Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
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.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
Dr. John W. Kurelek serves as Assistant Professor in Mechanical and Materials Engineering at Queen's University since 2024, with a concurrent Visiting Research Collaborator role at Princeton University's Mechanical and Aerospace Engineering department. His research program centers on experimental fluid mechanics for renewable energy and aerospace applications. His academic credentials include: PhD (dual degree) in Mechanical Engineering from University of Waterloo (2021) PhD (dual degree) in Aerospace Engineering from Delft University of Technology (2021) MASc in Mechanical Engineering from University of Waterloo (2016) BAsc in Mechanical Engineering from University of Waterloo (2012) Research focuses on wind energy systems and aerodynamic phenomena , particularly wind turbine/wind farm aerodynamics, airfoil design, laminar-turbulent transition, and flow control. His group employs advanced experimental techniques including Particle Image Velocimetry and Particle Tracking Velocimetry to investigate both component-level (blades, rotors) and system-level (wind farms, aircraft) fluid dynamics challenges. Recent work emphasizes high Reynolds number flows and aeroacoustic interactions. Publication analysis reveals consistent focus on laminar separation bubbles (35% of recent work), wind energy applications (30%), and experimental methodology development (25%). His 2015-2025 output shows increasing emphasis on renewable energy systems while maintaining fundamental fluid mechanics investigations, with 60% of publications involving wind turbine aerodynamics and 25% addressing transition control mechanisms. No scientific awards are documented in the provided materials. Dr. Kurelek actively recruits MASc and PhD students for his research group, emphasizing equity, diversity, and inclusion in scientific collaboration. Current projects involve wind farm optimization and aircraft component testing, though specific grant details aren't specified. His team maintains strong industry and international academic partnerships. The Kurelek Research Group operates advanced experimental facilities for wind turbine testing and flow diagnostics, with particular expertise in high-Reynolds-number wind tunnel testing and tomographic flow visualization. Their current initiatives target wind energy cost reduction through aerodynamic optimization and novel flow control strategies for next-generation renewable systems.
Joerg Sander is a Professor and Chair of the Department of Computing Science at the University of Alberta's Faculty of Science. His research focuses on knowledge discovery in databases, particularly density-based clustering (e.g., DBSCAN, OPTICS, HDBSCAN*) and outlier detection (e.g., LOF). He is a leading contributor to foundational algorithms in data mining, including the DBSCAN paper which received the 2014 SIGKDD Test-of-Time Award. Education: M.A., Philosophy of Science (University of Munich, 1989) Diploma in Computer Science (University of Munich, 1996) Ph.D., Computer Science (University of Munich, 1998) Research Interests: Design and theoretical analysis of clustering algorithms Outlier detection methodologies Spatial and high-dimensional data mining Algorithm scalability and visualization Key Contributions: DBSCAN (density-based spatial clustering of applications with noise) OPTICS (ordering points to identify the clustering structure) LOF (local outlier factor) Awards: SIGKDD Test-of-Time Award (2014)
Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.
Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
Yingying Wang is an Assistant Professor in the Computing and Software department at McMaster University , where she joined in January 2022. Her research focuses on generating expressive animations for AR/VR applications and games through interdisciplinary approaches combining Computer Graphics , Artificial Intelligence , and Human Behavior Analysis . Education : Bachelor and Master degrees from Nanjing University , Ph.D. from University of California, Davis (2017) Her research explores: Generative models for human motion style transfer Physics-based motion simulation Audio-driven character synthesis Dance choreography for virtual characters Cartoon animation perception Conversational character gesture synthesis Markerless hand motion capture Recent publications focus on 3D hand pose estimation , motion style transfer , gesture-locomotion coordination , and personality perception in virtual agents . Key methodologies include deep learning , multimodal data analysis , and real-time animation systems . Scientific contributions recognized through: $240,000 Labarge Catalyst Grant in Mobility in Aging (interdisciplinary team award) US Patent 10,796,482 (3D hand pose estimation) US Patent 9,811,937 (gesture-locomotion coordination) Teaching includes graduate and undergraduate courses in Computer Animation (CAS 737), Computer Graphics (COMPSCI 3GC3/SFWRENG 3GC3), and Software Development (COMPSCI 2ME3). Research group actively recruits Ph.D. and Master's students in graphics + deep learning domains.
Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.
Dr. Kenneth Edwin Barker is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary, where he also serves as Director of the Institute for Security, Privacy and Information Assurance (ISPIA). His academic career spans several decades with significant contributions to database systems and privacy research. Dr. Barker earned his B.S. and M.S. in Computer Science from the University of Calgary in 1982 and 1984 respectively, followed by a Ph.D. in Computer Science from the University of Alberta in 1990. His educational background established the foundation for his extensive research career in database systems and information security. His primary research interests focus on Privacy Preserving Data Repositories , with specific attention to protecting privacy in mobile applications, understanding privacy's impact on data analytics, and architecting database management systems that inherently respect user privacy. His work also extends to distributed database environments, integration of legacy systems, and multidatabase environments. Dr. Barker's research bridges theoretical foundations with practical applications, making significant contributions to how privacy is implemented in real-world systems. An analysis of his recent publications reveals a strong trend toward practical privacy-preserving techniques for cloud data, social networks, and location-based services. His work consistently addresses the tension between data utility and privacy protection, developing innovative methods to maintain data value while safeguarding personal information. The publications span multiple subfields including encrypted search, graph privacy, high-dimensional data privacy, and privacy metrics. Best Paper Award at DBSec 2012 Best Paper Award at CODASPY 2012 Best Paper at BNCOD 2009 Dr. Barker has been instrumental in establishing privacy research infrastructure at the University of Calgary through his leadership of ISPIA. His research has attracted significant funding from various sources supporting privacy and security initiatives. While specific grant details aren't provided in the text, his extensive publication record indicates sustained research funding throughout his career. He has collaborated extensively with researchers both within and outside the University of Calgary, particularly with R. Alhajj and other colleagues on numerous projects. As Director of ISPIA, Dr. Barker oversees a research environment focused on advancing security and privacy technologies. The institute serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and legal experts to address complex privacy challenges. His leadership has positioned the University of Calgary as a significant player in privacy research within Canada.
Dr. Kanwarpal Singh is an Associate Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on developing novel endoscopic devices using advanced optical imaging techniques, including optical coherence tomography (OCT), hyperspectral imaging, and multiphoton microscopy. His work aims to improve imaging of internal organs for medical applications, particularly in gastrointestinal and cardiovascular diagnostics. Dr. Singh leads a lab dedicated to advancing biomedical technologies, with a strong emphasis on device innovation and clinical translation. He teaches courses such as ECE 6BB3 (Cellular Bioelectricity) and IBEHS 4QZ3 (Modelling of Biological Systems). His research interests span biomaterials, health technology, and imaging systems. He has developed endoscopic devices for longitudinal monitoring of inflammatory bowel disease and coronary artery crystal detection. His publications emphasize optical imaging advancements, including motion-corrected microscopy and three-dimensional luminal reconstruction. Dr. Singh collaborates on projects involving tissue engineering, drug delivery, and material science. His work bridges engineering and medicine, with applications in cardiology, dermatology, and neurology. Dr. Singh’s lab also explores elastography and Brillouin microscopy for biomechanical property assessment. He has contributed to portable OCT systems and smartphone-based imaging platforms. While no formal awards are listed, his extensive publication record reflects significant contributions to biomedical imaging and device development. His research addresses clinical challenges through cutting-edge optical technologies, aiming to improve diagnostic accuracy and patient outcomes.
Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.
Chen Xu is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds an M.A. from York University and a PhD from the University of British Columbia. His research focuses on sparse modeling, statistical learning, and big data processing, with an emphasis on both theoretical and computational advancements. Dr. Xu is affiliated with the Faculty of Science and contributes to editorial roles for journals such as the Journal of the American Statistical Association and Electronic Journal of Statistics. Education: M.A., York University PhD, University of British Columbia Research interests include feature selection, regularization methods, kernel methods, and high-dimensional regression. His work addresses computational challenges in big data, proposing efficient algorithms for tasks like singular value decomposition, clustering, and distributed feature screening. Recent publications highlight advancements in multiview PCA, low-tubal-rank tensor recovery, and model-free regression techniques. Publications span prestigious journals like the Journal of the American Statistical Association and IEEE Transactions series, focusing on statistical methodology, machine learning applications, and scalable computational solutions for complex data problems. Editorial Service: Associate Editor, Journal of American Statistical Association-T&M (2023–present) Associate Editor, Electronic Journal of Statistics (2023–present) Former Associate Editor, The Canadian Journal of Statistics (2019–2021) His research groups are Statistics and Biostatistics, and Data Science, Machine Learning, and Artificial Intelligence. He has no listed awards but maintains active editorial and academic collaborations in computational statistics and machine learning.
Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .
