Nalini Singh

MIT HST/EECS Student

Nalini Singh is a researcher in the field of computer science and medical imaging, with a focus on applying machine learning and computer vision techniques to inverse problems in imaging. Here are some key details about her background and accomplishments:

Education and Research

Nalini Singh completed her PhD at the Massachusetts Institute of Technology (MIT), where she was advised by Professor Polina Golland.1 2 Her doctoral research focused on physics-inspired deep learning for inverse problems in MRI.2 Prior to her PhD, she earned a Bachelor's degree in Electrical Engineering and Computer Science from MIT.2

Fellowships and Awards

During her graduate studies, Singh received several prestigious fellowships and awards:

Google PhD Fellowship for Health research1 4
NSF Graduate Research Fellowship2 5
NIH Neuroimaging Training Program fellowship5

She was also recognized with an outstanding reviewer award at CVPR and ICLR conferences.2

Research Focus

Singh's research interests include:

Medical imaging
Machine learning
Inverse problems
Computer vision
Signal processing
Magnetic resonance imaging (MRI)1 3

Her work often combines physics-based models with deep learning approaches to improve medical imaging techniques, particularly in MRI reconstruction and motion correction.2 5

Current Position

As of October 2024, Nalini Singh is a postdoctoral researcher at the University of California, Berkeley, working with Laura Waller in the Visionary Optical Imaging Lab (VOILA!).5 6 Her current research continues to focus on applications of machine learning and computer vision to inverse problems in imaging.5

Publications and Projects

Singh has authored several influential papers in her field, including work on:

Deep learning for MRI reconstruction and motion correction
Joint frequency and image space learning for MRI
Applications of machine learning in neuroimaging3 5

Her research has been published in prestigious venues such as Nature Medicine and presented at conferences like Medical Imaging with Deep Learning.3 5

Nalini Singh's work bridges the gap between theoretical computer science and practical medical imaging applications, contributing to advancements in healthcare technology and diagnostic capabilities.