Welcome
Thank you for visiting my portfolio.
I am a graduate research associate in the School for Engineering of Matter, Transport and Energy at Arizona State University.
With a proven track record of technical contributions, innovation and a demonstrated history of successful project completion,
I bring expertise in technical competencies such as Statistical Analysis, Deep Learning, Image Processing and Numerical Methods.
My previous work has been funded by US-DOT and NASA, with a focus on scene understanding, uncertainty quantification, and safety-critical systems.
Competencies
For more details, hover over the icons.Professional Engagements
Delivered a guest lecture on Bayesian Statistics for a graduate class in Probabilistic Methods for Engineering Analysis.
Vision-Based Decision Support for Improved Situational Awareness in General Aviation
Presented at INFORMS Conference 2023 as an Invited Talk
By Rahul Rathnakumar and Yongming Liu
"Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network" accepted at Reliability Engineering & System Safety
Successfully defended my PhD proposal at Arizona State University.
Delivered a guest lecture on Deep Learning for a graduate class in Probabilistic Methods for Engineering Analysis.
Uncertainty-Aware Defect Detection for In-Line Inspection using a Stereo-Vision system
Presented at PRCI REX 2022 Poster Session
By Rahul Rathnakumar and Yongming Liu
Improving Situational Awareness for General Aviation Operations
Presented at NASA ULI Annual Meeting 2022
By Rahul Rathnakumar and Yongming Liu
A Novel Structured Light-Based Sensing and Probabilistic Diagnostic Technique for Pipe Internal Corrosion Detection and Localization
Presented at PRCI REX 2021 Poster Session
By Mohand Alzuhiri, Rahul Rathnakumar, Yongming Liu and Yiming Deng
Invited to present my current and future research agenda at the PHM Doctoral Symposium.
AI-enabled Interactive Threats Detection using a Multi-camera Stereo Vision System
Presented at the US DOT Annual Meeting 2020
By Rahul Rathnakumar, Yang Yu, Omar Serag, Kailing Liu, Chinmay Dixit, Yongming Liu
Articles
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Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network
Reliability Engineering & System Safety, 2023
Rahul Rathnakumar, Yutian Pang, Yongming Liu
Uncertainty-Aware Decision Support for Pilot Situational Awareness Using On-Board Vision DataSubmitted to Expert Systems with Applications, Preprint on SSRN
Rahul Rathnakumar, Yongming Liu
Defect Segmentation with Limited Labeled Data Using Consistency Regularization and Activation Map InterpolationSubmitted to Engineering Applications of Artificial Intelligence, Preprint on SSRN
Rahul Rathnakumar, Yongming Liu
Bayesian Entropy Neural Networks for Physics-Aware PredictionUnder preparation
Rahul Rathnakumar, Jiayu Huang, Hao Yan, Yongming Liu
Geometry-aware neural network for defect segmentation in gas pipelinesUnder preparation
Rahul Rathnakumar, Gowtham Dakshnamurthy, Abhishek Srinivas, Yongming Liu