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May 2019

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Change Seminar, Tuesday, May 7: Fahad Pervaiz, Understanding Challenges in the Data Pipeline for Development Data
by Galen Weld 06 May '19

06 May '19

Please join us for the Change Seminar next Tuesday, May 7, at 12pm in CSE 203. *Abstract:* The developing world is relying more and more on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched efforts for more frequent information gathering. Overall, the amount of data collected is tremendous, yet we face significant issues in doing useful analysis. Most of these barriers are around data cleaning and merging, and they require a data engineer to support some parts of the analysis. This thesis aims to understand the pain points of cleaning development data. It also proposes solutions that harness the thought process of a data engineer to reduce the manual workload of the tedious process of cleaning such data. To achieve these goals, two research areas are critical: (1) to discern current data usage patterns and to build a taxonomy of data cleaning in the developing world; and (2) to create algorithms to support automated data cleaning, which target selected problems including matching transliterated names. With these goals, this thesis will empower regular data users to easily do the necessary data cleaning and scrubbing for analysis. *Bio:* Fahad Pervaiz <https://homes.cs.washington.edu/~fahadp/> is a graduating computer science PhD student at Paul G. Allen School, University of Washington, advised by Richard Anderson <https://www.cs.washington.edu/people/faculty/anderson> in the ICTD Lab <http://ictd.cs.washington.edu/>. Within the broad spectrum of developing technology for low income countries, his interest includes data processing, data infrastructure, HCI and public health. His research focuses on understanding the challenges in processing data collected in low income countries and explore solutions that are tailored to mitigate various issues in the data pipeline.

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