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Kyros KutulakosView profile

Professor

• Computational Imaging
• Computer Vision
• 3D Sensing
• +9 more

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

Hassan Qudrat-UllahView profile

Professor

• Sustainability
• Global/Climate Change
• System Dynamics
• +3 more

Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

• Computer Vision
• Self-Supervised Learning
• Computational Photography
• +7 more

Alexei A. Efros is a Professor in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, where he holds the Howard Friesen Professorship and is affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. He previously served on the faculty at the Robotics Institute of Carnegie Mellon University (CMU) and completed a postdoctoral fellowship at the University of Oxford. His research spans data-driven computer vision, self-supervised learning, computational photography, and applications to computer graphics and robotics. His research interests include: Data-Driven Computer Vision Self-Supervised and Unsupervised Learning Generative Models and Image Synthesis Visual Representation Learning Applications in Robotics and Human-Computer Interaction Intersections with Human Vision and the Humanities The recent publications highlight a strong trend toward self-supervised learning, visual reasoning, and generative modeling, particularly diffusion models and 3D scene understanding. His work increasingly bridges computer vision with language, robotics, and cognitive science, emphasizing interpretability and real-world applicability. There is a clear focus on leveraging unlabeled data and developing methods for robust, generalizable AI systems. His scientific awards and recognitions include: Berkeley Fellowship Google Fellowship Soros Fellowship NSF Fellowship SIGGRAPH Outstanding Doctoral Dissertation Award Facebook Fellowship Adobe Fellowship CMU School of Computer Science Distinguished Dissertation Award ACM Doctoral Dissertation Honorable Mention Alexei Efros has advised numerous PhD students and postdocs, many of whom have gone on to faculty positions at top institutions including CMU, Stanford, MIT, Columbia, NYU, and Georgia Tech. His lab has received research funding from major tech companies and federal agencies, though specific grants are not detailed in the text. He teaches core computer vision and machine learning courses at both undergraduate and graduate levels at UC Berkeley. His research group is highly active, with ongoing projects in 3D perception, generative modeling, and vision-language systems. He leads a vibrant research lab at UC Berkeley, part of the BAIR consortium, collaborating with leading researchers such as Jitendra Malik, Trevor Darrell, Pieter Abbeel, and Angjoo Kanazawa. His lab fosters strong interdisciplinary connections with institutions worldwide, including Oxford, INRIA, and École Normale Supérieure.

Anamaria CrisanView profile

Assistant Professor

• Human-Computer Interaction
• Data Visualization
• Applied AI/ML
• +3 more

Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.

Aishwarya AgrawalView profile

Assistant Professor

• Multimodal learning
• Deep learning
• Natural language processing
• +3 more

Aishwarya Agrawal is an Assistant Professor at Université de Montréal in the Department of Computer Science and Operations Research (DIRO), affiliated with Mila – Quebec Institute of Artificial Intelligence and a Canada CIFAR AI Chair. She also serves as a research scientist at Google DeepMind, spending one day weekly there. Education: B.E. in Electrical Engineering (IIT Gandhinagar, 2014), Ph.D. in Computer Science (Georgia Tech, 2019). Her research focuses on multimodal learning , deep learning , natural language processing , and computer vision , particularly in developing AI systems that 'see' and 'communicate' effectively. Grants & Awards: Canada CIFAR AI Chair, 2020 Sigma Xi Best PhD Thesis Award, NVIDIA Fellowship (2018–2019), and multiple fellowships from Google and Facebook. She leads projects like Advancing Multimodal Vision-Language Learning (CRSNG-funded) and StarDoc: Document Structure Extraction (MITACS). Research Contributions: Pioneered benchmarks like CulturalVQA and UI-Vision , and frameworks such as PROGRESS for efficient VLM training. Her work emphasizes cross-modal alignment, robust evaluation, and cultural understanding in AI systems. Labs/Teams: Active in Mila’s core academic group and collaborates with Google DeepMind on multimodal and vision-language research. Supervises a dynamic team of PhD and master’s students in Montreal.

Enrica Dall'AraView profile

Associate Professor

• Practice-based research
• Public space design
• Urban renewal
• +3 more

Dr. Enrica Dall'Ara serves as the Associate Dean of Planning and Landscape Architecture and an Associate Professor at the University of Calgary's School of Architecture, Planning and Landscape. Her work bridges academic research and professional practice, exemplified through her firm PARC, which specializes in public space design, urban renewal, and the rehabilitation of industrial sites. She emphasizes interdisciplinary collaboration, integrating fields like environmental engineering and biology into landscape architecture. Education: Laurea in Architettura (Master Degree equivalent in Architecture), University of Ferrara, Italy Master in Landscape Architecture, ETSAB, Polytechnic University of Catalonia, Spain PhD in Landscape Design, University of Florence, Italy Research Interests: Dr. Dall'Ara focuses on practice-based research, public space design, service landscapes, and the integration of cultural advocacy into landscape architecture. Her work explores site-specific design solutions, the role of agriculture in urban-rural dynamics, and the preservation of traditional rural landscapes. She advocates for designs that serve life through poetic, functional, and culturally sensitive approaches. Contributions: Through her teaching, Dall'Ara encourages students to view design as research, fostering critical thinking and interdisciplinary dialogue. Her firm’s projects, such as the roundabout gardens in Imola, Italy, demonstrate her commitment to merging ecological, functional, and aesthetic considerations in landscape interventions. Professional Affiliations: Canadian Society of Landscape Architects (CSLA) Alberta Association of Landscape Architects (AALA) Associazione Italiana di Architettura del Paesaggio (AIAPP) Ordine degli Architetti, Pianificatori, Paesaggisti e Conservatori (Province of Forlì-Cesena, Italy)

Qianwen WangView profile

Assistant Professor

• Interactive Visualization
• Interpretable Machine Learning
• Human-AI Collaboration
• +5 more

Qianwen Wang is a tenure-track Assistant Professor in the Computer Science department at the University of Minnesota, Twin Cities. Her research combines interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. She actively seeks motivated students, research assistants, and interns to join her dynamic research team at UMN CS. Dr. Wang's research focuses on three primary themes: Human-AI Collaboration, where she designs tools to facilitate Human-AI interaction; Automatic & Intelligent Visualization, where she develops techniques to make visualizations accurately interpreted and easily used; and VIS+(X)AI in Biomed/Healthcare, where she studies how visualization and explainable AI can promote scientific discoveries in biomedicine and healthcare. Her work has made significant contributions to visualization, human-computer interaction, and bioinformatics, with particular applications in biomedical knowledge graphs and single-cell omics analysis. Her recent publications demonstrate a strong trend toward integrating visualization with large language models and graph neural networks for biomedical applications. She has published extensively at top venues including IEEE VIS, ACM CHI, and Nature Medicine, with a focus on making AI systems more interpretable and useful for domain experts, particularly in healthcare contexts. Her work often bridges theoretical advances in visualization with practical applications in genomics and healthcare. Two IEEE VIS Honorable Mention Awards (2022, 2024) Best Paper Award from IMLH@ICML 2021 Two Best Abstract Awards from BioVis ISMB (2021, 2022) HDSI Postdoctoral Research Fund Award Research covered by MIT News and Nature Technology Features Dr. Wang actively contributes to the academic community through service roles including General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and Program Committee member for IEEE VIS, ACM CHI, and ACM IUI. Her research has been supported by various grants that enable her team to develop innovative visualization techniques and explore their practical applications in biomedical domains. Her research group maintains an active presence in the visualization and HCI communities, with members participating in major conferences and workshops. The lab focuses on creating tools that bridge the gap between complex AI models and human understanding, with particular emphasis on making these technologies accessible and useful for domain experts in biomedical research.

David B. LindellView profile

Assistant Professor

• Computational Imaging
• Computer Vision
• Non-Line-of-Sight Imaging
• +5 more

David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.

Freda ShiView profile

Assistant Professor

• computational linguistics
• natural language processing
• computational multilingualism
• +5 more

Freda Shi is an Assistant Professor at the David R. Cheriton School of Computer Science at the University of Waterloo and a Faculty Member at the Vector Institute, where she holds a Canada CIFAR AI Chair. She joined the University of Waterloo in July 2024 after completing her Ph.D. at the Toyota Technological Institute at Chicago. Educational Background: Ph.D. in Computer Science, Toyota Technological Institute at Chicago (2024), advised by Professors Karen Livescu and Kevin Gimpel Bachelor's degree in Intelligence Science and Technology (Computer Science Track) with a minor in Sociology, Peking University (2018) Dr. Shi's research focuses on computational linguistics and natural language processing, particularly on deeper understandings of natural language and the human language processing mechanism. She is especially interested in learning language through grounding, computational multilingualism, and related machine learning aspects. Her work aims to inform the design of more efficient, effective, safe, and trustworthy NLP systems. She leads the CompLING Lab at the University of Waterloo, which investigates how language models process spatial relationships and acquire linguistic structures through grounded experiences. Her publication record shows a consistent trajectory of high-impact research, with recent work focusing on spatial reasoning in vision-language models, multilingual chain-of-thought capabilities, and grounded language acquisition. She has published in top-tier conferences including ACL, EMNLP, ICLR, and NAACL, with several papers receiving notable recognition including Best Paper Nominee status at multiple venues. Her research bridges theoretical linguistics with practical NLP applications, demonstrating how linguistic insights can improve AI systems. Scientific Recognition: Canada CIFAR AI Chair (2024) Google Ph.D. Fellowship Thesis of Distinction for her doctoral work Multiple Best Paper Nominee awards at major NLP conferences Dr. Shi teaches CS 784: Computational Linguistics and CS 486/686: Introduction to Artificial Intelligence at the University of Waterloo. She actively contributes to the NLP research community through conference participation, program committee service, and collaborative projects. Her research has significant implications for creating more robust, human-like language understanding systems and advancing the field of grounded language learning in artificial intelligence.

Enamul Hoque PrinceView profile

Associate Professor

• Information Visualization and Visual Analytics
• Natural Language Processing
• Human-Computer Interaction

Enamul Hoque Prince is an Associate Professor and Director of the School of Information Technology at York University. He leads the Intelligent Visualization Lab, funded by the Canada Foundation for Innovation (CFI) and Ontario Research Funds (ORF). He holds a PhD in Computer Science from the University of British Columbia and completed postdoctoral work at Stanford University. His research integrates information visualization, human-computer interaction (HCI), and natural language processing (NLP) to address information overload challenges. Dr. Prince's educational background includes a PhD from UBC, an MSc from Memorial University of Newfoundland, and a BSc from Chittagong University of Engineering & Technology. He has conducted research at institutions like Tableau Software and the Qatar Computing Research Institute and serves on committees for top conferences like ACL and IEEE Vis. His work is supported by grants from NSERC, CFI, and others. Research interests focus on NLP-driven visual analytics, user-adaptive visualization, and accessible interfaces. Notable projects include Evizeon (natural language interfaces for visual analytics), ConVisIT (topic modeling for online conversations), and CIDER (concept-based image search). Publications highlight trends in multimodal systems, chart comprehension, and accessibility. Key awards include the NSERC Discovery Grant (2019) and a Best Paper Honorable Mention at DIS 2021. He supervises graduate and undergraduate students in areas like visualization, NLP, and HCI. Labs and collaborations emphasize interdisciplinary approaches, with the Intelligent Visualization Lab advancing tools for data exploration and user-centered design. Teaching includes courses on design principles and information visualization.

• Cultural Studies
• Globalization and Culture
• Entrepreneurship
• +3 more

Tina Dacin is a Professor of Strategy and Organizational Behavior at the Smith School of Business, Queen's University, Canada. She holds the Stephen J.R. Smith Chair and serves as Director of the Community Impact Research Program. Her roles include former Director of the Smith School’s Centre for Social Impact, former University Senate member, and former Chair of the Principal’s Innovation Fund Committee. She co-founded the Community of Social Innovation Chair and holds affiliations with the Academy of Management and Informs. Education: Ph.D. (1993) in Business Administration from the University of Toronto and M.A. in Psychology from the University of New Brunswick. Research focuses on cultural heritage, social innovation/entrepreneurship, strategic alliances, and globalization. Key themes include custodianship, tradition preservation, place-making, and institutional dynamics. Recent work explores craft-based ventures, place-sensitive organizational practices, and institutional renewal in crises like the Detroit water wars. Awards: None explicitly listed, though her chair position reflects academic distinction. Teaching includes courses on global social impact and analytics/ethics policy. Labs/Teams: Leadership in the Community Impact Research Program and the Scotiabank Centre for Customer Analytics.

Joey PaquetView profile

Associate Professor

• Design and implementation of programming languages
• Context-driven computing
• Demand-driven computing

Dr. Joey Paquet is a Tenured Associate Professor and Department Chair in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He holds a PhD and specializes in research areas including Design and Implementation of Programming Languages, Context-Driven Computing, and Demand-Driven Computing. His work focuses on advancing programming paradigms and their applications in cybersecurity, distributed systems, and autonomic computing. Dr. Paquet is actively involved in thesis supervision across Computer Science and Software Engineering programs at both master's and doctoral levels. Research interests are centered around programming language design, particularly in demand-driven and context-aware systems. His contributions include frameworks like GIPSY and OpenISS, enabling scalable data processing and forensic computing. Recent work explores autonomic intent-driven networking, real-time gesture recognition, and IoT forensics. These efforts highlight his expertise in both theoretical constructs and practical implementations. His advising role supports students in MCompSc, MASc, and PhD programs. While specific grants are not detailed, his projects often involve interdisciplinary collaboration. Dr. Paquet is affiliated with platforms like LinkedIn and ResearchGate, reflecting an active academic presence. Key technical contributions include pioneering work on forensic computing backends, intent expression languages, and multimodal interaction systems. His research addresses challenges in cybersecurity, distributed systems, and service composition, with a focus on resilience and scalability.

Euijin ChooView profile

Assistant Professor

• Data-driven Cybersecurity
• Big Data Analytics
• Graph Mining
• +6 more

Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.

Baris FidanView profile

Professor

• Control Systems
• Autonomous Vehicles
• Robotics
• +5 more

Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.