E-mail: lowd at cs dot uoregon dot edu
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
Latest version: 1.1.1, released on 3/28/2015.
I was interviewed for a BBC article on adversarial machine learning, "How to Fool Artificial Intelligence".
Pedram Rooshenas successfully defended his dissertation and completed his Ph.D. Congratulations, Dr. Rooshenas!
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).
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).
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)
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.)
Our paper Discriminative Structure Learning of Arithmetic Circuits was published in the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) (with Pedram Rooshenas).
The Libra Toolkit for Probabilistic Models has been published in the JMLR Open Source Software track! (with Pedram Rooshenas)