Paroma Varma

Ph. D. Student - Electrical Engineering - Stanford University

paroma [at] stanford [dot] edu

I am a third year Ph.D. student advised by Prof. Chris Ré and affiliated with the DAWN and Infolab groups. I am supported by the Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship.

My research interests revolve making machine learning easily usable for domain experts who do not have access to the massive datasets required for training complex models. I am also interested in exploring the intersection between machine learning and programming languages to improve how models are supervised and reduce user effort in training these models.

My CV is here.


Coral: Enriching Statistical Models with Static Analysis
NIPS 2017, NIPS ML4H 2017, MED-NIPS 2017

We introduce a weak supervision framework to efficiently label image and video training data given a small set of user-defined heuristics. We identify correlations among heuristics using static analysis and incorporate this information into a generative model that can optimally assign probabilistic labels to training data. We apply this method to video querying and medical image classification tasks, outperforming fully supervised models in some cases. [pdf] [blogpost] [video]

Socratic Learning: Finding Latent Subsets in Training Data
HILDA 2017, NIPS FILM 2016

We explore how we can find latent subsets in training data that affect the behavior of weak supervision sources. We automatically identify these subsets using disagreements between the discriminative and generative models and correct misspecified generative models accordingly. We improve upon existing relation extraction and sentiment analysis tasks and make these latent subsets interpretable for users. [pdf] [workshop] [blogpost] [video]

Babble Labble: Learning from Natural Language Explanations

Braden Hancock and I explore how we can use natural language explanations for why crowd workers provide the labels they do to label training data more efficiently. We automatically parse these explanations into executable functions and apply them to large amounts of unlabeled data. We find that collecting explanations allows us to build high quality training sets much faster than collecting labels alone. [pdf soon!] [blogpost] [video]

In the Past

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.


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.



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

Automated Training Set Generation for Aortic Valve Classification
Vincent Chen, Paroma Varma, Madalina Fiterau, James Priest and Christopher Ré.
In Machine Learning for Health (ML4H), Neural Information Processing Systems (NIPS), 2017

Generating Training Labels for Cardiac Phase-Contrast MRI Images
Vincent Chen, Paroma Varma, Madalina Fiterau, James Priest and Christopher Ré.
In Medical Imaging meets NIPS (MED-NIPS), 2017

Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré

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


Socratic Learning
Paroma Varma, Rose Yu, Dan Iter, Christopher De Sa, Christopher Ré
In Future of Interactive Learning Machines Workshop (FILM), Neural Information Processing Systems (NIPS), 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