Kurt Tadayuki Miller

Employment

I am a quantitative researcher at PDT Partners, one of the top quantitative finance firms in New York City. 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.

For five years from 2014-2018, I was on the adjunct faculty at NYU every spring semester helping out with the Machine Learning and Computational Statistics class in the Center for Data Science.

Education

• UC Berkeley
  Ph.D. Computer Science, M.A. Statistics
  Advisor: Michael I. Jordan
• 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

• NYU DS-GA-1003, Machine Learning and Computational Statistics, Adjunct, Spring 2018
• NYU DS-GA-1003, Machine Learning and Computational Statistics, Adjunct, Spring 2017
• NYU DS-GA-1003, Machine Learning and Computational Statistics, Adjunct, Spring 2016
• NYU DS-GA-1003, Machine Learning and Computational Statistics, Adjunct, Spring 2015
• NYU DS-GA-1003/CSCI-GA.2567, Machine Learning and Computational Statistics, Adjunct, Spring 2014
• 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

Miscellaneous

• I tutored high school students from Breakthrough New York (BTNY) from 2011 through 2017 and co-coordinated my work's tutoring program of about a dozen tutors for BTNY students from 2012-2017.
• 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 by multiple research groups.
• During the 2008-2009 and 2009-2010 academic years, I co-organized the Machine Learning Tea at Berkeley.