I am a Computer Science and Engineering Ph.D. student at the University of Washington, where I work with Pedro Domingos. I received my B. Eng. in Computer Engineering from the University of Victoria in British Columbia, Canada.
My current research is on developing tractable models and inference algorithms, particularly with respect to probabilistic inference and nonconvex optimization. Longer term, I am interested in developing general learning techniques for artificial intelligence that are domain agnostic and rely on fewer assumptions than modern methods.
In previous work, I have explored nonparametric Bayesian modeling, planning and decision making, reinforcement learning, imitation, and goal inference. I've had the pleasure of being advised by both Rajesh Rao and David Wingate, and have had the good fortune to visit other research labs, including MIT in the labs of Leslie Kaelbling and Josh Tenenbaum, Berkeley with Tom Griffiths in the Computational Cognitive Science Lab, and an internship at Intel Labs in Seattle, working with Dieter Fox on Human Robot Interaction.
Here is a link to my CV.
Recent News4/25/2016 - Our paper "The Sum-Product Theorem: A Foundation for Learning Tractable Models" was accepted to ICML 2016!
7/27/2015 - The code for RDIS is available! (from our Distinguished Paper on "Recursive Decomposition for Nonconvex Optimization") Get it from my github repo here.
6/3/2015 - Our paper on Recursive Decomposition for Nonconvex Optimization was chosen as the winner of the Distinguished Paper Award at IJCAI 2015!
Abram L. Friesen and Pedro Domingos (2016). The Sum-Product Theorem: A Foundation for Learning Tractable Models. To appear in Proceedings of the International Conference on Machine Learning. New York, New York. June, 2016.
Abram L. Friesen and Pedro Domingos (2015). Recursive Decomposition for Nonconvex Optimization. In Proceedings of the 24th International Joint Conference on Artificial Intelligence. Buenos Aires, Argentina. July, 2015.
Winner of the Distinguished Paper Award!
(pdf) (supplement) (code)
Abram L. Friesen and Pedro Domingos (2014). Exploiting Structure for Tractable Nonconvex Optimization. In Proceedings of the 1st Workshop on Learning Tractable Probabilistic Models at ICML 2014. Beijing, China. June, 2014.
Abram L. Friesen and Pedro Domingos (2013). Nonconvex Optimization is Combinatorial Optimization. In Proceedings of the 6th Workshop on Optimization for Machine Learning at NIPS 2013. Lake Tahoe, Nevada. December, 2013.
Yanping Huang, Abram L. Friesen, Timothy D. Hanks, Michael N. Shadlen, and Rajesh P. N. Rao (2012). How prior probability influences decision making: a unifying probabilistic model. In Proceedings of the 26th Annual Conference on Neural Information Processing Systems. Lake Tahoe, Nevada. December, 2012.
Joseph L. Austerweil, Abram L. Friesen, and Thomas L. Griffiths (2011). An ideal observer model for identifying the reference frame of objects. In Proceedings of the 25th Annual Conference on Neural Information Processing Systems. Granada, Spain. December, 2011.
Abram L. Friesen and Rajesh P. N. Rao (2011). Gaze Following as Goal Inference: A Bayesian Model. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Boston, MA: Cognitive Science Society. July, 2011.
Abram L. Friesen and Rajesh P. N. Rao (2010). Imitation Learning with Hierarchical Actions. In Proceedings of the International Conference on Learning and Development (ICDL), Ann Arbor, MI, USA. August, 2010.
Kristi Morton, Abram Friesen, Magdalena Balazinska, and Dan Grossman (2010). Estimating the Progress of MapReduce Pipelines. In Proceedings of the 26th IEEE International Conference on Data Engineering (ICDE), Long Beach, CA, USA. March, 2010.