AMCSOur 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 410: Advanced Evolution
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
Alternatives/Additions:
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 900: Advanced Probability Theory
STAT 901 (OPIM931): Stochastic Processes II
MEAM 613 (CBE 617, CIS 613, ESE 617): Nonlinear Control Theory
BMB 604 (BE 619): Statistical Mechanics
MATH 590: Advanced Applied Mathematics
Alternatives/Additions
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