جست‌وجوی استاد و پژوهشگر

• 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 .

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.

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.

Tom Wooمشاهده پروفایل

استاد

• Theoretical Chemistry
• Physical Chemistry
• Catalysis
• +۶ مورد دیگر

Tom Woo is a Professor in the Department of Chemistry at the University of Ottawa's Faculty of Science. His research focuses on computational quantum chemistry, catalysis, and energy-related systems. Using advanced molecular simulations, his group explores microscopic chemical processes and develops novel methods for energy storage, pharmaceutical catalysis, and material design. Key projects include studying catalytic systems for energy conversion and pharmaceutical synthesis, leveraging computational tools to uncover reaction mechanisms inaccessible via experiments. Research interests span computational chemistry, quantum chemistry, molecular dynamics, and nanotechnology. The Woo Group applies these methods to design metal-organic frameworks (MOFs) for CO2 capture, hydrogen storage, and other energy applications. Their work bridges theoretical models with experimental validation, emphasizing high-throughput screening and machine learning. Publications highlight breakthroughs in MOF design, computational validation of material databases, and carbon capture technologies. Collaborations focus on interdisciplinary challenges in energy sustainability and pharmaceutical catalysis.

• Algorithmic Databases
• Data Mining
• Computational Geometry
• +۵ مورد دیگر

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). He joined UIC after a postdoctoral fellowship at the University of Chicago, where he was part of the ChiData group under Sanjay Krishnan. He earned his Ph.D. in Computer Science from Duke University in 2020, advised by Pankaj K. Agarwal. His research focuses on designing efficient algorithms for databases, data mining, and computational geometry . Key themes include: Theoretical guarantees for practical problems Compact indexing structures for query efficiency Geometric optimization combined with database systems Fairness in algorithmic design (e.g., Fair Set Cover, FairHash) Temporal data analysis and dynamic query processing Notable scientific contributions include a Best Paper Award at ICDT 2024 for work on range entropy queries. His publications span top venues like SIGMOD, PODS, VLDB, and SODA, covering areas such as clustering algorithms, synthetic query witnesses, and temporal join optimizations.

Kenneth Edwin Barkerمشاهده پروفایل

استاد

• Privacy Preserving Data Repositories
• Database Management Systems
• Data Privacy and Security
• +۴ مورد دیگر

Dr. Kenneth Edwin Barker is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary, where he also serves as Director of the Institute for Security, Privacy and Information Assurance (ISPIA). His academic career spans several decades with significant contributions to database systems and privacy research. Dr. Barker earned his B.S. and M.S. in Computer Science from the University of Calgary in 1982 and 1984 respectively, followed by a Ph.D. in Computer Science from the University of Alberta in 1990. His educational background established the foundation for his extensive research career in database systems and information security. His primary research interests focus on Privacy Preserving Data Repositories , with specific attention to protecting privacy in mobile applications, understanding privacy's impact on data analytics, and architecting database management systems that inherently respect user privacy. His work also extends to distributed database environments, integration of legacy systems, and multidatabase environments. Dr. Barker's research bridges theoretical foundations with practical applications, making significant contributions to how privacy is implemented in real-world systems. An analysis of his recent publications reveals a strong trend toward practical privacy-preserving techniques for cloud data, social networks, and location-based services. His work consistently addresses the tension between data utility and privacy protection, developing innovative methods to maintain data value while safeguarding personal information. The publications span multiple subfields including encrypted search, graph privacy, high-dimensional data privacy, and privacy metrics. Best Paper Award at DBSec 2012 Best Paper Award at CODASPY 2012 Best Paper at BNCOD 2009 Dr. Barker has been instrumental in establishing privacy research infrastructure at the University of Calgary through his leadership of ISPIA. His research has attracted significant funding from various sources supporting privacy and security initiatives. While specific grant details aren't provided in the text, his extensive publication record indicates sustained research funding throughout his career. He has collaborated extensively with researchers both within and outside the University of Calgary, particularly with R. Alhajj and other colleagues on numerous projects. As Director of ISPIA, Dr. Barker oversees a research environment focused on advancing security and privacy technologies. The institute serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and legal experts to address complex privacy challenges. His leadership has positioned the University of Calgary as a significant player in privacy research within Canada.

Yangjun Chenمشاهده پروفایل

استاد

• Computer Science
• Database Systems
• Graph Databases
• +۶ مورد دیگر

Dr. Yangjun Chen is a Full Professor in the Department of Applied Computer Science at the University of Winnipeg, Canada. He holds a Ph.D. from the University of Kaiserslautern, Germany (1995). His research focuses on database systems, graph algorithms, computational complexity, and theoretical computer science. Key areas include Federated Databases, Deductive Databases, DNA Databases, and the P vs NP problem. Education: Ph.D. in Computer Science, University of Kaiserslautern, Germany (1995). Research interests span graph query processing, algorithm design, big data optimization, and NP-completeness. Recent work includes polynomial-time solutions for 2-MAXSAT and advancements in string matching algorithms for DNA databases. Publications highlight contributions to graph indexing, efficient reachability queries, and algorithmic efficiency in databases. His work often bridges theoretical foundations with practical database applications. Awards: Excellent Merit Award (2022-2023, 2021-2022) - University of Winnipeg Best Article, ACTA Scientific Computer Sciences (2022) Multiple Best Paper Awards at conferences like DBKDA 2016 and CyberC Summit Teaching includes Advanced Databases, Distributed Database Systems, and Algorithms courses at both undergraduate and graduate levels. Active in supervising research in database systems and theoretical computer science. Laboratory and team focus on database innovation, including projects on graph databases and efficient query processing techniques.

Guillaume Bourqueمشاهده پروفایل

استاد

• Bioinformatics
• Computational Genomics
• Comparative Genomics
• +۵ مورد دیگر

Guillaume Bourque is a Professor in the Department of Human Genetics at McGill University's Faculty of Medicine. He serves as an Investigator at the Victor Phillip Dahdaleh Institute of Genomic Medicine and as the Scientific Director of the Canadian Centre for Computational Genomics (C3G). His laboratory is based at 740 Dr Penfield Ave, Room 6103, Montréal, Québec, Canada, H3A 1A4, where he leads research in computational genomics and bioinformatics. Professor Bourque's research focuses on understanding mammalian genomes using comparative genomic and epigenomic analyses. His lab investigates the evolution of regulatory sequences , the role of transposable elements in gene regulation , and the impact of genome rearrangements in evolution and cancer . His team develops computational methods and resources for the functional annotation of genomes with special emphasis on sequencing-based assays including ChIP-seq, RNA-Seq, exome- and whole-genome sequencing, and single-cell analysis. The lab's work involves examining billions of DNA base pairs to interpret how variation impacts basic biology and disease. Recent publications (2024-2025) demonstrate Bourque's leadership in pangenome graph construction , transposable element analysis , epigenomic profiling , and cancer genomics . His work spans multiple disciplines from basic genome evolution to clinical applications in cancer and infectious disease. Notable projects include the development of tools like DeepPolisher for genome assembly polishing and contributions to understanding the genomic basis of long COVID. Bourque's laboratory is actively recruiting postdocs and graduate students with backgrounds in programming or statistics. The lab emphasizes quantitative approaches to biology, requiring applicants to have experience in quantitative biology as a plus. His collaborative work extends across multiple institutions and international consortia, reflecting the interdisciplinary nature of modern genomic research.

Meng Heمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Computational Geometry
• +۷ مورد دیگر

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.

J. Ian Munroمشاهده پروفایل

استاد

• Data Structures
• Space Efficient Data Structures
• Algorithm Design and Analysis
• +۷ مورد دیگر

J. Ian Munro is a University Professor and Canada Research Chair in Algorithm Design at the Cheriton School of Computer Science, University of Waterloo. He has been a faculty member at Waterloo since completing his PhD at the University of Toronto in 1971 and was appointed University Professor in 2006, one of the highest honors the university bestows on its faculty. Dr. Munro's research focuses on the design, analysis, and implementation of efficient algorithms and data structures, with particular emphasis on space-efficient solutions. His work spans several key areas including succinct data structures, tree algorithms, text search methods, priority queues, and memory management for data structures. His research has had significant impact on how we understand the theoretical limits of data representation while maintaining efficient query performance. The trajectory of his recent publications (2020-2024) reveals a continued focus on pushing the boundaries of space efficiency in data structures, particularly for graphs and trees. His work increasingly addresses dynamic scenarios where data structures must be updated efficiently while maintaining query performance. The research shows strong connections between information theory, combinatorics, and practical algorithm design, with applications spanning database systems, computational geometry, and text processing. Fellow of the Association for Computing Machinery (FACM) Elected Fellow of the Royal Society of Canada (FRSC) in 2003 Canada Research Chair in Algorithm Design Dr. Munro has supervised over 24 PhD students throughout his career and has been actively involved in teaching advanced courses on data structures and algorithms. His research has been consistently funded through competitive grants, including his Canada Research Chair position. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network across numerous publications. As a member of both the Algorithms and Complexity Group and the Data Systems Group at Waterloo, Dr. Munro contributes to interdisciplinary research that bridges theoretical computer science with practical systems challenges. His work continues to influence both theoretical understanding and practical implementations of efficient data management solutions.

Amal Zouaqمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Semantic Web
• +۵ مورد دیگر

Dr. Amal Zouaq is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds the FRQS (Dual) Chair in AI and Digital Health, serves as Director of the LAMA-WeST research laboratory, and is an Associate Member of MILA. Her work bridges artificial intelligence with applications in digital health, cultural heritage, and educational technologies, positioning her at the forefront of interdisciplinary AI research in Canada. Her research focuses on Artificial Intelligence , particularly Natural Language Processing and the Semantic Web . Specific interests include knowledge representation, ontology learning, SPARQL query generation, bias mitigation in language models, and clinical text processing. Her work spans multiple domains including healthcare, cultural heritage, and educational technology, with emphasis on developing practical AI solutions that address real-world challenges in knowledge management and information extraction. Analysis of her recent publications reveals a strong trajectory in advancing NLP techniques for knowledge-intensive applications. Her work increasingly focuses on domain-specific applications in healthcare and cultural heritage, with growing emphasis on ethical AI considerations like bias mitigation. The research demonstrates progression from foundational semantic web technologies toward more sophisticated neural approaches while maintaining strong theoretical grounding in knowledge representation. Scientific Recognition: Holder of the FRQS (Dual) Chair in AI and Digital Health Dr. Zouaq has supervised 23 graduate students to completion, including 1 PhD and 22 Master's theses, with research spanning ontology learning, knowledge representation, and NLP applications. Her supervision record demonstrates consistent mentorship in cutting-edge AI research with practical applications across multiple domains. She actively serves on program committees for major conferences in knowledge engineering, data mining, and semantic web technologies. She directs the LAMA-WeST (Web, Semantics and Text) laboratory , which specializes in natural language processing and artificial intelligence research. The lab focuses on knowledge representation, semantic technologies, and their applications in healthcare, cultural heritage, and educational contexts. As a member of IVADO and MILA, she collaborates with leading AI researchers across Montreal's vibrant AI ecosystem.

Vasiliki Kantereمشاهده پروفایل

استاد

• Big Data Management
• Big Data Analytics
• Machine Learning
• +۹ مورد دیگر

Dr. Vasiliki Kantere is a Professor at the School of Electrical Engineering and Computer Science within the Faculty of Engineering at the University of Ottawa . She has held previous academic positions, including: Senior Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens Maître d’Enseignement et de Recherche and Maître Assistante at the University of Geneva Junior Assistant Professor at the Department of Electrical Engineering and Information Technology, Cyprus University of Technology Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne Her research interests span a wide range of interdisciplinary domains, including: Big Data Management: data storage optimization, analytics workflows, and deployment on hybrid infrastructures Big Data Analytics: support for users of varying expertise and large data graphs Machine Learning: applications in resource management and query optimization Cloud Computing: cost-aware data management and service-level agreements P2P Systems: query processing and mobile peer environments Semantic Web: ontology mapping and semantic integration Distributed Systems: federated databases and grid computing Databases: query languages and semi-structured data management General: spatial data, privacy and security, and sensor networks No academic awards, funded grants, or advising roles are explicitly listed in the provided information. Her professional contributions are reflected through her extensive research collaborations and diverse academic experiences. The text does not mention any affiliated research labs or teams.