WELCOME TO THE DISASTER DATA SCIENCE LAB

We are a group of data scientists and trainees who research how to leverage data to help others before, during, and after disasters. Globally, frequency and intensity of disasters are rising. We tackle pressing and challenging problems of disaster research by collecting and analyzing data to suggest evidence-based remedies.

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THE COVID-19 PANDEMIC SEATTLE STREET VIEW CAMPAIGN

This project conducts longitudinal (repeat) street view surveys for 12 months across a broad cross-section of Seattle to collect data on the community impact of the pandemic. This project also develops and implements a series of open-source routines that automatically process the data to rapidly extract time-sensitive insights from the imagery. This project was featured by the UW News and several media outlets.
Support: National Science Foundation (CMMI-2031119)

PARTICIPATORY STATISTICAL INFERENCE OF INTERDEPENDENT CRITICAL INFRASTRUCTURE RECOVERY TIMES

The project will innovate a new methodological framework, as well as software tools to support this framework, for estimating post-event interdependent critical infrastructure recovery times. The core of the framework is a participatory process for eliciting recovery estimates from topical experts.
Support: National Science Foundation (CMMI-1824681)

DATA-ENABLED ACCELERATION OF STOCHASTIC COMPUTATIONAL EXPERIMENTS

This project will develop methods to accelerate stochastic computational experiments with the aid of heterogeneous data (for example, empirical observations, multi-fidelity simulations, and expert knowledge). These methods will help overcome the computational challenge associated with investigating unusual strings of events (for example, nuclear meltdown, cascading blackout, and epidemic outbreak) that are critical to the nation's economy, security, and health.
Support: National Science Foundation (DMS-1952781)

AUTOMATIC DAMAGE DETECTION ON POST-HURRICANE SATELLITE IMAGERY

The governing research question of the project is: Can a machine learning algorithm automatically annotate damages on post-hurricane satellite images? To answer the question, the project uses satellite imagery data on the Greater Houston area after Hurricane Harvey in 2017, and damage labels created by crowdsourcing. The left-hand side image shows crowdsourced labels (1: Flooded/Damaged; 2: Non-damaged) and the right-hand side image shows our algorithm's prediction. For more information, please see the project website, presentation videoslides, and GitHub repo.
Support: Data Science for Social Good Program of the eScience Institute

BUILDING BACK BETTER: INNOVATIVE METHODS TO MEASURE RESILIENCE

During the disaster recovery process, it is extremely challenging to continuously assess community health and well-being. Readily available data sources on community health and wellbeing during the disaster recovery period are essential to assessing community recovery of health and wellbeing and to building capacity for community resilience. This study aims to understand if and how data from personal health monitoring devices and apps, such as Fitbit and Strava, can be used to understand community resilience and inform recovery activities in the recovery period. This project is an interdisciplinary collaboration with the Collaborative on Extreme Event Resilience. (Image credit to Strava Global Heatmap: Santa Rosa's aggregated, public activities for 2017-2018)
Support: Population Health Initiative of the University of Washington

 
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PEOPLE

 

Lab Director &
Assistant Professor

Ph.D. (2016) Industrial & Operations Engineering
University of Michigan

M.A. (2016) Statistics
University of Michigan

B.S. (2010) Physics & Management Science
KAIST, Korea  

Graduate Researcher
(2017/9 – Present)

Ph.D. Student (2017 - Present) 
Industrial & Systems Engineering
University of Washington

M.Sc. (2016)
Financial Engineering
University of Illinois at Urbana Champaign, IL

B.Eng. (2011) Civil Engineering
Nanyang Technological University, Singapore

Graduate Researcher
(2019/9 – Present)

Ph.D. Student (2019 - Present)

Industrial & Systems Engineering
University of Washington

B. Eng. (2019)
Industrial & Systems Engineering
University of Minnesota

Graduate Researcher
(2019/9 – Present)

Ph.D. Student (2019 - Present)

Industrial & Systems Engineering
University of Washington

B.S. (2017)
Industrial & Systems Engineering

(Operations Research Concentration)

Georgia Institute of Technology

Graduate Researcher
(2019/2 – Present)

M.Sc. (exp. 2020)

Industrial & Systems Engineering
University of Washington

M.S. (2016) Statistics
Chulalongkorn University

CHRISTOPHER SALAZAR

Graduate Researcher
(2020/8 – Present)

Registered Professional Engineer (California)

M.Sc. (exp. 2021)

Industrial & Systems Engineering
University of Washington

B.S. (2013) & M.S. (2015) 

Civil and Environmental Engineering

University of California, Davis

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Graduate Researcher
(2020/10 – Present)

M.Sc. (exp. 2022)

Biostatistics
University of Washington

B.Sc. (2019)  

Pharmacology and Statistics

McGill University

Undergraduate Researcher
(2020/6 – Present)

B.Sc. (exp. 2021)

Industrial & Systems Engineering
University of Washington

VANESSA YANG

Undergraduate Researcher
(2020/6 – Present)

B.Sc. (exp. 2021)

Statistics and Informatics
University of Washington

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Undergraduate Researcher
(2020/10 – Present)

B.Sc. (exp. 2021)

Industrial & Systems Engineering
University of Washington

Alumni:

  • Zhanlin (Kevin) Liu, Ph.D. in Industrial Engineering, Graduate Researcher (2016-2020).

  • Aman Ankit, B.Sc. in Industrial Engineering, Undergraduate Researcher (2017-2020).

  • Ken Yamada, B.Sc. in Industrial Engineering, Undergraduate Researcher (2019-2020).

  • Chris Haberland, M.Sc. in Computational Linguistics, Graduate Researcher (2018-2020).

  • Summer Ai, B.Sc. in Statistics & Psychology, Undergraduate Researcher (2018-2019).

  • Amy Xu, B.Sc. in Computer Science, Undergraduate Researcher (2017-2018).

  • Trevor J. Aquiningoc, B.Sc. in Mechanical Engineering, Undergraduate Researcher (2018).

  • Yu-Ting Chen, B.Sc. in Statistics & Economics, Undergraduate Researcher (2017-2018).

  • Xiaoyan Peng, B.Sc. in Statistics & Economics, Undergraduate Researcher (2017-2018).

  • Danni Shi, B.Sc. in Statistics, Undergraduate Researcher (2017-2018).

  • Aryton Tediarjo, B.Sc. in Industrial Engineering, Undergraduate Researcher (2017-2018).

  • Dengxian (Dara) Yang, B.Sc. in Applied & Computational Math Sci., Undergraduate Researcher (2017-2018).

  • Ty Good, B.Sc. in Industrial Engineering, Undergraduate Researcher (2017-2018).

  • Li Ding, B.Sc. in Industrial Engineering, Undergraduate Researcher (2017-2018).

  • Zechariah Cheung, B.Sc. in Computer Science, Undergraduate Researcher (2017-2018).

  • Nick Monsees, B.Sc. in Computer Engineering, Undergraduate Researcher (2017-2018).

  • Mary Barnes, M.A. in International Studies, Volunteer Data Scientist (2017-2018).

  • Zach McCauley, M.Sc. in Industrial Engineering, Graduate Researcher (2017-2018).

  • Xuejiao Li, B.Sc. in Applied Physics & Applied Mathematics (Minor), Undergraduate Researcher (2017-2018).  

  • Megan Miyasaki, B.Sc. in Physics, Undergraduate Researcher (2017-2018).

  • Christine Dien,  B.Sc. in Bioengineering, Undergraduate Researcher (2017-2018).

  • Randy Christopher Wenan, B.Sc. in Industrial Engineering,  Undergraduate Researcher (2017-2018).

  • Daniel Colina,  B.Sc. in Informatics, Undergraduate Researcher (2017-2018)

  • Winter Meng, B.Sc. in Applied Physics & Applied Math, Undergraduate Researcher (2017-2018).


Openings: If you would like to be considered for participating in our lab's research, please send Prof. Choe an email with your resume and (unofficial) transcript. Those from traditionally underrepresented groups in STEM (e.g., women and minorities) are particularly encouraged to get in touch.

News

  • 7/30/2020: Youngjun Choe (director) is selected as a Fellow of the NSF-supported Operations and Systems Engineering Extreme Event Research (OSEEER) network’s Early Career Mentoring (ECM) program.

  • 10/21/2019: Zhanlin (Kevin) Liu (graduate researcher) is selected as the INFORMS QSR Best Paper Competition Finalist.

  • 5/20/2019: Aman Ankit (undergraduate researcher) and his research appear in the UW ISE News.

  • 11/5/2018: Daniel Cao Quoc Dung (graduate researcher) wins the 3rd place in the INFORMS Poster Competition at the INFORMS Annual Meeting, Phoenix, AZ and appears in the UW ISE News

CONTACT US

Aerospace & Engineering Research Building (AERB), 141F, Seattle, WA 98105

Prof. Youngjun Choe <ychoe@uw.edu>

 

©2017-2020 BY DISASTER DATA SCIENCE LAB.