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

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.

Databases, transaction processing, concurrency control, data repositories

Vinayak Gupta works with Prof. Tim Althoff in the Behavioral Data Science Lab. His research interests broadly lie in the intersection of data mining and machine learning. Specifically, he focuses on neural generative models, including graph-based and temporal point processes, and applies them to domains such as healthcare, behavioral streams, and recommendation systems.

Andrew McNutt is a postdoc working with Allen School professors Jeff Heer and Leilani Battle. His research interests include information visualization (including its theories and practices) as well as the design of programming interfaces (such as editors and DSLs). In Fall 2024 he will be joining the Kahlert School of Computing / Scientific Computing and Imaging Institute at the University of Utah as an assistant professor.

Xinyi Zhou is a postdoctoral scholar working with Allen School professors Tim Althoff and Amy Zhang. Her research interests are broadly in the intersection of data mining, machine learning, and social computing. Her research has and will continue striving to bridge the gap between social theories and (multimodal) ML/AI techniques to comprehend and improve the online information ecosystem that can breed misleading, untrusted, biased, threatening, insecure, and distracting messages.