Ramesh Raskar

Cambridge, MA, United States of America

Ramesh Raskar

This inventor holds 133 USPTO granted patents and 5 published patent applications, plus 2 CIPO patents and 11 EPO patents, primarily in Computer Vision (CPC class G01S17-89). Top assignees: Massachusetts Institute of Technology, Mitsubishi Electric Research Laboratories, Inc., Meta Platforms, Inc.. Active years: 2003-2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Ramesh Raskar. Retrieved from https://idiyas.com/inventor/ramesh-raskar

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 133 

% Patents Active = 53.4


 

Average Co-Inventor Count = 2.7

ph-index = 32

Forward Citations = 2,752(Granted Patents)

Forward Citations (Not Self Cited) = 2,636(Dec 10, 2025)


Inventors with similar research interests:


Location History:

  • Somerville, MA (US) (2003 - 2006)
  • Palo Alto, CA (US) (2018 - 2023)
  • Cambridge, MA (US) (2004 - 2024)

Company Filing History:

goldMedal54 out of 8,586 
 
Massachusetts Institute of Technology
 patents
silverMedal49 out of 1,698 
 
Mitsubishi Electric Research Laboratories, Inc.
 patents
bronzeMedal12 out of 9,904 
 
Meta Platforms, Inc.
 patents
4. 5 out of 142 
 
Intrinsic Innovation LLC
 patents
5. 3 out of 6,530 
 
Nokia Technologies Oy
 patents
6. 2 out of 52 
 
Mitsubishi Electric Research Labs.
 patents
7. 1 out of 5 
 
Boston Polarimetrics, Inc.
 patents
8. 1 out of 32 
 
Brelyon, Inc.
 patents
9. 1 out of 2 
 
Eyenetra, Inc.
 patents
10. 1 out of 2,193 
 
Georgia Tech Research Corporation
 patents
11. 1 out of 1,439 
 
Michigan State University
 patents
12. 1 out of 1 
 
Mitsubishi Eletric Research Labs, Inc.
 patent
13. 1 out of 8,283 
 
Nokia Corporation
 patents
14. 1 out of 132,108 
 
Samsung Electronics Co., Ltd.
 patents
15. 1 out of 3,073 
 
The General Hospital Corporation
 patents
16. 1 out of 16,025 
 
University of California
 patents
17. 2 out of 834,513 
Other
 patents
where one patent can have more than one assignee

Years Active: 2003-2025

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Areas of Expertise:
Transparent Object Segmentation
Polarization Cues
Deep Learning
Imaging Systems
3D Shape Reconstruction
Light Field Display
Retinal Imaging
Fluorescence Imaging
Near-Eye Display
Stress-Adaptive Virtual Experience
Gigahertz Time-of-Flight Imaging
Haptic Messaging
133 patents (USPTO):Explore Patents

Title: Ramesh Raskar: Innovating Privacy-Preserving Neural Networks and Advanced Imaging Techniques

Introduction:

Ramesh Raskar, an accomplished Associate Professor at the prestigious Massachusetts Institute of Technology (MIT) based in Cambridge, Massachusetts, is a leading figure in the field of innovations and patents. With numerous patents to his name, Raskar has made significant contributions to the development of secure training of multi-party deep neural networks and advanced imaging techniques. Let's delve into his latest patents, career highlights, collaborations, and his overall impact on the field of technology and innovation.

Latest Patents:

Raskar's latest patents showcase his groundbreaking work in two pivotal areas: secure training of multi-party deep neural networks and improved imaging through scattering media.

1. Secure training of multi-party deep neural network:

This patent focuses on preserving data privacy while training deep neural networks involving multiple entities, Alices and Bobs. By employing a "split" neural network, the approach ensures that Bob, the outside computing entity, can assist in computations without accessing Alices' data directly. The network consists of an Alice part, comprising at least three neural layers, and a Bob part, comprising at least two neural layers. This novel architecture allows efficient collaboration while safeguarding data privacy.

2. Methods and apparatus for improved imaging through scattering media:

Raskar's second patent addresses the challenge of imaging scenes obscured by fog or scattering media. The patent proposes using a time-resolved light sensor, such as avalanche photodiodes, to collect reflected light. By leveraging a probabilistic algorithm that accounts for the different distribution of photon arrival times between the fog and the target, the imaging system obtains both reflectance and depth information of the obscured scene. Additionally, the system employs techniques like spatial and temporal regularization, as well as deblurring, to enhance the quality of the captured images.

Career Highlights:

Throughout his illustrious career, Raskar has held key positions at renowned institutions and companies. Notable highlights include his work as an Associate Professor at MIT and a research scientist at Mitsubishi Electric Research Laboratories, Inc. His expertise and contributions have not only facilitated groundbreaking research but also stimulated the progress of technology at large.

Collaborations:

Raskar's impact extends beyond his individual achievements, as he has collaborated with talented individuals in his field. Amit Agrawal and Achuta Kadambi are just a couple of his notable coworkers who have contributed significantly to their shared research endeavors. By fostering collaboration and knowledge exchange, Raskar has advanced the collective knowledge of the innovation community.

Conclusion:

Ramesh Raskar's exceptional work in secure training of multi-party deep neural networks and imaging through scattering media has propelled the field of technology forward. His patents reflect groundbreaking solutions to critical challenges, ensuring privacy and unlocking valuable insights in diverse domains. Raskar's career highlights and collaborations demonstrate his commitment to advancing technology and inspiring future generations of inventors. As an Associate Professor at MIT and a leading innovator, Ramesh Raskar continues to shape the world of innovations and patents with his remarkable contributions.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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