Timothy Antonelli

Timothy Antonelli

Assistant Professor of Statistics and Probability

I work at the intersection of mathematics and biology. My dissertation was on modeling the release of genetically modified mosquitoes to combat dengue, an important mosquito-borne virus. My recent interests are varied and include statistics, probability, and number theory. I also work with a group at Worcester Polytechnic Institute on modeling the stabilization of RNA in tuberculosis.


Duke University
Biomedical and Electrical Engineering
North Carolina State University
Skills Biomathematics Statistics Probability Mathematical Modeling


Honor honor-iconCreated with Sketch.
NSF Graduate Research Fellowship
2012 Honorable Mention
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  • Papers
  • Other
Human-associated Pathogen Biogeographic Maps Read More
A Surprising Link Between Integer Partitions and Euler’s number e Read More
Transgenic Pests and Human Health: A Short Overview of Social, Cultural and Scientific Considerations. Read More


Shell Lab

Some of my current research involves mathematical modeling and statistical analysis of mRNA stabilization in tuberculosis with the Shell Lab at Worcester Polytechnic Institute.
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MA 150

Statistics I

Descriptive techniques, elementary probability, distribution of the sample mean, confidence intervals, hypothesis testing, linear regression and correlation.
3 credits
MA 180

Introduction to Functions

Properties and applications of linear, quadratic, polynomial, rational, exponential, and logarithmic functions; systems of linear equations.
3 credits
MA 303

Mathematical Modeling

The development, analysis, and application of continuous and discrete-time models from the physical, financial, and life sciences.
3 credits
MA 380


Basic principles of counting, continuous and discrete probability concepts including: conditional probability, independence, expectation and variance of univariate and multivariate distributions. This is the first course in a two-course sequence that prepares students for the first actuarial exam.
3 credits
MA 497

Mathematical Statistics

A rigorous mathematical presentation of statistical inference including: maximum likelihood estimators, sufficiency, consistency, likelihood ratio tests and Bayesian inference.
3 credits