Data Science for Biophysics

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Sponsoring College, Department, School or Center: 
Physics
Faculty Name: 
Steve Pressé
Preferred Skills or Majors: 

Passion for physics, applied math, machine learning, data science

Compensation: 
unpaid
Closing Date: 
August 31, 2022
Location or Campus: 
Tempe, PSF
Hours Per Week: 
7-15

Desired Academic Year: 
Freshman
Sophomore
Junior
Project Description: 

This is a Project for an Honors Thesis.

Current biophysical imaging and spectroscopy methods can probe time (< 10-6 s), length (10-9 m) and force (10-12 N) scales relevant to the life cycle of a cell. These methods have revealed that all steps involved in molecular biology's central dogma (transcription, translation and DNA replication) are intrinsically stochastic. Despite the wealth of experimental data, the ability to gain meaningful insight from experiments on such small scales is severely limited by fundamental challenges common to all biological systems: current methods cannot capture complex processes in their full multi-dimensional detail. Here we propose to adapt state-of-the-art tools from Data Science to probe processes relevant to life from the arrival of single photons. Students involved in this project will master tools of computational statistics, data science, and Bayesian inference.



Application Instructions and Contact Info: 

Please send CV to spresse@asu.edu