جست‌وجوی استاد و پژوهشگر

Mohamed Beshirمشاهده پروفایل

استاد

• Fire Dynamics
• Fire Safety Engineering
• Informal Settlements
• +۳ مورد دیگر

Mohamed Beshir is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa, Canada. His research focuses on fire safety engineering, urban fire spread in informal settlements, and enhancing resilience in low- and middle-income communities. He teaches courses such as Fundamentals of Fire Safety Engineering and Fire Dynamics I , contributing to advanced engineering education. Education : PhD in Fire Safety Engineering, University of Edinburgh (2022). Professional Experience : Previously worked as a fire engineer at a leading UK consultancy, addressing fire safety in residential, retail, and historical buildings. Prof. Beshir’s research interests include fire and smoke dynamics, combustion modeling, compartment fires, and fire safety resilience. His work addresses environmental and economic sustainability in construction, alongside human behavior in fire scenarios. His scientific contributions span fire dynamics in informal settlements, wind effects on flashover, and energy harvesting technologies. He has received awards like the Sheldon Tieszen Student Award (2020) and the Best MSc Thesis Poster award (IMFSE, 2017). Teaching : CIVE 5609/IPIS 5504 (Fire Safety Engineering), CIVE 5610 (Fire Dynamics I). Collaborations : Worked with researchers like R. Walls, Y. Wang, and D. Rush on large-scale fire experiments and CFD modeling.

• Information Visualization
• Visual Analytics
• Graph Drawing
• +۳ مورد دیگر

Tamara Munzner is a Professor of Computer Science at the University of British Columbia, with a confirmed email at tamara@cs.ubc.ca . Her research focuses on information visualization , visual analytics , and graph drawing , emphasizing practical design frameworks and theoretical foundations. Recent work explores visitor engagement with science museum exhibits large-scale data visualization challenges health informatics applications for chronic pain management advanced graph neural network visualization His publications in IEEE Transactions on Visualization and Computer Graphics and Eurographics conferences demonstrate her expertise in visual analytics. Scientific awards include the Best Panel Award at Euro Vis 2009 for her work on visualization education. Her research spans visualization design principles, dimensionality reduction techniques, and applications in genomic epidemiology and environmental sustainability.

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.

• 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.

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.

Angel Xuan Changمشاهده پروفایل

دانشیار

• Natural language processing
• 3D scene understanding
• Multimodal grounding of language
• +۵ مورد دیگر

Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.

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.

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.

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.

• Wireless Communications
• MIMO Systems
• Signal Processing
• +۳ مورد دیگر

Oussama Damen is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering. His research focuses on advanced wireless communication systems, particularly in MIMO (Multiple-Input Multiple-Output) systems, signal processing, and machine learning applications in telecommunications. He is actively involved in developing innovative solutions for beamforming, hybrid precoding, and distributed decoding in massive MIMO and millimeter-wave networks. Damen's work also extends to optical fiber communication, federated learning in wireless systems, and optimization of resource allocation in next-generation networks like 5G/6G. His research interests include wireless communication theory, antenna system design, channel modeling, and algorithm development for improving spectral and energy efficiency. He has contributed extensively to the theoretical foundations of MIMO detection, lattice reduction techniques, and statistical signal processing methods. Notable trends in his publications emphasize bridging theoretical performance limits with practical implementations, particularly in scenarios involving channel impairments, limited backhaul capacity, and multi-core fiber transmission. His work often addresses fairness and optimization in distributed systems, including federated learning frameworks and hybrid beamforming architectures. No scientific awards or grants are explicitly mentioned in the provided information. Damen has advised no listed students, and no specific lab affiliations are noted.

Pourang Iraniمشاهده پروفایل

استاد

• Human-Computer Interaction
• Information Visualization
• Ubiquitous Computing
• +۴ مورد دیگر

Pourang Irani is a Professor and Principal’s Research Chair in Ubiquitous Analytics at the University of British Columbia (Okanagan campus), within the Irving K. Barber Faculty of Science’s Department of Computer Science, Mathematics, Physics and Statistics. Previously, he served at the University of Manitoba for 19 years as a faculty member and Acting Associate Dean of Science. His research focuses on Human-Computer Interaction (HCI), Wearable Computing, and Information Visualization, with an emphasis on designing interactive systems for 'anytime, anywhere' sensemaking using emerging technologies like mixed reality and smart devices. He leads interdisciplinary projects such as the NSERC CREATE grant on Visual and Automated Disease Analytics, training data scientists in health analytics. Education: PhD in Computer Science (University of New Brunswick, 2002), supervised by Dr. Colin Ware. Research Interests: Wearable interfaces (smartwatches, smartrings, and AR/VR) Data visualization for health and pervasive systems Persuasive health technologies and data storytelling Mid-air and hands-free interaction techniques Embodied interaction through social robots Recent Work Trends: Articles emphasize innovations in smartwatch interaction (e.g., bezel-to-bezel gestures), tactile visualizations via skin-dragging, and voice assistant-driven health data queries. Projects like Databiting explore transient personal data exploration, while Data Videos focus on narrative-driven health communication. Grants & Leadership: Principal Investigator of NSERC CREATE Visual and Automated Disease Analytics program. Co-leads UBCO’s Digital Transparency cluster. Active in interdisciplinary teams addressing health informatics and HCI challenges. Labs & Teams: Leads a lab developing prototypes in wearable computing, spatial analytics, and immersive technologies. Collaborates with researchers in medicine, engineering, and data science to translate HCI innovations into practical solutions.

Pascale Bironمشاهده پروفایل

استاد

• Hydro-geomorphology
• River Restoration
• Floodplain Management
• +۳ مورد دیگر

Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.

Ryo Suzukiمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Robotics
• Tangible User Interfaces
• +۶ مورد دیگر

Ryo Suzuki is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science. His research focuses on Human-Computer Interaction (HCI) and robotics, particularly in tangible user interfaces, swarm robotics, and shape-changing interfaces. He holds a PhD in Computer Science from the University of Colorado Boulder (2020) and has conducted research internships at Stanford University, UC Berkeley, the University of Tokyo, and Adobe Research. His educational background includes advanced coursework in robotics and HCI, with a strong emphasis on interdisciplinary innovation. He teaches courses such as Human-Computer Interaction II, Human-Robot Interaction, and Special Topics in Mixed Reality Application Design. Research interests include designing novel interfaces for AR/VR/MR systems, haptic feedback mechanisms, and collaborative robotics. His work often bridges physical and digital spaces, such as through projects like LiftTiles (shape-changing building blocks) and RoomShift (room-scale haptic environments). Key awards include the UIST 2020 Honorable Mention Paper Award, DIS 2019 Best Paper Award, and the Ministry of Internal Affairs and Communications in Japan Innovation Award. His research has been featured in IEEE Computer Graphics and Applications, TechXplore, and Wired. He actively explores future work in symbiotic AI systems, AI-in-the-loop AR applications, and scalable shape-changing robotics. His lab emphasizes hands-on prototyping and user-centered design principles.