Kurt Tadayuki Miller

Current employment

I am currently a researcher at one of the top quantitative finance firms. If you are interested in pursuing a career in quantitative finance or have any questions about it, feel free to get in touch with me at [my_middle_name]@cs.stanford.edu.

Education

• UC Berkeley
  Ph.D. Computer Science, M.A. Statistics
  Advisors: Michael I. Jordan and Thomas L. Griffiths
• Stanford University
  M.S. Computer Science, B.S. Mathematics, B.A. Spanish

Research

• Bayesian Nonparametric Latent Feature Models.
  Kurt T. Miller.
  Ph.D. Dissertation, University of California, Berkeley, 2011.
• Non-exchangeable Bayesian Nonparametric Latent Feature Models.
  Kurt T. Miller, Michael I. Jordan, and Thomas L. Griffiths.
  International Society of Bayesian Analysis (ISBA) World Meeting, 2010.
• Nonparametric Latent Feature Models for Link Prediction.
  Kurt T. Miller, Thomas L. Griffiths, and Michael I. Jordan.
  Advances in Neural Information Processing Systems (NIPS) 22, 2009.
• Continuous Time Group Discovery in Dynamic Graphs.
  Kurt T. Miller and Tina Eliassi-Rad.
  Analyzing Networks and Learning with Graphs Workshop at NIPS 22, 2009.
• Latent Feature Models for Link Prediction.
  Kurt T. Miller, Thomas L. Griffiths, and Michael I. Jordan.
  Snowbird Machine Learning Workshop, 2009.
• Variational Inference for the Indian Buffet Process.
  Finale Doshi-Velez, Kurt T. Miller, Jurgen Van Gael, and Yee Whye Teh.
  Artificial Intelligence and Statistics (AISTATS), 2009.
  Tech report (extended version of the paper) with all the derivations.
  Matlab code for the linear-Gaussian model.
• Variations on Non-Exchangeable Nonparametric Priors for Latent Feature Models.
  Kurt T. Miller, Thomas L. Griffiths, and Michael I. Jordan.
  Nonparametric Bayes Workshop at ICML/UAI, 2008.
• The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features.
  Kurt T. Miller, Thomas L. Griffiths, and Michael I. Jordan.
  Uncertainty in Artificial Intelligence (UAI), 2008.
• Optic Flow Sensors for MAV (Micro Air Vehicle) Navigation.
  Geoffrey L. Barrows, Craig Neely, and Kurt T. Miller.
  Chapter 26 in Fixed and Flapping Wing Aerodynamics for Micro Air Vehicle Applications, T.J. Mueller, Ed.
  American Institute of Aeronautics and Astronautics (AIAA), 2001.
• Fusing Neuromorphic Motion Detector Outputs for Robust Optic Flow Measurement.
  Geoffrey L. Barrows, Kurt T. Miller, and Brian Krantz.
  International Joint Conference on Neural Networks (IJCNN), 1999.
• Feature Tracking Linear Optic Flow Sensor Chip.
  Kurt T. Miller and Geoffrey L. Barrows.
  International Symposium on Circuits and Systems (ISCAS), 1999.

Teaching

• UC Berkeley CS 170, Efficient Algorithms and Intractable Problems, Graduate student instructor, Fall 2010
• UC Berkeley Stat 260-01/CS 294-38, Bayesian Modeling and Inference, Graduate student instructor, Spring 2010
• UC Berkeley CS 294-34, Practical Machine Learning, Guest lecturer, Fall 2009
• UC Berkeley CS 294-34, Practical Machine Learning, Guest lecturer, Spring 2008
• UC Berkeley Machine Learning Workhop, Guest lecturer, Fall 2007
• Stanford University CS 221, Artificial Intelligence: Principles and Techniques, Teaching assistant, Fall 2004

JET Program

I spent 2003-2004 teaching English in Japan through the JET Program. I have posted some photos, but there are intentionally few pictures of people (including me) available to the public in this album. Feel free to get in touch with me if you are interested in learning more about the JET Program.

Miscellaneous

• LaTeX poster template. This template makes posters that look like this. Here is what it looks like when used in a real conference poster. Feel free to use and distribute this template. Contact me (e-mail address below) if you have any suggestions or to let me know you have used it and found it helpful. Posters using it have already appeared in multiple machine learning conferences and are now in use my multiple research groups.
• During the 2008-2009 and 2009-2010 academic years, I co-organized the Machine Learning Tea at Berkeley.
  
• A mini-guide for getting set up with Hadoop for machine learning people at Berkeley that I helped put together in 2009. We have provided sample code for how to do logistic regression with Hadoop, though this code is more of an example for how to use Hadoop than code you would actually want to run and has not been updated since 2009.