Manan has accepted an internship offer from Seagate Technology for Summer 2023 where he will work as an AI/Machine Learning Intern. He will work in Seagate’s highly dynamic Global Wafer Systems (GWS) Team and collaborate with a global team of Data Scientists and Machine Learning Engineers to shape the future of new data products in […]
Author: mananm2@illinois.edu
Paper on Clustered Federated Learning published in IEEE Transactions on Industrial Informatics
Our paper titled “A greedy agglomerative framework for clustered federated learning” has been published in the IEEE Transactions on Industrial Informatics. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) has received widespread attention for supporting the training of deep learning models across multiple IoT devices while preserving data privacy. […]
Paper on Federated Learning-based Defect Detection for Additive Manufacturing published in JMS
Our paper titled “Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) is an emerging machine learning (ML) paradigm which allows several participants (manufacturers) to collaboratively train a model while […]
Paper on adaptive sampling for multi-task Gaussian processes published in JMS
Our paper titled “Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Multi-task learning (MTL) is a machine learning technique used to enhance learning performance in similar-but-not-identical tasks. However, the accuracy of MTL […]