Arian Azarang | Research

About

Arian Azarang earned his B.S. degree with First Rank Honors from Shiraz University, Iran, in 2015, followed by an M.S. in Electrical Engineering from Tarbiat Modares University, Iran, in 2017. He completed his Ph.D. in Electrical Engineering at the University of Texas at Dallas in 2021, where he received an Honorable Mention for the David Daniel Thesis Award from the Erik Jonsson School of Engineering and Computer Science. From 2021 to 2024, Dr. Azarang served as a Postdoctoral Research Associate at the University of North Carolina at Chapel Hill, earning the Research Excellence Award from Lampe joint Biomedical Engineering Department in 2022. He is now a Research Assistant Professor in the Department of Radiology at UNC Chapel Hill. His research focuses on real-time signal and image processing, development and deployment of deep learning models in biomedical fields, and speech recognition and enhancement. Dr. Azarang has authored or co-authored 24 scholarly publications in these areas. Since 2021, he has also served as an Associate Editor on the Editorial Boards of Journal of Real-Time Image Processing and Signal, Image, and Video Processing.

News

[03/2026] Our +10,000 post-dive Doppler ultrasound audio recordings repository is now live at An Echo from the past.
[02/2026] One abstract was accepted by 2026 Voice AI Symposium.
[02/2026] One abstract was accepted by 2026 Radiology Departmental Research Symposium.
[12/2025] Our paper titled "An echo from the past: open access repository of over 10,000 annotated Doppler audio recordings of venous gas emboli" was accepted to Diving and Hyperbaric Medicine.

Teaching

BMME 575: Practical Machine Learning, Fall 2026
BMME 575: Practical Machine Learning, Fall 2025
BMME 575: Practical Machine Learning, Fall 2024
BMME 575: Practical Machine Learning, Fall 2023
BMME 575 & BMME 890: Practical Machine Learning, Fall 2022
BMME 490: Signals And Systems: From MATLAB To Smartphones (cancelled), Summer 2022

Research

Applied AI for Health

Designing robust machine learning and deep learning tools that address real clinical and healthcare challenges.

Medical Imaging

Developing image-based AI pipelines for ultrasound and other imaging modalities to support guided acquisition, and real-time early screening workflows.

Biomedical Signals

Analyzing physiological and acoustic signals including voice biomarkers to improve detection of health-related conditions.

Awards

2026, Department Teaching Awards, Lampe Joint Deparment of Biomedical Engineering
2025, President's Best Poster Award, Undersea and Hyperbaric Medical Society
2025, Best Poster Award, Bridge2AI Voice Symposium
2023, President's Best Presentation Award, Undersea and Hyperbaric Medical Society
2022, Postdoctoral Research Excellence Award, Lampe Joint Deparment of Biomedical Engineering
2022, Honorable Mention of the David Daniel Thesis Award , The University of Texas at Dallas
2021, Best Teaching Assistant Award, Erik Jonsson School of Engineering at the University of Texas at Dallas

Selected Publications

Featured Works

An Echo from the past: open access repository of over 10,000 annotated Doppler audio recordings of venous gas emboli [Dataset]
Convolutional Autoencoder-Based Multispectral Image Fusion [Article] [Code]
Image Fusion in Remote Sensing: Conventional and Deep Learning Approaches [Book]
Deep Learning-Based Venous Gas Emboli Grade Classification in Doppler Ultrasound Audio Recordings [Article] [Code]
Improving deep speech denoising by noisy2noisy signal mapping [Article]
A Review of Deep Learning-based Contactless Heart Rate Measurement Methods [Editor's Choice] [Article]
Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative [Article]