About me

Hi, I am Sahil. I am student at the University of Massachusetts Amherst. I am pursuing Masters of Science in Computer Science with Concentration in Data Science. I am aspiring to be a Software Developer and really interested in Backend development in distributed systems and ML applications. The coding languages I am familiar with are Python, C++ and Java.

I have been involved in many projects during my masters, my undergrad and in my internships. I have also published a research paper to detect meaning behind an infant cry using speech processing and Deep Learning techniques. I recently did my internship in a startup called sheeva.ai. There I developed a software to detect parking spaces using satellite images, made a data simulator of cars for internal testing and also built a service to detect the nearest parking spot given the current location. I also designed and built a scalable movie recommender system recently using Spark. In my software engineering course, my team built a navigation system which takes elevation into account so that instead of finding the shortest route, we can also find a route which requires less effort and would help in hiking. Another notable project was the project called the Research paper tagger which took a research paper as an input to detect the research track. The aim of this project was to help young researchers with their paper submission process.

I believe I am detail-oriented and like to think of myself as a team player. I aim to utilize my skills in a variety of technical and engineering-related applications.

Resume

Education

  1. The University of Massachusetts Amherst

    2021 — 2023

    CGPA: 3.86/4

  2. Indian Institute of Technology Jammu

    2016-2020

    CGPA: 8.21/10

Experience

  1. Sheeva.ai

    Software Developer Intern
    May 2022 — Aug 2022

    • > Developed data pipeline, designed detection and recognition models and deployed API's for Sheeva's ADAS systems to automatically map parking spaces using satellite images. Achieved an average precision of 95%.
    • > Integrated Harman Cloud Data for vehicle status and Sheeva API for testing using Spring Boot framework. Deployed it on AWS Lambda for production.
    • > Developed service and API to detect nearest available parking spot using vehicle location using Apache Lucene.

  2. Indian Institute of Technology Jammu

    Research
    July 2020 — June 2021

    • > Built ensemble models to detect the emotional behaviour response of an infant using its cries and achieved test accuracy of 88%.
    • > Published research paper indicating the importance of using spectrogram images for infant cry analysis.

  3. The University of Auckland, New Zealand

    Summer Internship
    May 2019 — Aug 2019

    • > Built dataset of toxic comments by scraping GitHub using REST APIs and using discussions in the Linux Kernel Mailing list.
    • > Fine-tuned KNN and Decision tree classifiers which were pre-trained on Wikipedia Comments and achieved 90% precision using emotional analysis of the collected dataset.

Publication

  1. S. Jindal, K. Nathwani, and V. Abrol "Classification of Infant Behavioural Traits using Acoustic Cry: An Empirical Study" in IEEE 12th Int'l Symposium on Image and Signal Processing and Analysis, 2021.
    LINK

Skills

    Languages

  • Python
    C++
    Java
    MySQL/PostGresSQL
  • Technologies

  • Spring Boot
    Docker
    AWS
    Tensorflow/Pytorch
    FastAPI
    Git
    Linux
    MATLAB

Blog

Contact

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