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Vincent WadeView profile

Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +8 more

Professor Vincent Wade is a prominent academic and co-founder of the ADAPT SFI Research Centre, holding the Professorial Chair of Computer Science (established 1990) and a Personal Chair in Artificial Intelligence at Trinity College Dublin's School of Computer Science and Statistics. He co-directs the DREAL Centre for Research Training and leads ADAPT, a globally recognized centre for digital media technology and AI research. His work spans intelligent systems, personalisation, machine learning, and ethical AI applications in healthcare and education. Research interests include AI-driven personalisation, multimodal interaction, knowledge graphs, and ethical considerations in digital technologies. He has published over 350 peer-reviewed papers, earned the prestigious Provost Innovation Award (2018), and holds patents in personalisation technologies. He co-founded EmpowerTheUser, a TCD spin-out focused on simulation-based learning analytics. Major Achievements: 2018 Provost Innovation Award (Trinity College Dublin) 2010 European Language Label Award Fellow of Trinity College Dublin Over 350 scientific publications Key Contributions: Developed the ADELE corpus for social conversation analysis Pioneered cross-site personalisation frameworks Advanced adaptive e-learning systems through platforms like Slicepedia and AMASE His research bridges technical innovation with societal impact, addressing challenges in healthcare, education, and digital ethics.

• Interactive Information Retrieval Systems
• Search Engines
• Recommendation Systems
• +3 more

Mark D. Smucker is a Professor in the Department of Management Science and Engineering at the University of Waterloo , cross-appointed with the David R. Cheriton School of Computer Science (Faculty of Math). His research focuses on designing and evaluating interactive information retrieval systems, including search engines and recommendation systems. He co-organized the TREC Health Misinformation Track (2019–2022) and currently leads the TREC DRAGUN Track, addressing trustworthiness assessment of news. He holds a PhD in Computer Science from the University of Massachusetts Amherst, a Master's from the University of Wisconsin-Madison, and dual Bachelor's degrees in Physics and Computer Science from Iowa State University. His teaching includes courses like Search Engines (MSCI 541/MSE 541) and Introduction to Computer Programming (MSCI 121/MSE 121), emphasizing practical programming and information systems design. He has received awards including the ACM SIGIR 2012 Best Paper Award and the University of Waterloo Engineering Society Teaching Excellence Honourable Mention (2014). His work bridges theory and practice, with contributions to evaluation methodologies like time-based calibration of metrics and preference graphs. He collaborates on projects like the TREC tracks to improve search systems' real-world applicability, particularly in health contexts and misinformation detection.

Chris WhiddenView profile

Assistant Professor

• Algorithms and Bioinformatics
• Computational Biology
• Phylogenetics
• +10 more

Chris Whidden is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, where he leads research in algorithms and bioinformatics. His work bridges theoretical computer science with practical applications in computational biology and ocean data analytics. Whidden's research interests include approximation and fixed-parameter algorithms, computational biology, evolutionary trees and networks, graph theory, hybridization and lateral gene transfer, NP-hardness, and ocean data analytics. He develops efficient algorithms and software to solve NP-hard problems, particularly in the context of phylogenetics and large-scale biological data. His work applies both theoretical algorithm design and practical software engineering to create novel solutions for understanding biodiversity, bacterial and viral evolution, and oceanographic systems. His recent publications reflect a strong trend toward interdisciplinary research, combining deep learning and machine learning with oceanographic data analysis, fish detection and classification, echosounder data processing, and environmental monitoring. Many of his algorithmic contributions focus on phylogenetic tree comparison, including SPR distances, maximum agreement forests, and supertree construction. He has developed several widely used software tools such as rspr, SPR Supertrees, uspr, and phylogenetic topographer. NSERC Killam Trusts Tula Foundation NSF Simons Foundation (via Life Sciences Research Foundation) DeepSense (industry-academic collaboration) He is actively involved in mentoring and has funding available for PhD and MCS students in computer science, particularly in algorithms, bioinformatics, and data analytics. He teaches courses such as Algorithm Engineering (CSCI 4118/6105), Software Development (CSCI 2134), and Design and Analysis of Algorithms (CSCI 3110). Whidden has collaborated extensively with industry through DeepSense, working on projects that apply data analytics and machine learning to the ocean sector, including predictive modeling for ocean buoys, automated fish detection, and tidal energy monitoring.

Colleen LairdView profile

Assistant Professor

• Japanese Cinema
• Videographic Criticism
• Gender and Media Studies
• +3 more

Dr. Colleen Laird is an Assistant Professor of Japanese Film in the Department of Asian Studies at the University of British Columbia (UBC). Her research focuses on Japanese cinema, particularly examining female directors, gendered image production, and the intersection of film and videographic criticism. Ph.D., University of Oregon (East Asian Languages and Literatures, 2012) M.A., University of Oregon (East Asian Languages and Literatures, 2006) B.A., Macalester College (Asian Studies & Biology, 2002) Her work interrogates the roles of women in the Japanese film industry, the pedagogy of Japanese cinema, and the use of videographic criticism as a scholarly and educational tool. She has produced educational video series such as J-Horror Video Series and Formal Analysis and Japanese Cinema Series , available as open educational resources. Dr. Laird’s recent publications span videographic essays and peer-reviewed articles in journals like [in]Transition , Tecmerin , and Journal of Japanese and Korean Cinema . Her research frequently explores themes of domestic spaces, gendered narratives, and transnational cultural exchanges. SSHRC Connection Grant: Reframing the Argument (2025) British Film Institute’s Sight and Sound Best Video Essays (2022–2024) Hampton Fund Research Grant (2020–2022) At UBC, she co-leads the Collaborative Digital Heritage Studio (CoDHerS) and teaches courses on Japanese film, gender and media, and videographic criticism, emphasizing multimodal learning and creative scholarship.

Michael DoveView profile

Assistant Professor

• Public History
• Canadian Sports History
• Beer and Brewing History
• +4 more

Michael Dove is an Assistant Professor in the Department of History at the University of Western Ontario, serving as Director and Internship Coordinator for the M.A. Public History Field. He holds a PhD from the University of Western Ontario (2007) and a Certificate in Documentary Filmmaking from the New York Film Academy (2023). His research focuses on public history, Canadian sports history, beer and brewing history, and maritime commerce in the Early Modern Period. Dove has pioneered projects on Labatt Memorial Park, Canada's O-Pee-Chee Company, and the Hudson's Bay Company's commercial networks. Teaching responsibilities include courses on beer history, public history methodologies, and maritime history. Notable community projects include the Labatt Breweries Collection Virtual Exhibit, historical walking tours of London, Ontario, and oral history initiatives with the Canadian Museum of Immigration. He has contributed to national organizations such as the Ontario Historical Society and Museum of Ontario Archaeology. Awards include the 2023 London Heritage Award for public history work and the 2018 Vice-Provost Teaching Excellence Award. Current projects include an oral history book on O-Pee-Chee and documentary films exploring Canadian cultural heritage.

Stephen BrownView profile

Associate Professor

• Environmental Chemistry
• Analytical Chemistry
• Environmental Monitoring
• +4 more

Stephen Brown is an Associate Professor at Queen’s University, jointly affiliated with the School of Environmental Studies and the Department of Chemistry. His expertise lies in developing innovative environmental analysis methods, particularly for detecting small organic contaminants in aqueous samples. He specializes in optical sensor technology and enzyme-based assays, focusing on sensitivity and selectivity in environmental monitoring. His research integrates instrument design, biochemical detection methods, and field applications to address real-world environmental challenges. Dr. Brown holds a B.Sc. (1985) from Dalhousie University, an M.Sc. (1988) and Ph.D. (1992) from the University of Toronto. His work emphasizes practical solutions for water quality assessment, including wastewater surveillance, microbial source tracking, and oil toxicity evaluation. Recent projects include analyzing groundwater contamination patterns, SARS-CoV-2 monitoring in wastewater, and risk assessments for private well users. His research trends reflect a focus on interdisciplinary approaches, combining big data analytics, machine learning, and environmental chemistry. Key themes include improving detection accuracy in dynamic systems, understanding microbial interactions in contaminated environments, and advancing sensor technology for field deployment. He also explores public health dimensions, such as consumer risk perception and behavioral responses to water quality issues. Dr. Brown’s contributions span academic publications, patents (e.g., on switchable ionic strength water and optical sensors), and collaborations with environmental agencies. His lab in Chernoff Hall develops cutting-edge tools for environmental analysis, including enzyme immobilization techniques and fiber-optic sensor systems. His work addresses critical challenges in environmental health and sustainability, with implications for policy and community resilience.

Jonathan GammellView profile

Assistant Professor

• Autonomous Systems and Control
• Robotics
• Artificial Intelligence
• +4 more

Jonathan Gammell is an Assistant Professor at Queen's University's Department of Electrical and Computer Engineering and a member of the Ingenuity Labs Research Institute. He holds an adjunct fellowship at the Oxford Robotics Institute (University of Oxford), where he previously taught. His expertise spans autonomous systems, robotics, and AI, with a focus on motion planning for diverse applications, including medical devices, self-driving cars, and aerial robotics. He leads the Estimation, Search, and Planning (ESP) group, which develops algorithms for autonomous systems. Educations: BASc in Mechanical Engineering and Physics from the University of Waterloo MASc and PhD in Robotics from the University of Toronto Institute for Aerospace Studies Research Interests: Dr. Gammell's work addresses fundamental motion planning challenges in robotics, emphasizing asymptotically optimal algorithms like BIT*, AIT*, and FCIT*. His research integrates robotics with medical applications (e.g., knee replacement implants) and autonomous systems (e.g., aerial mapping for emergency response). His algorithms are widely adopted in industry and academia, including collaborations with NASA JPL and Oxford orthopaedic surgeons. Publications: His recent work explores advanced motion planning frameworks (e.g., AORRTC, Osprey), multimotion visual odometry (MVO), and medical robotics applications. These contributions highlight his dual focus on theoretical algorithm development and practical, high-impact applications. Awards: CIPPRS Award for Best Canadian PhD Thesis (Medical Imaging/Robotics) NASA Group Achievement Award (2021, 2022) IROS Best Paper Shortlist (2020) Advising & Grants: He advises on projects involving autonomous systems, medical robotics, and AI. His grants support collaborations with industry and academic partners, including NASA JPL and Oxford's robotics and medical teams. The ESP group's open-source tools (e.g., Planner Developer Tools) enable reproducible motion-planning research. Labs/Teams: He leads the ESP research group at Queen's University and collaborates with the Oxford Robotics Institute and Ingenuity Labs, focusing on cutting-edge robotics solutions for real-world challenges.

David WagnerView profile

Professor

• Mathematics Education
• Social Justice in Education
• Indigenous Mathematics Education
• +5 more

David Wagner is a Professor in the Department of Secondary Education at the Faculty of Education, University of New Brunswick. He holds adjunct positions at the University of South-Eastern Norway and Malmö University. His academic journey includes a PhD (2004) and M.Ed (2002) from the University of Alberta, followed by a B.Ed (1991), B.A. in Mathematics (1989), and B.R.S. from Mennonite Brethren Bible College (1989). Prior to his PhD, he taught grades 7–12 in Canada and in eSwatini, shaping his research focus on the cultural dimensions of mathematics education. His research explores human interaction in mathematics, emphasizing social justice, Indigenous education, and sustainability. Key projects include the 'Show Me Your Math' initiative engaging Indigenous communities and developing culturally responsive pedagogies. He has authored/co-authored over 100 publications, including books and chapters on mathematics education, equity, and critical discourse analysis. Wagner serves as Co-Editor-in-Chief of *Educational Studies in Mathematics*, and has held roles such as Associate Dean for Graduate Programs at UNB, Managing Editor of *For the Learning of Mathematics*, and leadership in organizations like the Canadian Mathematics Education Study Group. His work intersects with global non-killing initiatives and sustainable development, advocating for mathematics education to address societal challenges. His peer-reviewed articles span equity in mathematics education, classroom discourse analysis, and policy advocacy. He actively promotes ethical research practices, including guidelines for peer review and addressing systemic inequities in academic publishing.

Philippe GachonView profile

Professor

• Hydro-climatology
• Climate Risk Assessment
• Extreme Weather Events
• +4 more

Philippe Gachon is a Professor in the Department of Geography at the University of Quebec at Montreal (UQAM), Director of the ESCER Research Centre, and leader of the Quebec InterSectoral Flooding Network (RIISQ). He specializes in hydro-climatology and climate risk assessment, with extensive experience at Environment Canada and as a Strategic Research Chair holder on Hydrometeorological Risks Related to Climate Change. His research examines climate change impacts on extreme weather events including floods, droughts, and heat waves. Recent work focuses on improving risk identification and adaptation strategies through intersectoral approaches. Key research themes include: Regional climate modeling and validation Hydro-meteorological hazard intensification Systemic risk assessment Community-scale resilience building Climate services development His recent publications demonstrate advanced statistical approaches for modeling non-stationary flood regimes, evaluating wildfire risks under climate change, and quantifying vector-borne disease transmission. Collaborative projects with 16 universities address physical climate risks and socioeconomic vulnerabilities across Quebec.

Kelly L. LockwoodView profile

Associate Professor

• Sport Biomechanics
• Exercise Physiology
• Athlete Development
• +3 more

Dr. Kelly L. Lockwood is an Associate Professor in the Department of Kinesiology at Brock University, within the Faculty of Applied Health Sciences. Her research focuses on sport biomechanics, exercise physiology, and athlete development, particularly in on-ice sports like hockey. She collaborates with industry to enhance sport equipment engineering, exploring material properties and design to improve athlete performance. Dr. Lockwood teaches courses such as Coaching Theory, Advanced Coaching Theory, and Harnessing Athletic Potential. She is a Learning Facilitator for the National Coaching Certification Program (NCCP) and the Founder & President of The Skating Lab Inc., a consultancy focused on sport science applications. Her research interests include skating mechanics, long-term athlete development (LTAD), and equipment design optimization. She has contributed to peer-reviewed presentations and publications on topics like blade alignment effects in goaltenders, skate boot design limitations, and relative age effects in hockey player development. Dr. Lockwood’s work bridges theoretical frameworks with practical applications, impacting both athletic training and equipment innovation. Professional affiliations include the Canadian Society of Exercise Physiology (CSEP), International Society of Biomechanics of Sport (ISBS), and the International Skating Union (ISU). Her academic contributions emphasize translating sport science into actionable strategies for coaches, athletes, and equipment manufacturers.

Chengzhou TangView profile

Assistant Professor

• 3D Computer Vision
• Deep Learning
• Robotics
• +3 more

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.

Cuneyt Gurcan AkcoraView profile

Associate Professor

• Data Science on Complex Networks
• Graph Mining
• Topological Data Analysis
• +3 more

Cuneyt Gurcan Akcora is an Associate Professor of Computer Science at the University of Central Florida (UCF) and an Adjunct Professor in Computer Science and Statistics at the University of Manitoba, Canada. He holds a Ph.D. from Università degli Studi dell’Insubria (Italy) and an M.S. from SUNY Buffalo (USA) as a Fulbright Scholar. His research focuses on data science applied to complex networks and graph mining, particularly in blockchain and online social networks. He has collaborated with institutions like Yahoo! Research Barcelona, Qatar Computing Research Institute, and Huawei. Education: PhD in Computer Science, 2014 – Università degli Studi dell’Insubria, Italy MEng in Computer Science, 2010 – SUNY Buffalo, USA BSc in Electrical & Electronics Engineering, 2008 – Karadeniz Technical University, Turkey Research Interests: Explainable AI and Graph Machine Learning Topological Data Analysis (TDA) for Blockchain Networks Large-Scale Graph Analysis and Anomaly Detection Nonparametric Statistical Methods Key Contributions: His work includes developing chainlet theory for Bitcoin price prediction, scalable TDA algorithms for blockchain forensics, and interdisciplinary grants exploring blockchain applications in insurance. Notable projects include the Chartalist Dataset for blockchain network analysis and ChainNet , a topological graph learning framework. Awards & Grants: NSERC Discovery Grant (2020) UCF Interdisciplinary Grant for Blockchain in Insurance (2020) Fulbright Scholarship (M.S., SUNY Buffalo) Labs & Teams: Active in UCF’s FData Lab and leads blockchain data analytics initiatives. His work bridges academia and industry through collaborations with Huawei, QCRI, and others.

Kirk LutherView profile

Assistant Professor

• Forensic Psychology
• Investigative Interviewing
• Legal Rights Comprehension
• +3 more

Kirk Luther is an Assistant Professor in the Department of Psychology at Carleton University, within the Faculty of Arts and Social Sciences. His research focuses on safeguarding legal rights for adults and youth, advancing investigative interviewing methodologies, and understanding cognitive and social factors influencing comprehension and information provision during interviews. He leads the PRISM Lab, which explores theoretical models of legal rights comprehension and interviewee behavior. Luther holds a Ph.D. from Memorial University. His work integrates forensic psychology, cognitive science, and legal studies to improve practices in criminal justice contexts. He actively seeks motivated students for collaborative research projects. Key research areas include the administration of youth interrogation rights, rapport-building in interviews, and the efficacy of techniques like the sketch procedure for enhancing recall. His contributions address real-world challenges in law enforcement, emphasizing ethical and evidence-based practices. Contact: Office A539 LOEB BUILDING, Email: kirk.luther@carleton.ca .

Alan TsangView profile

Assistant Professor

• Artificial Intelligence
• Multi-agent Systems
• Game Theory
• +7 more

Alan Tsang is an Assistant Professor at Carleton University's School of Computer Science. He specializes in Multi-agent Systems and Computational Social Choice, focusing on strategic interactions in social networks. Previously, he was a Post-Doctoral Researcher at the National University of Singapore and earned his PhD from the Cheriton School of Computer Science at the University of Waterloo under Dr. Kate Larson. His research integrates game theory and agent-based simulations to address fairness, voting mechanisms, and social network dynamics. He is affiliated with Carleton's Institute for Data Science and the Department of Human-Computer Interaction (HCI), and serves as Information Officer of ACM SIGAI. His educational background includes a Master of Mathematics in Graph Theory and a Bachelor of Mathematics in Bioinformatics, both from the University of Waterloo. His work explores interdisciplinary applications, such as modeling pandemic impacts and ethical computing challenges. He teaches courses like Multiagent Systems and Computing, Society, and Ethics, and actively mentors students in graduate and undergraduate research programs. Key research areas include algorithmic fairness, social choice mechanisms, and agent-based modeling of societal systems. He leads the Games, Agents, and Incentives Workshop and collaborates on projects analyzing voting behavior in homophilic networks and fair allocation strategies.

Carson LeungView profile

Professor

• Big Data Science
• Data Mining
• Machine Learning
• +5 more

Dr. Carson Kai-Sang Leung is a Full Professor in Computer Science at the University of Manitoba's Faculty of Science. He founded and directs the Database & Data Mining Lab. His research focuses on big data science, data mining, machine learning, health informatics, and visual analytics. He holds SMIEEE and SMACM fellowships, reflecting his contributions to the field. Education: B.Sc., M.Sc., and Ph.D. from the University of British Columbia (UBC). Research interests include human-centered exploratory data mining, image databases, and scalable algorithms. He emphasizes user-driven constraints in mining processes and has developed techniques like the segment support map and OSSM for optimized frequency counting. His work on subimage queries in large image databases addresses real-world challenges in visual data retrieval. Publications span data mining, healthcare analytics, and transportation systems. His lab collaborates on projects like visual analytics for motor vehicle accidents and environmental data science for smart cities. He is affiliated with institutions such as the Institute of Industrial Mathematical Sciences (IIMS) and TRLabs. Key awards: Senior Member of IEEE (SMIEEE) and Senior Member of ACM (SMACM).