Eli Shlizerman

About

Eli Shlizerman is an Associate Professor in the Department of Applied Mathematics at the University of Washington. He completed his B.Sc. in Mathematics and Computer Science (magna cum laude) in 2002, and earned both an M.Sc. and Ph.D. in Applied Mathematics from the Weizmann Institute of Science in 2005 and 2009, respectively. His research group combines dynamical systems theory with data analysis to develop realistic data-driven dynamical models, focusing on inference of network architecture and modeling the dynamics of networks. His work is at the interface of computational approaches and biological and physical system modeling, with particular emphasis on neurobiological networks underlying insect sensory systems and neural dynamics of simple organisms.

Research topics

• Artificial Intelligence
• Computer Science

Selected publications

• PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 210 citations

  Senior authorCorresponding

  We propose a novel system for unsupervised skeleton-based action recognition. Given inputs of body-keypoints sequences obtained during various movements, our system associates the sequences with actions. Our system is based on an encoder-decoder recurrent neural network, where the encoder learns a separable feature representation within its hidden states formed by training the model to perform the prediction task. We show that according to such unsupervised training, the decoder and the encoder…

  DOI

• Transferable polychromatic optical encoder for neural networks

  Nature Communications · 2025-07-01 · 8 citations

  articleOpen access

  Artificial neural networks have fundamentally transformed the field of computer vision, providing unprecedented performance. However, these neural networks for image processing demand substantial computational resources, often hindering real-time operation. In this work, we demonstrate an optical encoder that can perform convolution simultaneously in three color channels during the image capture, effectively implementing several initial convolutional layers of the network. Such an optical encodi…

  PublisherOA PDFDOI

• CaloChallenge 2022: a community challenge for fast calorimeter simulation

  Reports on Progress in Physics · 2025-10-14 · 6 citations

  articleOpen accessCorresponding

  Abstract We present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and mo…

  PublisherDOI

• Hearing Anywhere in Any Environment

  2025-06-10 · 5 citations

  article

  In mixed reality applications, a realistic acoustic experience in spatial environments is as crucial as the visual experience for achieving true immersion. Despite recent advances in neural approaches for Room Impulse Response (RIR) estimation, most existing methods are limited to the single environment on which they are trained, lacking the ability to generalize to new rooms with different geometries and surface materials. We aim to develop a unified model capable of reconstructing the spatial…

  PublisherDOI

• Brain-to-text decoding with context-aware neural representations and large language models

  Journal of Neural Engineering · 2025-08-12 · 3 citations

  articleOpen accessSenior author

  Abstract Objective . Decoding attempted speech from neural activity offers a promising avenue for restoring communication abilities in individuals with speech impairments. Previous studies have focused on mapping neural activity to text using phonemes as the intermediate target. While successful, decoding neural activity directly to phonemes ignores the context dependent nature of the neural activity-to-phoneme mapping in the brain, leading to suboptimal decoding performance. Approach . In this…

  PublisherDOI

Recent grants

• Inference of Network Dynamics and Architecture in Neural Systems with Data-Driven Methods

  NSF · $879k · 2014–2020

• CRCNS Research Proposal: Collaborative Research: Electrophysiome: comprehensive recording and integrated modeling of the C. elegans nervous system

  NSF · $318k · 2021–2025

Frequent coauthors

• J. Nathan Kutz

  31 shared

• Kun Su

  Xi'an University of Architecture and Technology

  19 shared

• Xiulong Liu

  Tianjin University

  16 shared

• Jinlin Xiang

  11 shared

• Jeffrey A. Riffell

  University of Washington

  11 shared

• Jimin Kim

  University of Washington

  10 shared

• Edwin Ding

  Azusa Pacific University

  7 shared

• Julia A. Santos

  Hospital das Clínicas da Universidade Federal de Minas Gerais

  6 shared

Labs

• Eli Shlizerman LabPI

Education

• B.S., Mathematics and Computer Science

  Weizmann Institute of Science

  2002

• M.S., Applied Mathematics

  Weizmann Institute of Science

  2005

• Ph.D., Applied Mathematics

  Weizmann Institute of Science

  2009

Awards & honors

• NSF Funds A3D3 Institute to Integrate AI into Scientific Res…

Similar researchers at University of Washington

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• Aleksandr Aravkinh-84