Siddharth Khullar

Siddharth Khullar

Stockholm, Stockholms län, Sverige
6 tn följare Fler än 500 kontakter

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building the first AI-embedded OS for Complex Manufacturing

Researcher / Product…

Aktivitet

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Erfarenhet

  • Aris Machina-bild

    Aris Machina

    Stockholm, Stockholm County, Sweden

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    Stockholm, Stockholm County, Sweden

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    Stockholm, Stockholm County, Sweden

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    San Francisco Bay Area

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    Cupertino, California

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    Cupertino, California

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    Cupertino, California

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    Cupertino, California

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    Cambridge, MA

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    Redmond, WA

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    Albuquerque, New Mexico Area

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    Greater Seattle Area

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    Cambridge, MA

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Utbildning

  • Rochester Institute of Technology-bild

    Rochester Institute of Technology

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    Aktiviteter och föreningar:Graduate Speaker, Academic Convocation, 2013 Graduate Delegate (College of Science), College Commencement, 2013

    - Developed experiments to understand neural processes behind human vision and how they can be utilized to build Human-Computer Interaction based medical devices.
    - Working towards development of novel image analysis methods applied to Functional Brain Imaging modalities (mainly fMRI).

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Publikationer

  • Enhanced disease characterization through multi network functional normalization in fMRI.

    Frontiers in neuroscience

    Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not…

    Conventionally, structural topology is used for spatial normalization during the pre-processing of fMRI. The co-existence of multiple intrinsic networks which can be detected in the resting brain are well-studied. Also, these networks exhibit temporal and spatial modulation during cognitive task vs. rest which shows the existence of common spatial excitation patterns between these identified networks. Previous work (Khullar et al., 2011) has shown that structural and functional data may not have direct one-to-one correspondence and functional activation patterns in a well-defined structural region can vary across subjects even for a well-defined functional task. The results of this study and the existence of the neural activity patterns in multiple networks motivates us to investigate multiple resting-state networks as a single fusion template for functional normalization for multi groups of subjects. We extend the previous approach (Khullar et al., 2011) by co-registering multi group of subjects (healthy control and schizophrenia patients) and by utilizing multiple resting-state networks (instead of just one) as a single fusion template for functional normalization. In this paper we describe the initial steps toward using multiple resting-state networks as a single fusion template for functional normalization. A simple wavelet-based image fusion approach is presented in order to evaluate the feasibility of combining multiple functional networks. Our results showed improvements in both the significance of group statistics (healthy control and schizophrenia patients) and the spatial extent of activation when a multiple resting-state network applied as a single fusion template for functional normalization after the conventional structural normalization. Also, our results provided evidence that the improvement in significance of group statistics lead to better accuracy results for classification of healthy controls and schizophrenia patients.

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  • The influence of visuospatial attention on unattended auditory 40 Hz responses

    Frontiers in human neuroscience

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  • Computational Retinal Imaging via Binocular Coupling and Indirect Illumination

    ACM SIGGRAPH

    We present an interactive device that enables end-users to capture and visualize images of their own retina. Our portable solution simplifies constraints on traditional devices that need to provide precise alignment of the camera optics with the human eye.

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  • ICA-fNORM: Spatial Normalization of fMRI data using Intrinsic Group ICA-networks

    Frontiers in Systems Neuroscience

    A novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual's brain is organized functionally.

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  • Wavelet-based fMRI analysis: 3-D denoising, signal separation and validation metrics

    NeuroImage

    A novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain.

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  • Wavelet-based denoising and independent component analysis for improving multi-group inference in fMRI data

    IEEE International Symposium on Biomedical Imaging

    Invited Paper, Student Paper Award Nominee (Diagnostic Imaging).

    A 3-D framework for wavelet based fMRI analysis that includes denoising and signal separation followed by a detailed illustration of the benefits and improvements when applied to multi-group
    (healthy/patient) fMRI studies.

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  • A new metric to measure shape differences in fMRI activity

    SPIE – Medical Imaging

    A novel shape metric for quantification of shape differences between the
    spatial components obtained from independent component analysis (ICA) of group
    functional magnetic resonance imaging (fMRI) data.

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  • Improved 3D wavelet-based denoising of fMRI data

    SPIE – Medical Imaging

    A novel wavelet-based 3-D technique to remove noise in fMRI data while preserving the spatial features in the component maps obtained through group independent component analysis (ICA).

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  • Automatic Multi-resolution Spatio-Frequency Analysis for Evaluation of Print Mottle

    SPIE CIC

    Algorithm that uses a scanned image to quantify the low frequency variation or mottle in what is supposed to be a uniform field. ‘Banding’ and ‘Streaking’ effects are explicitly ignored and the proposed algorithm scales the test targets from “Flat print” (Good) to “Noisy print” (Bad) based on Mottle only. The evaluation procedure is modeled as feature computation in different combinations of spatial, frequency and wavelet domains.

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