I am advised by Prof. Chris Ré and affiliated with Infolab and DAWN groups. I am supported by the Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship.
My research interests revolve around making machine learning easily usable for domain experts who do not have access to the powerful systems and massive datasets required for training complex models. I have recently focused on the bottleneck of gathering high quality training data. My projects include automatically correcting generative models, debugging training sets, and learning model structure efficiently using priciples from static analysis.
My CV is here.
Coral: Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin and Christopher Ré.
In Neural Information Processing Systems (NIPS), 2017
Correcting Misspecified Generative Models using Discriminative Models
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré
A short blogpost
Flipper: A Systematic Approach to Debugging Training Sets
Paroma Varma, Dan Iter, Christopher De Sa and Christopher Ré.
In Workshop on Human-In-the-Loop Data Analytics (HILDA), 2017
Paroma Varma, Rose Yu, Dan Iter, Christopher De Sa, Christopher Ré
In Future of Interactive Learning Machines Workshop (FILM), Neural Information Processing Systems, 2016
Efficient 3D Deconvolution Microscopy with Proximal Algorithms
Paroma Varma, Gordon Wetzstein
In Computational Optical Sensing and Imaging, Imaging and Applied Optics, 2016
Nonlinear Optimization Algorithm for Partially Coherent Phase Retrieval and Source Recovery
Jingshan Zhong, Lei Tian, Paroma Varma, Laura Waller
In IEEE Transactions on Computational Imaging, 2016
Source Shape Estimation in Partially Coherent Phase Imaging with Defocused Intensity
Jingshan Zhong, Paroma Varma, Lei Tian, Laura Waller
In Computational Optical Sensing and Imaging, Imaging and Applied Optics, 2015
Design of a Domed LED Illuminator for High-Angle Computational Illumination
Zachary Phillips, Gautam Gunjala, Paroma Varma, Jingshan Zhong, Laura Waller
In Imaging Systems and Applications, 2015
At UC Berkeley, I was a teaching assistant for the first offering of EE16A: Designing Information Devices and Systems and helped develop course material for the class as well. I was also a teaching assistant for EE20: Structure and Interpretation of Signals and Systems.
Previously, I worked on problems related to computational imaging. As an undergraduate at UC Berkeley, I studied phase retrieval via partial coherence illumination and digital holography in Prof. Laura Waller’s Computational Imaging Lab. I also rotated with Prof. Gordon Wetzstein’s Computational Imaging Group and looked at solving 3D deconvolution problems more efficiently.