Ayush Chopra

Camera Culture
  • Research Assistant

Current: I am a second-year graduate student advised by Prof Ramesh Raskar.  My motivation is to  help reimagine decision making for large populations in our data-driven but privacy-sensitive world! To achieve this, I want to invent tools that allow us to realistically simulate large heterogenous populations by privately sampling data from the real-world. My research intersects (multi-agent, inverse) simulations, (distributed) machine learning and (private) imaging, with applications in health and biology. My resume is here.

Past: Prior to MIT, I was a researcher at Adobe where I focused on enabling decentralized commerce through interactive & personalized retail experiences in the browser. I was also the youngest recipient of the Adobe Outstanding Young Engineer Award [My talk at Adobe Marketing Summit 2020].  I have been a technical (AI) advisor at RemoteHQ, which is pioneering the transition to decentralized work through video platforms for distributed SaaS teams to collaborate productively [RemoteHQ voted #1 on ProductHunt]

Output : … View full description

Current: I am a second-year graduate student advised by Prof Ramesh Raskar.  My motivation is to  help reimagine decision making for large populations in our data-driven but privacy-sensitive world! To achieve this, I want to invent tools that allow us to realistically simulate large heterogenous populations by privately sampling data from the real-world. My research intersects (multi-agent, inverse) simulations, (distributed) machine learning and (private) imaging, with applications in health and biology. My resume is here.

Past: Prior to MIT, I was a researcher at Adobe where I focused on enabling decentralized commerce through interactive & personalized retail experiences in the browser. I was also the youngest recipient of the Adobe Outstanding Young Engineer Award [My talk at Adobe Marketing Summit 2020].  I have been a technical (AI) advisor at RemoteHQ, which is pioneering the transition to decentralized work through video platforms for distributed SaaS teams to collaborate productively [RemoteHQ voted #1 on ProductHunt]

Output : My research has been published (and received best paper awards) at several top-tier AI conferences/ interdisciplinary journals and has resulted in 25 patents filed across 5 countries. My projects have been covered by various digital media platforms including Tech Crunch, Reuters, Venture Beat, Weather Channel, Ad-Week, Women's Wear Daily and reproduced by national media outlets in over 15 countries (and 6 languages).

Collab: We have a lot exciting research and development projects going on! If you would like to know more or collaborate, please reach out to me at [firstname + c] [at] [mit.edu]

Recent News:  

i) Agent-based Modeling

DeepABM accepted at WSC 2021 as Oral! Making large-scale agent-based epi simulations  fast and differentiable.  Potential for data-driven public policy.

Clinical simulations paper (collab with Mayo) published at The BMJ 2021. Study public health impact of delaying 2nd dose of mRNA-based vaccine through simulations.

ii) Private Imaging and Distributed Machine Learning

-CBNS (preprint) mechanisms for conditionally private sampling of 3D point clouds to protect user privacy while preserving perception utility (eg: protect user privacy while: the roomba navigates obstacles when cleaning your house or you use face-id to login to your iPhone; etc). Potential for deploying ML systems in privacy-sensitive environments.

- AdaSplit (preprint) cross-platform distributed ML infra that can adapt to variable resource budgets. Jointly learn from  the diverse smart-devices (phone, watch, home-assistant, smart-car etc.) deployed  in your house, and across diverse parts of the world -- all while protecting privacy. Potential for deploying ML systems in low-resource and privacy-sensitive environments.

DISCO accepted at CVPR 2021. Conditionally private inference in deep neural networks. Potential for deploying ML systems in privacy-sensitive environments.

iii) In-browser commerce: search and synthesis

-SAC accepted at WACV 2022.  Editing visual representations with text feedback. Potential for enabling the last mile of product search.

ZFlow accepted at ICCV 2021. Data-driven estimation of appearance flow field for transforming deformable objects. Potential for photorealistic virtual try-on in the browser! (with a single RGB image)