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Siddharth X Khullar

from San Jose, CA
Age ~39
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Also known as:
Siddharth Khuller, Siddhath Khumar
Phone and address:
500 Golfview Dr, San Jose, CA 95127
Connected to:
Aditi Khullar, 42Ashish Khullar, 49Bhavna Khullar, 38Denesh K Khullar, 46Divya Khullar, 31Manik Khullar, 50Neelam J Khullar, 73Piyush Khullar, 41Pooja Khullar, 34Rajesh Khullar, 48

Siddharth Khullar Phones & Addresses

  • 500 Golfview Dr, San Jose, CA 95127
  • Santa Clara, CA
  • Sunnyvale, CA
  • Malden, MA
  • Redmond, WA
  • Albuquerque, NM
  • Melrose, MA
  • Rochester, NY

Work

Company: Quanttus, inc. Mar 2014 Position: Principal data scientist

Education

School / High School: Chester F. Carlson Center for Imaging Science- Rochester, NY 2009 Specialities: PhD in Imaging Science

Skills

Programming languages: MATLAB • MEX interfacing • C/C++ (OpenCV) • Beginner in Android SDK • iOS SDK. Design/Scripting Too... • MS Visio • Office • LaTeX • Python. Neuroimaging Tools: SPM • AFNI • Group-ICA Toolbox (GIFT) • MRICro. OS: Linux (Ubuntu • CentOS) • Windows • OS-X.

Resumes

Resumes

Siddharth Khullar Photo 1

Siddharth Khullar Redmond, WA

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Work:
Quanttus, Inc.

Mar 2014 to 2000
Principal Data Scientist
Microsoft Research

Jan 2013 to 2000
Post-Doctoral Researcher
Microsoft

Jun 2012 to 2000
Research Intern (MSR)
Mind Research Network for Neurodiagnostic Discovery

Jun 2009 to 2000
Graduate Research Associate, Medical Image Analysis Group
MIT Media Lab, Massachusetts Institute of Technology
Cambridge, MA
Sep 2011 to Jun 2012
Research Assistant, Camera Culture Group
Digital Imaging and Printing Labs, RIT, NY, USA

Mar 2008 to May 2009
Research Assistant
Signal Processing and WiComm Labs at NIEC
Delhi, Delhi
Jan 2006 to Jun 2007
Research Assistant, Multimedia Application Development
Education:
Chester F. Carlson Center for Imaging Science
Rochester, NY
2009 to 2013
PhD in Imaging Science
Rochester Institute of Technology
Rochester, NY
2007 to 2009
M.S. in Electrical Engineering
G.G.S Indraprastha University
New Delhi, Delhi
2003 to 2007
Bachelor of Technology in Electrical Engineering
Skills:
Programming languages: MATLAB, MEX interfacing, C/C++ (OpenCV), Beginner in Android SDK, iOS SDK. Design/Scripting Tools: Adobe Creative Suite3+, MS Visio, Office, LaTeX, Python. Neuroimaging Tools: SPM, AFNI, Group-ICA Toolbox (GIFT), MRICro. OS: Linux (Ubuntu, CentOS), Windows, OS-X.

Publications

Us Patents

Methods And Apparatus For Retinal Imaging

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US Patent:
20130208241, Aug 15, 2013
Filed:
Feb 13, 2013
Appl. No.:
13/766751
Inventors:
Matthew Everett Lawson - Cambridge MA, US
Jason Boggess - Cambridge MA, US
Siddharth Khullar - Albuquerque NM, US
Ramesh Raskar - Cambridge MA, US
International Classification:
A61B 3/14
US Classification:
351206, 351246
Abstract:
In exemplary implementations, this invention comprises apparatus for retinal self-imaging. Visual stimuli help the user self-align his eye with a camera. Bi-ocular coupling induces the test eye to rotate into different positions. As the test eye rotates, a video is captured of different areas of the retina. Computational photography methods process this video into a mosaiced image of a large area of the retina. An LED is pressed against the skin near the eye, to provide indirect, diffuse illumination of the retina. The camera has a wide field of view, and can image part of the retina even when the eye is off-axis (when the eye's pupillary axis and camera's optical axis are not aligned). Alternately, the retina is illuminated directly through the pupil, and different parts of a large lens are used to image different parts of the retina. Alternately, a plenoptic camera is used for retinal imaging.

Food Logging From Images

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US Patent:
20170323174, Nov 9, 2017
Filed:
Apr 17, 2017
Appl. No.:
15/489267
Inventors:
- Redmond WA, US
Siddharth Khullar - Seattle WA, US
T Scott Saponas - Redmond WA, US
Daniel Morris - Bellevue WA, US
Oscar Beijbom - La Jolla CA, US
Assignee:
Microsoft Technology Licensing, LLC - Redmond WA
International Classification:
G06K 9/34
G06F 17/30
G06K 9/00
G06Q 50/12
Abstract:
A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Video-Based Pulse Measurement

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US Patent:
20150302158, Oct 22, 2015
Filed:
Apr 21, 2014
Appl. No.:
14/257671
Inventors:
- Redmond WA, US
Siddharth Khullar - Malden MA, US
Neel Suresh Joshi - Seattle WA, US
Timothy Scott Saponas - Woodinville WA, US
Desney S. Tan - Kirkland WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 19/00
A61B 5/00
A61B 5/024
Abstract:
Aspects of the subject disclosure are directed towards a video-based pulse/heart rate system that may use motion data to reduce or eliminate the effects of motion on pulse detection. Signal quality may be computed from (e.g., transformed) video signal data, such as by providing video signal feature data to a trained classifier that provides a measure of the quality of pulse information in each signal. Based upon the signal quality data, corresponding waveforms may be processed to select one for extracting pulse information therefrom. Heart rate data may be computed from the extracted pulse information, which may be smoothed into a heart rate value for a time window based upon confidence and/or prior heart rate data.

Restaurant-Specific Food Logging From Images

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US Patent:
20150228062, Aug 13, 2015
Filed:
Feb 12, 2014
Appl. No.:
14/179101
Inventors:
- Redmond WA, US
Siddharth Khullar - Seattle WA, US
T Scott Saponas - Redmond WA, US
Daniel Morris - Bellevue WA, US
Oscar Beijbom - La Jolla CA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06T 7/00
G06K 9/34
G06K 9/62
G06K 9/66
G06Q 50/12
G06F 17/30
Abstract:
A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.

Determining Pulse Transit Time Non-Invasively Using Handheld Devices

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US Patent:
20140249398, Sep 4, 2014
Filed:
Mar 4, 2013
Appl. No.:
13/783395
Inventors:
- Redmond WA, US
T. Scott Saponas - Woodinville WA, US
Desney S. Tan - Kirkland WA, US
Morgan Dixon - Seattle WA, US
Siddharth Khullar - Rochester NY, US
Harshvardhan Vathsangam - Los Angeles CA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
A61B 5/021
A61B 5/00
A61B 5/0408
A61B 5/0404
US Classification:
600393, 600480
Abstract:
A system and method to determine pulse transit time using a handheld device. The method includes generating an electrocardiogram (EKG) for a user of the handheld device. Two portions of the user's body are in contact with two contact points of the handheld device. The method also includes de-noising the EKG to identify a start time when a blood pulse leaves a heart of the user. The method further includes de-noising a plurality of video images of the user to identify a pressure wave indicating an arterial site and a time when the pressure wave appears. Additionally, the method includes determining the PTT based on the de-noised EKG and the de-noised video images.
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Siddharth X Khullar from San Jose, CA, age ~39 Get Report