Maneesh John

PhD student @ Cornell University
Electrical and Computer Engineering

About

I am a first-year PhD student in ECE at Cornell, working with Yi Wang at Weill Cornell Medicine in New York City. I am broadly interested in machine learning, computer vision, and optimization, as well as their applications in medical imaging.

Currently, I am exploring deep learning algorithms for quantitative susceptibility mapping (QSM). I am also fascinated by generative modeling, particularly diffusion models and energy-based models for image generation. Before joining Cornell in 2024, I received my BSE and MS in ECE from the University of Iowa. I was advised by Mathews Jacob, and my research focused on robust and efficient deep learning algorithms for undersampled MRI reconstruction.

Publications

Local monotone operator learning using non-monotone operators: MnM-MOL

M. John, J. Rikhab Chand, and M. Jacob

IEEE Transactions on Computational Imaging (Apr. 2024)

journal paper pdf code

Local monotone operator learning using non-monotone operators: MnM-MOL

M. John, J. Rikhab Chand, and M. Jacob

IEEE International Symposium on Biomedical Imaging (2024)

conference paper

Memory-efficient and robust model-based deep learning using non-montone monotone operator learning (MnM-MOL)

M. John, J. Rikhab Chand, and M. Jacob

ISMRM Annual Meeting (2024)

conference abstract link

ENSURE: A general approach for unsupervised training of deep image reconstruction algorithms

H. K. Aggarwal, A. Pramanik, M. John, and M. Jacob

IEEE Transactions on Medical Imaging (Nov. 2022)

journal paper pdf