جست‌وجوی استاد و پژوهشگر

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 .

• 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.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto, specializing in large-scale data management, data systems, and applied machine learning. His research integrates machine learning techniques into scalable data platforms to enhance the analysis of massive datasets. He holds a PhD from the University of Toronto, an MSc from the University of Maryland, and a Bachelor's degree from the University of Patras. Education: PhD, University of Toronto MSc, University of Maryland at College Park Bachelor's degree, University of Patras, Greece Research Interests: Relational Deep Dive (ReDD): Natural language query execution over unstructured documents Streaming Video Queries (SVQ): Interactive query processing for video streams Reliable Text-to-SQL: Generating accurate SQL queries with human-in-the-loop assistance Machine Learning Integration in Data Systems Publications: Focus on video analytics, query processing, and reliable natural language interfaces. Notable works include optimizing video queries, declarative frameworks for temporal constraints, and abstention-based SQL generation. Awards: Inventor of the Year (1st Prize), University of Toronto (2011) Best Paper Awards at international conferences Entrepreneurship & Advising: Co-founder of Sysomos (Meltwater Group), Aislelabs (Constellation Software), and Workorb Advisor to mapintent and ktau Labs & Teams: Leads research groups developing systems like ReDD and SVQ, emphasizing collaboration between academia and industry.

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.

Xiaohui Yuمشاهده پروفایل

استاد

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

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

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.

Jonathan Cannonمشاهده پروفایل

استادیار

• Timing and Rhythm Perception
• Neural Dynamics
• Dynamical Systems Theory
• +۶ مورد دیگر

Jonathan Cannon is an Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University's Faculty of Science. His research focuses on timing and rhythm in perception and action, with particular interest in timing-related neural dynamics in the basal ganglia, cerebellum, and supplementary motor area. His work combines mathematical modeling with experimental approaches to understand the neural basis of rhythm perception and production. Dr. Cannon's research interests span timing and rhythm perception , neural dynamics , dynamical systems theory , Bayesian cognition , neural oscillations , and autism research . His approach centers on formulating and simulating neurophysiological and cognitive models, drawing on dynamical systems theory and Bayesian cognitive frameworks. His work incorporates psychophysics, EEG experiments, and collaborations with experimentalists to investigate how the brain processes rhythmic information. Analysis of his recent publications reveals a strong focus on the intersection of rhythm perception, motor control, and autism spectrum disorder. His work demonstrates how beat perception co-opts motor neurophysiology, with particular attention to predictive processes in rhythmic cognition. His research shows reduced precision of motor and perceptual rhythmic timing in autistic adults, while also finding intact sequence learning abilities in certain contexts. Dr. Cannon teaches advanced courses including Machine Learning Methods for Brain Modelling and Neural Data Analysis (PSYCH 734), Computational Models in Neuroscience (NEUROSCI 3MN3), and Neuroscience Seminars. His teaching reflects his interdisciplinary approach that bridges mathematics, neuroscience, and cognitive science. Beyond his academic work, Dr. Cannon is an active musician who performs on violin and guitar, particularly in klezmer and folk music contexts. He has also demonstrated entrepreneurial spirit through founding Flying Leap Games and developing the storytelling game 'Wing It,' which successfully crowdfunded and reached numerous retailers.

• Artificial Intelligence
• Computer Gaming and Animation
• Computer Graphics
• +۴ مورد دیگر

Yingying Wang is an Assistant Professor in the Computing and Software department at McMaster University , where she joined in January 2022. Her research focuses on generating expressive animations for AR/VR applications and games through interdisciplinary approaches combining Computer Graphics , Artificial Intelligence , and Human Behavior Analysis . Education : Bachelor and Master degrees from Nanjing University , Ph.D. from University of California, Davis (2017) Her research explores: Generative models for human motion style transfer Physics-based motion simulation Audio-driven character synthesis Dance choreography for virtual characters Cartoon animation perception Conversational character gesture synthesis Markerless hand motion capture Recent publications focus on 3D hand pose estimation , motion style transfer , gesture-locomotion coordination , and personality perception in virtual agents . Key methodologies include deep learning , multimodal data analysis , and real-time animation systems . Scientific contributions recognized through: $240,000 Labarge Catalyst Grant in Mobility in Aging (interdisciplinary team award) US Patent 10,796,482 (3D hand pose estimation) US Patent 9,811,937 (gesture-locomotion coordination) Teaching includes graduate and undergraduate courses in Computer Animation (CAS 737), Computer Graphics (COMPSCI 3GC3/SFWRENG 3GC3), and Software Development (COMPSCI 2ME3). Research group actively recruits Ph.D. and Master's students in graphics + deep learning domains.

Christian Messierمشاهده پروفایل

استاد

• Forest Ecology
• Tree Resilience to Global Changes
• Urban Forestry
• +۵ مورد دیگر

Christian Messier is a Professor at the University of Quebec in Outaouais (UQO) in the Department of Natural Sciences and at the University of Quebec in Montreal (UQAM) in the Department of Biological Sciences. He serves as Scientific Director of the Institute of Temperate Forest Sciences (ISFORT) and holds two prestigious research chairs: the Canada Research Chair on Tree Resilience to Global Changes and the NSERC/Hydro-Québec Chair on Tree Growth Control. His academic career spans over three decades since completing his Ph.D. in forest sciences from the University of British Columbia in 1991. Dr. Messier's research focuses on two primary areas: the complex functioning of managed natural forest systems to develop management approaches that promote resilience in the face of global changes, and the study of trees and urban forests to reconcile the needs of minimizing negative impacts of trees on human infrastructure while maximizing ecosystem services. His work integrates field studies, simulation modeling, and network theory to address pressing challenges in forest ecology and management. His recent publications demonstrate a strong emphasis on urban forest resilience, functional diversity in forest ecosystems, and nature-based solutions for climate adaptation. The research shows a clear trajectory toward more applied, solution-oriented approaches that bridge fundamental ecological understanding with practical forest management applications across diverse spatial scales from individual trees to entire landscapes. Francqui Foundation Chair (Belgium) - 2020 Member of The Royal Society of Canada - 2019 Personality of the Year 'Radio-Canada/Le Droit' - 2017 Humboldt Research Award - 2016 Prix Acfas – Michel-Jurdant - 2010 Canadian Forestry Scientific Achievement Award - 2006 Dr. Messier has supervised over 40 graduate students throughout his career and currently leads multiple major research projects including the Canada Research Chair on Forest Resilience, the NSERC/Hydro-Québec Chair on Tree Growth Control, and the SylvCIT project for urban forest immunization against global change. His research has been supported by significant grants from NSERC, Canada Research Chairs program, Hydro-Québec, and other major funding agencies. He directs the Messier lab with research assistants Kim Bannon and Fanny Maure, and collaborates with numerous researchers across Canada and internationally. His team studies diverse aspects of forest ecology including soil dynamics, functional composition of tree communities, nature-based solutions for climate adaptation, and ecophysiology of maple water flow. The lab maintains strong connections with the International Diversity Experiment Network with Trees (IDENT) and other major forest research initiatives.

Steven Livingstoneمشاهده پروفایل

دانشیار

• Data Science
• Affective Computing
• Human Computer Interaction
• +۴ مورد دیگر

Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +۵ مورد دیگر

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

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.

Amir Shmuelمشاهده پروفایل

استاد

• Neuroimaging
• fMRI
• Visual Perception
• +۵ مورد دیگر

Amir Shmuel is a Professor at McGill University's Faculty of Medicine, holding appointments in the Department of Neurology and Neurosurgery, Department of Biomedical Engineering, and Department of Physiology. He serves as Director of the Brain Imaging Signals Lab and Core Faculty at the McConnell Brain Imaging Centre of the Montreal Neurological Institute. His leadership includes chairing the 2018 International Society for Brain Connectivity conference and securing an $18.7M Canada Foundation for Innovation grant for Quebec's first large-bore 7 Tesla MRI scanner. Dr. Shmuel's research focuses on understanding neuronal mechanisms underlying functional brain imaging signals and visual information processing. His integrative approach combines fMRI, optical imaging, multi-channel neurophysiological recordings, and optogenetics across multiple spatial and temporal scales. Current research priorities include resting-state functional connectivity mechanisms, cortical lamina-resolved neurophysiology, and computational modeling of brain signals. His lab emphasizes parallel model development with experimental data acquisition. Recent publications demonstrate strong trends in multimodal neuroimaging integration, with emphasis on high-resolution fMRI techniques (especially 7T applications), resting-state connectivity analysis across species, and computational modeling of neurovascular coupling. Key subfields include laminar-specific activity mapping, artifact detection in medical imaging using deep learning, and cross-species functional connectivity frameworks. Dr. Shmuel's research is currently funded by the Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada (NSERC), and the US Department of Defense. His lab maintains active collaborations through initiatives like the International Society for Brain Connectivity and the PRIME-DE database consortium. Operating within the Brain Imaging Signals Lab at the Montreal Neurological Institute, Shmuel's team develops and applies advanced multimodal techniques including simultaneous fMRI-electrophysiology, voltage-sensitive dye imaging, and computational modeling frameworks. The lab maintains strong ties with the McConnell Brain Imaging Centre and participates in major open-science initiatives including the Tanenbaum Open Science Institute.

• 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.