Graduate Student (EE)
Graduate Student (EE)
Postdoc (Joint with Prof. Krishna Shenoy)
Graduate Student (CS)
Research Assistant (Statistics)
Graduate Student (CS)
Graduate Student (ICME)
Postdoc (Joint with Prof. Corey Keller)
With Celia Beron, Shay Neufeld, and Bernardo Sabatini. bioRxiv, 2021
We relate logistic regression models, drift diffusion models, and ideal Bayesian observer models of mouse behavior in a two-armed bandit task.
With Xinwei Yu and the Leifer Lab. eLife, 2021
We present an automated method to track and identify neurons in C. elegans using Transformer networks.
With Isabel Low, Alex Williams, Malcolm Campbell, and Lisa Giocomo. Neuron, 2021
MEC representations exhibit remarkable remapping even in the absence of any changes to sensory cues or task demands.
With Libby Zhang, Jesse Marshall, Tim Dunn, and Bence Ölveczky. AISTATS, 2021
We develop GIMBAL, a Bayesian hierarchical model for 3D pose estimation.
With Alex Williams, Anthony Degleris, and Yixin Wang.
We develop Neyman-Scott processes—a type of point process model—for discovering sequences in neural spike trains.
With Josh Glaser, Matt Whiteway, John Cunningham, and Liam Paninski. NeurIPS, 2020
State space models for simultaneous recordings of multiple neural populations.
With Robert Johnson and the Engert Lab. Current Biology, 2020
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 David Zoltowski and Jonathan Pillow. ICML, 2020
We unify and generalize drift diffusion models as recurrent switching linear dynamical systems.
With Ruoxi Sun, Ian Kinsella, and Liam Paninski.
Recurrent SLDS models for smoothing voltage imaging data.
With Ella Batty, Matt Whiteway, Liam Paninski, and many others. NeurIPS, 2019
Combining convoluational autoencoders and autoregressive hidden Markov models for neural and behavioral data.
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.
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 Ryan Adams. ICML 2014
Combining Hawkes processes with generative network models to uncover latent patterns of influence.