DIANA CAI
University of Chicago,
Department of Statistics
Email: dcaiatuchicagodotedu
Research interests: I am broadly interested in statistical modeling, theory, and applications, with an emphasis on scalable, nonparametric learning.
Recent News
 I will be presenting "Edgeexchangeable graphs and sparsity" at NIPS 2016 in Barcelona, Spain. I'll also have work at the workshops on Practical Bayesian Nonparametrics and Adaptive and Scalable Nonparametric Methods in Machine Learning .

I will be visiting and presenting work
at the following institutions this
summer:
 Massachusetts Institute of Technology, Machine Learning Tea seminar: July 11, 2016
 Isaac Newton Institute (INI) workshop, "Bayesian methods for networks": July 2527, 2016
 I am coorganizing the 11th Women in Machine Learning Workshop (WiML 2016), colocated at NIPS 2016 in Barcelona, Spain. Abstract submission deadline August 26, 2016. Registration deadline November 1, 2016.
 I will be attending NIPS 2015 in Montreal; I'm presenting a poster at the Women in Machine Learning (WiML) Workshop, giving a talk and poster at the workshop Bayesian Nonparametrics: the Next Generation, and presenting work in the workshop Networks in the Social and Informational Sciences. Thanks to support from an ISBA@NIPS travel award and a WiML student travel award.
 I will be giving a talk on "Priors on exchangeable directed graphs" at the ISBA 10th Conference on Bayesian Nonparametrics (BNP10), 2015. [[slides]]
Selected Work

Exchangeable trait allocations.
arxiv:1609.09147.
Trevor Campbell, Diana Cai, Tamara Broderick.

Edgeexchangeable graphs and
sparsity.
Diana Cai, Trevor Campbell, Tamara Broderick.
Advances in Neural Information Processing Systems (NIPS), to appear, 2016.

Priors on exchangeable directed graphs.
arxiv:1510.08440.
Diana Cai, Nate Ackerman, Cameron Freer.
Electronic Journal of Statistics (EJS), to appear, 2016.

An iterative stepfunction estimator for graphons.
arXiv:1412.2129.
Diana Cai, Nate Ackerman, Cameron Freer.
About Me
I am a Ph.D. student in Department of Statistics at the University of Chicago. I received an A.B. in computer science and statistics from Harvard University. I was a member of the Harvard Intelligence Probabilistic Systems (HIPS) Group, and was advised by Ryan P. Adams. I have also worked on applying machine learning to natural language processing problems at several startups and am excited about developing new machine learning methodology that can be applied in practice.
Projects
Projects, by topic:
Research Projects
Graphs and networks
Edgeexchangeable graphs, sparsity, and power laws
(with Trevor Campbell and Tamara Broderick).
Advances in
Neural Information Processing
Systems (NIPS), to appear, 2016.
Completely random measures for modeling power laws in sparse graphs.
NIPS workshop on
Networks in the Social and Informational
Sciences, 2015.
[[pdf]]
Edgeexchangeable graphs and
sparsity.
NIPS workshop on
Networks in the Social and Informational
Sciences, 2015.
[[pdf]]
Edgeexchangeable graphs,
sparsity, and power laws.
NIPS Workshop on
Bayesian Nonparametrics: The Next
Generation, 2015.
[[pdf]]
Priors on exchangeable directed graphs
(with Nate Ackerman and Cameron Freer).
Exchangeable directed graphs are characterized
by a sampling procedure given by the
AldousHoover theorem,
determined by specifying a distribution
on measurable objects known as
digraphons.
We present a new Bayesian nonparametric
model for exchangeable directed random
graphs.
Electronic Journal
of Statistics (EJS),
to appear, 2016.
In the NIPS Workshop on
Bayesian Nonparametrics, 2015.
[
arXiv
]
10th Conference on Bayesian Nonparametrics,
2015.
Iterative stepfunction estimation for graphons
(with Nate Ackerman and Cameron Freer).
A method for estimating graphons
(symmetric, measurable functions from which we can sample
exchangeable graphs) by iteratively
forming stepfunctions obtained by grouping
vertices by edge density.
Submitted, 2016.
[ PDF 
arXiv
]
In the Women in Machine Learning Workshop,
2014.
Times series modeling
Efficient Variational Approximations for
Online Bayesian Changepoint
Detection
(with Ryan P. Adams).
We develop a scalable method for online
changepoint detection in latent variable
models using sparse variational
approximations.
[ Github ]
New
England Machine Learning Day
Workshop, 2014.
In the Women in Machine Learning Workshop,
2013.
Other projects
The Ratio Project: Analyzing Online
Recipes
We analyzed online recipes using computational
methods and exploratory visualizations.
The Boston Globe, Dec 2011.
[[link]]
Bakery Mapping Visualization: [[pdf]]
 Slides:
[[pdf]]
(with Elaine Angelino and Michael Brenner)
Cocktails Visualization, Jun 2013.
(with Elaine Angelino, Gabrielle Ehrlich,
Brent Heeringa,
Michael Mitzenmacher,
Naveen Sinha).
Papers
Preprints

Exchangeable trait allocations.
Trevor Campbell, Diana Cai, Tamara Broderick.
arxiv:1609.09147 [math.ST]. 
An iterative stepfunction estimator for
graphons.
Diana Cai, Nate Ackerman, Cameron Freer.
arXiv:1412.2129 [math.ST, stat.ML, stat.CO].
Journal and Conference Papers

Edgeexchangeable graphs and
sparsity.
Diana Cai, Trevor Campbell, Tamara Broderick.
Advances in Neural Information Processing Systems (NIPS), to appear, 2016.

Priors on exchangeable directed graphs.
arxiv:1510.08440.
Diana Cai, Nate Ackerman, Cameron Freer.
Electronic Journal of Statistics (EJS), to appear, 2016.
Workshop Papers

Paintboxes and probability functions for
edgeexchangeable graphs.
Diana Cai, Trevor Campbell, Tamara Broderick.
NIPS Workshop on Adaptive and Scalable Nonparametric Methods in Machine Learning, 2016. 
A paintbox representation for exchangeable
trait allocations.
Trevor Campbell, Diana Cai, Tamara Broderick.
NIPS Workshop on Practical Bayesian Nonparametrics, 2016. 
Priors on
exchangeable directed graphs.
Diana Cai, Nate Ackerman, Cameron Freer.
NIPS Workshop on Bayesian Nonparametrics: The Next Generation, 2015. [[pdf]]
ISBA@NIPS Special Travel Award for Contributed Paper, 2015. 
Completely random measures for modeling
power laws in sparse graphs.
Diana Cai, Tamara Broderick.
In the NIPS workshop on Networks in the Social and Informational Sciences, 2015. [[pdf]]
arxiv:1603.06915 [stat.ML, math.ST, stat.ME].

Edgeexchangeable graphs, sparsity, and
power laws.
Tamara Broderick, Diana Cai.
In the NIPS Workshop on Bayesian Nonparametrics: The Next Generation [[pdf]]
ISBA@NIPS Special Travel Award for Contributed Paper, 2015.
In the NIPS workshop on Networks in the Social and Informational Sciences, 2015. [[pdf]]
arxiv:1603.06898 [math.ST, stat.ME, stat.ML].
Theses

Scalable methods for Bayesian online
changepoint detection.
Advisor: Ryan P. Adams
Senior Thesis, Harvard University, 2014.
Selected Talks

Edgeexchangeable graphs, sparsity, and
power laws.
Invited talk in the Isaac Newton Institute (INI) workshop on Bayesian methods for networks, July 2016. [[video link]] 
Edgeexchangeable graphs, sparsity, and
power laws.
Invited talk at the Massachusetts Institute of Technology, Machine Learning Tea seminar, July 2016. 
Edgeexchangeable graphs, sparsity, and
power laws.
Contributed talk in the NIPS workshop on Bayesian Nonparametrics: the Next Generation, December 2015. [pdf] 
Priors on exchangeable directed graphs.
Contributed talk in The 10th Conference on Bayesian Nonparametrics, June 2015. [pdf] 
Efficient online variational changepoint
detection.
Machine Learning Tea Seminar, Harvard University, Feb 2013.
Poster presentations

Completely random measures for modeling
power laws in sparse graphs.
NIPS workshop on Networks in the Social and Informational Sciences, Dec 2015. 
Edgeexchangeable graphs, sparsity, and
power laws.
NIPS workshop on Bayesian Nonparametrics: The Next Generation , Dec 2015.
NIPS workshop on Networks in the Social and Informational Sciences, Dec 2015. 
Priors on exchangeable directed graphs.
NIPS workshop on Bayesian Nonparametrics: The Next Generation, Dec 2015.
Women in Machine Learning Workshop, Dec 2015. 
An iterative stepfunction estimator for
graphons.
Women in Machine Learning Workshop, Dec 2014.  Efficient variational approximations for online Bayesian changepoint detection. New England Machine Learning Day Workshop, May 2014.
 Efficient variational approximations for online Bayesian changepoint detection. Women in Machine Learning Workshop, Dec 2013.
Teaching
University of Chicago
 STAT 20000: Elementary Statistics. Teaching Assistant: Fall 2016.
 STAT 22000: Statistical Methods and Applications. Teaching Assistant: Winter 2016, Spring 2016
Harvard University
 CS181: Machine Learning. Teaching Fellow, Spring 2014