Abhishek Sharma

About Me

  I am a graduate student at Computer Science Department, University of Maryland, College Park. My research advisor is Prof. David Jacobs. I have done my undergrad in Electrical Engineering from Indian Institute of Technology . In the winters of 2006, I was touched by the miracles of String Theory and fell in love with Maths. My primary interest lies in mathematical modelling and inference from images and textual data. So far, I have worked in Biometrics with a focus on pose and illumination invariant face recognition using latent space modelling. I have also worked on multi-modal correlation filters for infra-red image understanding and fusion. Recently I have been interested in deep learning; inspired by the great success of Deep Convolutional Neural Networks for large scale object recognition. However, unlike the use of deep convnets as a black-box for object classification, I am more interested in the application of CNNs on novel tasks such as image understanding, semantic feature extraction etc..

CV and Google Scholar

Research Internship

  • Mitsubishi Electric Research Lab Cambridge, Dec'13 - Jun'14 with Dr. Oncel Tuzel
  • Microsoft Research Redmond, Summer 2014 with Dr. Trishul Chilimbi and Dr. John C. Platt
  • Toyota Technological Institute Chicago , Summer 2012 with Dr. Racquel Urtasun
  • Defense Research Lab Dehradun, Summer 2009 with Dr. Amit Aran
  • Victorial University Melbourne, Summer 2008 with Dr. Hao Shi
  • Contact Information

    Address - Room 3250, AV Williams
                    Computer Science Department
                    University of Maryland, College Park
    Phone -    240-476-8060
    E-mail -

                    (more frequently used)



    A. Deep Learning

    Recently, deep learning based methods have shown significantly improved performances on both visual and textual understanding tasks. I have pursued the use of deep learning for visual and textual understanding tasks, but instead of using them as black-box classifier I am more interested in exploring them for novel tasks such as semantic scene segmentation, text composition, cross-domain transfer etc..

    B. Latent Space Models for Multi-view learning

    Data often arrives in different forms/views/modalities with similar or complementary information. It's a challenge to retrieve or classify samples in different view using a model trained on some other view or combine complementary information from different views. It is because different views span different feature spaces and there is no natural correspondence betweent the representations that can be utilized for aforementioned tasks. A natural and intuitive way to tackle these problems is to utilize a generative model from a common latent space to the observed samples spaces and pool the information from multiple views in the common latent space. The advantage of a commom latent space is evident for cross-view classification and retrieval in that all different view samples can be first mapped to the common latent space prior to classification and retrieval. I have pursued the idea of latent space representation for multi-view learning problems and came up with some interesting solutions to commonly occuring multi-view problems such as - Pose and lighting invariant face recognition, text-image retrieval etc..

    C. Miscellaneous