Data Management

Databases, cloud computing, big-data analytics, scientific data management, machine learning and data management, image and video analytics, data management for VR/AR

Machine learning, speech/language/bioinformatics/music, submodularity & discrete optimization

Machine learning, artificial intelligence, data science

Data visualization and analysis, human-computer interaction and social computing

Databases, scientific data management and visualization

Software testing and program analysis, in particular efficient mutation testing, partial test oracles, and security testing.

Design, implementation, and analysis of high-performance computing and communication systems; data-intensive discovery (eScience); information technology and public policy

Data management: uncertain and probabilistic databases, data privacy and security, complexity of parallel query evaluation, data pricing.

Artificial intelligence, human computer interaction, natural language processing

Databases, transaction processing, concurrency control, data repositories

Nick is a postdoctoral researcher with the Taskar Center for Accessible Technology doing work in mapping and analyzing individual-level pedestrian accessibility of public spaces with graph theoretic interpretations of equity. His doctoral work involved the development of the OpenSidewalks and AccessMap projects that, respectively, define and collect open pedestrian transportation network data for a wide set of use cases and interpret that data into a user-friendly and individually-customizable web map.

Waylon Brunette works with Richard Anderson in the ICTD Lab. His research focuses on designing, building, and evaluating resilient mobile technologies optimized for usage in resource-constrained and infrastructure-constrained environments. His interdisciplinary research focuses on technology for global health, humanitarian assistance, and international development. He received his Ph.D. from the Allen School in 2020 and is one of the Open Data Kit (ODK) project founders.

Adam Richie-Halford works with professor Ariel Rokem in the eScience Institute on the development of statistical learning techniques and software for the analysis of neuroimaging data. Adam's current research interests lie in extracting the biophysical properties of the brain's major white matter connections by leveraging large open datasets containing diffusion MRI images. He earned a Ph.D. in physics from the University of Washington.