Affiliated Graduate Programs
Applied Linear Algebra: ES_APPM 395
Nearly every discipline with a quantitative component including engineering, physical sciences, social sciences, finance, computer graphics, big data, and machine learning rely on linear algebra. Numerical computation greatly enables modeling the data analysis in these fields. In order to utilize linear algebra and computing for problem solving, it is essential to understand how to set up problems (in the linear framework and numerically), determine when well defined solutions exist, write programs and algorithms in MATLAB to solve these problems, evaluate whether the algorithm will find the solution efficiently, and evaluate the accuracy of the computation.
Professor: Niall Mangan
Bioinformatics: Biological Sequence and Structure Analysis: BIO_SCI 323
BIO_SCI 323 explores through case studies and classroom discussions, the principles and practical applications of computational tools in contemporary molecular and structural biology research. Besides gaining an appreciation for the algorithmic aspects of these tools, students will learn to code with python and R, design and perform experiments in silico, and critically evaluate results.
Professor: Ishwar Radhakrishnan
Models in Applied Mathematics: ES_APPM 421
Applications to illustrate typical problems and methods of applied mathematics. Mathematical formulation of models for phenomena in science and engineering, problem solution, and interpretation of results. Examples from solid and fluid mechanics, combustion, diffusion phenomena, chemical and nuclear reactors, and biological processes.
Professor: Daniel Abrams
Numerical Methods for Random Processes: ES_APPM 448
Analysis and implementation of numerical methods for random processes: random number generators, Monte Carlo methods, Markov chains, stochastic differential equations, and applications.
Professor: Hermann Riecke
Principles and Methods in Systems Biology: IBIS 404
IBIS 404 uses mathematical-based experimental analysis and modeling to study biological problems. The class will introduce quantitative techniques, computational tools and biological systems that help investigators analyze heterogeneous complex data about molecular networks to uncover meaningful relationships about key components.
Professor: Richard Carthew
Quantitative Analysis of Biology: BIO_SCI 354/ ES_APPM 395-0
BIO_SCI 354 will be a course where we cover some the landmark results in quantitative biology. Every module (of which there are 5-6) will end with analysis of a data set acquired from the authors of studies and reanalysis and re-plotting of a central result from the paper. En route to that I will teach you the biology mathematics physics and statistics required to make the plots. The landmark papers will span from studies in gene regulation, developmental biology, sequencing etc.
We will also have various crash courses in coding, image analysis, etc.Introduction to landmark insights into quantitative biology. Random genetic processes, gene expression, cell adaption, cell cycle, developmental morphogens, phylgenomics.
Course website: https://madhavmani.wixsite.com/qbiocourse
Professor: Madhav Mani
Quantitative Biology: IBIS 410
Quantitative approaches to molecular and cell biology, focused on developing an understanding of connections between biomolecule structure and dynamics, and behavior of cells. The course will also include review of topics from statistics of random variables and statistical data analysis relevant to biology and biophysics.
Professor: John Marko
Quantitative Experimentation in Biology: BIO_SCI 359
BIO_SCI 359 is taught in 4 modules, which will involve students repeating experiments from some of the landmark papers in quantitative biology. Groups of students will work together and learn how to do experiments in a wet lab, perform live-imaging, conduct sequencing based studies, analyze their own data, and presentation skills.
Professor: Richard Carthew