| Office: 1313 Wean Hall |
| Phone: 412-268-5940 |
| jkbradle (yes, without the y) at cs dot cmu dot edu |
I'm a second-year in the Ph.D. Program in Machine Learning in the Machine Learning Deparment at Carnegie Mellon University. I'm advised by Carlos Guestrin and am part of the Select Lab. My interests are in machine learning and probabilistic models.
I am currently researching query-specific structure learning for graphical models, along with Anton Chechetka: Probabilistic Graphical Models, such as Bayes or Markov networks, model distributions over sets of random variables V such that it is tractable to compute queries such as P(A) or P(A | B = b) where A,B are subsets of V. Currently, a single model is usually learned from data and then used to answer multiple queries of form P(A | B = b). The specific queries which are important to the user are not taken into account by the learning algorithm. In this project, we aim to learn Probabilistic Graphical Models which are tailored to answering a specific query P(A | B = b) or a set of such queries. Intuitively, we hope to be able to learn simpler (more tractable) models by taking advantage of the locality of the query variables or of query-specific independence introduced by the evidence B = b.
My first year in grad school, I was advised by Eric Xing and worked on Population Genetics, modeling ancestral populations of single species using latent variable models.
Before coming to grad school, I was an undergraduate at Princeton University, where I received a B.S.E. in Computer Science. At Princeton, my main research was with Robert E. Schapire. We researched boosting in the filtering framework, where the learner does not use a fixed training set but rather has access to an example oracle which can produce an unlimited number of examples from the target distribution. This setting is useful for modeling learning with datasets too large to fit into a computer, learning in memory-limited situations, or learning from an online source of examples (e.g. from a web crawler).
Joseph K. Bradley and Robert E. Schapire.
FilterBoost: Regression and Classification on Large Datasets.
In
Advances in Neural Information Processing Systems 20, 2008.
Paper (PDF)
Appendix (PDF)
Slides (PPT) from oral at NIPS
More coming soon, I hope.
I do competitive Latin and Standard (and a little Smooth) ballroom dancing. It's awesome. You should do it too.
I like traveling too.
Before going to college, I was born and grew up in Birmingham, Alabama, where I spent less time in front of a computer. I like returning to my former state.