable solutions for modern data analytics systems, particularly in graph processing and distributed computing. He leads the Parallel Data and Computing Lab (PDCL), developing systems like Peregrine , GraphBolt , and GraphBolt . Contact: TASC1 9419, keval@sfu.ca. Education: PhD in Computer Science from the University of California, Riverside (2017). Previously worked at Morgan Stanley on low-latency trading software. Teaching: Courses include Distributed Systems (CMPT 431) and Special Topics in Networks and Systems (CMPT 982). Advises graduate and undergraduate students on projects involving distributed systems and graph analytics. Research Interests: Parallel/Distributed Computing, Irregular Big Data Processing, High-Performance Computing. His work emphasizes efficient techniques with provable guarantees for large-scale systems. Software Contributions: Peregrine (pattern-based analytics), GraphBolt (dynamic graph processing), and Lumos (disk-based graph processing). These systems address challenges in scalability, efficiency, and real-time data handling.

Qizhen Zhangمشاهده پروفایل

استاد

• Hyperscale Data Processing
• Cloud Computing
• Network-Centric System Design
• +۴ مورد دیگر

Qizhen Zhang is a Professor in the Department of Computer Science at the University of Toronto's Faculty of Arts and Science. Specializing in hyperscale data processing systems, Zhang leads research at the intersection of cloud computing, distributed systems, and data center networking. Recent work focuses on network-centric designs for efficient large-scale data processing, including pioneering contributions to disaggregated data center architectures. Research interests center on hyperscale data processing , network-aware system design , and disaggregated infrastructure . Current projects investigate DPU-accelerated systems (dpBento, DPDPU), memory disaggregation (Cowbird, Redy), and blockchain scalability (FlexChain). Zhang's approach systematically integrates network characteristics into distributed system optimization, addressing challenges in trillion-item workloads through novel shuffle layers (TeShu) and compute pushdown mechanisms (TELEPORT). Zhang advises multiple graduate students working on satellite networking (SaTE), federated learning, and DPU-optimized storage (DDS). Professional service includes program committees for SIGMOD, VLDB, NSDI, and EuroSys (2023-2026), plus journal reviews for ACM TODS and IEEE/ACM Transactions on Networking. Industrial collaborations with Microsoft Research have yielded production-oriented systems like Redy and CompuCache. Key contributions include MimicNet for scalable network simulation (SIGCOMM 2021), GraphRex for network-aware graph processing (SIGMOD 2019), and foundational work on disaggregated data centers (CIDR 2020, VLDB 2020). Teaching responsibilities encompass graduate courses CSC2235 (Cloud-native Data Management) and undergraduate CSCC43 (Databases) at the University of Toronto.

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.

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.