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Bindiya AroraView profile

Researcher

• Atomic Physics
• Quantum Mechanics
• Theoretical Physics
• +3 more

Bindiya Arora is a PSI Fellow in the Academic Staff at the Perimeter Institute for Theoretical Physics (PITP), focusing on roles related to training, educational outreach, and scientific programs. Her research spans atomic physics, quantum mechanics, and theoretical physics, with applications in precision measurements, atomic clocks, and quantum technologies. She has contributed to studies on dispersion coefficients, Rydberg atoms, and sensor technologies. Her work bridges fundamental physics and practical applications, including collaborations on atomic data portals and computational tools. She actively engages in educational outreach through lectures at institutions like KNUST and Jaypee Institute of Information Technology, covering topics from quantum theory to ethics in science. Her seminars emphasize quantum computation, scientific communication, and addressing challenges like imposter syndrome in academia. Notable projects include investigations into THz atomic clocks, quantum electrometric sensors, and the radiative properties of ions for astrophysical applications. She advises graduate students on topics like dispersion interactions and quantum memory systems. Her research outputs span high-impact journals like Physical Review A and Atoms, showcasing expertise in both theoretical and applied physics domains.

Mohamed KamelView profile

Professor

• Machine Learning
• Biometrics
• Robotics
• +7 more

Mohamed Kamel was a Professor in the Department of Electrical and Computer Engineering, co-founding director of the Centre for Pattern Analysis and Machine Intelligence (CPAMI). He contributed extensively to interdisciplinary research across machine learning, robotics, biometrics, and vehicular networks. His work integrated computational methods with ecological, medical, and engineering challenges. Research interests span machine learning applications, biometric systems, robotics, image analysis, and VANET (Vehicular Ad Hoc Network) technologies. Notable contributions include biometric identification systems, human-robot interaction frameworks, and environmental modeling using Maxent techniques. He also explored advanced battery systems and genetic diagnostics for psoriasis treatment. Publications reflect a focus on interdisciplinary topics: from underwater gesture recognition to protein sequence analysis, and from Li-ion battery cathodes to medical imaging algorithms. His work bridges theoretical advancements with practical implementations in safety systems, conservation biology, and clinical diagnostics. Awards and recognitions are not explicitly stated in available texts. His legacy includes foundational contributions to the CPAMI and mentorship through collaborative research projects.

• Computer Science Education
• Computer Systems
• Object-Oriented Programming
• +5 more

Mark A. Holliday is a Professor of Computer Science at Western Carolina University (WCU), where he has served since 2004. He holds a Ph.D. in Computer Science from the University of Wisconsin–Madison (1986) and has extensive experience in academia, including roles as Interim Department Head (2007–2009) and Visiting Research Scientist at institutions like the University of Portsmouth (UK) and the University of Toronto. His primary affiliation is with the Department of Mathematics and Computer Science at WCU. Education Background: Ph.D. in Computer Science, University of Wisconsin–Madison (1986) Master's in Computer Science, University of Wisconsin–Madison (1982) B.A. in Mathematics and Economics, University of Virginia (1978) Research Interests: Holliday focuses on enhancing computer science education, particularly in teaching methodologies for programming concepts (e.g., memory diagrams for object-oriented programming), computer systems, and networking. His work includes developing educational tools like interactive animations for network concepts and memory diagram assessments. He also explores performance analysis of parallel architectures and distributed computing. Prior Research Areas: Included grid computing, web services, memory reference traces in parallel systems, and shared memory multiprocessors. His recent projects include the National Storm Surge Database and applications for public access to environmental data. Advising & Grants: While no specific students are listed, Holliday has led curriculum development initiatives and collaborated on projects funded by institutions like the National Oceanic and Atmospheric Administration (NOAA). He has served on the ABET Computing Accreditation Commission since 2023, contributing to accreditation standards. Labs/Teams: His work involves interdisciplinary collaboration, including contributions to the Distributed Systems Group (University of Portsmouth) and the development of the Storm Surge Viewer application.

Svetlana BrzevView profile

Adjunct Professor

• Masonry Structures
• Seismic Design and Retrofitting
• Earthquake Engineering
• +4 more

Dr. Svetlana Brzev is an Adjunct Professor in the Department of Civil Engineering at the University of British Columbia (UBC), within the Faculty of Applied Science. Her expertise lies in structural and seismic design of masonry and reinforced concrete structures, with a focus on retrofitting and seismic risk assessment. She has contributed to national and international standards, including the Canadian masonry design standard (CSA S304-14) and Eurocode 8 provisions for masonry buildings. Her global initiatives include the World Housing Encyclopedia and Confined Masonry Network, promoting earthquake-safe housing in seismically active regions. Dr. Brzev holds notable roles such as Director of the International Association for Earthquake Engineering and has been recognized with awards like the 2016 Global Earthquake Model’s Outstanding Achievement Award and Engineers Canada’s Fellowship. Her research spans seismic behavior of masonry structures, energy dissipation systems, and post-earthquake damage assessment. She has authored textbooks on masonry and reinforced concrete design, and her work integrates experimental studies with real-world earthquake reconnaissance, such as analyses of the 2019 Albania and 2020 Petrinja earthquakes. Education: Doctorate in Civil Engineering (Details unspecified). Global Contributions: Leadership in EERI, Masonry Society, and international risk assessment projects. Her articles highlight advancements in confined masonry design, seismic retrofitting methodologies, and the application of computer vision for damage assessment. Collaborative fieldwork and policy advocacy underscore her commitment to enhancing structural resilience globally.

• Data Mining
• Machine Learning
• Natural Language Processing (NLP)
• +5 more

Aijun An is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. She holds a Ph.D. from the University of Regina (1997) and has extensive academic experience, including postdoctoral work and roles at the University of Waterloo before joining York in 2001. Her research focuses on data mining, machine learning, and NLP, with contributions to pattern mining, parallel deep learning, sentiment analysis, and generative AI. Key projects include outbreak detection systems using social media, ethical AI in humanitarian crises, and adaptive data stream mining. Dr. An has led numerous grants from NSERC, SSHRC, and other agencies, including initiatives like the SMART-ART space exploration project and One Health Modelling for emerging infections. She actively supervises graduate students in both master’s and doctoral programs. Teaching responsibilities include courses on data mining, big data systems, and database management. Recent courses include EECS 4412/6412 (Data Mining), EECS 4415 (Big Data Systems), and EECS 2031 (Software Tools).

• Medical Imaging
• Artificial Intelligence
• Biomedical Engineering
• +3 more

Amir Jabbarpour is a Researcher affiliated with the Department of Physics at Carleton University, within the Faculty of Science. His work focuses on advancing medical imaging technologies through artificial intelligence and machine learning. Key research areas include pulmonary embolism detection via V/Q scintigraphy, deep learning segmentation, and image enhancement techniques. His contributions span both technical innovations (e.g., AI-annotated databases, U-Net models) and methodological advancements (e.g., spatial normalization strategies, crowdsource annotation workflows). Recent studies address challenges in image resizing effects, unsupervised PET correction, and pseudo-CT generation for radiotherapy. Jabbarpour's publications (2022-2025) reflect a strong emphasis on bridging AI capabilities with clinical imaging needs, particularly in pulmonary and oncological applications. No awards or grants are explicitly mentioned in the provided materials.

Stephen ThomasView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Big Data
• +4 more

Stephen W. Thomas is an Assistant Professor & Distinguished Teaching Fellow of Management Analytics at the Smith School of Business, Queen’s University. He holds a PhD in Computer Science from Queen’s University and has expertise in Natural Language Processing (NLP), machine learning, and data analytics. His research focuses on practical and fair AI applications, including bias detection and ethical considerations in NLP models. He has held roles as Executive Director of Smith's Analytics & AI Ecosystem and directs the Master of Management Analytics and Master of Management in Artificial Intelligence programs. His work bridges academia and industry, with consulting experience in big data and AI. Education: PhD (Computer Science, Queen’s University 2012), M.S. (Computer Science, University of Arizona 2009), B.S. (Computer Science, New Mexico State University 2006). Research Interests: NLP applications in messy real-world data, fair AI, AutoML for NLP, temporal databases, and mining software repositories. He has published in IEEE Transactions journals and led initiatives like τBench for temporal benchmarking. Awards: Scotiabank Scholar, multiple Professor of the Year awards, Queen’s Graduate Award Scholarship, and Raytheon Advanced Scholarship Program recipient. He has advised over 15 students in analytics and AI projects. Teaching: Courses include Machine Learning & AI, Text Analytics, Big Data, and Mathematical Analysis across Smith’s programs. He is a frequent speaker on AI ethics and management applications.

Dorothea BlosteinView profile

Professor

• Computer Science
• Biomechanics
• Graphics Recognition
• +6 more

Roles and Affiliations: Dorothea Blostein is a Professor in the School of Computing at Queen's University, part of the Faculty of Arts and Science. She has held this position since 1988 after earning her Ph.D. (1987) and B.Sc. (1978) from the University of Illinois and an M.Sc. (1980) from Carnegie Mellon University. Research: Her work focuses on the interface between paper and electronic documents, with specialties in graphics recognition, document analysis, and biomedical computing. Key areas include tensegrity structures for biomechanical modeling, adaptive systems, and artificial fascial networks. She also explores music notation recognition and software engineering document classification. Collaborations include projects with NASA on tensegrity robotics and studies on concussion prevention through helmet fitting systems. Teaching: She teaches courses such as CISC 859 (Pattern Recognition) and CISC 324 (Operating Systems). Her pedagogical approach emphasizes hands-on projects and theoretical foundations in AI and algorithms. Labs and Teams: Her research leverages tools like the NASA Tensegrity Robotics Toolkit and ArtiSynth. She collaborates with institutions like the Gordon Lab for embryonic modeling and biomechanical engineers on cytoskeleton simulations.

• Algorithms
• Big data analytics
• Blockchain
• +12 more

Dr. Wojciech Golab is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on distributed systems, concurrency control, and persistent memory algorithms. He has received recognition for his work on shared memory algorithms from ACM Computing Reviews. His academic career includes teaching courses like ECE 252, ECE 356, and ECE 454 in the past five years. Education: PhD in Computer Science, University of Toronto (2010) M.Math in Computer Science, University of Waterloo (2004) BSc in Computer Science, University of Toronto (2002) Dr. Golab's research interests span algorithms, blockchain, distributed systems, fault tolerance, and scalable storage solutions. His work emphasizes practical applications of theoretical computer science, such as optimizing transaction processing and ensuring consistency in distributed environments. His recent publications explore topics like detectable synchronization objects for persistent memory, blockchain sharding protocols, and efficient mutual exclusion algorithms under system failures. These contributions highlight advancements in both theoretical foundations and real-world distributed system design. Awards: ACM Computing Reviews 'Notable Computing Items' recognition (2012) Dr. Golab has collaborated with industry partners like Hewlett-Packard Labs, Google, and Cisco to advance research in distributed computing and cloud systems. He currently oversees graduate studies in his department and welcomes applications from prospective students.

Joanne M. AtleeView profile

Professor

• Software Engineering
• Software Product Lines
• Feature Interaction Detection
• +3 more

Joanne M. Atlee is a Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. Her research focuses on software engineering , particularly improving software quality through feature modeling and feature interaction management in complex systems like software product lines , systems-of-systems , and the Internet of Things . She develops theories of composition, interaction detection algorithms, coordination architectures, and proof systems for interoperability. Key research areas include Feature Interaction Management Software Product Line Engineering Model-Based Engineering Formal Verification Her recent publications explore AI-generated code detection (2024), variability-aware analysis (2024), and dynamic assertion generation (2023). Collaborations span institutions like McGill University , INRIA , and Siemens Canada , with applications in automotive software and cyber-physical systems .

Ladan TahvildariView profile

Professor

• Software Engineering
• Cloud Computing
• Self-Adaptive Systems
• +5 more

Ladan Tahvildari is a Full-time Professor in the David R. Cheriton School of Computer Science at the University of Waterloo. Her research focuses on software engineering, self-adaptive systems, cloud computing, and test case prioritization. She leads the Software Technologies Applied Research Laboratory (STAR Lab), emphasizing hands-on industry collaboration and practical software solutions. Her work spans over two decades, with notable contributions to adaptive software systems, runtime adaptation frameworks (GRAF), and cloud modeling (StratusML/Adoop). She has pioneered techniques for flaky test detection (FlaKat), spatiotemporal auto-scaling (STaleX), and POMDP-based uncertainty management for security systems. Key research areas include: Self-protecting software systems Component-based software evolution Defect detection and prioritization Autonomic computing decision models Cloud infrastructure optimization Her recent work bridges academia and industry through hands-on learning approaches and frameworks like Semeru Cloud Compiler for performance enhancement. She has served as workshop chair for ACSOS 2023 and contributed to IBM tool integration in educational curricula. Her lab focuses on transforming theoretical concepts into practical tools like StarMX for self-managing systems and ReLACK for VoIP steganography. Current efforts emphasize adaptive machine learning frameworks and scalable microservice architectures.

Ihab F. IlyasView profile

Professor

• Data Quality
• Machine Learning
• Database Systems
• +4 more

Ihab F. Ilyas is a Professor and holds the Thomson Reuters–NSERC Industrial Research Chair in Data Quality at the University of Waterloo's Department of Computer Science. His research focuses on data quality, machine learning for data enrichment, probabilistic data management, and knowledge graph systems. He leads projects like Holoclean (a probabilistic data repair system) and Data Civilizer (a data unification platform). His work bridges database systems and machine learning to address challenges in data integration, error detection, and large-scale knowledge representation. Education: PhD in Computer Science from Purdue University (2004), MSc and BSc from Alexandria University, Egypt (1999 and 1995). His publications span vector search, knowledge graph construction, and data curation techniques, with recent emphasis on adaptive indexing and scalable systems. He has contributed to open-source tools and frameworks for data cleaning and machine learning integration. Research interests include probabilistic data management, machine learning applications for data quality, and scalable systems for big data. His work often addresses real-world challenges in data integration and semantic representation, with a focus on practical, deployable solutions.

Tamer ÖzsuView profile

Professor

• Distributed Data Management
• Graph Databases
• RDF Systems
• +2 more

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

• Compiler Testing
• Program Analysis
• Static Binary Taint Analysis
• +5 more

Chen Sun is an Adjunct Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong. His research focuses on compiler testing, program analysis, static binary taint analysis, and software engineering methodologies. Notable areas include leveraging large language models (LLMs) for compiler testing baselines, probabilistic delta debugging, and enhancing program reduction techniques across multiple languages. Recent work addresses ransomware defense mechanisms, fuzzing countermeasures, and database management system (DBMS) bug discovery through configuration transformations. Research interests span compiler optimization, fault localization, cybersecurity, and algorithmic efficiency. His publications from 2023-2025 highlight trends in combining AI with traditional software engineering challenges, such as improving type inference for Java code and developing syntax-guided program reduction frameworks. Articles frequently address practical security concerns like protecting embedded devices from protocol fuzzing and mitigating memory leaks through sanitizer-based localization. No scientific awards are explicitly mentioned. Advising and grants sections remain underdeveloped in available data. Current affiliations emphasize academic-industrial collaboration through adjunct roles focused on cutting-edge software reliability and security research.

• Neurodevelopment
• Cognitive Development
• Pediatrics
• +7 more

Sarah Lippé is a Full Professor in the Department of Psychology at the University of Montreal, affiliated with the Faculty of Arts and Sciences. She is also a Senior Scientist at Sainte-Justine Hospital (FRQ-S) and Director of the Neuroscience of Early Development Lab (NED). Her research focuses on neurodevelopmental mechanisms in infants and children, particularly investigating how genetic and environmental factors influence brain development, sensory processing, and learning capacities in healthy and at-risk populations. Education: Ph.D. in Clinical and Research Neuropsychology, Université de Montréal Master’s in Neuropsychology, Université de Montréal Postdoctoral training in neuroscience (Atomic Energy Commission, France; Baycrest Centre, Toronto) Research Interests: Dr. Lippé’s work examines cerebral mechanisms of learning in infants/children, neurodevelopmental disorders (e.g., Fragile X syndrome), and biomarker development for early diagnosis and treatment efficacy. Her lab employs EEG, neuroimaging, and behavioral assessments to study genetic risk factors (e.g., CNVs) and their impact on sensory processing and cognitive outcomes. Key Projects: - Leading the first intergenerational genetic-neuropsychology-EEG cohort (400+ families) for neurodevelopmental disorders - Co-directing international clinical trials for autism and FXS using EEG as an outcome measure - Developing EEG databases for infant brain maturation assessments Awards: FRQ-S Senior Scientist Canada Research Chair in Pediatric Neurodiversity CIHR Project Grants Azrieli Foundation Funding Advising & Grants: Supervised over 30 graduate students and led >50 research projects totaling millions in funding from FRQS, CIHR, and international networks. Active in multidisciplinary initiatives like the Kids Brain Health Network and Quebec 1000 Projects. Labs/Teams: - Neuroscience of Early Development Lab (NED) - Member of BRAMS (Brain Music Sound), CerebrUM, KBHN, and TACC networks - Director of Quebec’s Neuroelectrophysiology Research Clusters