Skip to main content

This project invites physics, engineering, computer science, statistics, chemistry and agriculture majors to experience an active research environment targeted at the analysis/collection of large high-precision time series datasets related to stochastic physics of biological dynamics; the goal is to provide a data-driven quantitative understanding of what life is

Using a combination of high-precision experiments and first-principles-based physics theory, the Iyer-Biswas group (Purdue, Physics) finds emergent rules governing the stochastic, active and non-equilibrium dynamics of living systems. Specifically, the students will have the opportunity to be involved in one of two projects: 1) multigenerational individual bacterial cell dynamics in time varying environments or 2) emergence of social rules via individual-individual interactions in a population of eusocial animals such as ants. This undergraduate research opportunity will allow the student to work at the exciting interface of physics, statistics, computer science and biology, and apply theoretical, computational and experimental techniques to elucidate the physical principles stochastic dynamics in these contexts. The student will have an opportunity to become familiar with stochastic processes theory, i.e., the physics of random fluctuations. This theory has applications in many other modern contexts, including quantitative finance. They will get to work with computational algorithms for data processing tens of terabytes of data per experiment. They will also get to work on highly interdisciplinary projects involving advanced optics, microfluidics, cutting-edge image and data analysis and genetic engineering. Additionally, they will have an opportunity to work in an environment that is welcoming of diversity of identity, experience and perspective.

In addition, physics students with a special interest in theory development will have an opportunity to combine data-science approaches with information theory and modern statistical physics techniques to elucidate how the arrow of time, i.e., irreversibility, emerges in the dynamics of living systems. 

 

Eligibility

Open to any undergraduate student with a major in Agriculture, Engineering, or Science and an interest in the Data Sciences

Residential Component

  • Students from this learning community must reside in Hillenbrand Hall
  • Information on this residence hall can be found here
  • Students who are required to reside in a different residence hall (e.g. due to the Honors College or athletics participation) or who do not sign a housing contract may not participate in this learning community
  • Your roommate in most cases will be a member of the learning community
  • Completing a housing contract is a separate process from applying for a learning community

Duration

Fall and Spring semesters

Associated Classes

Fall 2020

  • PHYS 39000 (1 credit seminar and 2 credit lab) 
  • STAT 19000, 29000, 39000, or 49000 (1 credit) The Data Mine I or III or V or VII

Spring 2021

  • PHYS 39000 (1 credit seminar and 2 credit lab) 
  • STAT 19000, 29000, 39000, or 49000 (1 credit) The Data Mine The Data Mine II or IV or VI or VIII

Events and Activities Included:

  • Weekly dinners with LC participants
  • Tour of Purdue’s computational facilities
  • Social gatherings with LC members
  • Seminars by visiting speakers including practicing actuaries and data scientists
  • Meals with campus and community leaders
  • Game / recreation nights
  • Career and graduate school panels

Information above is subject to change. If you are placed in the LC, the associated courses will be on your schedule prior to you registering for the rest of your courses.

Learning Communities ERHT 1275 1st Street, West Lafayette, IN 47906 - (765) 494-5785 or (765) 494-8571, learningcommunities@purdue.edu

© 2018 Purdue University | An equal access/equal opportunity university | Copyright Complaints | Maintained by Student Life Marketing

If you have trouble accessing this page because of a disability, please contact Learning Communities at learningcommunities@purdue.edu.