I am a Research Scientist in the Algorithmic Systems Group within the Analog Garage, which is the research incubator of Analog Devices, Inc. My research is about computational imaging and sensing algorithms.
I defended my PhD in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in May, 2023. I was a part of the Computational Imaging Science Lab, advised by Prof. Mark A. Anastasio.
My PhD research was about developing ML-based algorithms for solving ill-posed imaging inverse problems, with applications to medical imaging. I have also worked on compressive microscopy, phase retrieval, physics of optical tomography, statistical optics and objective assessment of image quality.
I completed my undergrad (B.Tech.) in Engineering Physics from Indian Institute of Technology Madras in July 2017, and my MS in ECE from UIUC in August 2019.
I have done research internships at Mitsubishi Electric Research Labs (summer 2022), Algorithmic Systems Group at Analog Garage, Analog Devices Inc. (summer 2019), and LIGO 40m Lab, Caltech (summer 2016). I worked on various signal processing and imaging problems throughout these internships.
|Apr 5, 2023||I gave a talk about our work on evaluating generative models in the 6th Health Data Analytics Workshop hosted at University of Illinois! Recording available here.|
|Jul 14, 2022||I was invited to give a talk on our work on assessing GANs at the AAPM Annual Meeting tutorial session on Assessment of Deep-Learning Technologies in Medical Imaging! Link to the talk here.|
|Jun 5, 2022||I was invited to give a talk and lead a session at the Gordon Research Seminar on Imaging Science for early career researchers.|
|Feb 1, 2022||Our paper on quantifying hallucinations in image reconstruction was covered by several leading newsletters including IEEE Spectrum and within a Forbes article on AI bias. Read the paper here.|
|Mar 22, 2021||I was awarded the Oak Ridge Institute and FDA fellowship to evaluate GANs for medical imaging applications. Read more here.|
- IEEE TMIAssessing the ability of generative adversarial networks to learn canonical medical image statisticsIEEE transactions on medical imaging 2023
- ICMLIn Proceedings of the 38th International Conference on Machine Learning (ICML) 2021
- IEEE TMIIEEE Transactions on Medical Imaging 2021
- SPIE JMIJournal of Medical Imaging 2021
- IEEE TCIIEEE Transactions on Computational Imaging 2021
- Nat. Photon.Nature Photonics 2020