Daniel Lowd

E-mail: lowd at cs dot washington dot edu
Office: 430 Allen Center

Mailing Address:
University of Washington
Dept. of Computer Science and Engineering
Mailstop 352350
University of Washington
Seattle, WA, 98195-2350

I am a sixth-year graduate student in Computer Science and Engineering at the University of Washington.

I expect to graduate in the summer of 2009. You can find my C.V. here.

Research

My focus is on statistical relational learning, but I'm also interested in recommender systems, spam filtering, and machine learning in general. I am currently working with Pedro Domingos on miscellaneous things that are somehow related to Markov logic networks.

Statistical Relational Learning

Statistical relational learning seeks to represent the complexity and uncertainty present in most real-world problems by combining first-order logic with probability. The main challenges are in developing effective representations and effective algorithms. One of my projects has been Recursive Random Fields (RRFs), a multi-layer generalization of Markov logic networks that resolves a number of inconsistencies in the Markov logic representation (Lowd and Domingos, 2007a [pdf] [ppt] [ppt+audio]). I have also worked on applying quadratic optimization algorithms to Markov logic weight learning, resulting in more accurate models in much less time than before (Lowd and Domingos, 2007b [pdf] [ppt] [video]).

Learning for Efficient Inference

Inference in Bayesian networks and Markov networks is intractable in general, but many special cases are tractable. Often, a tractable subclass such as naive Bayes mixture models yields comparable accuracy while offering exponentially faster inference (Lowd and Domingos, 2005 [pdf] [ppt] [appendix]). Furthermore, by incorporating a preference for tractable models into the learning algorithm, we can guarantee efficient inference without restricting ourselves to any particular class (Lowd and Domingos, 2008 [pdf] [pdf+proofs]).

Adversarial Machine Learning

I spent the summer of 2004 at Microsoft Research working with Chris Meek on the problem of spam. We looked at a common technique spammers use to defeat filters: adding "good words" to their emails. We developed techniques for evaluating the robustness of spam filters, as well as a theoretical framework for the general problem of learning to defeat a classifier (Lowd and Meek, 2005ab [pdf] [pdf]).

Slides from a talk at Oregon State University (7/14/2006).

Slides from a talk at the 2007 NIPS Workshop on Machine Learning in Adversarial Environments for Computer Security (12/8/2007).

Slides from a talk at the University of Cagliari, Italy (7/3/2008).

Publications

Book chapters

Just Add Weights: Markov Logic for the Semantic Web. Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, and Parag Singla. In P. C. G. Costa, C. d'Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, and M. Pool (eds.), Uncertain Reasoning for the Semantic Web I, 2008. New York: Springer.
Markov Logic. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, and Parag Singla. In L. De Raedt, P. Frasconi, K. Kersting and S. Muggleton (eds.), Probabilistic Inductive Logic Programming (pp. 92-117), 2008. New York: Springer.

Refereed conference papers

Using Salience to Segment Desktop Activity into Projects. Daniel Lowd and Nicholas Kushmerick. Proceedings of the Thirteenth International Conference on Intelligent User Interfaces (IUI), 2009. Sanibel Island, Florida: ACM Press.
Learning Arithmetic Circuits. Daniel Lowd and Pedro Domingos. Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2008. Helsinki, Finland: AUAI Press. (Extended version with proofs) (Poster)
Efficient Weight Learning for Markov Logic Networks. Daniel Lowd and Pedro Domingos. Proceedings of the Eleventh European Conference on Principles and Practices of Knowledge Discovery in Databases (PKDD), 2007. Warsaw, Poland: Springer Verlag. (Slides) (Video)
[Updated PDF file fixes several formula errors.]
Recursive Random Fields. Daniel Lowd and Pedro Domingos. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007. Hyderabad, India: IJCAI. (Slides) (Slides+Audio)
Naive Bayes Models for Probability Estimation. Daniel Lowd and Pedro Domingos. Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), 2005. Bonn, Germany: ACM Press. (Slides) (Online appendix)
Good Word Attacks on Statistical Spam Filters. Daniel Lowd and Christopher Meek. Second Conference on Email and Anti-Spam (CEAS), 2005. Palo Alto, CA. (Slides)
Adversarial Learning. Daniel Lowd and Christopher Meek. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005. Chicago, IL: ACM Press. (Poster)

Workshop papers

Recursive Random Fields (workshop version). Daniel Lowd and Pedro Domingos. Proceedings of the ICML-2006 Workshop on Open Problem in Statistical Relational Learning, 2006. Pittsburgh, PA: IMLS. (Slides)

Technical reports

Challenges and Solutions for Synthesis of Knowledge Regarding Collaborative Filtering Algorithms. Daniel Lowd, Olivier Godde, Matthew McLaughlin, Shuzhen Nong, Yun Wang, and Jonathan L. Herlocker. Oregon State University, School of Electrical Engineering and Computer Science. 2009 (written in 2004). URI: http://hdl.handle.net/1957/10940

Miscellaneous

CSE Poetry

Here is a villanelle I wrote for architecture class. I had to write it in order to get a one-week extension on the final project. Writing was quite enjoyable... alas, there is no quals course in poetry!

Fortunately, one of the questions on the architecture final asked me to answer a question of my own creation. I received full credit on it, too.

The following quarter inspired this creation. I was taking advanced complexity at the time.

Music

World of Warcraft meets the Beach Boys: Ungoro (MP3). Lyrics, vocals, and production by me.

I used to sing with the Seattle Men's Chorus.

Other Interests

My wife, Mary Lowd, is a science fiction writer. You can read one of her short stories online for free. Her web page includes some excellent pictures of our daughter and our pets.

I don't plan on ever getting a tattoo, but if I did, it would have to be something really cool. Like a praying mantis. Playing a guitar. Eating a butterfly.