Daniel Lowd
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E-mail: lowd at cs dot uoregon dot edu
Mailing Address: |
I am an Associate Professor in the Department of Computer and Information Science at the University of Oregon.
My research interests include learning and inference with probabilistic graphical models, adversarial machine learning, and statistical relational machine learning.
I also maintain Libra, an
open-source toolkit for Learning and Inference in
Bayesian networks, Random fields, and Arithmetic
circuits.
Latest version: 1.1.1, released on 3/28/2015.
PROSPECTIVE STUDENTS
Interested in doing machine learning research at the University of Oregon? Please check out the FAQ for more information, then email me if you still have questions.NEWS
May 2017
I was awarded tenure! I'm now an Associate Professor (effective September 2017).
April 2017
I was interviewed for a BBC article on adversarial machine learning, "How to Fool Artificial Intelligence".
March 2017
Pedram Rooshenas successfully defended his dissertation and completed his Ph.D. Congratulations, Dr. Rooshenas!
February 2017
Our work on Collective Classification of Social Network Spam (with Jonathan Brophy) has been published in the 2017 AAAI Workshop on Artificial Intelligence and Cyber-Security (AICS).
October-December 2016
Two new publications on stance classification in tweets (with Javid Ebrahimi and Dejing Dou): Weakly Supervised Tweet Stance Classification by Relational Bootstrapping (COLING'16) and A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets (EMNLP'16).
July 2016
Ali Torkamani successfully defended his dissertation and completed his Ph.D. Congratulations, Dr. Torkamani!
I gave an invited talk on "Adversarial Statistical Relational AI" at the IJCAI 2016 Workshop on Statistical Relational AI (StarAI). (Slides)
June 2016
We've received funding from the DARPA Media Forensics (MediFor) program! Over the next 4 years, we will develop Markov logic networks and algorithms to reason about fraudulent images and videos. (Joint team with SRI Princeton and NYU.)
May 2016
Our paper Discriminative Structure Learning of Arithmetic Circuits was published in the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) (with Pedram Rooshenas).
February 2016
Our paper A Probabilistic Approach to Knowledge Translation (with Shangpu Jiang and Dejing Dou) was published in AAAI 2016
January 2016
The Libra Toolkit for Probabilistic Models has been published in the JMLR Open Source Software track! (with Pedram Rooshenas)