Phytoplankton Trends

Discovered 2 types of phytoplankton by using machine learning and data visualization on Flow Cytometry data.


This is a data science based project that is going on at UW Seattle. The work on this project was performed as a Capstone project primarily with the goal of gaining better comprehension of Marine biology by analyzing the Flow Cytometry data available.

Oceanographers use Flow Cytometry to measure the optical properties of a given sample of water through radial dispersion. This is done by attaching Flow Cytometers to the bottom of ships that conduct research, thus enabling coverage of a vast body of water.

We procured Flow Cytometry data obtained at 3 minute time intervals (might change), and used suitable clustering technique to identify regions in the water body that have similar trends in microscopic life form population.

To learn more about the project, please see the wiki.

Roles: Program Manager, Developer (Machine Learning + Data Visualization), Presenter

Key Activities: Acquiring client, gathering requirements, setting goals, creating a roadmap of deliverables, coordinating events with the stakeholders and ensuring that deliverables are on time, literature review, coding, data visualization, presentation


The primary contributor of this project’s repository are:

  1. Abhigyan Kaustubh
  2. Elton Dias
  3. Tanmay Modak

This repo was compiled and documented by Abhigyan Kaustubh.


  1. Bill Howe, eScience Institute, UW CSE
  2. Sophie Clayton, UW Oceanography
  3. Jeremy Hyrkas, UW CSE
  4. Daniel Halperin, UW CSE
  5. UW Oceanography Researchers (eScience Institute)

Timeline: Dec 2014 to Jun 2015 (7 months)

Sponsor: UW eScience

Poster for Presentation


Screen Shot 2016-03-29 at 5.08.22 AM