Bill Howe

Associate Professor, Information School
Adjunct Associate Professor, Computer Science & Engineering
Adjunct Associate Professor, Electrical Engineering
Co-Founding Director, Center for Responsible AI Systems and Experiences
Director, Urbanalytics Lab
Founding Program Director and Faculty Chair, UW Data Science Masters Degree
Founding Associate Director and Senior Data Science Fellow, UW eScience Institute
University of Washington
Short Bio | Curriculum Vitae | NSF-style biosketch | NIH-style biosketch
billhowe at | Office: MGH 310 in the iSchool DataLab

I am an Associate Professor in the Information School, Adjunct Associate Professor in Computer Science & Engineering and Electrical Engineering. I was Founding Associate Director of the UW eScience Institute where I remain a Senior Data Science Fellow. I currently Direct the Urbanalytics group and Co-Direct the Center for Responsible AI Systems and Experiences. I am a co-founder of Urban@UW. I created a first MOOC on Data Science through Coursera, and I led the creation of the UW Data Science Masters Degree, where I serve as its first Program Director and Faculty Chair. I serve on the Steering Committee of the Center for Statistics in the Social Sciences.

My group's research aims to make the techniques and technologies of data science broadly accessible, especially in the public sector where issues of equity, privacy, and compliance are paramount. Our methods are rooted in database models and languages, though we also study machine learning, visualization, HCI, and high-performance computing. We are an applied, systems-oriented group, frequently sourcing projects through collaborations in the physical, life, and social sciences. Our current interests are in algorithmic fairness, machine learning for heterogeneous data, and urban computing.



EZLearn+Automatic Claim Validation
We are developing an end-to-end system for validating scientific claims against open data repositories using NLP, machine learning, and data integration techniques.
Privacy-Preserving Synthetic Data
We are developing usable, general tools to generate shareable synthetic datasets with strong privacy guarantees from any input dataset.
Data Science for Social Good
Building on our data science incubator program and the University of Chicago's Data Science for Social Good program, we ran an interdisciplinary summer program for...
Machine vision, machine learning, and bibliometric analysis to understand how visualization is used to convey ideas in the scientific literature.
Myria Middleware for Polystores
Part of the Myria project, RACO (the Relational Algebra COmpiler) is a polystore middleware system that provides query translation, optimization, and orchestration across complex multi-system...
Clustering Billion-Edge Graphs
Working at the intersection of network science, databases, and high-performance computing, we developed a series of novel parallel algorithms based on Infomap serial graph clustering...
Scalable Flow Cytometry
We have developed algorithms, methods, systems, and applications in support of the Seaflow project in the Armbrust Lab in the UW department of Oceanography.
SQLShare: DB-as-a-Service
SQLShare aims to increase uptake of databases in data science and shed light on how data scientists work with data
VizDeck + Visualization Recommenders
VizDeck recommends visualizations based on the statistical properties of the data tempered by perception heuristics. Dashboards are assembled through a card-game UI.

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