PhD candidate David Hallac, et al, receive 2017 ACM SIGKDD Research Track Award

David Hallac, EE PhD candidate
September 2017

David Hallac, EE PhD candidate, is the lead author of "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data," which has been selected to receive the KDD 2017 Conference Best Paper Runner-Up Award and the Best Student Paper runner-up Award. Co-authors include research assistant Sagar Vare (CS), professor Stephen Boyd (EE) and professor Jure Leskovec (CS).

ACM SIGKDD is the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining. The award recognizes papers presented at the annual SIGKDD conference, KDD2017, that advance the fundamental understanding of the field of knowledge discovery in data and data mining.

Their paper will received both the KDD 2017 Best Paper runner-up Award, as well as the Best Student Paper runner-up Award at the KDD 2017 ceremonies held in Halifax, Canada in August. The group will receive individual award plaques as well as a check.

 

Congratulations to David, Sagar, Stephen and Jure on this special recognition!

 

 

 

View "Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data" Abstract.