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AISB event Bulletin Item

2nd CfP - NIPS 2010 Workshop on Modeling Human Communication Dynamics

Modeling Human Communication Dynamics
NIPS Workshop, Whistler, British Columbia, Canada Friday, December 10th, 2010 
Extended submission deadline: October 18th, 2010
    *** List of invited speakers completed. See below. ***


Face-to-face communication is a highly interactive process in which the participants mutually exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and the dynamic interactions among an individual's perceptual, cognitive, and motor processes are swift and complex. How people accomplish these feats of coordination is a question of great scientific interest. Models of human communication dynamics also have much potential practical value, for applications including the understanding of communications problems such as autism and the creation of socially intelligent robots able to recognize, predict, and analyze verbal and nonverbal behaviors in real-time interaction with humans.

Modeling human communicative dynamics brings exciting new problems and challenges to the NIPS community.  The first goal of this workshop is to raise awareness in the machine learning community of these problems, including some applications needs, the special properties of these input streams, and the modeling challenges.  The second goal is to exchange information about methods, techniques, and algorithms suitable for modeling human communication dynamics.  After the workshop, depending on interest, we may arrange to publish full-paper versions of selected submissions, possibly as a volume in the JMLR Workshop and Conference papers series.


We therefore invite submissions of short high-quality papers describing research on Human Communication Dynamics and related topics.  Suitable themes include, but are not limited to:

* human behavior modeling for virtual agents and human-robot interactions
* modeling methods robust to semi-synchronized streams (gestural, lexical, prosodic, etc.)
* learning methods robust to the highly variable response lags seen in human interaction
* coupled models for the explicit simultaneous modeling of more than one participant
* ways to combine symbolic (lexical) and non-symbolic information

* learning of models that are valuable for both behavior recognition and behavior synthesis
* algorithms robust to training data whose labeling is incomplete or noisy
* feature engineering
* online learning and adaptation

* models of moment-by-moment human interaction that can also work for longer time scales
* specific applications and potential applications
* failures and problems observed when applying existing methods to such tasks
* insights from experimental or other studies of human communication behavior

Invited speakers (partial list):

* Janet Bavelas (University of Victoria)
* Marian Stewart Bartlett (University of California, San Diego)
* Jeff Bilmes (University of Washington)
* Dan Bohus (Microsoft Research)
* Justine Cassell (Carnegie Mellon University)
* Noah D. Goodman (Stanford University)

Submission guidelines:

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS latex style. NIPS style files and formatting instructions can be found at (although we will not enforce the double blind rule). Work that was recently published or presented elsewhere is allowed, provided that the extended abstract mentions this explicitly; work earlier presented at non-machine-learning venues is especially encouraged. Please send your submission by email to before October 18th, 2010 at 11:59pm PDT. 

Important dates:

Submission deadline: October 18th, 2010, 11:59pm PDT
Notification of acceptance: November 7th, 2010
Workshop: December 10th, 2010


Louis-Philippe Morency (University of Southern California)
Daniel Gatica-Perez (IDIAP)
Nigel Ward (UTEP)
Previous Conferences:
Prediction Models of Human Communication Dynamics, Los Angeles, August 4-6, 2010. Co-organized by Louis-Philippe Morency and Nigel Ward
PASCAL 2: Pattern Analysis, Statistical Modelling and Computational Learning