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نمایش ۱۶-۳۰ از ۱۳۶ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Sageev Ooreمشاهده پروفایل

دانشیار

Dalhousie UniversityCanada

Sageev Oore is an Associate Professor in the Faculty of Computer Science at Dalhousie University, a Research Faculty Member at the Vector Institute for Artificial Intelligence, and a Canada CIFAR AI Chair. He previously served as Associate Professor and Chairperson in the Department of Mathematics & Computer Science at Saint Mary’s University and spent 2016–2018 as a Visiting Research Scientist at Google Brain, working on the Magenta team. Faculty of Computer Science, Dalhousie University Vector Institute for Artificial Intelligence Google Brain (2016–2018) Saint Mary’s University (former) Sageev Oore's research centers on machine learning and deep learning, with a strong focus on creative applications in music, audio processing, and computational creativity. His work bridges the gap between technical innovation and artistic expression, developing systems that generate and interact with music using neural networks. He has made significant contributions to generative models for music, including the development of PerformanceRNN and other interactive systems. His recent publications highlight advancements in out-of-distribution detection (Gram-OOD), interactive music generation, and deep learning tools for creative domains. These works reflect a consistent trend toward building intelligent, user-centered systems that enhance human creativity through AI. Canada CIFAR AI Chair (2018) Best Paper Award, CVPR ISIC Workshop (2020) Outstanding Demonstration Award (Runner-up), NeurIPS (2020) Best Demonstration Award, AAAI (2017) Best Demonstration Award, NeurIPS (2016) Sageev Oore actively mentors graduate and undergraduate students, with well-funded research positions available for motivated candidates. His collaborations span academia and industry, including major projects with Google Brain and interdisciplinary work with artists. He leads research initiatives in AI-driven creativity and is deeply involved in the Canadian AI ecosystem through the Vector Institute and CIFAR. His work is supported by significant grants and affiliations, including the Canada CIFAR AI Chair program, which funds his research in foundational AI and its applications. He is also part of the Magenta project at Google, contributing to open-source tools for art and music generation. Sageev Oore leads a research group focused on deep learning for creative applications, with projects in music generation, audio synthesis, and human-AI interaction. His lab collaborates with musicians, artists, and healthcare researchers, fostering a transdisciplinary approach to AI innovation.

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Pascal Vincentمشاهده پروفایل

دانشیار

University of MontrealCanada

Pascal Vincent is an Associate Professor at the Department of Computer Science and Operational Research , University of Montreal, and a key member of the Montreal Institute for Learning Algorithms (MILA) . He holds a PhD in Computer Science from the University of Montreal and has been pivotal in advancing machine learning and artificial perception. Education: PhD in Computer Science (University of Montreal, 2003) His research spans machine learning , deep learning , representation learning , and neural networks , focusing on unsupervised methods and geometrically inspired algorithms. He explores how intelligent systems can autonomously build meaningful representations from raw data, driven by principles like the manifold hypothesis . Key projects include generative stochastic networks , contractive autoencoders , and high-dimensional sequence transduction . His work has resulted in 15+ recent publications in top venues like NIPS, ICML, and CVPR. Scientific Awards : Best student-paper award at ICML 2012 Honorable mention at NIPS 2011 Funded by FCI, FRQNT, CRSNG, CIFAR, and IBM Pascal has supervised 15+ doctoral and Master’s students , including Florian Bordes, Tom Bosc, and Nicolas Boulanger-Lewandowski, across topics like representation learning and generative models . He is also a co-founder of the UNIQUE (Union Neurosciences & Intelligence Artificielle Québec) research consortium.

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Paul Tupperمشاهده پروفایل

استاد

Simon Fraser UniversityCanada

Paul Tupper is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He holds a Ph.D. in Scientific Computing from Stanford University (2002). His research focuses on applied mathematics with emphasis on mathematical modeling in epidemiology, speech perception, neural networks, and computational linguistics. He teaches advanced courses in probability, numerical linear algebra, and calculus for social sciences. His work bridges theoretical mathematics and real-world applications, particularly in understanding complex systems like disease transmission dynamics and cognitive processes. Recent studies include modeling the transition of pandemics to endemic states, genomic analysis of viral spread, and audio-visual perception mechanisms in speech. He actively contributes to public health policy discussions through epidemic modeling research. Professor Tupper's research has been published in high-impact journals and conferences, with notable contributions to diversity metrics in biology and geometry, stochastic differential equations, and connectionist models of linguistic phenomena. His courses reflect interdisciplinary interests, integrating mathematical rigor with practical computational methods.

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Ozgur Yilmazمشاهده پروفایل

استاد

University of British ColumbiaCanada

Ozgur Yilmaz is a Professor in the Department of Mathematics at the University of British Columbia (UBC). He is the Director of the Pacific Institute for the Mathematical Sciences (PIMS) and has held roles such as Interim Deputy Director at PIMS and Deputy Director at the Banff International Research Station (BIRS). His research focuses on applied harmonic analysis, signal processing, compressed sensing, and seismic signal processing. Education: PhD in Applied and Computational Mathematics from Princeton University (2001), B.Sc. in Mathematics and Electrical Engineering from Boğaziçi University (1997). Research Interests: Mathematical problems in analog-to-digital conversion, blind source separation, sparse approximations, compressed sensing, and their applications in seismic exploration. He has contributed to advancements in sigma-delta quantization, low-rank matrix recovery, and compressed sensing algorithms. Funding: Recipient of NSERC Discovery Grants, UBC Data Science Institute grants, and leadership in collaborative research groups (CRGs) on high-dimensional data analysis and applied harmonic analysis. His work bridges theoretical mathematics with practical applications in signal processing and AI-driven medical imaging. Students and Postdocs: Supervised numerous PhD and MSc students in areas like compressed sensing, seismic data reconstruction, and machine learning. Current advisees include Aaron Berk and Xiaowei Li. Former students hold positions at academic institutions and tech companies. Labs and Collaborations: Affiliated with UBC’s Data Science Institute (DSI), Centre for Artificial Intelligence Decision-making and Action (CAIDA), and the Institute of Applied Mathematics (IAM). Collaborates on projects integrating AI with scientific discovery, such as retinal biomarker identification using deep learning.

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Robert Hamiltonمشاهده پروفایل

استاد

McMaster UniversityCanada

Robert Hamilton is a Professor in the Department of Communication Studies & Media Arts at McMaster University's Faculty of Humanities in Hamilton, Canada. With an extensive career as both an academic and practicing artist, he has established himself as a significant figure in contemporary media arts education and creation since joining McMaster University. Hamilton holds dual MFA degrees from the Art Institute of Chicago and the Jan van Eyck Academie in Maastricht, Netherlands, complemented by a Diploma from the Alberta College of Art + Design. His academic journey spans from 1981-1985 at the Alberta College, followed by advanced studies from 1991-1993 at the Jan van Eyck Academie. His research and creative practice centers on visual media with particular focus on video art, photography, and animation. Hamilton's work consistently explores themes of identity, community, and the evolving urban environment, often reflecting on societal events and contemporary issues. His artistic approach combines technical mastery with conceptual depth, resulting in works that challenge viewers' perceptions of everyday spaces and social structures. He has developed a distinctive methodology that blends documentary techniques with artistic interpretation, creating works that are simultaneously observational and transformative. Hamilton's recent body of work demonstrates a clear trajectory toward examining human relationships with urban spaces, particularly during times of societal transformation like the pandemic. His projects increasingly incorporate digital technologies and AI elements while maintaining a strong foundation in traditional media arts. The thematic continuity across his work reveals a persistent concern with how individuals navigate and make meaning within constructed environments. German Video Art Prize Silver Hugo Award at the Chicago Film Festival Work featured in National Gallery of Canada collection As an educator, Hamilton has mentored numerous students through senior thesis projects and specialized courses in media production. His teaching portfolio spans animation, video art, digital cinema, photographic techniques, and emerging media. While specific grant information isn't detailed in the available materials, his sustained creative output suggests successful acquisition of research funding to support his artistic practice. His work has been exhibited internationally at venues including Maison des Arts de Laval, Museum of Contemporary Art in Castello, Spain, and Transmediale in Berlin. Hamilton maintains an active studio practice that directly informs his teaching approach. His creative process involves both digital and traditional techniques, often experimenting with the intersection of physical and virtual spaces. He has developed specialized approaches to documenting urban environments that reveal hidden patterns and structures within everyday landscapes, creating what might be described as visual commentaries on contemporary life.

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Bon Woo Kooمشاهده پروفایل

استادیار

Toronto Metropolitan UniversityCanada

Dr. Bon Woo Koo is an Assistant Professor at the School of Urban and Regional Planning , Toronto Metropolitan University. His expertise lies in geospatial urban analytics, walkability, and GIS applications, focusing on urban design for public health equity and innovative data science tools. He holds a PhD in City & Regional Planning from Georgia Institute of Technology, a Master’s in Landscape Architecture from Seoul National University, and a Bachelor’s in Interior Design from Kookmin University. Education: PhD in City and Regional Planning, Georgia Institute of Technology Master of Landscape Architecture, Seoul National University Bachelor of Interior Design, Kookmin University Research Interests: Dr. Koo investigates urban environments’ impact on health and well-being, equity in environmental amenities (e.g., tree canopies), and advanced GIS techniques. He develops automated audit methods for walkability and explores spatial modeling for urban sustainability. His work bridges data science with policy, contributing to CDC health surveillance and smart city initiatives. Publications: His research appears in journals like Landscape and Urban Planning , Environment and Behavior , and Health and Place , with a focus on walkability audits, urban tree equity, and audio-based pedestrian sensing. Recent work addresses post-pandemic mental health and broadband equity strategies. Professional Engagement: He has advised the CDC’s technical panel on leveraging big data for health policy, presented at conferences like the Association of Collegiate Schools of Planning, and collaborated with institutions like Universitas Gadjah Mada and the Atlanta Regional Commission.

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Urs Hengartnerمشاهده پروفایل

دانشیار

University of WaterlooCanada

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.

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Benoit Champagneمشاهده پروفایل

استاد

McGill UniversityCanada

Benoit Champagne is a Full Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal. His research focuses on statistical signal processing, with applications in wireless communications, multi-antenna systems, and adaptive filtering. He has held academic positions since 1990, including roles at INRS-Telecom before joining McGill in 1999. He teaches graduate and undergraduate courses such as ECSE 305 (Probability and Random Signals), ECSE 512 (Digital Signal Processing), and ECSE 617 (Array Signal Processing). Education: B.Eng. (Electrical Engineering) and M.Sc. (Physics) from Université de Montréal (1983, 1985), Ph.D. in Electrical Engineering from University of Toronto (1990). His research spans signal detection/estimation, speech enhancement, MIMO systems, and physical layer security, with over 150+ publications in top journals and conferences. He has supervised numerous graduate students and holds grants from NSERC, CFI, and industry partners like Nortel and Bell Canada. His work emphasizes practical implementations, including hybrid analog/digital beamforming for mmWave systems and energy-efficient resource allocation in D2D communications. He has contributed to IEEE standards through editorial roles (e.g., IEEE Transactions on Signal Processing) and conference organization (e.g., IEEE VTC 2016). Current research explores machine learning integration with signal processing for next-generation wireless systems. Notable contributions include advancements in subspace tracking, cognitive radar systems, and distributed adaptive filtering. His lab collaborates internationally, addressing challenges in 5G/6G networks, massive MIMO, and secure communications.

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Glenn Gulakمشاهده پروفایل

استاد

University of TorontoCanada

Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.

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Elizabeth Johnsonمشاهده پروفایل

استاد

University of TorontoCanada

Dr. Elizabeth Johnson serves as Professor of Psychology at the University of Toronto Mississauga (UTM), holding the Canada Research Chair in Spoken Language Acquisition. She is cross-appointed to the Graduate Department of Linguistics and directs the University of Toronto's Tri-Campus Graduate Program in Psychology, leading interdisciplinary research on language acquisition through the Child Language and Speech Studies (CLASS) Lab in UTM's Communication, Culture and Technology Building. Her academic foundation includes a B.A. in Brain and Cognitive Sciences from the University of Rochester (with a Take Five program in Developmental Biology & Evolution), followed by M.A. and Ph.D. degrees in Psychological & Brain Sciences from Johns Hopkins University. She completed postdoctoral training at the Max Planck Institute for Psycholinguistics and spent semesters at MIT's Speech Group during her doctoral studies. Dr. Johnson's research examines how perceptual, cognitive, and social mechanisms enable children's rapid language mastery, with emphasis on speech perception, multilingual development, accent processing, and infant speech segmentation. Her work bridges developmental psychology, linguistics, and speech sciences to investigate topics including audio-visual speech perception, word-learning heuristics, prosody acquisition, and voice recognition—all contextualized within UTM's linguistically diverse community. Analysis of her recent publications reveals intensifying focus on real-world applications of language acquisition research, particularly voice assistant technology for children, pandemic impacts on language development, and sociolinguistic factors in educational settings. Her work increasingly explores accent variation effects across development and the cognitive mechanisms underlying talker recognition. Dr. Johnson's scientific recognition includes: Canada Research Chair in Spoken Language Acquisition She actively mentors graduate researchers: PhD Students: Emily Shroads (June and David Scott Fellowship recipient) Recent PhD Graduates: Priscilla Fung (SSHRC Postdoctoral Fellow), Madeleine Yu Undergraduate Support: NSERC USRA (Christopher Khalaf), SSHRC UTEA (Gabriella Di Maio, Alexandra Maracz) Research Funding: SSHRC Insight Grants, NSERC Discovery Grants The CLASS Lab functions as an interdisciplinary hub collaborating with UTM's Developmental Science Cluster, Centre for Biological Communication Systems, and Infant and Child Studies Centre. Its community-engaged research leverages linguistic diversity in the Greater Toronto Area to translate foundational science into practical applications for child development and educational technology.

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Ewan Dunbarمشاهده پروفایل

استادیار

University of TorontoCanada

Ewan Dunbar is an Assistant Professor of Computational Linguistics at the University of Toronto (downtown campus), affiliated with St. Michael's College. He holds appointments in the Department of French, Department of Linguistics, and Department of Computer Science. His research explores human speech perception, technological advancements in speech processing, and related areas such as unsupervised learning and language acquisition. Educational Background: While specific degrees aren't listed, his research and appointments indicate advanced training in linguistics and computational sciences. Research Interests: Dunbar focuses on how humans perceive speech, developing computational models of speech perception, and advancing unsupervised learning techniques. He leads the Perceptimat Research Group and organizes the Zero Resource Speech Challenge, a machine learning competition promoting unsupervised approaches to speech processing. Awards: He has received notable grants including the 2028 NSERC Discovery Grant, 2028 NSERC Discovery Launch Supplement, and 2023 Connaught New Researcher Award, all supporting his work on computational psycholinguistics and speech perception models. Advising & Grants: While specific student names aren't listed, his grants indicate involvement in mentoring undergraduates (e.g., 2024 NSERC Undergrad Studentship Grant). His labs focus on creating speech perception tools and benchmarks like Perceptimatic. Labs/Teams: Director of the Perceptimat Research Group, organizer of the Zero Resource Speech Challenge.

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Zhao Zhaoمشاهده پروفایل

استادیار

University of GuelphCanada

Zhao Zhao is an Assistant Professor at the School of Computer Science, University of Guelph. Her work focuses on wearable systems, human-robot interaction, and gamification for health and education. She holds a PhD from Carleton University (2019) and completed a postdoctoral fellowship at the University of Toronto (2019–2023) before roles at McMaster University and her current position. Education includes a BSc in Computer Science from University of Electronic Science and Technology of China (2011), MSc from Carleton University (2014), and PhD in Electrical and Computer Engineering (2019). Research interests span physiological computing, AI-assisted creativity tools, and adaptive systems leveraging wearable sensors. Key research areas include: 1) Wearable-based gamification for health and education, 2) Emotion sensing via physiological signals, and 3) Human-robot interaction enhanced by wearable data. Her lab uses Empatica EmbracePlus wristbands and Emotiv EEG headsets to analyze real-time physiological responses in diverse interaction scenarios. Recent publications (2020–2025) emphasize personalized exergame systems, child-robot interaction studies, and AI linguistic competency analysis. She actively seeks graduate students and industry partnerships in wearable technology, education tech, and HRI.

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Jeff Lupkerمشاهده پروفایل

استادیار

Western UniversityCanada

Jeff Lupker serves as an Assistant Professor at Western University's Don Wright Faculty of Music, specializing in the intersection of artificial intelligence and musical creativity. His work develops computational tools that augment human composition through deep learning algorithms and interactive systems, positioning him at the forefront of AI-driven music innovation. Lupker completed his entire academic training at Western University: PhD in Composition (2021) Master of Music in Composition (2016) Bachelor of Music in Theory and Composition (2014) His research program focuses on artificial intelligence applications for musical creation, including deep learning models for algorithmic composition, sentiment analysis of social media as compositional input, and mobile-based spatial audio systems. Lupker investigates how transformer architectures generate musical structures and how real-time web applications enable collaborative performance, emphasizing practical tools that combat writer's block while expanding composers' stylistic range through AI-assisted creativity in electroacoustic and popular music contexts. Analysis of his 2021 publications reveals a cohesive research trajectory leveraging cutting-edge AI methodologies to solve creative challenges in music. The works demonstrate how deep learning transforms composition through systems like Score-Transformer and explore mood-pattern recognition using machine learning, collectively establishing foundational work for human-AI creative collaboration that bridges music theory with big data analytics. As founder of Staccato, Lupker leads the development of an AI co-writing platform that functions as an intelligent creative partner. The system generates lyrical content from keywords and suggests musical continuations, helping composers overcome creative blocks while expanding artistic possibilities across diverse genres through accessible, user-friendly interfaces.

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Stefano Gregoriمشاهده پروفایل

استاد

University of GuelphCanada

Stefano Gregori is a Professor at the University of Guelph's School of Engineering. He specializes in analog and mixed-signal integrated circuit design, with a focus on low-power systems, sensor networks, and microsystem integration. His research involves collaborations with industry leaders like STMicroelectronics and TSMC, emphasizing practical applications in IoT, energy efficiency, and biomedical devices. He holds a PhD and is a Professional Engineer (PEng). His work bridges theoretical design and real-world applications, such as secure cryptographic circuits and sustainable materials for biomedical composites. He actively supervises graduate students and has mentored numerous scholars, many of whom now work in leading tech companies like Qualcomm and Thales. Gregori's funding sources include NSERC, CMC Microsystems, and the Ontario Centres of Excellence. He emphasizes ethics in engineering, advocating for safety and environmental responsibility. His lab, located in Richards Building Room 3521, focuses on cutting-edge projects like energy-efficient audio amplifiers and blockchain-based IoT security.

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Richard Dansereauمشاهده پروفایل

استاد

Carleton UniversityCanada

Richard Dansereau is a Professor and Associate Dean (Graduate Studies) in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. His research focuses on signal processing, including biomedical applications, compressive sensing, medical imaging, and fractal complexity analysis. He has led the Signal Processing and Machine Learning Lab and advised numerous PhD students in areas like PET image reconstruction and drone detection through Riemannian geometry. His academic roles include serving as Clerk of Senate and Academic Editor for IET Signal Processing. Key research contributions span deep learning for compressive sensing (e.g., DEQ-based networks), cervical cell segmentation, and biomedical signal processing. Over 150 publications highlight his work on topics like PET-MRI fusion, audio-visual speech enhancement, and radar systems. Collaborations include projects on cardiac PET imaging and drone detection algorithms. Awards include the IEEE Senior Member designation. His teaching spans courses such as Digital Signal Processing, Wavelets, and Biomedical Systems across Carleton and the Georgia Institute of Technology.

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