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Eric Fischer

Statistics Ph.D. student with a specialization in natural language processing

University of California, Los Angeles

About Me

I’m a Ph.D. student with a specialization in natural language processing from the Department of Statistics at the University of California, Los Angeles. I perform research on energy-based generative models applied to language problems at the Center for Vision, Cognition, Learning, and Autonomy at UCLA. My advisors are Dr. Ying Nian Wu and Dr. Song-Chun Zhu. Here is my Github.

I earned a Master of Science from the Department of Computer Science at UCLA, submitting a thesis “Deep Generative Classifier with Short Run Inference.” A deep generative classifier uses Short Run Markov Chain Monte Carlo inference, Langevin dynamics, and backpropagation through time to achieve similar classification accuracy as an analogous discriminative classifier, i.e., a convolutional neural network, while it has the advantages that it can generate data, it can learn unsupervised with additional unlabeled data, and it exhibits robustness to adversarial attacks due to the stochasticity of the Langevin equation and the top-down architecture of the underlying generator network.

Before my M.S., I worked as a Full Stack Software Engineer in the San Francisco bay area for two to three years, most recently at NatureBox in Redwood City. Before this, I earned a Bachelor of Arts from the Department of Philosophy at UCLA, focusing my studies on the philosophy of language and propositional and first-order logic.

Education

  • Ph.D. Statistics, Present

    University of California, Los Angeles

  • M.S. Computer Science with Thesis, 2020

    University of California, Los Angeles

  • B.A. Philosophy, 2013

    University of California, Los Angeles

Publications

Deep Generative Classifier with Short Run Inference

Deep generative classifier uses Short Run Markov Chain Monte Carlo inference, Langevin dynamics, and backpropagation through time to …

Learning Multi-Layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

Short-run MCMC residual network outperforms a variational autoencoder in terms of image reconstruction error and image synthesis …

Statistical Models for Marr’s Paradigm

Wrote two chapters and edited several others of “Statistical Models for Marr’s Paradigm,” a textbook authored by my Ph.D. …

Stochastic Grammars for Scene Parsing

Edited several chapters of “Stochastic Grammars for Scene Parsing,” a textbook authored by my Ph.D. advisor Dr. Song-Chun …

Research

Exact Sampling with Coupled Markov Chains and Swendsen-Wang Cluster Sampling of the Ising Model

Convergence analysis of exact sampling with Gibbs sampler and coupled Markov chains vs. cluster sampling with Swendsen-Wang algorithm

Topology Adaptive Snakes Improve Mask Generation for Image Inpainting

T-snake deformable models, which can segment complex-shaped structures, improve generated masks passed to a GAN for image inpainting

Implementation and Convergence Analysis of First-Order Optimization Methods for a CNN

Convergence analysis and Python implementations of SGD, SGD with momentum, SGD with Nesterov momentum, RMSprop, and Adam optimizers

Formulation of Variational Lower Bound and Application of VAE to MNIST Dataset

Formulation of the evidence lower bound (ELBO) for the variational autoencoder and an application to synthesizing binary images

Experience

 
 
 
 
 

Teaching Assistant

University of California, Los Angeles

Mar 2020 – Present Los Angeles, CA
Have served as a Teaching Assistant and Grader for many undergraduate and graduate statistics courses at UCLA
 
 
 
 
 

Graduate Researcher

Center for Vision, Cognition, Learning, and Autonomy

Dec 2018 – Present Los Angeles, CA
Perform research on energy-based generative models applied to language problems
 
 
 
 
 

Full Stack Software Engineer

NatureBox

Mar 2016 – Dec 2017 Redwood City, CA
Core contributor to new Flux/React web application created after company added direct-to-consumer business; Led various projects including a payment processor migration, addition of Amazon payments, and a 2nd version of API
 
 
 
 
 

Software Engineer

Cinemagram

Sep 2015 – Dec 2015 San Francisco, CA
Worked with Python, Ruby, and SQL code to construct internal data management interfaces and tools