Bio

I am a postdoc in the SLI lab with John Fisher III at MIT CSAIL. My research interests are in machine learning and graphical models. My work aims to create more flexible variational inference that can be applied to many complex applications involving spatio-temporal processes and physical models. My application interest span domains such as computer vision, computational biology, and signal processing. Prior to joining MIT I completed my PhD with Erik Sudderth at Brown University. For more details see my CV.

Publications

Variational Approximations with Diverse Applications
Ph.D. Thesis, Dept. of Computer Science, Brown University, Apr. 2016
( paper )

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
J. Pacheco and E. B. Sudderth
International Conference on Machine Learning (ICML), Jul. 2015
( paper )   ( talk slides )   ( talk video )   ( code )

Preserving Modes and Messages via Diverse Particle Selection
J. Pacheco, S. Zuffi, M. J. Black and E. B. Sudderth
International Conference on Machine Learning (ICML), Jun. 2014
( paper )   (supplement )   ( talk slides )   ( talk video )

Minimization of continuous Bethe approximations: A positive variation
J. Pacheco and E. B. Sudderth
Advances in Neural Information Processing Systems (NIPS), Dec. 2012
( paper )   ( supplement )

Improved variational inference for tracking in clutter
J. Pacheco and E. Sudderth
IEEE Statistical Signal Processing, Aug. 2012
( paper )

Max-product particle Belief Propagation
R. Kothapa, J. Pacheco and E. Sudderth
Technical Report, Brown University, May. 2011
( paper )

Temporal decomposition for online multitarget multisensor tracking
J. Pacheco and M. Sellmann
Technical Report, Brown University, May. 2008
( paper )

Invited Talks

Diverse Particle Selection for Inference in Continuous Graphical Models   ( talk slides )
Virginia Tech, Feb. 2016;   MIT, June. 2016

Diverse Particle Max-Product: Multi-extremal Optimization for Continuous Graphical Models
Division of Applied Mathematics, Brown University, Apr. 2014

Graphical Models, Variational Inference, and Message Passing
Naval Undersea Warfare Center, Division Newport RI, Feb. 2012

Expectation Propagation for target tracking in clutter
Brown University Dept. of Comp. Sci., Feb. 2011







Contact Information

Email: 
  32 Vassar Street
  32-D475A
  Cambridge, MA 02139