Data Science Lab

Data Science LabThe Data Science Lab (or “DataLab”) is the research arm of the Institute for Advanced Analytics. It brings together investigators from various disciplines to focus on the intersection of analytical and computational challenges organizations face in extracting meaningful insights from a vast quantity and variety of data.

Areas of Interest

  • The detection of anomalous patterns or rare events within and across various realms of data, and determining whether such patterns are meaningful outliers or artifacts. This might include such varied activities as detecting fraudulent financial transactions or cheating in massive multiplayer online games.
  • The detection of commonalities in behavior or sentiment in the analysis of text on the Web or other content systems. This might include detecting commonality in software bug reports or customer service calls, the strength and direction of opinion in consumer product evaluations, or sentiment toward broader issues concerning the public.
  • The application of analytics to develop methods and approaches to assure security of a wide range of software-based systems by analyzing both static and dynamic characteristics of the systems, large-scale data-streams, and human behaviors.
  • New visualization methods to aid researchers in the analysis of data, as well as developing ways to visually represent complex data to enhance human perception, understanding and decision-making.
  • Developing new algorithms to improve the speed and efficiency of queries in extremely large relational databases.
  • The development of complex predictive models including combinatoric searches and evolutionary computational techniques for high throughput data.
  • The application of analytics to geospatial data.


  • Sudipta Dasmohapatra – Customer Analytics
  • Hugh Devine – Geospatial Analytics
  • Christopher Healey – Data Visualization
  • Ilse Ipsen – Randomized algorithms
  • Aric LaBarr – Time Series
  • Carl Meyer – Dynamical Clustering
  • Alison Motsinger-Reif – Predictive Modeling
  • Michael Rappa – Web Analytics
  • Oleg Veryovka – Social and Game Analytics
  • Andrea Villanes – Text Mining
  • Ben Watson – Data Visualization
  • Laurie Williams – Software Security Analytics

Doctoral students:

  • Mark Cusick – Machine Learning