Find Academic Supervisors

Aaron SmithView profile

Associate Professor

• Applied Probability
• Computational Statistics
• Monte Carlo Methods
• +3 more

Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.

Piotr ZwiernikView profile

Associate Professor

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +7 more

Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.

Charles PerinView profile

Assistant Professor

• Information Visualization
• Human-Computer Interaction
• Personal Visualization
• +5 more

Charles Perin is an Assistant Professor of Computer Science at the University of Victoria, leading the UViz research group. He holds a PhD from Université Paris-Sud (2014) and has held roles including Post-doc at the University of Calgary and Lecturer at City, University of London. His research focuses on information visualization, personal visualization, human-computer interaction, and sports visualization. Education: PhD in Computer Science (2014), Université Paris-Sud; Post-doc at University of Calgary (InnoVis lab); MS and earlier studies in Computer Science and HCI in France. Research interests include designing interactive visualization tools for personal data reflection, health data communication, and sports analytics. He emphasizes authoring tools for non-experts and physical/tangible visualization systems. Recent work explores embedded data physicalizations and mobile visualization design. His articles span topics like data storytelling, patient-generated health visualizations, and soccer data analysis, often appearing in top venues like IEEE VIS, CHI, and Eurovis. He has advised over 20 students across PhD, MSc, and undergraduate levels. Teaching includes courses on Information Visualization and HCI at UVic, City, and other institutions. He co-organized workshops on topics like Personal Visualization and Sports Data at IEEE VIS. His UViz lab collaborates internationally with institutions like Monash University and the National Archives (UK).

Jian ZhaoView profile

Associate Professor

• Information Visualization (InfoVis)
• Human-Computer Interaction (HCI)
• Data Science
• +6 more

Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.

Andrew Rau-ChaplinView profile

Professor

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +14 more

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Aurélie LabbeView profile

Professor

• Data Science
• Statistical Learning
• Machine Learning
• +7 more

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

Andrew McArthurView profile

Professor

• Bioinformatics
• Genomics
• Antimicrobial Resistance
• +5 more

Andrew McArthur is a Professor in the Department of Biochemistry & Biomedical Sciences at McMaster University, where he leads the McArthur Laboratory. His research focuses on bioinformatics , genomics , and computational biology with a specialization in genomic surveillance of antimicrobial resistance (AMR) . He spearheads the Comprehensive Antibiotic Resistance Database (CARD) and collaborates with the Canadian Anti-Infective Innovation Network (CAIN) , GenEpio Consortium , and IRIDA Platform for pathogen genomics. Education : PhD in Biochemistry (University of Victoria, 1996), Postdoctoral work at Marine Biological Laboratory (1998-1999) and National Museum of Natural History (1996-1998) His research spans antimicrobial resistance surveillance , machine learning applications , viral genomics (including SARS-CoV-2), and pathogen evolution . He has developed tools like CARD and IRIDA to standardize AMR data and enable rapid infectious disease analysis. Recent work includes machine learning models for predicting AMR, cloud-based pathogen detection , and resistome profiling in clinical and environmental contexts. He has mentored graduate students including Jalees Nasir (HSGSA Impact Award 2025), Dirk Hackenberger , Autumn Arnold , and Emily Bordeleau , as well as postdoctoral fellows like Sheridan Baker . His teaching includes courses in practical bioinformatics and biomedical consulting at McMaster University.

Kenneth Edwin BarkerView profile

Professor

• Privacy Preserving Data Repositories
• Database Management Systems
• Data Privacy and Security
• +4 more

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.

Chen XuView profile

Associate Professor

• Sparse Modeling
• Statistical Learning
• Feature Selection
• +5 more

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.

Divya RamachandranView profile

Assistant Professor

• New-age/Emerging Technologies
• Digital Marketing
• Empirical Marketing Strategy
• +3 more

Divya Ramachandran is an Assistant Professor and F. Ross Johnson Fellow at the Asper School of Business, University of Manitoba. She holds a Ph.D. in Business Administration (Marketing) from Georgia State University, an M.S. in Marketing Research from Michigan State University, and an MBA in Marketing from S.P. Jain Institute of Management and Research, India. Prior to academia, she worked in sales, marketing research, and strategy roles in India and the U.S. Her research focuses on empirical marketing strategy, particularly the impact of new-age technologies (AI, IoT, blockchain) and digital marketing on customer engagement, marketing outcomes, and societal wellbeing. She employs quantitative and qualitative methods, including econometrics and machine learning. Current projects include evaluating digital marketing in nonprofits and studying sharing economies and labor platforms. Her work appears in journals like the International Journal of Research in Marketing and the Journal of Retailing. She teaches Marketing Research, Marketing Management, and Digital Marketing. No scientific awards are explicitly listed. Her advising and grant activities are not detailed here. She is affiliated with the Marketing Department at the Asper School of Business and contributes to academic communities through research and teaching.

Elena TuzhilinaView profile

Assistant Professor

• Machine Learning
• Applied Statistics
• Computational Biology
• +5 more

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 .

Mostafa MilaniView profile

Assistant Professor

• Data Management
• Databases
• Data Cleaning
• +7 more

Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.

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.

• Construction Engineering
• Simulation Science
• Information Technology in Construction
• +3 more

Simaan Abourizk, PhD, PEng, is the Dean of the Faculty of Engineering at the University of Alberta and holds the rank of Professor in Construction Engineering and Management. He also served as the NSERC/Alberta Construction Industry Research Chair (1997–2011) and Canada Research Chair in Operations Simulation (2001–2008). His roles include Executive Board membership at the Construction Research Institute of Canada. Education: Doctor of Philosophy in Construction Engineering and Management from Purdue University (1990), Master of Science and Bachelor of Civil Engineering (Honors) from Georgia Institute of Technology (1985 and 1984). Research focuses on advancing simulation technologies for construction and natural resource industries, including: Development of the Simphony simulation environment and COSYE framework Integration of AI, visualization, and scheduling tools (e.g., SmartEst, MTRACK) Optimization of tunneling, modular assembly, and industrial fabrication processes Key awards include the E. Whitman Wright Award (2002), E.W.R. Steacie Memorial Fellowship (2001), and Walter Shanly Award (2001). Current projects emphasize synthetic environments, safety analytics, and data-driven decision support systems in construction. He leads initiatives on risk simulation, resource allocation, and industrial automation.

• Archival Science
• Digital Documentation
• Classification Systems
• +6 more

Sabine MAS is a Full Professor at the School of Library and Information Studies, Faculty of Arts and Sciences at the University of Montreal. Her research focuses on classification systems for digital archives, typology of contemporary archives, and user-centric information management. She has supervised 5 major theses and leads/co-leads 9 research projects including the Centre de recherche interuniversitaire sur les humanités numériques (CRIHN). Teaches courses like Introduction à l'archivistique and Diplomatique contemporaine Active in digital humanities and legal documentation fields Recipient of multiple grants from CRSH and FRQSC Research highlights include development of faceted indexing models for legal documents and studies on emotion-driven archival description. She co-edited Typologie des documents des organisations (2019) and modernized the Portail International Archivistique Francophone (PIAF). Grants include a 2024-2028 strategic research grant (FQRSC) and 2023 CRSH exploration grant for studying archival practices. Her work bridges archival theory with practical applications in digital environments.