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Englander, Janos Associate Professor

Positions

Research Areas research areas

Research

research overview

  • Dr. Englander's research concerns spatial branching particle systems and superprocesses, and their relationship to nonlinear partial differential equations. Special focus is on interactions and random environments. Furthermore, some non-classical random walks are studied.

keywords

  • Spatial branching processes, superprocesses, nonlinear partial differential equations, random environments, interacting particle systems, non-classical random walks

Publications

selected publications

Teaching

courses taught

  • APPM 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Spring 2018
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as STAT 5520 and MATH 4520 and MATH 5520.
  • APPM 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Spring 2018
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: one semester calculus-based probability course, such as MATH 4510 or APPM 3570. Same as STAT 4520 and MATH 4520 and MATH 5520.
  • MATH 2400 - Calculus 3
    Primary Instructor - Spring 2019
    Continuation of MATH 2300. Topics include vectors, three-dimensional analytic geometry, partial differentiation and multiple integrals, and vector analysis. Department enforced prerequisite: MATH 2300 or APPM 1360 (minimum grade C-). Degree credit not granted for this course and APPM 2350.
  • MATH 4520 - Introduction to Mathematical Statistics
    Primary Instructor - Spring 2018
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Same as MATH 5520 and STAT 4520 and STAT 5520.
  • MATH 5520 - Introduction to Mathematical Statistics
    Primary Instructor - Spring 2018
    Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distribution-free methods. Department enforced prerequisite: MATH 4510 or MATH 5510 or APPM 3570. Same as MATH 4520 and STAT 4520 and STAT 5520.
  • MATH 6550 - Introduction to Stochastic Processes
    Primary Instructor - Spring 2018
    Systematic study of Markov chains and some of the simpler Markov processes, including renewal theory, limit theorems for Markov chains, branching processes, queuing theory, birth and death processes, and Brownian motion. Applications to physical and biological sciences. Department enforced prerequisite: MATH 4001 or MATH 4510 or APPM 3570 or APPM 4560. Instructor consent required for undergraduates. Same as APPM 6550.

Background

International Activities

global connections related to teaching and scholarly work (in recent years)

Other Profiles