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: EME206
 Education                    Vitae                        Research                         Publications                      Projects


10/06/2017: I got an intern offer from Honeywell for summer 2018 and accepted it!
08/17/2017: I got an opportunity to attend Google Summer of Code 2017 Mentor Summit, Oct. 13th - 14th, Sunnyvale, CA!
07/17/2017: I was 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: 3.8 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. Our study identifies and models the patterns of human daily routines in 74 smart homes with diverse participants, and provides vital information of behavior patterns and detection of deviations that indicate potential health problem. Not only can this lead to more effective medical interventions, but these findings may benefit other fields.

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

2017 Publications (Journal Articles)
      • 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).

Projects: Indoor Air Quality    Population Modeling
Indoor Air Quality
Part 1: data visualization and transformation: smarthome-based human behavior detection (click a graph to zoom in).
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 (click a graph to zoom in)..

Part 2: outlier detection and model fitting (click a graph to zoom in).
             Part 2.1: outlier detection and model fitting for an activity named work.

             Part 2.2: outlier detection and model fitting for nine activities of daily living (ADLs).
              ( nine activities of daily living: work, sleep, bed to toilet transition, relax, cook, eat, personal hygiene, enter home, wash dishes. )

Part 3: summarized results of modeling at population level and among subpopulations