Daniel Lowd

Portrait of Daniel Lowd

E-mail: lowd at cs dot uoregon dot edu
Office: 262 Deschutes Hall
Phone: 541-346-4154
Fax: 541-346-5373

Mailing Address:
Department of Computer and Information Science
University of Oregon, Eugene, OR 97403

I am an Assistant 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.


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)

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