My current research interests are fundamentally rooted in the areas of machine learning and network analysis. For over a decade, I have developed and studied large margin machine learning algorithms in the context of a range of perception tasks, such as object classification in video, sonar classification, hyperspectral anomaly detection and acoustic classification. In recent years, I have turned my attention to the field of social network analysis and the plethora of challenges associated with understanding dynamic social networks in the online realm.

My core interests in the theoretical aspects of statistical machine learning are driven by the following fundamental questions:

• How can machines efficiently acquire context from a human
  expert over time to solve a pattern recognition task?



• How can a machine identify deficiencies in its own understanding
  of the environment in order to request more context from the user?



• How does one estimate the generalization performance of a model efficiently?



• What approaches allow a designer to impose explicit constraints on the
  computational complexity of a classifier during learning?

These questions have motivated my work in incremental and active learning, novelty detection, generalization performance assessment and sparse kernel machine learning.

My pursuits in machine learning are motivated by an overarching desire to build analytic processes that aid a user/analyst in deriving and capturing trusted insights in a timely fashion from large data sources. In any exploratory process, the analyst will want to make specifications of relevance that a machine can learn from to accelerate the process of insight generation. This is inherently a collaborative process between the analyst and machine, requiring a synnergistic mix of machine learning, visualization and interaction paradigms tailored to the analytic task at hand.

Network analysis provides a rich domain in which to develop and apply these concepts. As social media technologies transform the ways in which millions of individuals communicate and collaborate each day, there are new opportunities to observe and understand how networks develop and evolve online. I am particularly interested in methods that help us understand the evolution of social networks within formal organizations and networked groups. As we improve our capabilities to understand factors that are driving the evolution of a network, we may then move toward analytic processes that help decisionmakers take actions to purposefully amplify or degrade the future performance of the network as needed.