MATH 3090: Advanced Calculus - Fall 2006
MATH/CSCI 2112: Discrete Structures I - Winter 2007
MATH 2051: Problems in Geometry - Fall 2007
MATH/CSCI 2113: Discrete Structures II - Winter 2008
MATH 1115: Mathematics for Commerce - Winter 2011
MATH 1115: Mathematics for Liberal Arts - Winter 2012
MATH/STAT 3460: Intermediate Statistical Theory - Winter 2014
ACSC/STAT 3740: Predictive Analytics - Winter 2023
I have interests in a wide variety of topics in areas of pure mathematics, data science and actuarial science.
In data science, I am interested in the application of abstract mathematical modelling to statistical methodology. I am interested in general statistical methodology, and in development of statistical techniques to analyse microbiome data. Some particular topics of interest include understanding the temporal dynamics of the microbiome; and handling the unique measurement error structure in microbiome data. In other areas of data science, I am working on a project on automatic diagnosis of emergency department data. This is a big project involving dealing with many difficulties from a statistical standpoint including: the large quantities of free text data; the amount of missing data, with the potential for the missing pattern to change between training and test data; and the structure of the data and diagnoses. In statistical methodology, I have several projects relating to various areas such as ranking problems; measurement error and variable selection. I have recently been working on unfolding and deconvolution problems which aim to estimate the distribution of a quantity from a sample with measurement error.
I am also interested in general machine-learning methods. These methods use mathematical structures to create flexible functions that can approximate any true distribution of the data. While the flexibility allows these functions to approximate the complicated relationships that can arise in real data, it also results in hard-to-interpret predictions. Furthermore, avoiding overfitting can be a challenge. By studying the mathematical structures underlying the methods, I hope to find improvements that address these issues.
I am beginning to develop a research program in Actuarial Science. Topics of interest include: estimating utility functions to find optimal insurance choices for individuals; and assessing the value of information for calculating insurance costs.
In pure mathematics, my research interests mostly fall between Category theory, universal algebra, logic and combinatorics. Particular areas of interest include partition and congruence lattices; and Coxeter groups. I have also recently worked on topological convexity spaces.
|C. Liu, T. Kenney, R. Beiko , H. Gu.||The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles.||Systematic Biology, (2022) syac052 (16 pages)|
|L. Liu, H. Gu, J. Van Limbergen and T. Kenney.||SuRF: a New Method for Sparse Variable Selection, with Application in Microbiome Data Analysis.||Statistics in Medicine 40 (2021), 897-919.|
|T. Kenney, T. Huang and H. Gu.||Poisson PCA: Poisson Measurement Error corrected PCA, with Application to Microbiome Data.||Biometrics 77 (2021) 1369-1384|
|T. Kenney, J. Gao and H. Gu.||Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes||BMC Bioinformatics 21(2020), 450 (32 pages)|
|W. Chen*, T. Kenney*, J. P. Bielawski and H. Gu.||Testing Adequacy for DNA Substitution Models.||BMC Bioinformatics 20(2019) 349 (16 pages). *‐co-first authors.|
|K. A. Dunn*, T. Kenney*, H. Gu and J. P. Bielawski.||Improved inference of site- specific selection pressures under a generalized parametric model of codon evolution.||BMC Evolutionary Biology 19(2019) 19:22 (19 pages). *‐co-first authors.|
|Y. Cai, H. Gu and T. Kenney||Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization.||Microbiome 5 (2017), (27 pages)|
|T. Kenney||Partial Sup Lattices||Theory and Applications of Categories 30 (2015), 305-331|
|M. Abeysunderra, T. Kenney, C. Field and H. Gu||Combining Distance Matrices on Identical Taxon Sets for Multi-Gene Analysis with Singular Value Decomposition.||PLoS ONE 9 (2014), e94279. doi:10.1371/journal.pone.0094279 (14 pages)|
|T. Kenney||Coxeter Groups, Coxeter Monoids and the Bruhat Order.||Journal of Algebraic Combinatorics 39 (2014), 719-731|
|T. Kenney and H. Gu||Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications||Statistical Applications in Genetics and Molecular Biology, 11 (2012), issue 4, article 14|
|T. Kenney and R. Paré||Categories as Monoids in Span, Rel and Sup,||Cahiers de e Topologie et Géométrie Différentielle Catégoriques, 52 (2011), 209-240|
|T. Kenney||The Path Relation for Directed Planar Graphs, and its Relation to the Free Diad.||Discrete Mathematics 311 (2011), 441-456|
|T. Kenney||Injective Power Objects and the Axiom of Choice||Journal of Pure and Applied Algebra 215 (2011), 131-144|
|T. Kenney||Graphical algebras - a new approach to congruence lattices||Algebra Universalis 64 (2010), 313-338|
|H. Gu, T. Kenney and M. Zhu||Partial Generalized Additive Models: an Information-Theoretic Approach to Selecting Variables and Dealing with Concurvity.||Journal of computational and graphical statistics 19 (2010), 531-551|
|T. Kenney||The General Theory of Diads||Appl. Cat. Struct. 18 (2010), 523-572|
|T. Kenney and R. J. Wood||Tensor Products of Sup Lattices and generalized sup-arrows.||Theory and Applications of Categories 24 (2010), 266-287|
|T. Kenney||Diads and Their Application to Topoi,||Appl. Cat. Struct. 17 (2009), 567-590|
|T. Kenney||Copower Objects and their applications to Finiteness in Topoi,||Theory and Applications of Categories 16 (2006), 923-956|
|T. Kenney||Generating Families in a Topos,||Theory and Applications of Categories 16 (2006), 896-922|
|T. Kenney, H. He and H. Gu.||Prior Distributions for Ranking Problems.||ArXiv|
|T. Kenney.||Consistency of Ranking Estimators.||ArXiv|
|L. Xu, X. Xu, D. Kong, L. Wang, H. Gu And T. Kenney||Stochastic Generalized Lotka-Volterra Model with An Application to Learning Microbial Community Structures||ArXiv|
|Y. Cai, H. Gu And T. Kenney||Deconvolution density estimation with penalised MLE.||ArXiv|
|X. Zhang, H. Gu And T. Kenney||Simultaneous Feature and Structure Selection of Dense Neural Network.|
|W. Zhang, T. Kenney And L. Ho.||Evolutionary shift detection with ensemble variable selection.||ArXiv|
|T. Kenney||Stone Duality for Topological Convexity Spaces.||ArXiv|
|Y. Cai, H. Gu And T. Kenney||Rank Selection for Non-negative Matrix Factorization.|
|S. Ling, T. Kenney, C. Field, H. Gu.||Model Combination for Block Missing Data.|
|M. Wang, T. Kenney.||The Influence of Utility Functions on Life Insurance Choices.|
|T. Kenney.||Euclidean Abstract Convexity Spaces.|
|R. Doig, T. Kenney, H. Gu||Negative Binomial PCA for Overdispersed Count Data|
|L. Liu, H. Gu, and T. Kenney.||The influence of long tailed distributions on LASSO-based variable selection methods.|
|Lihui Liu||PhD. (Co-supervised with H. Gu)||Variable selection methods with application to microbiome data.|
|Xinyue Zhang||PhD. (Co-supervised with H. Gu)||Use of Convolutional Neural Networks in Estimating House Prices.|
|Wensha Zhang||PhD. (Co-supervised with L. Ho)||Variable selection with dependant data.|
|Shanglun Li||PhD. (Co-supervised with H. Gu)||State-space models for microbiome data.|
|Fatemah Tofighi Khelejan||PhD. (Co-supervised with H. Gu)||Model adequacy tests for phylogenetic models.|
|Shuangming Yang||PhD. (Co-supervised with H. Gu)||Modelling Microbiome Temporal Dynamics Using Nonlinear Stochastic Differential Equations|
|Yurunyun Wang||PhD. (Co-supervised with H. Gu)||Predicting Osteoarthritis from X-ray Images with Machine Learning|
|Yun Cai||PhD. (Co-supervised with H. Gu)||Measurement Error Deconvolution Methods and Rank Selection for Non-Negative Matrix Factorization with Applications in Microbiome Data.|
|Shen Ling||PhD. (Co-supervised with H. Gu and C. Field)||A New Method for Multi-Class Classification with Multiple Data Sources, with Application to Abdom- inal Pain Diagnosis.|
|Wanru Jia||MSc. (Co-supervised with H. Gu)||Edge Detection Operators for X-ray Images based on Hessian Matrices.|
|Junqiu Gao||MSc. (Co-supervised with H. Gu)||Ornstein-Uhlenbeck Process and Optimal Sampling for Analysis of Microbiome Data.|
|Mingzhu Wang||MSc.||The Influence of Utility Functions on Insurance Choices|
|Tianshu Huang||MSc. (Co-supervised with H. Gu)||Semi-Parametric Principal Component Analysis for Poisson Count Data with Application to Microbiome Data Analysis.|
|Hao He||MSc. (Co-supervised with H. Gu)||Robust Ranking and Selection with Heavy-tailed Priors and its Application to Market Basket Analysis.|
|Li Li||MSc. (Co-supervised with H. Gu)||Recombination Detection Based on Likelihood and Clustering for DNA and Amino Acid Sequences.|
|Yun Cai||MSc. (Co-supervised with H. Gu)||Non-negative matrix factorisation for classification of metagenomic data.|
|Wei Dai||MSc. (Co-supervised with H. Gu)||A new Test to Build Confidence Regions using Balanced Minimum Evolution.|
These guides give advice on what are new topics for a number of graduate students, and are based on common mistakes I have seen when supervising students.
COLD (Codon Optimal Likelihood Discoverer) is a program for estimating phylogenetic parameters using maximum likelihood for codon models.
Simple Plot is a program for adaptively projecting multidimensional data into two dimensions.
Adequate Bootstrap is a program for estimating confidence intervals for parameter values that take into account the uncertainty due to model misspecification.
Here are a number of
R packages written by me or my students. These will probably be submitted to CRAN when the relevant papers are published.