جست‌وجوی استاد و پژوهشگر

Farida Cherietمشاهده پروفایل

استاد

• Biomedical technology
• Artificial intelligence
• Medical imaging
• +۴ مورد دیگر

Dr. Farida Cheriet is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . She serves as Director of the 4D Imaging and Vision Laboratory (LIV4D) and holds membership in the Institute of Biomedical Engineering and the Institute for Data Valorization (IVADO) . Her research spans medical imaging , biomedical technology , artificial vision , and 3D movement estimation . Ph.D. (Montreal), DEA (France), Ing. (Algeria) Her work focuses on interpretable AI models for retinal pathology diagnosis, 3D anatomical reconstruction from medical imaging, and automated segmentation of vascular and spinal structures. She leads innovations in deep learning applications for diabetic retinopathy screening, coronary artery analysis , and spine surgery planning . Recent publications highlight her leadership in computer vision for biomedical applications, with 15 featured articles covering ICU readmission prediction, retinal vessel analysis, dental crown design, and spinal ultrasound processing. She received CFI funding (2021) for cutting-edge facilities and collaborates on pan-Canadian ophthalmic image databases (2018). Dr. Cheriet has supervised 28 Ph.D. students and 46 Master's students in biomedical computing, including projects on scoliosis analysis, retinal imaging, and cardiac MRI. Her teaching includes digital communications and computer graphics courses.

Laks V.S. Lakshmananمشاهده پروفایل

استاد

• Data Management
• Graph Computing
• Machine Learning
• +۵ مورد دیگر

Laks V.S. Lakshmanan is a Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. His research focuses on data management, graph computing, machine learning, and algorithms, with notable contributions to dense subgraph discovery, influence maximization, and healthcare informatics. He teaches advanced courses on databases and data management, including CPSC 404 (Advanced Relational Databases) and CPSC 534L (Topics in Data Management). His awards include the ACM SIGMOD Research Highlight Award, the IEEE Data Science Best Paper Award, and recognition as an ACM Distinguished Scientist (2016). His work bridges theoretical algorithm design with practical applications in social networks, bioinformatics, and healthcare. Key research themes include optimizing graph algorithms for large-scale data, combating misinformation through network analysis, and developing efficient methods for subgraph enumeration and influence propagation. His recent publications explore topics like clinical event prediction (TRACE), cost-effective LLM selection (ThriftLLM), and cross-modal consistency in AI systems. Education: Details not explicitly provided in sources. Grants & Funding: Recipient of NSERC Discovery Accelerator Supplements. Labs/Teams: Engaged in UBC's data management research groups and collaborative initiatives with industry partners.

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

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.

Michael Winterمشاهده پروفایل

استاد

• Programming Languages
• Semantics of Programming Languages
• Program Verification
• +۵ مورد دیگر

Michael Winter is a Professor in the Department of Computer Science at Brock University, Canada. He holds a Habilitation from the University of the Federal Armed Forces, Munich (2002), a Ph.D. (Dr. rer. nat.) from the same institution (1998), and a Master of Computer Science (Dipl. Inform.) (1993). His research focuses on programming languages, semantics, program verification, relational methods, category theory, and fuzzy logic. He is the Managing Editor of the Journal on Relational Methods in Computer Science . Winter's academic roles include teaching courses such as COSC 2P05 (Programming Languages), COSC 3P91 (Advanced Object-Oriented Programming), and COSC 5P02 (Logic in Computer Science). He has advised numerous graduate and undergraduate students on topics including fuzzy relational analysis, program verification, and formal methods. His research contributions span over 100 publications in journals and conferences, emphasizing relational algebra, category theory, and their applications in computer science. His work integrates theoretical foundations with practical applications, such as developing tools like RelView for relational computation and exploring applications in ambient intelligence and data visualization. Winter collaborates internationally, contributing to conferences like RAMiCS and publishing in journals like Fuzzy Sets and Systems and Journal of Logical and Algebraic Methods in Programming .

Sean Chesterمشاهده پروفایل

استادیار

• Data Management
• Parallel Computing
• Algorithm Engineering
• +۵ مورد دیگر

Sean Chester is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He is affiliated with the Faculty of Engineering and Computer Science and specializes in scalable data analytics, with a focus on data management, parallel computing, and algorithm engineering. His research interests include GPU-native algorithms, multicore optimization, spatio-temporal data processing, and graph-based analysis. He actively contributes to open-source projects and course materials on platforms like GitHub, emphasizing open science and education. Recent work highlights include advancements in skyline computation, GPU-accelerated algorithms, and efficient processing of large-scale datasets. His publications span topics such as kNN optimization, social network anonymization, and vectorized k-core decomposition. Sean is involved in teaching courses like CSC 485C/586C on data management on modern hardware and CSC 370 on database systems. No scientific awards or grants are explicitly mentioned in the provided texts. He collaborates with students and researchers through platforms like GitHub, where he maintains repositories related to algorithm engineering and educational materials.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +۱۱ مورد دیگر

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

Bin Guoمشاهده پروفایل

استادیار

• Parallel and Distributed Computing
• Graph Algorithms
• Graph Mining
• +۵ مورد دیگر

Bin Guo is an Assistant Professor at the Computer Science Department of Trent University (since Jan. 2024) and an Adjunct Assistant Professor at the Computing & Software Department of McMaster University. He holds a PhD in Computer Science from McMaster University (2023) and an MSc in Applied Computer Science from Winnipeg University (2018). His research focuses on parallel/distributed computing, graph algorithms, and computer security for data analytics, with notable contributions to federated k-core decomposition and secure distributed algorithms. He teaches courses in database systems, operating systems, and computer security at Trent University and has taught at McMaster University. Education: PhD in Computer Science, McMaster University (2023) MSc in Applied Computer Science, Winnipeg University (2018) Research Interests: Parallel and Distributed Computing Graph Algorithms and Mining Computer Security & Privacy Federated Learning Concurrent Data Structures Advising & Grants: Current advisees: Gregory Prouty, Michael Abiona, Syed Zarif Past advisees: Igor Jardim-Martins, Issec Lee Funding sources: Graduate Teaching Assistantships, Research Fellowships, Trent University Research Development Grants Labs & Teams: Leading research projects in parallel graph algorithms and federated security algorithms Collaborating with McMaster University on PhD/Master's co-supervision

• Theoretical Computer Science
• Algorithms
• Logic
• +۳ مورد دیگر

Michael Soltys is an Adjunct Professor in the Department of Computing and Software at McMaster University. His work bridges theoretical computer science, algorithms, and practical applications in cybersecurity and education. Research interests include logic, circuit complexity, and pairwise comparisons. Publications span 2002–2021, with recent focus on cloud computing education, digital forensics, and graph theory. Active in academic service through editorial contributions (e.g., Foreword for Franco-Canadian workshop proceedings). His scholarly activity maps to subdisciplines such as Computer Systems Theory, Logic, Discrete Applied Mathematics, and Operations Research. Dr. Soltys' work on pairwise comparisons and clique covers provides foundational insights for decision systems and graph optimization. He has also contributed to cloud curriculum development and malware analysis frameworks. As an educator, he co-authored An Introduction to the Analysis of Algorithms (2018, 2012, 2009), a textbook exploring algorithmic foundations. Current affiliations include the McMaster Experts database, with collaborations across theoretical computer science and applied cybersecurity research.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۳ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

Robert Baileyمشاهده پروفایل

دانشیار

• Discrete Mathematics
• Algebraic Graph Theory
• Combinatorial Design Theory
• +۷ مورد دیگر

Robert Bailey is an Associate Professor of Mathematics at the School of Science and the Environment, Grenfell Campus, Memorial University, with a cross-appointment to the Department of Mathematics and Statistics in St. John's. He joined Grenfell in 2013 after postdoctoral positions at Ryerson University, the University of Regina, and Carleton University. Education : M.Math. (Hons.) from the University of Leeds (2002), Ph.D. from Queen Mary, University of London (2006), with an exchange year at the University of Waterloo. Research Interests center on discrete mathematics, particularly algebraic graph theory, combinatorial design theory, and permutation groups. His work focuses on distance-regular graphs, metric dimension, error-correcting codes, and group theory, with applications in network modeling and computational combinatorics. Research Trends from his 15 most recent articles include advanced studies in metric dimension for polar and incidence graphs, classification of distance-regular structures, and innovative applications in coding theory and design theory. His interdisciplinary approach bridges pure mathematics with real-world problems in computer networks and information theory. Scientific Awards NSERC Discovery Grant (2024-29) PIMS Postdoctoral Fellow (2009-11) University start-up grants and Seed Fund support Advising and Grants involve mentoring numerous undergraduate and postdoctoral researchers. His funded projects explore symmetry breaking, distance-regular graph databases, and combinatorial coding, supported by NSERC and institutional funds. Labs and Teams include the Distance-Regular Graph Database developed with students, and collaboration with the Combinatorics Group at St. John's campus. He organized major conferences like the 16th East Coast Combinatorics Conference (2024) and Symmetry Breaking in Discrete Structures (2018).

Ursula Eickerمشاهده پروفایل

استاد

• Urban energy systems
• 3D City modeling
• Urban data and simulation platform development
• +۷ مورد دیگر

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

Emmanuel Stefanakisمشاهده پروفایل

استاد

• Geospatial Data Science
• Digital Earth
• Discrete Global Grid Systems (DGGS)
• +۱۷ مورد دیگر

Emmanuel Stefanakis is a Professor and Department Head of Geomatics Engineering at the Schulich School of Engineering, University of Calgary. He holds a PhD in Electrical and Computer Engineering (National Technical University of Athens, 1997), an MScE in Geodesy and Geomatics Engineering (University of New Brunswick, 1994), and a Dipl.Eng in Rural and Surveying Engineering (National Technical University of Athens, 1992). His research focuses on Geospatial Data Science, Discrete Global Grid Systems (DGGS), GeoAI, and applications of Geomatics in natural hazards, transportation, and climate change . He has led over 140 student projects and authored/co-authored five textbooks and 150+ articles. Notable awards include the 2023 Canadian Cartographic Association’s Award of Distinction and the 2023 UCalgary Teaching Excellence Award. Recent articles emphasize high-performance trajectory analysis, DGGS integration, and geospatial quantum computing . His work bridges theoretical advancements with practical tools for flood modeling, epidemiology, and urban planning. Grants include NSERC Discovery Grants and collaborations with industry partners like McElhanney Ltd. Professional memberships span the Canadian Institute of Geomatics, Canadian Cartographic Association, and APEGA. He served as Editor-in-Chief of Cartographica (2014–2022) and actively contributes to international conferences on geoinformatics. His educational initiatives include innovative course designs in online and distance learning. Current roles include leadership in the HALOS and Geospatial Quantum Computing projects, advancing geomatics engineering education in the digital era.

Faramarz Famil Samavatiمشاهده پروفایل

استاد

• Computer Graphics
• Interactive Games and Graphics
• 3D Imaging and Modeling
• +۱ مورد دیگر

Faramarz Famil Samavati is a Professor in the Department of Computer Science at the Faculty of Science, University of Calgary. His research focuses on computer graphics, interactive games and graphics, 3D imaging and modeling, and scientific computations. He holds a PhD in Mathematics and Computer Science from Sharif University of Technology (1999), an M.S. in Mathematics (1991), and a B.S. in Mathematics and Computer Science (1987), all from Sharif University of Tech. Samavati teaches courses such as CPSC 453 (Introduction to Computer Graphics), CPSC 589 (Modelling for Computer Graphics), and advanced computational techniques for graphics and visualization. His awards include the Best Paper Award at the ACM/Eurographics Expressive Conference (2017), Outstanding Leadership in Alberta Technology (ASTech Foundation), and multiple recognitions for graduate supervision excellence, including the Killam Graduate Supervision and Mentoring Award (2011). His research contributions span innovative work in discrete global grids, spherical curves, and hierarchical data structures, with applications in digital earth systems and geospatial modeling. He actively contributes to academic leadership and has been honored for his role in fostering technological innovation at the University of Calgary.

Hao Zhouمشاهده پروفایل

دانشیار

• Generative AI
• Molecular Modeling
• Protein Engineering
• +۳ مورد دیگر

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.