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Analysis in Motion Initiative

AIM is accelerating knowledge discovery from high-volume data streams by inventing new techniques to help automate the process of creating and validating hypotheses.

In a world where data are continually streaming from distributed and diverse sources—from scientific instruments, to web traffic, to live imagery—making timely discoveries requires computing capabilities that can keep pace with rapidly evolving phenomena. AIM is developing a new analysis paradigm to provide continuous, automated synthesis of new knowledge and to enable measurement systems to be steered in response to emerging knowledge, rebalancing the effort between humans and machines.

Improving How Humans and Machines Interact

We are transforming the feedback loop in joint human and machine reasoning by capturing human background knowledge through new interaction techniques, allowing AIM systems to evaluate candidate hypotheses rapidly and steer models and data collectors in response to evolving knowledge.

Automating Hypothesis Generation and Testing

We are creating automated methods for generating human-useful hypotheses from data, including incremental machine learning from incomplete data and streaming deductive inference to identify and preserve interesting assertions from live data.

Creating New Streaming Analysis Algorithms

We are developing streaming data characterization methods that can identify and tag important features in high rate data streams and exploring the possibility that streaming algorithms can use dynamic sampling strategies to apply more powerful algorithms to increasingly challenging data rates.

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  • August 6, 2015. "Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization" was presented by at the Microscopy and Microanalysis Conference, Portland, OR.
  • July 27, 2015. was a speaker at DMIN'15: The 11th International Conference on Data Mining, Las Vegas, NV on July 27, 2015. He presented "Selecting a Classification Ensemble and Detecting Process Drift in an Evolving Data Stream." Learn more...
  • July 29, 2015. presented "A Machine Learning Approach for Business Intelligence Analysis Using Commercial Shipping Transaction Data" at WORLDCOMP'15 - the International Conference on Data Mining, in Las Vegas, NV. Visit the website for more information.
  • The American Association for the Advancement of Science (AAAS), Federal Bureau of Investigation (FBI), and United Nations Interregional Crime and Justice Research Institute (UNICRI) recently completed a year-long study of the benefits and risks of big data, and the related national security implications. served on the study's panel of experts and was an editor for the report National and Transnational Security Implications of Big Data in the Life Sciences. Read the full article.

Employment and Collaboration Opportunities

Want to join us? We're hiring! We're looking for more world-class machine learning and human-computer interaction researchers to join our team. ; tell us what you're passionate about and discuss the possibilities.

We're also interested in partnering with individuals and organizations. If you have expertise in the areas of machine learning or human-computer interaction, and are interested in partnership opportunities, then we'd like to talk with you. .

Analysis in Motion

Current Projects