E-mail: email@example.com CV
With Robert Johnson and the Engert Lab. bioRxiv, 2019
Rob collected a huge dataset of larval zebrafish behavior in natural environments. We developed and fit point process models to see how past behavior and environmental input shape action selection.
With Annika Nichols, David Blei, Manuel Zimmer, and Liam Paninski. bioRxiv, 2019
We develop hierarchical and recurrent state space models for whole brain recordings of neural activity in C. elegans. We find states of brain activity that correspond to discrete elements of worm behavior and dynamics that are modulated by brain state and sensory input.
With Josue Nassar, Monica Bugallo, and Il Memming Park. ICLR, 2019
We develop an extension of the rSLDS to capture hierarchical, multi-scale structure in dynamics via a tree-structured stick-breaking model. We recursively partition the latent space to obtain a piecewise linear approximation of nonlinear dynamics. A hierarchical prior smooths dynamics estimates, and inference is performed via an augmented Gibbs sampling algorithm.
With Anuj Sharma, Robert Johnson, and Florian Engert. NeurIPS 2018
We develop deep state space models with point process observation models to capture structure in larval zebrafish behavior. The models combine discrete and continuous latent variables. We marginalize the discrete states with message passing and perform inference with bidirectional LSTM recognition networks.
With Christian Naesseth, Rajesh Ranganath, and David Blei. AISTATS 2018
We view SMC as a variational family indexed by the parameters of its proposal distribution and show how this generalizes the importance weighted autoencoder. As the number of particles goes to infinity, the variational approximation approaches the true posterior.
With Gonzalo Mena, Hal Cooper, Liam Paninski, and John Cunningham. AISTATS 2018
How to perform gradient-based variational inference over permutations and matchings via a continuous relaxation to the Birkhoff polytope.
With Sam Gershman. Current Opinion in Neurobiology, 2017
Top-down and bottom-up methods are joined in a theory-driven analysis pipeline. We view theories as priors for statistical models, perform Bayesian inference, criticize, and revise.
With Christian Naesseth, Fran Ruiz, and David Blei.
Best Paper Award
Reparameterization gradients through rejection samplers for automatic variational inference in models with gamma, beta, and Dirichlet latent variables.
With Matt Johnson, Andy Miller, Ryan Adams, David Blei, and Liam Paninski. AISTATS, 2017
Bayesian learning and inference for models with co-evolving discrete and continuous latent states.
2016 Leonard J. Savage Award
My dissertation work at Harvard University on networks, point processes, and state space models for neural data analysis.
With Ryan Adams and Jonathan Pillow. NIPS 2016
We combine network priors, nonlinear autoregressive models, and Pólya-gamma augmentation to reveal latent types and features of neurons using spike trains alone.
With Matt Johnson and Ryan Adams. NIPS 2015
We use a stick-breaking construction and Pólya-gamma augmentation to derive block Gibbs samplers for linear Gaussian models with multinomial observations.
With Chris Stock and Ryan Adams. NIPS 2014
We propose a time-varying generalized linear model whose weights evolve according to synaptic plasticity rules, and we perform Bayesian inference with particle MCMC.
With Ryan Adams. ICML 2014
Combining Hawkes processes with generative network models to uncover latent patterns of influence.