Categorization Processes Professors and Supervisors in Canada | ApplySharif

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Renée J. MillerView profile

Professor

University of TorontoCanada

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).

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Martin Willis MonroeView profile

Assistant Professor

University of New BrunswickCanada

Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.

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Dirk Bernhardt-WaltherView profile

Professor

University of TorontoCanada

Professor Dirk Bernhardt-Walther is an academic at the University of Toronto, serving as Program Director of the Cognitive Science Program and Department of Psychology . He investigates neural and computational mechanisms underlying high-level sensory perception, focusing on real-world scenes, mid-level vision, and visual aesthetics. Education: PhD in Computation and Neural Systems (Caltech, 2006), M.Phil (University of Cambridge) Research Focus: His lab employs fMRI, MEG, EEG , and GAN-generated stimuli to study scene categorization, perceptual organization, and aesthetic processing. Recent work explores curvature perception, emotion representation in scenes, and neural dissociations between computational and subjective visual metrics. Laboratory Members: The Bernhardt-Walther Lab includes PhD students like Gaeun Son (scene perception), Charlotte Leferink (scene representation), and Dela Farzanfar (aesthetic processing), alongside postdocs and collaborators. Advising: Supervises graduate students in projects combining computational modeling, psychophysics, and neuroimaging, particularly those with backgrounds in computer science or cognitive neuroscience.

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Diana InkpenView profile

Professor

University of OttawaCanada

Professor Diana Inkpen is a faculty member at the School of Electrical Engineering and Computer Science, University of Ottawa. She holds a Ph.D. from the University of Toronto's Department of Computer Science and degrees from the Technical University of Cluj-Napoca, Romania (M.Sc. and B.Eng.). Her research focuses on computational linguistics, natural language processing (NLP), and artificial intelligence, with specialties in natural language understanding/generation, lexical semantics, and semantic web agents. Education: Ph.D., Computer Science, University of Toronto M.Sc., Computer Science and Engineering, Technical University of Cluj-Napoca B.Eng., Computer Science and Engineering, Technical University of Cluj-Napoca Affiliations: Director, NLP Lab Editor-in-Chief, Computational Intelligence journal Associate Editor, Natural Language Engineering journal Her research has led to roles such as program co-chair for AI 2012 and leadership in organizing international workshops. She has received funding from NSERC, SSHRC, and OCE, and was honored with a Visiting Professor title at the University of Wolverhampton, UK. Her teaching spans courses like Information Retrieval, Natural Language Processing, and Prolog programming. Professor Inkpen's work bridges NLP innovations with societal challenges, including mental health surveillance via social media analysis and bias mitigation in AI systems. She actively contributes to interdisciplinary projects, such as detecting hate speech and legal text entailment, while maintaining a global research network through collaborations and international conference involvement.

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Xiaohui YuView profile

Professor

York UniversityCanada

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.

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Huiyan LiView profile

Associate Professor

University of GuelphCanada

Huiyan Li, PhD, P.Eng., is an Associate Professor in the Department of Biomedical Engineering at the University of Guelph. Her research focuses on developing micro/nanoscale biosensors and lab-on-a-chip technologies for cancer diagnostics and personalized medicine. Dr. Li holds a Ph.D. in Biomedical Engineering from McGill University and completed postdoctoral training at Harvard Medical School/Massachusetts General Hospital. Her multidisciplinary research integrates biosensing, micro/nanofabrication, bio-optics/electronics, and computational tools to study cancer molecular complexity. Current openings exist for M.A.Sc. students interested in biosensing research. Key research areas include extracellular vesicle analysis, multiplexed immunoassays, magnetic/nanoparticle-enhanced bioassays, and graphene-based biomedical sensors. Recent work emphasizes point-of-care diagnostics and enhanced protein detection via novel material integration. Teaching responsibilities include ENGG 6301 (Advanced Micro/Nano Biotechnology) and undergraduate courses in bio-instrumentation and biomedical signal processing. Her work spans biomaterials classification, microfluidic systems, and antimicrobial nanocomposite development. Research outputs emphasize scalable microarray formats, EV-based biomarker discovery, and sensor sensitivity enhancement through nanomaterial innovations. Current projects address EV concentration measurement, 3D antibody microarrays, and magnetic-responsive hydrogel discs for bioassay improvements.

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Joerg SanderView profile

Professor

University of AlbertaCanada

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)

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Pascal VincentView profile

Associate Professor

University of MontrealCanada

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

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Neda MaghboulehView profile

Associate Professor

University of TorontoCanada

Neda Maghbouleh serves as Associate Professor of Sociology at the University of Toronto Mississauga (UTM), holding a cross-appointment to the graduate department at St. George campus. She holds the prestigious Canada Research Chair in Migration, Race, and Identity and received the Ontario Early Researcher Award. As Principal Investigator of the RISE (Refugee Integration, Stress, and Equity) Team, she leads a major SSHRC Insight Grant (2018-2023) examining refugee settlement experiences in Canada. Her research critically investigates racialization processes among Middle Eastern, North African, and South Asian populations in North America, challenging the Black-White racial binary. She specializes in refugee integration dynamics, Iranian diaspora identity formation, and gendered migration experiences. Her methodological approach combines critical race theory with feminist and qualitative frameworks, particularly in refugee motherhood studies and digital activism among Muslim-American youth. Analysis of her publications reveals evolving thematic focus from Iranian-American identity politics (2009-2017) to contemporary refugee integration challenges. Her work consistently examines how structural inequalities manifest in everyday interactions, with increasing emphasis on Syrian refugee experiences in Canada since 2018. Key contributions include reframing MENA racial categorization and developing trauma-informed frameworks for refugee settlement. Her scientific recognition includes: Canada Research Chair in Migration, Race, and Identity Ontario Early Researcher Award Through the RISE Team, Maghbouleh directs community-engaged research on refugee health equity, collaborating with scholars like Melissa Milkie and Ito Peng. Her SSHRC grant supports interdisciplinary investigation of Syrian mothers' settlement stressors and resilience factors, informing Canadian integration policies. Current projects emphasize participatory methodologies that center refugee voices in knowledge production. She leads the RISE research collective, an interdisciplinary team developing evidence-based interventions for refugee health equity through mixed-methods investigation of settlement challenges and community support systems.

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Anne BroadbentView profile

Professor

University of OttawaCanada

Anne Broadbent is a Full Professor at the University of Ottawa's Department of Mathematics and Statistics within the Faculty of Science. She holds a Tier 1 Canada Research Chair in Quantum Communications and Cryptography. Her academic journey includes an MSc and PhD from the University of Montreal. Her research focuses on quantum information processing and cryptography, with a particular emphasis on quantum communication protocols, encryption, and secure computation. She has contributed to groundbreaking work in quantum homomorphic encryption, uncloneable encryption, and quantum delegation protocols. Broadbent supervises graduate students including Daniel Lovsted, Sherry Wang, Pierre Botteron, Nagisa Hara, and Peter Yuen, often in collaboration with colleagues at institutions like Toulouse. Her research group explores discrete mathematics and analysis, alongside quantum information theory. Her recent articles highlight advancements in quantum proof systems, secure quantum communication architectures, and cryptographic primitives resistant to cloning. These contributions underscore her role in advancing theoretical and applied aspects of quantum information science.

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Marysa LaguëView profile

Assistant Professor

University of British ColumbiaCanada

Marysa Laguë is an Assistant Professor in the Department of Geography within the Faculty of Arts at the University of British Columbia. She is a climate scientist specializing in understanding how terrestrial processes impact the atmosphere and surface climate across scales from individual plants to entire planets. Her research focuses on how changes in the land surface modify energy and water fluxes between the land and atmosphere, and how these changes subsequently affect atmospheric dynamics and climate both locally and remotely. Dr. Laguë serves as a member of the Graduate and Postdoctoral Studies program at UBC. Dr. Laguë earned her PhD in Atmospheric Sciences and MSc degrees in both Atmospheric Sciences and Applied Mathematics from the University of Washington. Her educational background provides her with strong theoretical and modeling expertise that informs her comprehensive research approach. Her research can be broadly categorized into three interconnected areas: understanding land-atmosphere interactions in the modern climate system, exploring fundamental physical connections between terrestrial and planetary processes using idealized models, and climate model development. She studies how changes in vegetation impact cloud formation, temperatures, water vapor, and atmospheric circulation in ways that can feed back on surface climate. On the idealized side, she investigates how continental configurations fundamentally alter global-scale climate patterns. Her work often involves developing and using numerical models of varying complexity to quantify land's role in the coupled Earth System. She is particularly interested in understanding where the atmosphere cares about changes in the land surface, and what particular properties of the land surface it is that the atmosphere cares about. Dr. Laguë's recent publications demonstrate a consistent focus on land-atmosphere interactions, with particular emphasis on how terrestrial evaporation, surface albedo, and vegetation properties influence climate dynamics. Her research spans from local-scale processes to planetary-scale phenomena, including studies of exoplanet climates. She frequently employs both complex Earth system models like the Community Earth System Model and idealized modeling frameworks to isolate specific mechanisms. A recurring theme across her work is the development and application of the Simple Land Interface Model (SLIM), which allows researchers to test how individual land-surface properties modify energy and water fluxes to the atmosphere. Dr. Laguë is actively involved in mentoring graduate students and is interested in supervising Master's students, Doctoral students, and Postdoctoral Fellows. She supports interdisciplinary research collaborations and is open to supervising students interested in public scholarship through the Public Scholars Initiative. She also encourages experiential learning opportunities like internships for her graduate students and emphasizes the importance of interdisciplinary research approaches. She is the lead scientific developer of the Simple Land Interface Model (SLIM), an idealized land surface model that couples with the Community Earth System Model. This tool allows researchers to isolate the effects of individual land surface properties on the Earth system. Her work has significant implications for understanding climate change impacts, land management strategies, and even the potential habitability of exoplanets. Her research methodology includes using climate models, Earth system models, numerical Earth system models, Python, Jupyter, the Coupled Model Intercomparison Project (CMIP), and Fortran programming for climate model development.

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Alan BaleView profile

Professor

Concordia UniversityCanada

Dr. Alan Bale is a Professor in the Department of Classics, Modern Languages and Linguistics at Concordia University, specializing in semantics and syntax-semantics interfaces. His work integrates generative linguistics frameworks with experimental methods to explore grammatical representations of meaning, particularly focusing on number, comparison, and scalar competition across languages like Mi’gmaq, English, and Western Armenian. He investigates how conceptual development interacts with linguistic structures through both linguistic and non-linguistic meaning analysis. His research emphasizes teasing apart contributions from pure semantics, psychological categories, and pragmatic reasoning. Key topics include the mass-count distinction, scalar implicature, and experimental methodologies. He maintains a website and has published extensively on topics ranging from linguistic pragmatics to formal syntax. His CV provides additional details on his academic contributions. Bale's publications (2020–2025) reflect a sustained focus on scalar implicature mechanisms, mass-count distinctions, and cross-linguistic syntax-semantics interactions. Recent work explores how online experimental methods and cognitive load influence pragmatic reasoning. His research bridges formal linguistic theory with empirical cognitive science approaches.

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Svetlana YanushkevichView profile

Professor

University of CalgaryCanada

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

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Meng HeView profile

Professor

Dalhousie UniversityCanada

Meng He is a Professor in the Faculty of Computer Science at Dalhousie University. He obtained his PhD from the Cheriton School of Computer Science at the University of Waterloo in 2008 and held postdoctoral and research positions at Carleton University and the University of Waterloo before joining Dalhousie. He is affiliated with the Algorithms & Bioinformatics research cluster and is actively recruiting graduate students for master's and PhD studies, as well as supervising honors theses and USRA internships. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in the areas of computational geometry, databases, text retrieval, and bioinformatics. His work often involves developing succinct and dynamic data structures for fundamental problems in graph theory, trees, and geometric data. His recent publications show a strong focus on path and distance queries in various graph types (especially interval graphs and trees), range counting, mode queries, and succinct representations. The research trend emphasizes theoretical foundations combined with practical efficiency, often addressing dynamic and space-constrained scenarios. Alberto Apostolico Best Paper Award of CPM 2017 Dr. He has supervised numerous PhD and master's students and collaborators, frequently co-authoring with researchers such as J. Ian Munro, Travis Gagie, Gonzalo Navarro, and Norbert Zeh. His research has been supported by grants from NSERC and other funding agencies, though specific grant details are not listed in the provided text. He has also contributed significantly to the academic community through editorial work for journals like Computational Geometry - Theory and Applications and Algorithmica , and by organizing major conferences such as CCCG and WADS. He leads a research group focused on algorithms and data structures, fostering collaborations both within Dalhousie and internationally. Future work is likely to continue exploring the theoretical and practical aspects of dynamic and succinct data structures, with applications in large-scale data processing and information retrieval systems.

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Lynn DempseyView profile

Associate Professor

Brock UniversityCanada

Lynn Dempsey is an Associate Professor in Applied Linguistics and Associate Dean of Undergraduate Studies in the Faculty of Social Sciences at Brock University. She holds a PhD in Rehabilitation Sciences from the University of Western Ontario (2005), an M.Sc. in Speech-Language Pathology, and degrees in Psychology and Social Work from McMaster University. A certified Speech-Language Pathologist, her research focuses on narrative comprehension in preschool children with and without language impairments, emphasizing story representation, early literacy development, and the application of the ICF framework in clinical assessment. Dr. Dempsey directs the Child Language Laboratory and contributes to the LifeSpan Development Centre. Her teaching spans courses in child language acquisition, language disorders, and clinical evaluation. She has held leadership roles including Chair of the Department of Applied Linguistics (2020-2023), Graduate Program Director, and member of ethics and academic committees. Her publications emphasize methodologies for assessing story comprehension, the impact of play-based interventions, and literacy support for children with hearing impairments. Dr. Dempsey’s work bridges developmental psychology, speech-language pathology, and educational practices. Her research consistently examines how narrative structures, background knowledge, and clinical frameworks influence young children’s language development. Recent studies explore emotion categorization in children’s language and the validity of assessment tools for pre-readers.

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