Jason K. Eshraghian

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Assistant Professor

University of California, Santa Cruz

United States

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About

Jason K. Eshraghian is an Assistant Professor at the University of California, Santa Cruz. His research focuses on deep learning applications in cardiovascular disease classification, neuromorphic computing, and side-channel attack analysis. His recent publications include works on ECG feature extraction, neuron latency coding, and machine learning approaches for optimizing diagnostic strategies in coronary artery disease. Professor Eshraghian is dedicated to advancing technology and methodologies in the fields of health informatics and artificial intelligence.

Recent Grants

Grant: Close

Neuromorphic RRAM Integration

Open Date: 2019-08-01

Close Date: 2020-03-01

Grant: Close

RRAM-CMOS Integration for High-Speed Image Classification

Open Date: 2019-08-01

Close Date: 2020-03-01

Grant: Close

Turning Sight into Sound: Enabling the Visually Impaired to See with a Multi-Sensory Headset

Open Date: 2019-01-01

Close Date: 2020-01-01

Grant: Close

Memristive Implementation into an Artificial Vertebrate Retina for Sensor Systems

Open Date: 2016-07-01

Close Date: 2019-07-01

Articles (20)

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Collaborators (11)

Frank Sanfilippo

University of Western Australia

AUSTRALIA

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Max Ward

University of Western Australia

AUSTRALIA

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Tara Hamilton

University of Sydney

AUSTRALIA

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Wei Lu

Professor

University of Michigan

UNITED STATES

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Robert G. Elliman

University

AUSTRALIA

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Jonathon Stewart

University of Western Australia

AUSTRALIA

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Abdul Rahman Ihdayhid

University in Western Australia

AUSTRALIA

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Mohammed Bennamoun

University of Western Australia

AUSTRALIA

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Adrian Goudie

University in Western Australia

AUSTRALIA

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Nhan Truong

University of Sydney

AUSTRALIA

View details

Omid Kavehei

Associate Professor

University of Sydney

AUSTRALIA

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Jason K. Eshraghian

Research Interests

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Contact this professor

About

Recent Grants

Articles (20)

Neuromorphic deep spiking neural networks for seizure detection

Deep learning model to predict exercise stress test results

Side-Channel Attack Analysis on In-Memory Computing Architectures

Neuromorphic cytometry: implementation on cell counting and size estimation

A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction

BPLC + NOSO: backpropagation of errors based on latency code with neurons that only spike once at most

Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment

Guest Editorial Dynamical Neuro-AI Learning Systems: Devices, Circuits, Architecture and Algorithms

Modeling and hardware implementation of universal interface-based floating fractional-order mem-elements

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators

Spiking Neuron Implementation Using a Novel Floating Memcapacitor Emulator

FPGA Synthesis of Ternary Memristor-CMOS Decoders for Active Matrix Microdisplays

A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification

Low-Variance Memristor-Based Multi-Level Ternary Combinational Logic

How to Build a Memristive Integrate-and-Fire Model for Spiking Neuronal Signal Generation

A Leaky-Integrate-and-Fire Neuron Model of Spontaneous Reset of Thin-Film Metal-Oxide Resistive Switches.

Quantized Convolutional Neural Network Implementation on a Parallel-Connected Memristor Crossbar Array for Edge AI Platforms.

A Behavioral SPICE Model of a Binarized Memristor for Digital Logic Implementation

A Reconfigurable SRAM based CMOS PUF with Challenge to Response Pairs

High-Density Memristor-CMOS Ternary Logic Family

Collaborators (11)

Frank Sanfilippo

Max Ward

Tara Hamilton

Wei Lu

Robert G. Elliman

Jonathon Stewart

Abdul Rahman Ihdayhid

Mohammed Bennamoun

Adrian Goudie

Nhan Truong

Omid Kavehei

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