John Whelan Headshot

John Whelan

Professor, Applied Mathematics

School of Physics and Astronomy
College of Science

585-475-5083
Office Hours
Via zoom https://rit.zoom.us/j/96378996388 Mon/Fri 10:00-11:00am or by appointment (send email to jtwsma@rit.edu)
Office Location
Office Mailing Address
RIT School of Mathematics and Statistics 85 Lomb Memorial Drive Rochester, NY 14623

John Whelan

Professor, Applied Mathematics

School of Physics and Astronomy
College of Science

Education

BA, Cornell University; Ph.D., University of California at Santa Barbara

585-475-5083

Personal Links
Areas of Expertise

Currently Teaching

ASTP-612
3 Credits
This course provides an introduction to the applied mathematical and statistical tools used frequently in astrophysics including modeling, data reduction, analysis, and computational astrophysics. Topics will include Special Functions, Differential Equations, Probability and Statistics, and Frequency Domain Analysis.
ASTP-790
1-3 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
ASTP-791
1 Credit
Continuation of Thesis
ASTP-890
1-6 Credits
Dissertation research by the candidate for an appropriate topic as arranged between the candidate and the research advisor.
ASTP-891
1 Credit
Continuation of Thesis
PHYS-320
3 Credits
This course serves as an introduction to the mathematical tools needed to solve intermediate and upper-level physics problems. Topics include matrix algebra, vector calculus, Fourier analysis, partial differential equations in rectangular coordinates, and an introduction to series solutions of ordinary differential equations.
STAT-561
3 Credits
This course provides a comprehensive introduction to Bayesian statistical reasoning and methodology, emphasizing both theoretical foundations and practical applications. Students will learn how to formulate statistical models using prior distributions, update beliefs through observed data via Bayes’ theorem, and make inferences using posterior distributions. Topics include conjugate priors, linear models, hierarchical models, model comparison and selection, and computational methods such as Markov Chain Monte Carlo (MCMC) and Gibbs sampling. Students will also gain hands-on experience implementing Bayesian models in modern statistical software (e.g., R or Python).
STAT-661
3 Credits
This course provides a comprehensive introduction to Bayesian statistical reasoning and methodology, emphasizing both theoretical foundations and practical applications. Students will learn how to formulate statistical models using prior distributions, update beliefs through observed data via Bayes’ theorem, and make inferences using posterior distributions. Topics include conjugate priors, linear models, hierarchical models, model comparison and selection, and computational methods such as Markov Chain Monte Carlo (MCMC) and Gibbs sampling. Students will also gain hands-on experience implementing Bayesian models in modern statistical software (e.g., R or Python).

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