• Contact Info

Mullen, Zachary Kidder Instructor


Research Areas research areas


research overview

  • Dissertation in parameter estimation and computational concerns regarding Gaussian processes. Experienced with interdisciplinary collaborations in a variety of fields: applying linear modelling, spatial statistics, nonparameteric analysis, simulation, and other methods.


  • spatial statistics, computational methods, Gaussian processes


courses taught

  • APPM 1350 - Calculus 1 for Engineers
    Primary Instructor - Fall 2018
    Topics in analytical geometry and calculus including limits, rates of change of functions, derivatives and integrals of algebraic and transcendental functions, applications of differentiations and integration. Students who have already earned college credit for calculus 1 are eligible to enroll in this course if they want to solidify their knowledge base in calculus 1. For more information about the math placement referred to in the Enrollment Requirements, contact your academic advisor. Degree credit not granted for this course and APPM 1345 or ECON 1088 or MATH 1081 or MATH 1300 or MATH 1310 or MATH 1330.
  • APPM 1360 - Calculus 2 for Engineers
    Primary Instructor - Spring 2019
    Continuation of APPM 1350. Focuses on applications of the definite integral, methods of integration, improper integrals, Taylor's theorem, and infinite series. Degree credit not granted for this course and MATH 2300.
  • APPM 1390 - A Game for Calculus
    Primary Instructor - Fall 2018 / Spring 2019
    Coaches students to implement study strategies geared specifically toward APPM Calculus in a structured, supportive, small group environment. Department consent required.
  • APPM 4570 - Statistical Methods
    Primary Instructor - Summer 2018 / Summer 2019
    Covers basic statistical concepts with accompanying introduction to the R programming language. Topics include discrete and continuous probability laws, random variables, expectation and variance, central limit theorem, testing hypothesis and confidence intervals, linear regression analysis, simulations for validation of statistical methods and applications of methods in R. Same as APPM 5570.
  • COEN 1350 - Calculus 1 Work Group
    Primary Instructor - Fall 2018
  • CSCI 2824 - Discrete Structures
    Primary Instructor - Spring 2020
    Covers foundational materials for computer science that is often assumed in advanced courses. Topics include set theory, Boolean algebra, functions and relations, graphs, propositional and predicate calculus, proofs, mathematical induction, recurrence relations, combinatorics, discrete probability. Focuses on examples based on diverse applications of computer science. Same as CSPB 2824.
  • CSCI 2834 - Discrete Structures Workgroup
    Primary Instructor - Spring 2020
    Provides additional problem-solving practice and guidance for students enrolled in CSCI 2824. Students work in a collaborative environment to further develop their problem-solving skills with the assistance of facilitators. Does not count as Computer Science credit for Computer Science majors or minors.
  • CSCI 3022 - Introduction to Data Science with Probability and Statistics
    Primary Instructor - Fall 2019 / Fall 2020 / Spring 2021
    Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
  • CSCI 3202 - Introduction to Artificial Intelligence
    Primary Instructor - Fall 2020
    Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language. Same as CSPB 3202.
  • CSCI 4022 - Advanced Data Science
    Primary Instructor - Fall 2019 / Spring 2021
    Introduces students to advanced tools, methods, and theory for extracting insights from data. Covers computational tools for storing and working with large data sets and computational techniques for common big data scenarios like graph data, recommender systems, and dimensionality reduction. Emphasizes both the efficient implementation of algorithms as well as the mathematical foundations behind techniques.