جستجوی استاد راهنمای دانشگاهی

David Lie is a Professor at the University of Toronto, jointly appointed in the Edward S. Rogers Department of Electrical and Computer Engineering, Department of Computer Science, and Faculty of Law. He directs the Schwartz Reisman Institute for Technology and Society, co-founded the IT3 Lab, and serves as Associate Director at the Data Sciences Institute. His research focuses on securing computer systems through operating systems, architecture, and formal verification approaches. B.A.Sc (University of Toronto, 1998) M.S. (Stanford, 2001) Ph.D. (Stanford, 2004) His research emphasizes building secure systems for mobile platforms and cloud computing, with significant contributions to trusted execution environments (XOM architecture precursor to Intel SGX/ARM TrustZone) and Android permission mapping (PScout tool). Recent work spans cryptographic side-channels, web tracking detection, and AI safety. Key honors include SOSP 2003 Best Paper, Ontario MRI Early Researcher Award (2008), Connaught Global Challenge Award (2017), and Canada Research Chairs (Tier 2 2013-2018, Tier 1 current). He has secured over $30M in research funding and served as General Chair for CCS 2018. Lie leads the IT3 Lab (Technology and Policy Integration), collaborates with industry leaders (Google, VMware, Telus), and mentors graduate students working on practical security implementations. He co-teaches ECE1724: Privacy Problems with Lisa Austin from the Faculty of Law, reflecting his technology-policy interests.

• Computer Systems Security
• Trusted Execution Environments
• Mobile Platform Security
• +۷ مورد دیگر

Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.

• Three-way Decisions
• Granular Computing
• Rough Sets
• +۸ مورد دیگر

Christopher Lawson is an Assistant Professor in the Department of Chemical Engineering and Applied Chemistry at the University of Toronto, affiliated with the Faculty of Applied Science and Engineering. He serves as Principal Investigator of the Microbiome Engineering Lab and is part of BioZone – the Centre for Applied Bioscience and Bioengineering. His research focuses on engineering anaerobic microbiomes for resource recovery from waste streams using systems biology, synthetic biology, and machine learning approaches. B.A.Sc., M.A.Sc. (University of British Columbia) Ph.D. (University of Wisconsin-Madison) Postdoctoral Training (Berkeley Lab) Lawson's work addresses the challenge of controlling complex microbial interactions in engineered systems to enable scalable biotechnologies for renewable energy, chemicals, and materials. His lab develops high-throughput methods integrating automation and computational tools to optimize microbiome assembly and metabolic fluxes. Recent publications highlight advancements in metabolic modeling , isotope tracing , and systems-level analysis of anaerobic microbiomes, with applications in wastewater treatment , anammox granules , and bioenergy production . His research bridges fundamental microbiology with industrial-scale bioprocess engineering. Scientific Awards ISME/IWA BioCluster Rising Star Award (2022) Jacobs Engineering Group/AEESP Outstanding Doctoral Dissertation Award (2020) Wesley Eckenfelder Graduate Research Award (2019) WEF Canham Graduate Studies Scholarship (2018) NSERC Post-Graduate Scholarship – Doctoral (2014) Lawson actively mentors students and postdocs, emphasizing technical rigor, communication skills, and independence. His lab collaborates within BioZone and with industry partners to advance "team science" principles. Current projects focus on creating engineered microbiomes for commercial-scale waste valorization.

• Microbiome Engineering
• Systems Biology
• Synthetic Biology
• +۵ مورد دیگر

Dr. Mahesh Tripunitara is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Associate Chair for Undergraduate Studies. He holds a PhD (2005) and Master's (1995) in Computer Science from Purdue University, along with a BSc (1993) in Computer Science from Dalhousie University. His research focuses on information security, authorization mechanisms, cryptographic key management, and hardware security, with industry experience at Motorola's R&D labs and Silicon Valley. His work spans theoretical advancements like access control policy analysis and practical applications such as secure payments systems and IoT device reliability. Notable awards include the Best Student Paper at Usenix Security 2013 and Best Paper at ACM SACMAT 2013. He actively serves on program committees for major security conferences including CCS, CODASPY, and SACMAT. Recent publications highlight innovations in cellular security (SUCI-Catchers defense), role-mining optimization, and blockchain smart contract auditing. Teaching includes advanced algorithm design courses (ECE 406/606) and digital computation (BME 121). His research emphasizes balancing security rigor with usability in authorization systems and hardware protection mechanisms.

• Information Security
• Authorization and Access Control
• Cryptographic Key Transport
• +۷ مورد دیگر

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

• Data Management
• Database Systems
• Data Integration
• +۷ مورد دیگر

Ming Li is a Professor at the University of Waterloo , holding the Canada Research Chair in Bioinformatics . Affiliated with the David R. Cheriton School of Computer Science , his research spans Bioinformatics , Kolmogorov Complexity , Deep Learning , Natural Language Processing , and Computational Biology . His contact details include office Davis Center 3355 and email mli@uwaterloo.ca . Research Interests include Bioinformatics (protein/antibody sequencing, structure analysis), Deep Learning , Natural Language Processing , Kolmogorov Complexity and Applications , and Algorithms & Complexity (average-case analysis, information distance). Editorial Roles : Co-Managing Editor of the Journal of Bioinformatics and Computational Biology , Associate Editor-in-Chief for Journal of Computer Science and Technology , Editorial board member of multiple journals including SIAM Journal on Computing and Information and Computation . Professional Activities : Served on scientific advisory committees for Genome Prairie and Tsinghua University, and numerous international conference program committees (e.g., KDD 2007, CPM 2007, FOCS'99). Awards & Honors : Killam Prize (2010) IEEE Granular Computing Outstanding Contribution Award (2010) Premier's Discovery Award (2009) Fellow of Royal Society of Canada, ACM, and IEEE (2006) Killam Fellowship (2001) E.W.R. Steacie Memorial Fellowship (1996) Multiple Best Paper Awards Students & Postdocs : Mentored over 30 graduate students and postdocs, including current advisees Guangyu Feng, Anqi Cui, and alumni like Babak Alipanahi, Xin Chen, and Brona Brejova.

• Deep learning
• Natural language processing and automated conversation
• Kolmogorov complexity and its applications
• +۸ مورد دیگر

Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.

• Audio Processing
• Machine Learning
• Digital Signal Processing
• +۳ مورد دیگر

Alireza Yaseri serves as an Adjunct Assistant Professor in the Department of Civil Engineering within Smith Engineering at Queen's University. He maintains a dual affiliation with the GeoEngineering Centre, a leading research institute at Queen's specializing in geotechnical and geoenvironmental challenges, where he contributes to advanced computational modeling initiatives. Education: PhD in Geotechnical Engineering, Université Laval (2021) MSc in Geotechnical Engineering, Shiraz University (2012) Dr. Yaseri's research program centers on computational geomechanics with emphases on seismic soil-structure interaction, railway-induced vibrations, and constitutive modeling of granular materials. His work bridges theoretical mechanics and practical infrastructure challenges through advanced numerical techniques, particularly hybrid FEM-SBFEM implementations for dynamic analysis of earth dams, canyon systems, and transportation corridors. He investigates nonlinear soil behavior under monotonic/cyclic loading and develops predictive models for vibration propagation in complex geological settings. Analysis of his publication trajectory (2014-2024) reveals consistent innovation in computational geotechnics, with recent work focusing on 2.5D/3D modeling of train-induced vibrations and sophisticated sand constitutive frameworks. His 2024 publications demonstrate dual expertise in railway vibration prediction methodologies and critical-state soil mechanics, while his earth dam-canyon system analyses from 2020-2022 established foundational approaches for seismic amplification in flexible geological formations. As an active member of Queen's GeoEngineering Centre, Dr. Yaseri collaborates on interdisciplinary projects addressing infrastructure resilience, with particular relevance to dam safety and transportation geotechnics in seismic zones. His technical leadership in scaled boundary methods contributes to the center's reputation for computational innovation in geomechanics.

• Geotechnical Engineering
• Geomechanics
• Earthquake Engineering
• +۴ مورد دیگر

Dr. Joshua M. Pearce is a Professor at Western University, holding appointments in the Department of Electrical & Computer Engineering and the Ivey Business School. He is the John M. Thompson Chair in Information Technology and Innovation at the Thompson Centre for Engineering Leadership & Innovation and a Fellow of the Canadian Academy of Engineering. His research focuses on open-source appropriate technology for sustainability and poverty reduction, spanning solar photovoltaics, 3D printing, distributed recycling, and policy analysis. Ph.D. in Materials Engineering from Pennsylvania State University Former Richard Witte Professor at Michigan Tech Editor-in-Chief of HardwareX Author of multiple open-source sustainability books His work integrates engineering, economics, and policy to solve global sustainability challenges. Recent projects include agrivoltaic systems, open-source medical devices, and climate-resilient food production frameworks. He leads the Free Appropriate Sustainability Technology (FAST) research group, which has produced over 200 open-access publications cited in top-tier journals like Renewable and Sustainable Energy Reviews (IF=16.3) and HardwareX (IF=2). Dr. Pearce's scientific contributions include: Fulbright-Aalto University Distinguished Chair Top 0.06% most cited scientist (Elsevier metrics) Leading open-source hardware certification frameworks Developing low-cost scientific instruments His research team includes cross-disciplinary collaborators from Mechanical Engineering, Environmental Science, and Policy Studies. The FAST group emphasizes practical open-source solutions for energy, water, and food security in both developed and low-resource contexts.

• Renewable Energy
• Open Source Hardware
• 3D Printing
• +۳ مورد دیگر

Dr. Mahdi Taiebat is a Professor in the Department of Civil Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He specializes in theoretical and computational geomechanics, constitutive modeling, granular materials mechanics, and geotechnical earthquake engineering. His research addresses challenges at the intersection of engineering, physics, and applied mathematics. He leads the Theoretical & Applied Geomechanics (TAG) research group and has co-authored over 150 scientific papers. Educations: B.Sc. and M.Sc. in Civil Engineering, Sharif University of Technology, Iran (2001, 2003) Ph.D. in Civil Engineering, University of California, Davis, USA (2008) Research Interests: Theoretical and computational geomechanics Constitutive modeling of engineering materials Physics and mechanics of granular materials Geotechnical earthquake engineering Static and dynamic soil-structure interaction Awards & Honors: Norman Medal (ASCE, 2012) Discovery Accelerator Supplement (NSERC, 2015) Professor Appreciation Award (UBC Civil Engineering Undergraduate Student Club, 2011) Postdoctoral Research Fellowship, Norwegian Geotechnical Institute (2008-2009) His work includes advising graduate students on geomechanics and geotechnical earthquake engineering, with emphasis on programming skills (C++, Fortran, Python) and competency in solid/fluid mechanics. The TAG group focuses on advanced numerical methods and experimental validation, collaborating internationally on projects like the LEAP initiative. His research spans constitutive model development (e.g., SANISAND/SANICLAY), seismic site response analysis, and liquefaction mitigation strategies.

• Theoretical Geomechanics
• Computational Geomechanics
• Constitutive Modeling
• +۳ مورد دیگر

Brandon Karchewski is an Associate Head (Undergraduate) and Teaching Professor in the Department of Earth, Energy and Environment at the University of Calgary. He earned his PhD in Civil Engineering from McMaster University and teaches courses including Engineering Geology, Computational Methods, and Natural Disasters. His research program focuses on: Computational methods in geophysics and geomechanics Geoscience education innovation Climate change impacts on frozen soils Inverse modeling applications Karchewski has pioneered virtual field experiences and developed open-source tools for modeling climate impacts on permafrost. His educational research examines metaphor use in geoscience communication and field pedagogy. Recent computational work includes Python-based permafrost modeling and stochastic inversion methods for biogeochemical transport. He leads the geophysics field school program emphasizing team-based learning. Award recognition includes: Geoscience Teaching Award (2019) Best Poster Award for teaching innovation research (2018) Team Teaching Excellence award (2016) Multiple teaching assistant awards

• Computational Geoscience
• Engineering Geology
• Natural Hazards
• +۲ مورد دیگر

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +۱۲ مورد دیگر

Mehdi Pouragha is an Associate Professor in the Department of Civil and Environmental Engineering at Carleton University's Faculty of Engineering and Design in Ottawa, Canada. He holds a BSc and MSc from Sharif University of Technology in Iran and a PhD from the University of Calgary. His academic career focuses on advanced geomechanics research with applications in both theoretical and practical engineering contexts. Dr. Pouragha's research spans multiple interconnected areas within geomechanics, with particular emphasis on constitutive modeling of geomaterials, micromechanics of granular materials, thermo-hydro-mechanical behaviors, and computational geomechanics. His work integrates theoretical approaches with advanced numerical methods to address complex problems in soil mechanics, permafrost engineering, and unsaturated soil behavior. His research program bridges fundamental micromechanical understanding with practical engineering applications, particularly in cold regions and climate change adaptation contexts. Analysis of Dr. Pouragha's recent publications reveals a strong trend toward multiscale modeling approaches that integrate discrete element methods with continuum mechanics. His work increasingly incorporates machine learning techniques to enhance computational efficiency while maintaining physical accuracy. The research spans both fundamental theoretical developments in constitutive modeling and practical applications in permafrost engineering, tailings management, and soil-structure interaction problems. His publications consistently demonstrate a focus on connecting microscale mechanisms to macroscale material behavior. Dr. Pouragha teaches several core civil engineering courses including Geotechnical Engineering, Fundamentals of Geomechanics, Numerical Methods in Geotechnical Engineering, and Professional Practice. His teaching portfolio reflects his research expertise while providing students with both theoretical foundations and practical engineering skills.

• Geomechanics
• Constitutive Modeling
• Granular Materials
• +۷ مورد دیگر

Taher Ghaleb is an Assistant Professor in the Computer Science Department at Trent University, Peterborough, Canada. He completed his Ph.D. at Queen’s University under Prof. Jenny Zou, followed by postdoctoral research at the University of Ottawa and a senior research role at the University of Toronto. His research focuses on applying data science and AI to address software engineering challenges. Education: Ph.D., Queen’s University, Software Evolution & Analytics Lab (SEAL) Postdoctoral Fellow, University of Ottawa Senior Research Position, University of Toronto Research Interests: Data-Driven Software Analytics: Empirical analysis of software development practices. AI in Software Engineering: Leveraging generative AI for code generation and bug detection. Continuous Integration/DevOps: Optimizing CI/CD pipelines for efficiency and reliability. LLM-Oriented Development: Integrating language models into software lifecycle processes. Publications: Taher’s work spans CI/CD practices, test case minimization, and AI-driven software testing. Recent trends emphasize empirical studies on open-source Android apps and flaky test prediction using language models. Awards: No explicit awards listed, but holds multiple U.S. patents related to software engineering and compiler systems. Advising & Grants: Seeks Master’s students and interns to join his team. Teaches courses like Software Design & Modelling (COIS 2240) and Software Engineering Project (COIS 4000) at Trent University. Labs & Teams: Formerly part of the Software Evolution & Analytics Lab (SEAL) at Queen’s University.

• Data-Driven Software Analytics
• Mining Software Repositories
• Continuous Integration and Delivery (CI/CD) – DevOps
• +۳ مورد دیگر

John Tsotsos is Distinguished Research Professor of Vision Science at York University, holding the NSERC Tier I Canada Research Chair in Computational Vision. He is affiliated with the Lassonde School of Engineering, Department of Electrical Engineering and Computer Science, and serves as Adjunct Professor in Ophthalmology and Vision Sciences at the Temerty Faculty of Medicine, University of Toronto. His research focuses span computer vision, visual attention, active vision systems, human vision, and visually guided robotics. Post-Doctoral Fellow – Ontario Heart Foundation, Toronto General Hospital (1979-81) Ph.D. in Computer Science, University of Toronto (1980) M.Sc. in Computer Science, University of Toronto (1976) B.A.Sc. (Honours) in Engineering Science (Computer Science Option), University of Toronto (1974) His research integrates computer vision with robotics and human perception, emphasizing active vision and computational models of attention. Notable contributions include the Selective Tuning theory of visual attention, formal theorems on visual complexity, and implementations in autonomous robotic systems. Recent publications highlight advancements in task-driven gaze prediction, saliency mapping, domain adaptation, stereo vision tracking, and gait analysis. These works bridge computer science, neuroscience, and biomedical engineering. Fellow, Canadian Academy of Engineering (2024) Fellow, Asia-Pacific Artificial Intelligence Association (2022) CS-Can|Info-Can Lifetime Achievement in Computer Science (2020) IEEE Life Fellow (2024) Sir John William Dawson Medal, Royal Society of Canada (2015) Geoffrey J. Burton Memorial Lecture (2011) NSERC Tier I Canada Research Chair in Computational Vision (2003-2024) Royal Society of Canada Fellow (2010) With 200+ trainees and a long-standing directorship at York University’s Centre for Vision Research, Tsotsos has mentored numerous researchers who have become influential in academia and industry. His leadership extends to the Centre for Innovation in Computing @ Lassonde and collaborations with institutions including MIT, University of Toronto, and University of Pittsburgh. He pioneered the Laboratory for Active and Attentive Vision, contributing to fields like medical image analysis, autonomous robotics, and cognitive architectures. His work includes patents in touch-sensitive displays and face recognition systems.

• Computer Vision
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر