Sheikh Shams Azam bio photo

Sheikh Shams Azam

M.S. Student, Purdue ECE

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August 2019 - Present Purdue University
Graduate Research Assistant

Professional Experience

June 2021 - August 2021 Zillow Group, Seattle, WA
Applied Scientist-Intern

  • Unsupervised Multimodal Representation Learning
    • Developed an unsupervised multimodal (visual, lingual, spatial, etc. modes) representation learning framework that leverages the unlabeled raw documents (e.g. property documents) and weakly labeled image dataset (e.g. listings images and descriptions).
    • The learned transformations help improve performance on several downstream few-shot learning tasks including sequence classification, token classification, image attribute detection, and localization etc.

September 2018 - August 2019 Foundation AI, Los Angeles, CA
Research Scientist

  • Computer Vision and NLP for Document Understanding:
    • Development of novel CV methods for document analysis and OCR using GANs, CNNs and Graph convolutions.
    • Developed key-value pair extraction NLP model leveraging link prediction techniques on unstructured documents.

June 2015 - August 2018 Practo Technologies, Bangalore, India
Data Scientist, Senior Software Engineer, Software Engineer

  • Computer Vision for Medical Imaging:
    • Developed novel CV models for diagnosing lung-cancer, brain tumor, and diabetic retinopathy using radiology images.
    • Developed NLP solutions using LSTM and attention based deep learning methods for 90,000-class classification.
    • Developed semi-supervised text classifier for highlighting important phrases in clinical documents.
  • NLP for Automated Medical Document Annotation:
    • Developed methods such as Q-Map (fast retrieval of concepts from text documents; see Publication [J1]) and CASCADENET (hierarchical deep neural network for massively categorical classification; see Publication [C1]) for automated coding ICD-10 (International Code for Diseases), CPT (Current ProceduralTerminology) of clinical documents.
    • The Q-Map and CASCADENET play a vital role in development of subsequent clinical decision support system and other solutions with application in insurance claims automation, disease trend and epidemic outbreak characterization.