Edison Thomaz

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I am an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin, where I direct the Human Signals lab. I hold a bachelor's degree in Computer Science from UT Austin, a master's from the MIT Media Lab and a Ph.D. in Human-Centered Computing from Georgia Tech. I am a member of DICE, WNCG, and I am currently an Associate Editor of IMWUT. I also co-lead the Life Sensing Consortium.

My research focuses on the computational perception of human signals (e.g., behavioral, emotional, physiological) while leveraging ubiquitous and wearable sensing. A core area of interest is studying systems and methods for recognizing and modeling the entire span of people's everyday activities and context. This work is at the intersection of ubiquitous computing, hci, human-centered machine learning, and signal processing. I am particularly motivated by applications in the domain of health and personalized medicine such as building health models and tools that can characterize and forecast states of health and disease from sensor data.

Contact Info

ethomaz at utexas dot edu
Twitter: @ethomaz

EER 7.818
2501 Speedway
Austin, TX 78712

Office Hours

Wed: 4pm-5pm


Spring 2020

EE380L - Data Mining

Fall 2020

EE382V - Activity Sensing and Recognition



September 2019

I am co-organizing the Texas Wireless Summit 2019, where the theme is "Connectivity and Sensing at the Human-Machine Frontier". This is the flagship yearly event of the WNCG, our center for research and education in partnership with industry affiliates.

September 2019

Our work exploring Active Learning to minimize data annotation has been accepted to IMWUT and will be presented at Ubicomp 2019. Our group will be presenting 2 papers at the conference.

June 2019

I had the opportunity to co-instruct the "Ubiquitous Computing: Enabling Technologically Advanced Living" workshop at the University of Oulu in Finland with my colleagues Prof. Denzil Ferreira and Prof. Anind Dey.

March 2019

I am excited that I was awarded a 2-year Experiential Learning Initiative grant to re-design and update our software design and implementation class.


Recent Publications

Towards a Generalizable Method for Detecting Fluid Intake with Wrist-Mounted Sensors and Adaptive Segmentation
Keum San Chun, Ashley B. Sanders, Rebecca Adaimi, Necole Streeper, David E. Conroy, Edison Thomaz
IUI 2019

Edison Thomaz © 2019