Beiyu Lin

Ph.D. Student

Center for Advanced Studies in Adaptive Systems (CASAS) lab

School of Electrical Engineering and Computer Science

Washington State University


Lab: EME136
 Education                    Vitae                        Research                         Publications                      Projects



08/17/2017: I am selected to attend Google Summer of Code 2017 Mentor Summit, Oct. 13th - 14th, Google, CA!
07/17/2017: I am one of the students in receipt of EECS's Scholarship to atttend 2017 Grace Hopper Celebrating, Oct. 4th-6th, Orlando, FL!


Ph.D. in Computer Science, Washington State University Jan. 2016-Present. GPA: 3.79 Advisor: Dr. Diane J. Cook

I am a Ph.D. student and have been using data to find human behavior patterns as a preventative study to detect early signs of chronic diseases under the smart home environment. To do this, I have been applying big data and machine learning technologies to do data analysis by using Python and R with well-defined methods and algorithms as well as exploring and developing new methods and algorithms.

One project I have been working on is granted by the Department of Energy and by the U.S. Environmental Protection Agency's Science to Achieve Results (STAR) program. Our research is about Integrated Measurements and Modeling Using US Smart Homes to Assess Climate Change and Human Behaviors Impact on Indoor Air Quality.

My most recent work is about Population Modeling. Currently we spend billions of dollars treating the late-stage chronic disease when it’s often in vain, this preventative study is to detect chronic diseases earlier when it can be cured under the SH environment by applying big data and machine learning technologies.

My research interests include smart home, machine learning, technology applications for healthcare, and compute science education.

2017 Publications (Journal Articles)
    • Kirk, Max, Madeline Fuchs, Yibo Huangfu, Tom Jobson, Patrick O’Keeffe, Shelley Pressley, Von Walden, Beiyu Lin, Diane Cook, and Brian Lamb. Indoor Air Quality and Wild Fire Smoke Impacts in the Pacific Northwest, the ASHRAE Journal of Science and Technology for the Built Environment, 2017 (to be appear).
      • Correction: the selected VOCs, including formaldehyde, acetaldehyde, acetonitrile, methanol, ethanol, acetone, benzene, toluene, xylenes, styrene, and monoterpenes, were measured continuously with a proton transfer reaction mass spectrometer (PTR-MS, Ionicon Analytik, Innsbruck, Austria, Europe).

Indoor Air Quality
Part 1: data visualization and transformation: smarthome-based human behavior detection.
Part 2: finding the relationship between human behavior and indoor air quality based on three regression analyses and we report correlation coefficients that are moderate or large (r >= 0.3).
                    Summarized results
Part 3: feature extraction based on three learning algorithms.
                Results for aggregated dataset
                Results for IAQ1
                Results for IAQ2

Population Modelling

Part 1: point density for mobile phone data with different radium values.
Part 2: modelling for aggregated dataset.