I have been fortunate to participate in proposal writing and receive valuable training from my advisor. As a result, I have had the opportunity to draft several proposals, which I would like to share with you.
Machine Learning Enabled Data Fusion for Anomaly Detection
Agency/Company: Apple Inc.
Total Dollar Amount: $300,000
Period of Contract: 08/2022 – 08/2024
Status: funded
This proposal is an extension of my internship work at Apple Company in the summer, 2021, with the aim of addressing challenges in process monitoring and quality inspection. I assisted Professor Shi in writing the proposal, which included research problem identification and formulation, methodology development, and proposal composition. Initially, this proposal was for one year and was extended for an additional year due to our excellent work. I am the Ph.D. student working on this funded project also.
Anomaly Detection in Advanced Manufacturing Processes in A Data-rich but Label-rare Environment
Agency/Company: IDEaS and the School of Computer Science, Gatech
Total Dollar Amount: $66440 of Azure computing credits
Period of Contract: 11/2021 – 06/2024
Status: funded
I assisted Professor Shi in writing a proposal for the IDEaS program. Initially, this proposal was intended for one year and was later extended for an additional year due to our excellent work.
Robust Generative Inversion as Multipurpose Metrology and Inspection Tool in Semiconductor Manufacturing Process
Agency/Company: Samsung Company
Total Dollar Amount: $150k
Period of Contract: 01/01/2024-12/31/2024
Status: pending
This project is focused on advancing next-generation AI techniques for semiconductor metrology. The proposal is mainly based on our new publication, which addresses the adoption and further refinement of the Robust GAN inversion method. I assisted Professor Shi in developing this proposal and made contributions in research problem identification, formulation, methodology development, and proposal composition.
Scalable Standard Testing and Analysis Protocol with Advanced Data Analytics/Machine Learning
Agency/Company: NSF IUCRC
Total Dollar Amount: $180k
Period of Contract: 4/2022 – 3/2025
Status: funded
This project aims to develop a scalable standard testing and analysis protocol for characterizing the quality of composite material bonding processes and optimizing their design. I contributed by assisting with problem formulation and engaging in discussions on the idea. My key contribution to the proposal/project is proposing physics-informed machine learning methods in composite bonding testing, which has great potential to obtain a system model with less experimental testing efforts.