Big Data meet Complex Models – A UAI Application Workshop

Final Proceedings

The final workshop is now available. The formal workshop proceedings are at The links below are for the preliminary papers presented at the conference.


All times approximate

Combined Applications Workshop Introductions
9:00–10:15 Session I – Extracting information about individuals from complex data
  1. Kerr and Chung – Identifying Learning Trajectories in an Educational Video Game
  2. Almond, Kim, Shute, & Ventura – Using information to debug Complex Systems (Slides: PDF, PPTX)
  3. Rafatirad and Laskey – Transforming Personal Artifacts into Probabilistic Narratives (Slides: PDF, PPTX)
Discussion for Session I

Coffee Break

Session II – Combining Learning and Expert Opinion
  1. Babagholami-Mohamadabadi, Jourabloo, Zolfaghari, & Manzuir-Shalmani – Bayesian Supervised Dictionary Learning
  2. Sun, Hanson, Twardy, & Laskey – Learning Parameters by Prediction Markets and Kelly Rule for Graphical Models
  3. Yet, Marsh, Perkins, Tai, & Fenton – Predicting Latent Variables with Knowledge and Data: A Case Study in Trauma Care
Open Discussion of Session II

Break (lunch on your own)

Application Workshop II – Spatial, Temporal, and Network Models

Combined Applications Workshop Wrap-up and Planning Discussion

Call For Papers

As the capacity of modern computing has increased, so has the complexity of the models explored by the UAI community: complexity defined by many variables and many parameters which must be estimated from data or tuned to expert opinion. The increased capacity of modern computing has also made it easier and easier to collect fine-grained data from a wide variety of subject interactions with various computer systems. This is often called "Big Data" because the data sets can become very large. But the true difficulty often lies with the nature of the data, and not just the size. Big Data don't look like the carefully planned and cleaned data sets from experimental or observational studies. Instead, they are heterogeneous: encompassing a wide variety of types, quality and completeness. Because Big Data are often gathered in an opportunistic fashion, issues of missing responses and selection bias are often not ignorable.

The focus this year is on the intersection of the complex models studied by the UAI community with the emerging challenges of Big Data. In particular, we invite papers on the following themes:

This list is meant to be suggestive and not exhaustive; other papers with an application focus are welcome. Also welcome are papers which represent works in progress or which explore a practical problem or issue without a final resolution. Workshop papers will be selected with the goal of stimulating discussion of critical issues within the community of practice.

Instructions for Authors

Submissions will be peer reviewed and papers will be published online. Authors who wish to withhold their paper from publication (either because it contains references to proprietary data, or because they wish to publish it later at a different venue) can request that only the abstract be published. Papers should follow the general UAI conference guidelines as to format and length, although these will be more loosely enforced.

Abstracts should be submitted to by May 17, 2013, with full papers due on May 24, 2013. Author notifications are expected around June 7, 2013. For questions contact the chair at

There are several collocated workshops. Papers may only be submitted to one of them. If the program committee feels that a paper would be a better fit in a different workshop, with the authors will pass the paper along to the other workshop's committee.

Conference Details

This workshop is offered as part of the 29th Uncertainty in Artificial Intelligence conference (UAI 2013), July 11-15th in Seattle, WA. It will be collocated with other UAI workshops on the last day of the conference, July 15th. Registration is handled through the main conference web site, which also gives information about lodging and travel. One registration fee covers all of the workshops: people who register for this workshop may split their time between all the workshops.

Program Committee:

Chair: Russell Almond
Florida State University
Co-chair: Thomas O'Neill
The American Board of Family Medicine
Marek J Druzdzel
University of Pittsburgh and Bialystok University of Technology, Poland
Julia Flores
Universidad de Castilla-La Mancha, Spain
Linda van der Gaag
Utrecht University, The Netherlands
Lionel Jouffe
Bayesia SAS
Kathryn Laskey
George Mason University
Suzanne Mahoney
Innovative Decisions, Inc.