جستجوی استاد راهنمای دانشگاهی

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.

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

Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.

• High-Performance Computing
• Distributed Systems
• Graph Algorithms
• +۳ مورد دیگر

Dr. Sazia Mahfuz is an Assistant Professor at the Jodrey School of Computer Science, Acadia University. She holds a Ph.D. from Queen’s University, an M.Sc. from Acadia University, and a B.Sc. from the University of Dhaka, Bangladesh. Her research focuses on Streaming IoT Data Analytics, Lifelong Machine Learning, and Medical Image Analytics, addressing challenges in real-time data processing, adaptive learning systems, and healthcare technology. Dr. Mahfuz’s work spans interdisciplinary applications including human activity recognition, vehicular sensor data classification, and diabetic retinopathy detection using deep learning. Her recent contributions emphasize hybrid architectures (CNN-RNN), transformer-based models, and frameworks for multi-level data analytics. She has published extensively on topics ranging from wearable sensor optimization to distributed data stream processing. Her research trends highlight a strong emphasis on IoT-enabled healthcare solutions and lifelong machine learning algorithms that generate synthetic examples to improve model adaptability. While no awards are listed, her articles reflect significant engagement with both theoretical and applied computer science domains. No advising or grant information is provided in the text.

• Streaming IoT data analytics
• Lifelong machine learning
• Medical image analytics

Gilles Caporossi is a Full Professor in the Department of Decision Sciences at HEC Montréal. He holds a Master’s degree from HEC Montréal and a Ph.D. in engineering mathematics from Polytechnique Montréal. As a member of the Group for Research in Decision Analysis (GERAD), he specializes in complex networks, data mining, algorithmics, combinatorial optimization, and artificial intelligence. He leads the Data Science and Business Analytics specialization in the M.Sc. program at HEC Montréal. His research focuses on privacy-preserving techniques, network optimization, graph theory applications, and machine learning. Notably, he contributed to the 40th-anniversary special issue of the Journal of Global Optimization (2021) and served as Department Head (2019–2021). He has supervised numerous students in Ph.D., M.Sc., and project-based research, including work on federated learning, network analysis, and NLP applications. Caporossi received the 2007 Young Scientist Prize from the International Academy of Mathematical Chemistry. He teaches courses on optimization, big data analytics, and complex networks. His work addresses real-world challenges in telecommunications, energy systems, and marketing analytics, leveraging advanced computational methods and interdisciplinary collaboration.

• Complex Networks
• Data Mining
• Algorithmics
• +۵ مورد دیگر

Omair Shafiq is an Associate Professor at the School of Computer Science, Carleton University. His research focuses on advanced topics in natural language processing (NLP), machine learning, and their applications in knowledge graphs, blockchain, and network security. He leads projects that integrate large language models (LLMs) with structured data to enhance information retrieval and decision-making systems. His work spans areas like explainable AI, adversarial robustness, and real-time traffic prediction. Shafiq is affiliated with Herzberg Laboratories and contributes to interdisciplinary research, including smart contracts, encrypted traffic classification, and IoT security. Education details are not explicitly listed in the provided text. His research interests emphasize practical solutions for challenges in AI ethics, data privacy (e.g., GDPR compliance), and scalable analytics for big data applications. He has developed frameworks like ECSGen/iZen for NLP tasks and CARD-B for encrypted traffic classification. His recent publications (2022–2025) highlight innovations in ensemble learning, adversarial attacks defense, and blockchain-based solutions for secure transactions. Shafiq’s work often bridges theory and practice, addressing real-world problems such as cybersecurity in vehicular networks, stock market prediction using deep learning, and user behavior analysis on social platforms. His contributions include tools like RevDet for event detection in news feeds and HybLoc for indoor localization. Though no specific awards are mentioned, his prolific publication record reflects sustained academic impact.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر