Tong Lam is an Associate Professor in the Department of Historical Studies and the Graduate Department of History at the University of Toronto, where he also directs the Dr. David Chu Program in Asia-Pacific Studies at the Asian Institute. His research sits at the intersection of media studies, environmental history, and science and technology studies (STS), focusing on the politics and aesthetics of mobilization in China’s special zones across socialist and postsocialist eras. Employing interdisciplinary methodologies, his work explores themes such as state-precipitated violence, industrial ruination, and the socio-political implications of technology. He is a visual artist whose lens-based projects interrogate military violence, surveillance, and postindustrial landscapes. His recent scholarship analyzes postsocialist urbanism, digital governance, and the temporalities of infrastructure. While the absence of explicit scientific awards or student advisement records in the provided text precludes their inclusion here, his publications and creative practice reflect a sustained engagement with critical historical inquiry and contemporary socio-technological challenges.
Florian Shkurti is an Assistant Professor in the Department of Computer Science at the University of Toronto Mississauga (UTM), affiliated with the UofT Robotics Institute, Vector Institute, and Acceleration Consortium. His research focuses on robotics, machine learning, and computer vision, emphasizing safe and effective autonomous systems in dynamic environments. He directs the Robot Vision and Learning (RVL) lab, exploring areas like environmental monitoring, autonomous navigation, and mobile manipulation. Research Interests: His work spans robotics, machine learning, and computer vision. Key areas include robot perception, planning under uncertainty, safe exploration, imitation learning, and applications in field robotics, autonomous vehicles, and chemistry lab automation. He develops methods enabling robots to perceive, reason, and act safely in collaboration with humans. Publications: Recent work includes advancements in safe multitask learning, interactive crowd navigation, and diffusion models for trajectory planning. His research bridges theoretical foundations with real-world applications in environmental science and autonomous systems. Affiliations: Faculty Member, UofT Robotics Institute; Faculty Affiliate, Vector Institute; Faculty Member, Acceleration Consortium. He also holds positions at UTM's Mathematical & Computational Sciences department. Teaching: Courses include Imitation Learning for Robotics, Neural Networks, and Mobile Robotics. He emphasizes hands-on experience with autonomous systems through projects involving RC cars and simulation tools. Labs & Teams: Leads the RVL lab, collaborating on projects like RoboCulture (automated biological experimentation) and SICNav (safe crowd navigation systems). The lab focuses on cross-disciplinary robotics solutions for real-world challenges.
Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.
James T. Enns is a Professor and Distinguished University Scholar in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His research primarily focuses on the role of attention in human vision, with secondary interests in developmental psychology and human-machine interaction. Dr. Enns' research interests span perception, attention, vision, cognition, development, and human-machine interaction. His work explores how the human mind selects information, with particular emphasis on visual attention mechanisms. His laboratory research investigates the fundamental processes of visual perception and how attention modulates these processes across different contexts and developmental stages. Analysis of Dr. Enns' publication record reveals consistent engagement with visual perception, attentional mechanisms, and cognitive processing. His research demonstrates expertise in both theoretical frameworks and experimental methodologies related to visual attention, with applications spanning basic cognitive science to potential implementations in human-computer interaction systems. His work often bridges theoretical cognitive psychology with practical applications. Canadian Society for Brain, Behaviour and Cognitive Science Donald O. Hebb Distinguished Contribution Award (2013) Distinguished University Scholar, UBC (2004) Robert E. Knox Master Teaching Award (2004) Royal Society of Canada Fellow (2002) Killam Faculty Research Prize (1994) Killam Faculty Research Fellowship (1993) Society of Experimental Psychology Fellow Dr. Enns has served as Editor for the Journal of Experimental Psychology: Human Perception and Performance, and as Associate Editor for Psychological Science, Consciousness and Cognition, and Visual Cognition. His research has been supported by grants from NSERC, the Canadian Foundation for Innovation, the Australian Research Council, BC Health, and Nissan. He has authored textbooks on perception, edited research volumes on the Development of Attention, and published numerous scientific articles on vision, attention, and cognitive science. Dr. Enns is currently accepting graduate students and continues to mentor the next generation of cognitive scientists. Dr. Enns leads the UBC Vision Lab, which focuses on how the human mind selects information. The lab conducts research on visual attention, perception, and cognitive processes using a variety of experimental methodologies.
Anoop Sarkar is a Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Natural Language Lab. His research focuses on multilingual natural language processing, machine learning applications in NLP, and machine reading with visualization techniques. He has held significant grants including NSERC Discovery Accelerator Supplements (2012–2023) and multiple Google/IBM Faculty Awards. His teaching includes advanced NLP courses and compiler design. Over 38 students have graduated under his supervision, with a strong emphasis on computational linguistics and machine translation. His research explores areas such as statistical machine translation, decipherment of ancient scripts, and semi-supervised learning. Key software contributions include the TroFi toolkit and metaphor dataset. He has served as area chair for EMNLP (2023) and co-chair of NAACL (2015). The Natural Language Lab hosts weekly meetings open to collaboration. His work bridges theory and application, addressing challenges in low-resource language processing. Recent publications (2023–2024) highlight advancements in entity linking, cognate alignment, and structured prediction. Grants span foundational and applied NLP research, supported by NSERC and industry partnerships. His advising spans 12 PhD and 26 MSc graduates, reflecting a sustained impact on the next generation of NLP researchers.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Septimiu E. Salcudean is a Professor at the University of British Columbia's Department of Electrical and Computer Engineering, holding the C.A. Laszlo Chair in Biomedical Engineering and a Canada Research Chair. His research focuses on medical robotics, image guidance systems, and ultrasound elastography. He has contributed to advancements in haptic interfaces, teleoperation, and needle insertion modeling. Education: B.Eng and M.Eng from McGill University (1979-1981), Ph.D. from UC Berkeley (1986). He has held positions at IBM T.J. Watson Research Center and was a Killam Research Fellow at ONERA in France. His work spans robotics, biomedical engineering, and surgical systems. Research interests include medical robotics, real-time imaging, and surgical navigation. Notable projects involve ultrasound-guided surgery, vibro-elastography for tissue characterization, and haptic feedback systems. His lab, the Robotics and Control Laboratory (RCL), develops technologies like the da Vinci surgical system integration with ultrasound imaging. Awards include the NSERC Synergy Award, IEEE Fellowship, and UBC Killam Research Prize. He has advised over 30 graduate students and published extensively in robotics and biomedical journals.
Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.
Soo Jeon is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, part of the Faculty of Engineering. He holds a PhD from the University of California at Berkeley (2007) and prior degrees from Seoul National University. His research focuses on mechatronics, dynamic systems, and control, with applications in robotics, autonomous systems, and precision motion control. He has held roles as Assistant Professor (2009–2015), Associate Professor (2015–2024), and Full Professor (2024–present). Professor Jeon’s expertise includes intelligent sensing and control for mechatronic systems, nonlinear dynamics, and autonomous systems. He has received notable awards such as the 2022 Engineer of the Year Award (AKCSE/KOFST), 2015 NSERC Discovery Accelerator Supplement, and 2010 ASME Rudolf Kalman Best Paper Award. He serves as an Associate Editor for several journals, including the ASME Journal of Dynamic Systems and IEEE Transactions on Automation Science and Engineering. His research interests span robotics, control systems, and automation, with recent work on autonomous navigation, tactile exploration, and model predictive control. He supervises graduate students in MASc and PhD programs and teaches courses like ME 649 (Control of Machines and Processes) and ME 360 (Introduction to Control Systems). Jeon holds patents in areas like low-power magnetic locks and remote plasma source seasoning. His lab, the Waterloo Mechanical Systems & Control Laboratory (WMSCL), focuses on advanced mechatronics and robotics projects, including collaborations with international institutions such as the Korea Institute of Machinery & Materials (KIMM).
France Bouthillier is an Associate Professor and Associate Dean of Graduate and Postdoctoral Studies at McGill University's School of Information Studies. She holds a PhD from the University of Toronto and multiple advanced degrees in library science, administration, and education from Quebec institutions. Her research focuses on competitive intelligence, healthcare information systems, and small business information needs. She has led major grants including a SSHRC-funded study on children’s cyber-safety and CIHR projects on evidence dissemination in healthcare. Education: PhD, Faculty of Information Studies, University of Toronto MBSI, École de bibliothéconomie et des sciences de l'information, Université de Montréal C. Admin, Département des sciences de l'administration, UQAM BEd, Département des sciences de l'éducation, UQAM Research Interests: Dr. Bouthillier investigates digital resource assessment in healthcare, cross-cultural competitive intelligence practices, and information needs of marginalized communities. She emphasizes collaborative information monitoring and user-centered design in information systems. Her work bridges theory and practice, addressing gaps in evidence-based decision-making. Grants & Awards: Competia Award (2003) for co-authored book on CI software assessment SSHRC Standard Research Grant (2005-2008) on CI technology use CIHR grants for healthcare evidence dissemination (2006, 2008) Professional Involvement: Editorial Board member of the Journal of Information Science Theory and Practice, ASIST member, and SSHRC grant reviewer. She co-developed the eSRAP system for patient-oriented research monitoring. Her lab focuses on interdisciplinary projects in information visualization and collaborative tools.
Samia Khan is a Professor in the Department of Curriculum & Pedagogy at the University of British Columbia's Faculty of Education, where she also serves as Associate Dean of Research. Her academic work bridges educational technology, science education, and teacher preparation across K-16 contexts. Dr. Khan earned her PhD from the University of Massachusetts. Her educational background informs her interdisciplinary approach to learning sciences and technology integration. Her research centers on how digital technologies transform science learning , with emphases on model-based teaching , visualization tools , and equitable participation in STEM . She investigates simulation technologies, future-state modeling, and strategies to broaden science engagement through interpretive and mixed-methods research. Her work particularly examines teacher epistemologies, scientific reasoning development, and socio-cultural factors in technology-mediated learning environments. Analysis of her recent publications reveals three dominant trends: (1) International comparative studies of science curricula across Southeast Asia, (2) Efficacy of digital tools (PhET, GeoGebra, Symbolab) in conceptual understanding, and (3) Pre-service teacher development in model-based science instruction. Her research spans diverse contexts from Canadian classrooms to Rwandan and Vietnamese educational settings. Dr. Khan's contributions have been recognized through: New Scholar Award from the Canadian Society for Study in Education Prime Minister’s Award of Canada for Teaching Excellence in Science, Technology, and Mathematics As former MET Director (2021-2022) and author of foundational courses ETEC 530/533, she has significantly shaped UBC's educational technology programs. Her Faculty Associate role at the Institute of Resources, Environment, and Sustainability demonstrates cross-disciplinary engagement with sustainability education. Her research appears in leading journals including Journal of Technology and Teacher Education, Computers and Education, and Educational Technology Research and Development, with consistent citation as field-shaping work in educational technology.
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Frederick A. A. Kingdom is a Professor in the Department of Ophthalmology at McGill University's Faculty of Medicine, focusing on Perception, Cognition and Cognitive Neuroscience . His research explores the interplay between early visual feature detection (edges, bars) and intermediate stages forming contours, textures, and surfaces through spatial vision, color vision, stereopsis, texture perception, brightness/lightness perception, and transparency studies . Email: fred.kingdom@mcgill.ca Key research domains include: Perceptual Mechanisms : Lateral inhibition, contrast normalization, spatial bandpass filters, and their role in brightness/lightness perception and illusions like simultaneous brightness contrast. Color Vision : Red-green vs blue-yellow system distribution, chromatic contrast requirements for stereopsis, color-based depth processing limitations, and color-shading effects that parse surfaces vs illumination. Texture Analysis : Detection thresholds for orientation/frequency/contrast modulated textures, co-circularity in texture perception, and texture statistical sensitivity (e.g., kurtosis importance). Shape Processing : Shape-frequency/shape-amplitude aftereffects, global vs local shape coding, and contour inflection adaptation. His work combines psychophysics , fMRI , image processing , and computational modeling to dissect visual system architecture, particularly how color and luminance signals are integrated/separated in early cortical processing.
Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.
Pourang Irani is a Professor and Principal’s Research Chair in Ubiquitous Analytics at the University of British Columbia (Okanagan campus), within the Irving K. Barber Faculty of Science’s Department of Computer Science, Mathematics, Physics and Statistics. Previously, he served at the University of Manitoba for 19 years as a faculty member and Acting Associate Dean of Science. His research focuses on Human-Computer Interaction (HCI), Wearable Computing, and Information Visualization, with an emphasis on designing interactive systems for 'anytime, anywhere' sensemaking using emerging technologies like mixed reality and smart devices. He leads interdisciplinary projects such as the NSERC CREATE grant on Visual and Automated Disease Analytics, training data scientists in health analytics. Education: PhD in Computer Science (University of New Brunswick, 2002), supervised by Dr. Colin Ware. Research Interests: Wearable interfaces (smartwatches, smartrings, and AR/VR) Data visualization for health and pervasive systems Persuasive health technologies and data storytelling Mid-air and hands-free interaction techniques Embodied interaction through social robots Recent Work Trends: Articles emphasize innovations in smartwatch interaction (e.g., bezel-to-bezel gestures), tactile visualizations via skin-dragging, and voice assistant-driven health data queries. Projects like Databiting explore transient personal data exploration, while Data Videos focus on narrative-driven health communication. Grants & Leadership: Principal Investigator of NSERC CREATE Visual and Automated Disease Analytics program. Co-leads UBCO’s Digital Transparency cluster. Active in interdisciplinary teams addressing health informatics and HCI challenges. Labs & Teams: Leads a lab developing prototypes in wearable computing, spatial analytics, and immersive technologies. Collaborates with researchers in medicine, engineering, and data science to translate HCI innovations into practical solutions.
