Saman Muthukumarana

Personal Website

Recent Publications

• Morrissette, S., Muthukumarana, S. and Turgeon, M. (2023). Bayesian Model-based Clustering with Dissimilarities. Under Review.

• Afzali, E. and Muthukumarana, S. (2023). Gradient Free Kernel Conditional Stein Discrepancy. Under Review.

• Wanasundara, S., Wickramasinghe, A., Schaubroec, M. and Muthukumarana, S. (2023). Detecting Thermal Anomalies in Buildings using Frequency and Temporal Domains Analysis. Under Review.

• Szturm, T., Nariman, S., Lezen, A., Kanitkar, A., Szturm, S., Parmar, S., Eskicioglu, R. and Muthukumarana, S. (2023). A Game-Based Mechatronic Device for Interactive Rehabilitation of Hand Function Post Stroke: Design, Prototyping and Feasibility Study. Under Review.

• Wickramasinghe, L., Leblanc, A. and Muthukumarana, S. (2023). On Smoothed Dirichlet Distribution. Under Review.

• Ciupeanua, A., Wickramasinghe, A., Muthukumarana, S. and Arino, J. (2023). Investigating Air Travel Network Changes in Canada, USA and Europe During COVID-19 Using Open Source Data. Under Review.

• Wickramasinghe, A., Muthukumarana, S., Loewn, D. and Schaubroec, M. (2022). Temperature Clusters in Commercial Buildings Using K-means and Time Series Clustering. Energy Informatics, 5 (1), 1-14.

• Wickramasinghe, A. and Muthukumarana, S. (2022). Assessing the Impact of the Density and Sparsity of the Network on Community Detection using a Gaussian Mixture Random Partition Graph Generator. International Journal of Information Technology, 14 (2), 607-618.

• Munaweera, I., Harris, L., Moore, J.S., Tallman, R.F., Fisk, A.T., Gillis, D.M. and Muthukumarana, S. (2022). Estimating Survival Probabilities of Cambridge Bay Arctic Char Using Acoustic Telemetry Data and Bayesian Multi-state Capture-recapture Models. Canadian Journal of Fisheries and Aquatic Sciences, https://doi.org/10.1139/cjfas-2021-0262.

• Enns, J., Katz, A., Yogendran, M., Urquia, M., Muthukumarana, S., Matharaarachchi, S., Singer, A., Nickel, N., Star, L., Cavett, T., Keynan, Y., Lix, L. and, Sanchez-Ramirez, D. (2022). A population data-driven approach to identifying ‘Long COVID’ cases in support of diagnosis and treatment. International Journal of Population Data Science, 7(3). PMCID: PMC9644890.

• Wickramasinghe, A., Muthukumarana, S., Loewn, D., Schaubroec, M. and Wanasundara, S. (2022). Detection of Abnormal Behaviour of Wireless Sensors in School Buildings Using Dynamic Time Warping. Under Review.

• Matharaarachchi, S., Domaratzki, M., Marasinghe, C., Muthukumarana, S. and Tennakoon, V. (2022). Modeling and Inference with Feature Importance for Assessing the Quality of Sleep among Chronic Kidney Disease Patients. Journal of Sleep Epidemiology, https://doi.org/10.1016/j.sleepe.2022.100041.

• Matharaarachchi, S., Domaratzki, M. and Muthukumarana, S. (2022). Minimizing Features While Maintaining Performance in Data Classification Problems. PeerJ Computer Science, https://doi.org/10.7717/peerj-cs.1081.

• Matharaarachchi, S., Domaratzki, M., Katz, A. and Muthukumarana, S. (2022). Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets. Journal of Medical Internet Research – Formative Research. 6(9): e37984.

• Munaweera, I., Muthukumarana, S. and Jozani, M. J. (2022). A Generalized Quadratic Garrote Approach Towards Ridge Regression Analysis. Innovations in Multivariate Statistical Modeling, Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-031-13971-0_15.

• Mehnaz, J., Steeves, H.N., Fisher, J.T., Bonner, S.J., Muthukumarana, S. and Cowen, L.E. (2022). Shooting for Abundance: Comparing Integrated Multi-sampling Models for Camera Trap Data. Environmetrics. https://doi.org/10.1002/env.2761.

• Dharmasena, I., Domaratzki, M. and Muthukumarana, S. (2021). Comparison of Resampling Methods on Mobile Apps User Behavior. Internet of Things and Connected Technologies, 253-271.

• Cuny, L., Davies, K. and Muthukumarana, S. (2021). Order Restricted Bayesian Inference of the Simple Step-Stress Model Under Type-I Right Censoring With Weibull Distributed Lifetimes. Under Review.

• Doshi, A., Johnson, B. and Muthukumarana, S. (2021). Modelling and Visualizations of Community Structures in Networks. Under Review.

• Matharaarachchi, S., Domaratzki, M. and Muthukumarana, S. (2021). Assessing Feature Selection Method Performance with Class Imbalance Data. Machine Learning with Applications 6, 100170.

• Munaweera, I., Muthukumarana, S., Gillis, D.M., Watkinson, D.A., Charles, C. and Enders, E.C. (2021). Assessing Movement Patterns using Bayesian State-Space Models on Lake Winnipeg Basin Walleye. Canadian Journal of Fisheries and Aquatic Sciences, 78 (10), 1407-1421.

• Wickramasinghe, A. and Muthukumarana, S. (2021). Social Network Analysis and Community Detection on Spread of COVID-19. Model Assisted Statistics and Applications. 16 (1), 37-52.

• Zhang, W., Wang, X., Muthukumarana, S. and Yang, P. (2021). A Continual Reassessment Method Without Undue Risk of Toxicity. Communications in Statistics – Simulation and Computation. 1-13.

• Dharmasena, I., Domaratzki, M. and Muthukumarana, S. (2021). Modeling Mobile Apps User Behavior Using Bayesian Networks. International Journal of Information Technology. https://doi.org/10.1007/s41870-021-00699-7.

• Neufeld, L.,  Muthukumarana, S., Fischer, J., Ray, J., Siegrist, J and Fraser, K. (2021). Breeding latitude is associated with the timing of nesting and migration around the annual calendar among Purple Martin (Progne subis) populations. Journal of Ornithology. DOI:10.1007/s10336-021-01894-w.

• Wickramasinghe, L., Leblanc, A. and Muthukumarana, S. (2020). Bayesian Inference On Sparse Multinomial
  Data Using Smoothed Dirichlet Distribution. Under Review.

• Doshi, A., Johnson, B. and Muthukumarana, S. (2020). Temporal Preservation of Cliques in Preferential Attachment Models. Under Review.

• Anand, M., Rajapakse, A. Muthukumarana, S. and Bagen, B. (2020). Evaluation of a Stochastic Vehicle Travel Pattern Generation Model with Real-World Travel Data. IEEE Electric Power and Energy. Accepted.

• Wickramasinghe, L., Leblanc, A. and Muthukumarana, S. (2020). Model Based Estimation of Baseball Batting Metrics. Journal of Applied Statistics. DOI: 10.1080/02664763.2020.1775792.

• Muthukumarana, S., Martell, D. and Tiwari, R.C. (2019). Meta Analysis of Binary Data with Excessive Zeros in Two-arm Trials. Journal of Statistical Distributions and Applications 6, 1-17.

• Muthukumarana, S., Vincent, K. and Tichon, J. (2019). Bayesian Item Response Analysis of Method-of-Payment Habits in Banking Surveys. Journal of Mathematical Finance 9, 1-10.

• Muthukumarana, S. and Khargonkar, N. (2019). Modeling and Simulation of UEFA Champions League. 15th International Conference on Machine Learning and Data Mining, MLDM 2019 Proceedings Volume II, 820 – 834.

• Kpekpena, C. and Muthukumarana, S. (2018). Bayesian Equivalence Testing and Meta-Analysis in Two-Arm Trials with Binary Data. Computational and Mathematical Methods in Medicine, 1- 8.

• Wickramasinghe, L., Leblanc, A. and Muthukumarana, S. (2018). Modelling Bowling Performance in T20I Cricket. Under Review.

• Kroeker, K., Widdifield, J., Muthukumarana, S., Jiang, D. and Lix, L. (2017). Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis. BMJ open 7 (6), e016173.

• Kuwornu, J.P., Lix, L.M., Quail, J.M., Forget, E., Muthukumarana, S., Wang, X.E., Osman, M. and Teare, G.F. (2016). Identifying Distinct Healthcare Pathways During Episodes of Chronic Obstructive Pulmonary Disease Exacerbations. Medicine, 95 (9), e2888.

• Muthukumarana, S. and Tiwari, R.C. (2016). Meta-Analysis using Dirichlet Process. Statistical Methods in Medical Research 25 (1), 352-365.

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Recent Awards, Honors and Research Grants

• Research Manitoba Innovation Proof- of-Concept Research commercialization grant for Assessing and Identifying Indoor environmental Quality Gaps in Commercial Buildings using Wireless sensors and Big Data Analysis Tools (with ioAirFlow and Red River College, 2021 -2023).
• Research Manitoba Innovation Proof- of-Concept Research commercialization grant for Rehab@home: Bringing interactive rehabilitation devices to homes (with Szturm, T., Eskicioglu, R., Ethans, K., Sepehri N., 2021 -2023).
• CIHR Emerging COVID-19 Research Gaps and Priorities grant for Long-term Sequelae of COVID-19: Using Clinical and Administrative Data to Support Diagnosis and Treatment of Long COVID Patients (with Katz, A., Keynan, Y.,Lix, L., Nickel, N., 2021 -2022).
• Fisheries and Oceans Canada Grant for Mark-recapture and movement analysis of Anadromous Dolly Varden in the western Canadian Arctic (2020 – 2023).
• Mitacs Accelerate, Assessing and Identifying Indoor environmental Quality Gaps in Commercial
  Buildings using Wireless sensors and Big Data Analysis Tools (with Yang Wang, 2020 – 2023).
• Fisheries and Oceans Canada Contract Award for Analyzing Capture-Mark-Recapture and Acoustic Telemetry Data (2020 – 2022).
• The Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Team Project Grant for Addressing Spatial and Computational Issues in Integrated Analysis of Modern Ecological Data (with Laura Cowen (UVictoria) and Simon Bonner (Western), 2020 – 2023).
• Faculty of Science Interdisciplinary/New Directions Research Collaboration Initiation Grant for Investigating Avian Resiliency to Environmental Change using New Migratory Tracking Technologies and Statistical Modelling (with Kevin Fraser, 2020 -2021).
• Mitacs Accelerate, Modeling and Simulation Methods for Assessing Casino Player Behaviour (2019 – 2021).
• Faculty of Science Research Innovation and Commercialization Grant (2019 – 2020).
• The Canadian Statistical Sciences Institute (CANSSI) Conference Grant for Nexus 2019 – Art of the Possible, International Data Science Conference (2019).
• University of Manitoba Travel and Conference Grant for Nexus 2019 – Art of the Possible – International Data Science Conference (2019).
• NSERC Engage Grant for User Behavior Analytics and Software Development for Assessing RaceRunner Customers (2018 – 2019).
• Natural Sciences and Engineering Research Council (NSERC) of Canada – Discovery Grant; Bayesian Methods, Computation, Model Selection and Goodness of Fit with Complex Data (2018 – 2024).
• Ian C. P. Smith Integrated Science Faculty Scholar (SIS Faculty Scholar) (2017- 2020).
• Faculty of Science Interdisciplinary/New Directions Research Collaboration Initiation Grant for Modelling Environmental and Ecological Processes using Bayesian Methods (2018 -2019).
• Teaching and Learning Enhancement Fund (TLEF) Grant for Investigating New Trends in Interdisciplinary Science Education ( Co-applicant with other SIS Faculty Scholars, 2018 -2019).
• MITACS Globalink (2013 – 2018).
• Natural Sciences and Engineering Research Council (NSERC) of Canada – Discovery Grant; Bayesian Methods and Computation in Complex Models (2011 – 2018).

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Recent Conference and Seminar Presentations

• Assessments Using Data, ROBCONIM – IEEE Robotics,Control, Instrumentation and Measurement, University of Manitoba, October, 2022. (Invited).

• Future of Data Science, Research and Education in the Post-Pandemic Era, The Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka, June, 2022. (Invited).

• Essentials of Data Analysis for Research Design, 12th edition of the Conference on Excellence in Research and Education (CERE 2022) on Digital Decade – E-Learning, E-Working, and E-Business, IIM Indore, India, June, 2022. (Invited).

• Bayesian State-Space Models for Acoustic Telemetry Data, Joint Statistical Meeting (JSM), August 2021. (Topic Invited – Online).

• Comparison of Resampling Methods on Mobile Apps User Behavior, 6th International Conference on Internet of Things and Connected Technologies (ICIoTCT) 2021, July 2021. (Invited Online).

• Mark-recapture and Bayesian State Space Analysis of Fish Movements in the Region of Canadian Arctic, Western North American Region (WNAR) of the International Biometrics Society Meeting, June 2021. (Invited – Online).

• Bayesian Networks Learning for Complex Data, The Faculty of Computing Research Symposium, Kotelawala Defence University, December 2020. (Invited – Keynote – Online).
• Modeling and Simulation of Mobile Apps User Behavior, Department of Mathematics and Statistics Seminar Series, University of  Victoria, November 2020. (Invited – Online).
• Modelling and Simulation using Dirichlet Process with Applications, Machine Learning Special Interest Group Seminar Series, University of Manitoba, April 2020. (Invited – Online).
• Bayesian Networks for Assessing Mobile App User Behavior, St. Anne’s College – University of Oxford UK, January 2020. (Invited).
• Bayesian Methods with Applications, IEEE RobConIM Seminar Series, Winnipeg Section, December 2019. (Invited).
• Model Based Estimation of Baseball Batting Metrics, International Conference on Statistical Distributions and Applications (ICOSDA), Eberhard Conference Center, Grand Rapids USA, October 2019. (Invited).
• Bayesian Methods for User Behaviour Analysis of Customers, Asian Institute of Technology, Bangkok Thailand, July 2019. (Invited).
• Modeling and Simulation of UEFA Champions League, International Conference on Machine Learning and Data Mining, New York, July 2019.
• Sports Analytics: Models & Predictions, University School of Business, Chandigarh University India, July 2019. (Invited).
• Bayesian Methods & Computation with Applications, Department of Mathematics, Chandigarh University India, July 2019. (Invited).
• Bayesian Models for Assessing Lake Winnipeg Fish Movement Patterns, Meeting on Integrated Modelling, Western University, June 2019. (Invited).
• Meta analytic approaches and multiple treatments assessment in clinical trials, IISA Conference, University of Florida, Gainesville, Florida, May 2018. (Invited).
• Modeling and Simulation of UEFA Champions League, Joint Statistical Meetings, Baltimore, August 2017.
• Assessing Personality Traits in Ordinal Survey Data, 75th ICP Conference, Pace University, New York, July 2017. (Invited).
• Bayesian Methods with Biological Applications, Department of Biological Sciences Seminar Series, University of Manitoba, April 2017. (Invited).
• Meta-analytic Methods using Dirichlet Process, Department of Statistics Seminar Series, University of Manitoba, April 2017.
• Bayesian Approach for Analyzing Data Arising from Two-Arm Trials, ODRS Conference 2016, McMaster University Hamilton Ontario, August 7-10 2016. (Invited).
• Non-inferiority Hypothesis Testing in Two-arm Trials using Relative Belief Ratios, International Conference on Statistical Distributions and Applications (ICOSDA), Niagara Falls Ontario, October 14-16 2016. (Invited).
• Panelist on Speed Networking Session, Canadian Society for Epidemiology and Biostatistics National Student Conference, Brodie Centre(Bannatyne Campus) Winnipeg, June 8-10 2016. (Invited).
• Bayesian Inference in Two-arm Trials, IISA Conference, Riverside California, July 2014. (Invited)
• Bayesian Hypothesis Assessment in Two-arm Trials Using Relative Belief Ratios, Statistical Society of Canada annual Meeting, University of Toronto, May 2014.
• Statistical Methods for Noninferiority Trials, Joint Statistical Meetings, Montreal, August 2013. (Topic contributed)
• Statistical Heterogeneity in Multi-arm Clinical trials: A Meta Analysis Approach, ICSA/ISBS Joint Statistical Conference, Washington D.C, June 2013. (Invited).
• Bayesian Analysis and Model Assessment of Binary Item Response Data, Statistical Society of Canada annual Meeting, University of Alberta, Edmonton Alberta, May 2013.
• Panel Session on Methodological Innovations for Patient-Oriented Research, The George & Fay Yee Centre for Healthcare Innovation (CHI), University of Manitoba / Winnipeg Regional Health Authority, December 2012 (12/12/12/12 Noon). (Invited).
• Random-Effects Meta-Analysis Using Dirichlet Process, Joint Statistical Meetings, San Diego California, August 2012. (Topic contributed).
• Modelling Heterogeneity in Mark-recapture Data Using the Dirichlet Process, ISBA 2012 World Meeting, Kyoto Japan, June 2012. (Special Topic Session).
• Assessing Noninferiority in Multi Arm Clinical Trials, Statistical Society of Canada annual Meeting, University of Guelph, Guelph Ontario, June 2012. (Invited).

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Current and Recent Teaching

• STAT 7270 Bayesian Inference (Winter 2021)
• STAT 4150 Bayesian Analysis and Computing (Winter 2021)
• STAT 7270 Bayesian Inference (Fall 2019)
• STAT 2150 Statistics and Computing (Winter 2019)
• STAT 2150 Statistics and Computing (Fall 2018)
• STAT 2150 Statistics and Computing (Winter 2018)
• STAT 7270 Bayesian Inference (Winter 2017)
• STAT 7240 Advanced Computational Statistics (Fall 2016)
• STAT 3480 Statistical Methods for Research Workers 2 (Winter 2016)
• STAT 3470 Statistical Methods for Research Workers 1 (Fall 2015)
• STAT 7250 Bayesian Computational Analysis (Fall 2015)
• STAT 7360 Nonparametric Bayesian Models (Winter 2015)
• STAT 3480 Statistical Methods for Research Workers 2 (Winter 2015)
• STAT 7250 Bayesian Computational Analysis (Winter 2014)
• STAT 3480 Statistical Methods for Research Workers 2 (Winter 2014)
• STAT 1000 Basic Statistical Analysis 1 (Fall 2013)
• STAT 4200 Statistical Inference 2 (Winter 2013)
• STAT 4100 Statistical Inference 1 (Fall 2012)

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Biography

Saman joined the Department of Statistics as an Assistant Professor on July 1, 2010. He obtained a B.Sc. Honours special degree in Statistics from University of Sri Jayewardenepura, Colombo, Sri Lanka. In spring 2007, he completed a Master of Science degree in statistics under the supervision of Dr. Tim Swartz at Simon Fraser University. His thesis developed a Bayesian Analysis template for the Pacific Ocean Shelf Tracking project. This work was published in the Canadian Journal of Statistics as a discussed paper. He then continued to work with Dr. Tim Swartz for his doctoral thesis and completed his Ph.D. on June 2010. His thesis developed new theoretical and computational methodologies which deal with latent variable models and complex model structures for problems in sports, ordinal survey and network data. This work has been published in the Canadian Journal of Statistics and Australian & New Zealand Journal of Statistics. He was promoted to Associate Professor (with tenure)  in 2016 and then to full Professor in 2022.

His research interests lie broadly in Bayesian methods and computation for complex models which integrate multidisciplinary applications. Along with this main theme, he has developed methods to facilitate modelling and inference on non-standard complex data which lead to innovative analyses in the areas of social networks, health studies, sports, customer and user behaviour analytics, environmental and ecological studies. See the list of publications and presentations for further details.

His research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery program, NSERC Engage, Mitacs Globalink, Mitacs Accelerate, Manitoba Institute of Child Health (MICH), The Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Team Project Grant, Fisheries and Oceans Canada, Canadian Institutes of Health Research (CIHR), Research Manitoba innovation Proof-of-Concept grants, University of Manitoba internal grants and Faculty of Science Interdisciplinary grants.