Associate Professor, Information School
Adjunct Associate Professor, Computer Science & Engineering
Adjunct Associate Professor, Electrical Engineering
Program Director and Faculty Chair, UW Data Science Masters Degree
Director, Urbanalytics Lab
Founding Associate Director and Senior Data Science Fellow, UW eScience Institute
University of Washington Short Bio |
Curriculum Vitae |
NSF-style biosketch |
billhowe at uw.edu | Office: MGH 310 in the iSchool DataLab
I am an Associate Professor in the Information School, Adjunct Associate Professor in Computer Science & Engineering, and Associate Director and Senior Data Science Fellow at the UW eScience Institute. I am a co-founder of Urban@UW, and with support from the MacArthur Foundation and Microsoft, I lead UW's participation in the MetroLab Network. 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 dramatically more accessible, particularly at scale. Our methods are rooted in database models and languages, though we sometimes work in 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.
October 2018: Our paper led by Shrainik Jain on Database-Agnostic Workload Management was accepted to CIDR 2019! The proposed system uses NLP on the queries themselves to help predict errors, security violations, and approximate performance.
October 2018: I'm giving an invited talk at the TRIPODS PI meeting with Lise Getoor on Responsible Data Science
August 2018: Our panel at VLDB 2018 with Julia Stoyanovich, Jagadish, and Gerome Miklau attracted a full room!
July 26, 2018: I'm giving an invited talk at NSF on our work on algorithmic curation of science data repositories.
July 21, 2018: Best paper award Our paper on DRACO using answer set programming to model visualization recommendation rules won best paper at InfoVis 2018! Congratulations to Dominik and the whole team!
July 18, 2018: I presented our paper on EZLearn at IJCAI 2018 in Stockholm. EZLearn combines distant supervision and co-learning to avoid the need for training data in applications that have access to noisy free-text descriptions of data, especially in science.
July 10, 2018: Congratulation to Kanit (Ham) Wongsuphasawat for defending his thesis on visualization recommendation systems! Ham is joining Apple as a Research Scientist.
We are studying the technical foundations for responsible data science, including fair machine learning, semi-synthetic private data, data governance, automatic metadata attachment and curation, and...