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 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)
Our paper Ontology Matching with Knowledge Rules (with Shangpu Jiang and Dejing Dou) was published in DEXA 2015 and won the Best Paper Award!
Automated Attacks on Compression-Based Classifiers was accepted to the ACM AISec 2015 workshop (with Igor Burago).
I received an ARO Young Investigator Award for my proposal on "Inferring Trustworthiness and Deceit in Adversarial Relational Models"!
I will (again) be serving as Proceedings Chair for the Conference on Uncertainty in Artificial Intelligence (UAI 2015) and Workshops Cochair for AAAI 2016.
Libra version 1.1.1 is now available! Includes better documentation, better interface, bug fixes, and more!
New NSF grant funded -- "EAGER: Machine Learning to Combat Adversarial Attacks"!
Presented two papers at ICML 2014 - On Robustness and Regularization of Structural Support Vector Machines (with Ali Torkamani) and Learning Sum-Product Networks with Direct and Indirect Interactions (with Pedram Rooshenas)