# Mathematical Biology

Our understanding of evolutionary and ecological processes has a long history of interactions with mathematics and, more recently, computation. Much of the early development of statistics and probability theory was directly inspired by problems arising in population biology; and, later, an appreciation for complex and chaotic dynamics was first understood in the context of simple ODE models arising in ecology.  Theoretical population biology remains an area of very active research, both inspiring and consuming mathematical, statistical, and computational techniques.  Outstanding problems in the field include an understanding of what forces preserve genetic diversity in populations, what forces shape the evolutionary trajectories of populations, and how populations interact at longer, ecological timescales.

Year 1:

Core courses

BIOL 535: Mathematical Ecology

STAT 530: Probability Theory

STAT 512: Mathematical Statistics

STAT 531: Stochastic Processes

Electives

STAT 540: Statistical Methods and Computation

BIOL 536: Computational Biology

GCB 531: Introduction to Genome Science

MATH 582: Applied Mathematics and Computation

MATH 583: Applied Mathematics and Computation

MATH 432: Game Theory

BSTA 651: Introduction to Linear Models and Generalized Linear Models.

BSTA 630: Statistical Methods for Data Analysis I

BSTA 631: Statistical Methods and Data Analysis II

STAT 512 (BSTA621, STAT432): Mathematical Statistics

CIS 520: Artificial Intelligence and Machine Learning

Year 2:

All Electives

STAT 901 (OPIM931): Stochastic Processes II

MEAM 613 (CBE 617, CIS 613, ESE 617): Nonlinear Control Theory

BMB 604 (BE  619): Statistical Mechanics

BIOL 540 (CAMB541, MOLB541): Genetic Analysis

BMB 619: Protein Folding

CAMB 752: Genomics

BE 539 (ESE 539): Neural Networks, Chaos, and Dynamics: Theory and Application

STAT 927: Bayesian Statistical Theory and Methods

STAT 910: Forecasting and Time Series Analysis

BSTA 771: Applied Bayesian Analysis