( about . research . teaching . outreach . personal . calendar )

Lillian J. Ratliff

ratliffl at eecs.berkeley

Postdoctoral Researcher, EECS at UC Berkeley

I enjoy spending time mentoring high school and undergraduate students on projects that allow them to combine their interests with science in the hopes that it will encourage them to continue with STEM activities in their education. Below are some of the projects I have worked with students.

abstract algebra notes (summer 2012): (PDF)

warning: there maybe some mistakes. these notes are being prepared as part of an independent study on undergraduate abstract algebra with a high school student (amelia keller-boren). we are using Gilbert & Gilbert's book 'Elements of Modern Algebra'. many reviews suggest it is a good book to introduce algebra to high school students. so far this seems to be the case. we are covering chapters 1-3.

Amelia Keller-Boren:


J. Gilbert and L. Gilbert. Elements of Modern Algebra. Prindle, Weber & Schmidt Publishing Company, 3rd Ed., 1992.

S. Lang. Undergraduate Algebra. Springer-Verlag, 1987.

T. W. Hungerford. Abstract Algebra: An Introduction. Brooks Cole, 2nd Ed., 1996.

links: ( project euler) (learn python the hard way)

dancing nao robots (summer 2012): (link to project)

this project aimed to get two nao robots to dance in synchronization. a high school student (axenya kachen) did all the programming for this project using python. the dances that have been implemented include but are not limited to the cha cha, the waltz, and the jive.


Axenya Kachen:

search and rescue nao robots (summer 2013):

In this project, two freshman undergraduate students were tasked with designing and implementing a search and rescue mission with Nao humanoid robots. Specifically, the task was to position one robot in the lab at a location unknown to two other robots. Then these other two robots were to be programmed to collaborate in searching the lab space for the third robot and rescue it by engaging with it, providing it with code to return to home base.


Donald Cook, Purdue, School of Aeronautics and Astronautics

Omar Mahmood, Columbia, Applied Physics

beeEar: save the bees via machine learning (summer 2014):

we collected data from the bee hives in my backyard using a platform we built. the data we collected included sound, temperature, and humidity. using Python's scikit-learn , we applied machine learning techniques to the data in order to find anomalous behavior. the end goal is to build a low-cost sensing platform that can inform backyard beekeepers and small commercial beekeepers about the health of their hives. this is an ongoing project and collaborators include Aaron Bestick, Duncan Haldane, Dan Cook and the Mead Kitchen


Ingemar Anderson-Schmidt, High School student in bay area

Zoe Turin, High School student in bay area

Customer Segmentation for Demand-Side Management (summer 2014):

NSF TRUST sponsored REU Project

using RECS data set and Python's scikit-learn tools, Axenya implemented customer segmentation algorithms.


Axenya Kachen, UC Berkeley Undergraduate Student