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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.

»  View the Analysis in Motion Initiative flier

»  Learn more about AIM's R&D


  • Can artificial intelligence (AI) help advance scientific research? , with collaborators at the University of Southern California, Rensselaer Polytechnic Institute and Cornell University, addressed this question in the article “Amplify scientific discovery with artificial intelligence,” published in the October 10, 2014 issue of Science. Read the full article.
  • The Streaming Hypothesis Generation (Shyre) project has documented a use case for identifying possible compounds in a substance based on its NMR spectra, and prototyped the proposed, stream reasoning system to obtain preliminary results. For additional information on this and other developments, contact .
  • Bobbie-Jo Webb-Robertson, Lisa Bramer, and Sean Robinson attended The R User Conference 2014. The conference, which took place at the University of California, Los Angeles, will provide the team with specific training on big data and streaming applications with R. Bobbie-Jo hosted a short debrief presentation upon her return from the conference. For more information, please email or visit the conference website.
  • Bobbie-Jo Webb-Robertson and Lisa Bramer attended the Women in Statistics conference. Bobbie-Jo presented on a panel about balancing a career and children, and Lisa won a travel award and presented her work on the Analysis in Motion Initiative. During the conference, they met Grace Wahba. Grace, at 80 years old, still actively works and speaks at conferences. She is a pioneer in the statistics field, and worked on machine learning when there were nearly no women in the field. Find more about Grace, on Wikipedia, or to find out more about the conference, contact .

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