David Gotz is an Associate Professor at the UNC-Chapel Hill School of Information and Library Science (SILS). Dr. Gotz leads the Visual Analysis and Communications Lab (VACLab) where he conducts research related to the study and development of visual methods for information analysis and communication. He also serves as the Assistant Director for the Carolina Health Informatics Program (CHIP), and is an Associate Member at the UNC Lineberger Comprehensive Cancer Center. Dr. Gotz was also named a 2015 Data Fellow by the National Consortium for Data Science.
Prior to joining SILS, Dr. Gotz was a research scientist at the T.J. Watson Research Center at IBM Research. In his final years at IBM, Dr. Gotz was part of the Healthcare Systems and Analytics Research Department. He also spent several years working in the Intelligent Information Interaction Department which he joined immediately after earning his Ph.D. in Computer Science from UNC-Chapel Hill in 2005. Dr. Gotz earned his MS in Computer Science from UNC-Chapel Hill in 2001, and graduated with highest honors from Georgia Tech in 1999 with a BS in Computer Science and a certificate in Economics.
Industry Expertise (4)
Areas of Expertise (7)
2015 Data Fellow (professional)
National Consortium for Data Science (NCDS)
2013 IBM Research Accomplishment Award (professional)
IBM Research Accomplishment Award received for the scientific and commercial impact of
medical informatics research
2012 IBM Research Accomplishment Award (professional)
IBM Research Accomplishment Award received for the scientific and commercial impact of medical informatics research
University of North Carolina at Chapel Hill: Ph.D., Computer Science 2005
Dissertation Title: Scalable and Adaptive Streaming for Non-Linear Media
University of North Carolina at Chapel Hill: M.S., Computer Science 2001
Georgia Institute of Technology: B.S., Computer Science 1999
Certificate in Economics
Research Grants (1)
Interactive Ensemble clustering for mixed data with application to mood disorders
NSF DMS QuBBD Program
NSF DMS QuBBD Program (Collaborative Research) September 11, 2015 – September 10, 2016
Title: Interactive Ensemble clustering for mixed data with application to mood disorders
Total Award Amount: $100,000
Visual analytics is a science that involves the integration of interactive visual interfaces with analytical techniques to develop systems that facilitate reasoning over, and interpretation of, complex data. As the volume of health-related information continues to increase at unprecedented rates, there is a critical need to study effective ways to support the analysis of large amounts of data by leveraging human-computer interaction and graphical interfaces...
As medical organizations modernize their operations, they are increasingly adopting electronic health records (EHRs) and deploying new health information technology systems that create, gather, and manage their information. As a result, the amount of data available to clinicians, administrators, and researchers in the healthcare system continues to grow at an unprecedented rate. 1 However, despite the substantial evidence showing the benefits of EHR adoption, e-prescriptions, and other components of health information exchanges, ...
Data with multiple probabilistic labels are common in many situations. For example, a movie may be associated with multiple genres with different levels of confidence. Despite their ubiquity, the problem of visualizing probabilistic labels has not been adequately addressed. Existing approaches often either discard the probabilistic information, or map the data to a low-dimensional subspace where their associations with original labels are obscured...
As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-launching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual ...
Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (eg, with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (eg, thousands of event types). ...