Caleb Tung

Computer Science · Purdue University West Lafayette

Early career · publishing since 2017

Advanced Neural Network Applications CCD and CMOS Imaging Sensors Video Surveillance and Tracking Methods COVID-19 epidemiological studies Industrial Vision Systems and Defect Detection

Publications

Citations

146

Est. group size

Recurring co-author estimate

Active years

Publishing since 2017

Research summary

AI-generated

Caleb Tung's research focuses on making computer vision systems, particularly convolutional neural networks (CNNs, a common type of image-processing AI model), faster and more energy-efficient by identifying and skipping unnecessary image data. Related work has applied network camera footage to study real-world phenomena such as public social-distancing behavior during the COVID-19 pandemic. The work combines efficient AI model design with practical applications in surveillance and monitoring.

Efficient deep learning / CNN optimizationComputer vision and image processingCamera-based monitoring and surveillanceEnergy-efficient AI inferenceApplied AI for public health observation

Publication output rose from a low, steady rate before 2020 to a peak around 2020-2022, then has slowed somewhat in 2023-2024.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence

Publications per year over the last 10 years — averaging 2.4/year recently

Recent publications

An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
2024
MOT Challenge
TIB Data Manager · 2024
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
arXiv (Cornell University) · 2023
Restructurable Activation Networks
arXiv (Cornell University) · 2022
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision
arXiv (Cornell University) · 2022
Using Network Cameras to Observe COVID-19 Social Distancing
Loyola eCommons (Loyola University of Chicago) · 2020
Observing Responses to the COVID-19 Pandemic using Worldwide Network\n Cameras
arXiv (Cornell University) · 2020

Publishes in

arXiv (Cornell University)×9
ACM Transactions on Design Automation of Electronic Systems×1
IEEE Design and Test×1
IEEE Multimedia×1
Computer×1

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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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