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.
Nonconvex optimization problems are prevalent in both the scientific and machine learning communities. My current research is on developing a novel algorithm for nonconvex optimization that uses techniques from the combinatorial optimization literature to reduce the size of the search space by an exponential factor. We hope to use this algorithm to advance the state of the art in protein folding, one of the most important problems in biochemistry. Similarly, a significant advance in nonconvex optimization could drastically improve machine learning by redefining the types and complexity of models and algorithms that we can feasibly work with. 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Here is a link to my (slightly out of date) CV.
Projects and PublicationsPlanning and Decision Making
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. (pdf)
Inferring Object Reference Frames
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. (pdf)
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. (pdf)
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. (pdf)
Query Progress Estimation
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. (pdf)