Jabed Tomal, PhDAssociate Professor | Faculty of Science

Research Interests

Statistical machine learning; Bayesian statistical inference; statistical ecology; analysis of longitudinal and event history data; medical and health care data analysis

I am a statistician with research interest in statistical machine learning, Bayesian statistical inference and statistical ecology. My main focus in research is in developing ensemble method to improve its prediction performance by exploiting the richness of useful variables in high-dimensional data and identify important models and variables to answer important research question. I have shown application of my ensemble/model in (i) drug discovery to detect drug like active bio-molecules for development and (ii) protein homology to detect homologous proteins and produce evolutionary sequences of proteins. These days, I am focusing more on developing Bayesian and computational models for ecology, health-care, housing market, and genetics data

Note: I am currently hiring research students. For details, please check my website at faculty.tru.ca/jtomal/ under prospective students

Publications

Selected Journal Publications:

1. Chowdhury, RI., and Tomal, JH. (2022). Risk prediction for repeated measures health outcomes: A divide and recombine framework. Informatics in Medicine Unlocked (Elsevier). 28: 100847.
https://doi.org/10.1016/j.imu.2022.100847

2. Atkins, M., Howarth, C., Russello, M., Tomal, J., and Larsen, K. (2022). Evidence of intrapopulation differences in rattlesnake defensive behavior across neighboring habitats. Behavioral Ecology and Sociobiology (Springer). 76(3): 1-11.
https://doi.org/10.1007/s00265-021-03100-6

3. Tomal, JH., and Rahman, H. (2021). A Bayesian piecewise linear model for the detection of breakpoints in housing prices. METRON (Springer). 79(3): 361-381. https://doi.org/10.1007/s40300-021-00223-8

4. Hsu, GG., Tomal, JH., and Welch, WJ. (2021). EPX: An R package for the ensemble of subsets of variables for highly unbalanced binary classification. Computers in Biology and Medicine (Elsevier). 136: 104760.
https://doi.org/10.1016/j.compbiomed.2021.104760

5. Khan, JR., Tomal, JH., and Raheem, E. (2021). Model and Variable Selection using Machine Learning Methods with Applications to Childhood Stunting in Bangladesh. Informatics for Health and Social Care (Taylor and Francis). 0: 1-18. https://doi.org/10.1080/17538157.2021.1904938

6. Tomal, JH., Rahmati, S., Boroushaki, S., Jin, L., and Ahmed, E. (2021). The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach. METRON (Springer). 79: 57-91. https://doi.org/10.1007/s40300-021-00200-1

7. Tomal, JH., and Ciborowski, JJH. (2020). Ecological Models for Estimating Breakpoints and Prediction Intervals. Ecology and Evolution (Wiley). 10: 13500-13517. https://doi.org/10.1002/ece3.6955

8. Tomal, JH., Hsu, G., Welch, WJ., and Wang, M. (2020). EPX: Ensemble of Phalanxes. R package version 1.0.3. https://cran.r-project.org/web/packages/EPX/index.html

9. Evans, M., and Tomal, JH. (2018). Measuring Statistical Evidence and Multiple Testing. FACETS. 3: 563-583. https://doi.org/10.1139/facets-2017-0121

10. Tomal, JH., Welch, WJ., and Zamar, RH. (2017). Discussion of ``Random-projection Ensemble Classification'' by T.I. Cannings and R.J. Samworth. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 79(4): 1024-1025

11. Tomal, JH., Welch, WJ., and Zamar, RH. (2016). Exploiting Multiple Descriptor Sets in QSAR Studies. Journal of Chemical Information and Modeling. 56(3): 501-509

12. Tomal, JH., Welch, WJ., and Zamar, RH. (2015). Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery. The Annals of Applied Statistics, 9(1): 69-93

Courses Taught

1. Department of Mathematics and Statistics, Thompson Rivers University

(a) DASC 5420: Theoretical Machine Learning (Winter 2022, Summer 2021)
(b) DASC 6510: Bayesian Deep Learning (Fall 2021)
(c) STAT 1200: Introduction to Statistics (Winter 2022)
(d) MATH 1070: Mathematics for Business and Economics (Fall 2019, 2020, Winter 2020)
(e) STAT 2000: Probability and Statistics (Fall 2019, 2020, Winter 2019, 2020, 2021)


2. Department of Computer and Mathematical Sciences, University of Toronto Scarborough

(a) STAC58H3: Statistical Inference (Winter 2018)
(b) STAC23H3: Introduction to Statistics for the Social Sciences (Winter 2018)
(c) STAC62H3: Stochastic Processes (Fall 2017)
(d) STAC67H3: Regression Analysis (Fall 2014, Fall 2015, Fall 2017)
(e) STAB57H3: An Introduction to Statistics (Winter 2015, 2016, 2017, Fall 2016)
(f) STAB52H3: An Introduction to Probability (Fall 2014)
(g) STAD92H3: Logistic Regression and Parallel Computing using R (Summer 2015)

Timetable