Tejas Mahajan

Tejas Mahajan

Computer Science Graduate Student

Courant Institute of Mathematical Sciences (NYU)


I am graduate student pursuing my Masters in Computer science at Courant Institute of Mathematical Sciences, New York University. My current interests lie at the intersection of computer vision and natural language processing, more specifically on multi modal learning problems.

Prior to this, I was working as a data scientist in applied machine learning, more specifically on computer vision problems statements. I have an experience working in the fintech, regtech, data privacy, fashion and retail domains.

  • Computer Vision
  • MultiModal Learning
  • Natural Language Processing
  • Distributed Systems
  • Bachelors of Engineering in Computer Engineering, 2014-2018

    Savitribai Phule Pune University (formerly called University of Pune)

  • Masters in Computer Science, 2021-2022

    Courant Institute of Mathematical Sciences (New York University)


Data Scientist
Sep 2018 – Mar 2020 Mumbai, India


  • Part of the OCR team at Karza, developed end to end training and evaluation pipelines for text recognition models and fine tuned text detection models for financial documents.
  • Created data extraction pipelines from structered images pdfs using basic image processing for segmentation and simple heuristics for data validation.
  • Worked on the deployment pipeline for the various developed deep learning models.
Machine Learning Intern (Remote)
Aug 2018 – Mar 2018 New Delhi, India


  • Developed and fine tuned fashion image object detection models encompassing 20 classes spanning upper & low body clothes and footwear.
  • Extracted the color of the detected object and mapped the color to its closest name.
  • Deployed the models as REST APIs as microservices in docker containers.
IBM System Labs
Feb 2018 – Jun 2018 Pune, India


  • Developed a synthetic data generation pipeline for personally identifiable documents with focus to social security cards.
  • Finetuned an object detector to locate and classify the card in an image.
  • Redacted the PII data in the image by locating the text boxes and with simple heuristics to determine if box contains PII data or not.