b'Ramesh Raskar'

research

∙ 04/21/2022

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging

Cameras were originally designed using physics-based heuristics to captu...

Tzofi Klinghoffer, et al. ∙

research

∙ 04/11/2022

Physically Disentangled Representations

State-of-the-art methods in generative representation learning yield sem...

Tzofi Klinghoffer, et al. ∙

research

∙ 03/29/2022

Towards Learning Neural Representations from Shadows

We present a method that learns neural scene representations from only s...

Kushagra Tiwary, et al. ∙

research

∙ 01/25/2022

The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence Welfare in Social Choice

This work studies a fundamental trade-off between privacy and welfare in...

Ibrahim Suat Evren, et al. ∙

research

∙ 12/11/2021

Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning

In recent years, there have been great advances in the field of decentra...

Shraman Pal, et al. ∙

research

∙ 10/19/2021

Private measurement of nonlinear correlations between data hosted across multiple parties

We introduce a differentially private method to measure nonlinear correl...

Praneeth Vepakomma, et al. ∙

research

∙ 10/09/2021

DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks

We introduce DeepABM, a framework for agent-based modeling that leverage...

Ayush Chopra, et al. ∙

research

∙ 08/19/2021

Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity

Classes of set functions along with a choice of ground set are a bedrock...

Praneeth Vepakomma, et al. ∙

research

∙ 05/21/2021

Automatic calibration of time of flight based non-line-of-sight reconstruction

Time of flight based Non-line-of-sight (NLOS) imaging approaches require...

Subhash Chandra Sadhu, et al. ∙

research

∙ 05/02/2021

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

Wireless channels can be inherently privacy-preserving by distorting the...

Yusuke Koda, et al. ∙

research

∙ 03/01/2021

Safepaths: Vaccine Diary Protocol and Decentralized Vaccine Coordination System using a Privacy Preserving User Centric Experience

In this early draft, we present an end-to-end decentralized protocol for...

Abhishek Singh, et al. ∙

research

∙ 02/22/2021

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

Differential Privacy offers strong guarantees such as immutable privacy ...

Praneeth Vepakomma, et al. ∙

research

∙ 01/20/2021

MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes

In this early draft, we describe a user-centric, card-based system for v...

Joseph Bae, et al. ∙

research

∙ 01/05/2021

COVID-19 Tests Gone Rogue: Privacy, Efficacy,Mismanagement and Misunderstandings

COVID-19 testing, the cornerstone for effective screening and identifica...

Manuel Morales, et al. ∙

research

∙ 01/04/2021

Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

There is a growing need for spatial privacy considerations in the many g...

Rohan Iyer, et al. ∙

research

∙ 12/21/2020

COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

The COVID-19 pandemic has challenged scientists and policy-makers intern...

Rohan Sukumaran, et al. ∙

research

∙ 12/20/2020

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Recent deep learning models have shown remarkable performance in image c...

Abhishek Singh, et al. ∙

research

∙ 12/04/2020

Verifiable Proof of Health using Public Key Cryptography

In the current pandemic, testing continues to be the most important tool...

Abhishek Singh, et al. ∙

research

∙ 12/03/2020

Digital Landscape of COVID-19 Testing: Challenges and Opportunities

The COVID-19 Pandemic has left a devastating trail all over the world, i...

Darshan Gandhi, et al. ∙

research

∙ 09/26/2020

Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

In this work we propose a WiFi colocation methodology for digital contac...

Mikhail Dmitrienko, et al. ∙

research

∙ 09/25/2020

Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems

The adoption of digital contact tracing (DCT) technology during the COVI...

Ananya Gangavarapu, et al. ∙

research

∙ 09/10/2020

Bluetooth based Proximity, Multi-hop Analysis and Bi-directional Trust: Epidemics and More

In this paper, we propose a trust layer on top of Bluetooth and similar ...

Ramesh Raskar, et al. ∙

research

∙ 09/04/2020

Proximity Sensing for Contact Tracing

The TC4TL (Too Close For Too Long) challenge is aimed towards designing ...

Sheshank Shankar, et al. ∙

research

∙ 08/20/2020

NoPeek: Information leakage reduction to share activations in distributed deep learning

For distributed machine learning with sensitive data, we demonstrate how...

Praneeth Vepakomma, et al. ∙

research

∙ 08/15/2020

PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic

Several contact tracing solutions have been proposed and implemented all...

Priyanka Singh, et al. ∙

research

∙ 08/11/2020

Comparing manual contact tracing and digital contact advice

Manual contact tracing is a top-down solution that starts with contact t...

Ramesh Raskar, et al. ∙

research

∙ 08/07/2020

SplitNN-driven Vertical Partitioning

In this work, we introduce SplitNN-driven Vertical Partitioning, a confi...

Iker Ceballos, et al. ∙

research

∙ 07/06/2020

Splintering with distributions: A stochastic decoy scheme for private computation

Performing computations while maintaining privacy is an important proble...

Praneeth Vepakomma, et al. ∙

research

∙ 07/01/2020

Adding Location and Global Context to the Google/Apple Exposure Notification Bluetooth API

Contact tracing requires a strong understanding of the context of a user...

Ramesh Raskar, et al. ∙

research

∙ 07/01/2020

Adding Location and other Context to the Google/Apple Exposure Notification Bluetooth API

Contact tracing requires a strong understanding of the context of a user...

Ramesh Raskar, et al. ∙

research

∙ 06/04/2020

COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers

A number of groups, from governments to non-profits, have quickly acted ...

Ramesh Raskar, et al. ∙

research

∙ 06/02/2020

Automatic Differentiation for All Photons Imaging to See Inside Volumetric Scattering Media

Imaging through dense scattering media - such as biological tissue, fog,...

Tomohiro Maeda, et al. ∙

research

∙ 04/28/2020

Privacy Guidelines for Contact Tracing Applications

Contact tracing is a very powerful method to implement and enforce socia...

Manish Shukla, et al. ∙

research

∙ 04/25/2020

Privacy in Deep Learning: A Survey

The ever-growing advances of deep learning in many areas including visio...

Fatemehsadat Mirshghallah, et al. ∙

research

∙ 03/31/2020

Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic

Governments and researchers around the world are implementing digital co...

Alex Berke, et al. ∙

research

∙ 03/19/2020

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Containment, the key strategy in quickly halting an epidemic, requires r...

Ramesh Raskar, et al. ∙

research

∙ 12/27/2019

Split Learning for collaborative deep learning in healthcare

Shortage of labeled data has been holding the surge of deep learning in ...

Maarten G. Poirot, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

Peter Kairouz, et al. ∙

research

∙ 10/12/2019

Recent Advances in Imaging Around Corners

Seeing around corners, also known as non-line-of-sight (NLOS) imaging is...

Tomohiro Maeda, et al. ∙

research

∙ 10/09/2019

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

Recently, there has been the development of Split Learning, a framework ...

Vivek Sharma, et al. ∙

research

∙ 10/05/2019

ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

In this work we introduce ExpertMatcher, a method for automating deep le...

Vivek Sharma, et al. ∙

research

∙ 09/27/2019

Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest

In this paper we investigate the usage of adversarial perturbations for ...

Indu Ilanchezian, et al. ∙

research

∙ 09/18/2019

Detailed comparison of communication efficiency of split learning and federated learning

We compare communication efficiencies of two compelling distributed mach...

Abhishek Singh, et al. ∙

research

∙ 05/14/2019

Data Markets to support AI for All: Pricing, Valuation and Governance

We discuss a data market technique based on intrinsic (relevance and uni...

Ramesh Raskar, et al. ∙

research

∙ 05/10/2019

On Unlimited Sampling and Reconstruction

Shannon's sampling theorem is one of the cornerstone topics that is well...

Ayush Bhandari, et al. ∙

research

∙ 01/13/2019

Light-Field for RF

Most computer vision systems and computational photography systems are v...

Manikanta Kotaru, et al. ∙

research

∙ 12/08/2018

No Peek: A Survey of private distributed deep learning

We survey distributed deep learning models for training or inference wit...

Praneeth Vepakomma, et al. ∙

research

∙ 12/06/2018

A Review of Homomorphic Encryption Libraries for Secure Computation

In this paper we provide a survey of various libraries for homomorphic e...

Sai Sri Sathya, et al. ∙

research

∙ 12/03/2018

Split learning for health: Distributed deep learning without sharing raw patient data

Can health entities collaboratively train deep learning models without s...

Praneeth Vepakomma, et al. ∙

research

∙ 11/10/2018

Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning

More than half of the world's roads lack adequate street addressing syst...

Ilke Demir, et al. ∙

Ramesh Raskar

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