John Whelan Headshot

John Whelan

Professor

School of Mathematical Sciences
College of Science

585-475-5083
Office Hours
Office hours by appointment (send email to jtwsma@rit.edu)
Office Location
Office Mailing Address
RIT School of Mathematical Sciences 85 Lomb Memorial Drive Rochester, NY 14623

John Whelan

Professor

School of Mathematical Sciences
College of Science

Education

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

585-475-5083

Areas of Expertise

Currently Teaching

ASTP-711
3 Credits
This is an advanced course in statistical inference and data analysis for the astrophysical sciences. Topics include Bayesian and frequentist methods of parameter estimation, model selection and evaluation using astrophysical data. Specific applications, such parameter estimation from gravitational wave signals, or analysis of large data sets from imaging, spectroscopic or time domain surveys will be discussed. Computational methods including Markov Chain Monte Carlo, with other topics such as machine learning, and time series analysis included at the discretion of the instructor.
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
0 Credits
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
0 Credits
Continuation of Thesis
MATH-251
3 Credits
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications.
MATH-381
3 Credits
This course covers the algebra of complex numbers, analytic functions, Cauchy-Riemann equations, complex integration, Cauchy's integral theorem and integral formulas, Taylor and Laurent series, residues, and the calculation of real-valued integrals by complex-variable methods.
STAT-345
3 Credits
This course is an in-depth study of inferential procedures that are valid under a wide range of shapes for the population distribution. Topics include tests based on the binomial distribution, contingency tables, statistical inferences based on ranks, runs tests and randomization methods. A statistical software package is used for data analysis.
STAT-753
3 Credits
The emphasis of this course is how to make valid statistical inference in situations when the typical parametric assumptions no longer hold, with an emphasis on applications. This includes certain analyses based on rank and/or ordinal data and resampling (bootstrapping) techniques. The course provides a review of hypothesis testing and confidence-interval construction. Topics based on ranks or ordinal data include: sign and Wilcoxon signed-rank tests, Mann-Whitney and Friedman tests, runs tests, chi-square tests, rank correlation, rank order tests, Kolmogorov-Smirnov statistics. Topics based on bootstrapping include: estimating bias and variability, confidence interval methods and tests of hypothesis.

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