Rutwik Kulkarni

Rutwik kulkarni

bookmark on deepenrich
location of Rutwik KulkarniWashington DC-Baltimore Area
Phone number of Rutwik Kulkarni+91 xxxx xxxxx
Followers of Rutwik Kulkarni2000 followers
  • Timeline

    May 2017 - Jul 2017

    Intern

    Persistent Systems
    Jun 2018 - Jun 2019

    Project Intern

    Prescient Technologies
    Jun 2018 - Sept 2018

    Intern

    Periwinkle Technologies Pvt. Ltd.
    Aug 2019 - May 2021

    Graduate Research Assistant

    Virginia Tech
    Blacksburg, Virginia, United States
    Current Company
    Jun 2021 - now

    Software Engineer

    MicroStrategy
  • About me

    Software Engineer @MicroStrategy. Cloud | AWS | GCP| Kubernetes| Genomics

  • Education

    • Pune institute of computer technology

      2015 - 2019
      Bachelor of engineering - be computer engineering cgpa: 9.51
    • Virginia tech

      2019 - 2021
      Master's degree computer science
    • Fergusson college

      2013 - 2015
      Hsc

      12th Science

  • Experience

    • Persistent systems

      May 2017 - Jul 2017
      Intern

      Worked on Web Development and Persistent Artificial Intelligence platform. Successfully Implemented POC right from SRS to Automated Testing and successfully coordinated in the software engineering process. Learnt and implemented REST API'sSkills learnt : Software Engineering, PHP, Drupal, Python, Selenium, MySQL, AIML, XML,REST

    • Prescient technologies

      Jun 2018 - Jun 2019
      Project intern

      Our solution aims at detecting suspended impurities and foreign objects in beverages. We are working specifically on buttermilk for quality detection and assurance with the help of computer vision and sensors, which will act as a contact-free technology for foreign object detection. The data (images of bottles on the assembly line and that gathered from the sensors) is collected and classified for further QA using Deep Learning.

    • Periwinkle technologies pvt. ltd.

      Jun 2018 - Sept 2018
      Intern

      I worked on the Machine Learning and Computer Vision aspects of a live project, ‘Smart Scope™’. It involved software development for the detection of cervical cancer at an early stage with the help of images taken by doctors. Abnormalities like cervicitis could also be detected at early stages. Specifically, I built the algorithm required for cervix segmentation and examined the applicability of transfer learning for solving the given problem. I also deployed the ML platform on Amazon Web Services. In a resource-constrained country like India, the application enables remote screening of cervical cancer and provides real time diagnosis for prompt action. The Government of India has also shown a keen interest in the application and provided sponsorship for the same. Show less

    • Virginia tech

      Aug 2019 - May 2021

      1. Worked on the development of a ML model DeepARG+ that predicts antibiotic resistance from large scale genomic data. Paper to be published in a high impact factor journal 2. Led a team on NSF funded project CI-WARS for creating a pipeline for end to end resistance analysis3. Developed a visualization tool in D3.js for anomaly detection for monitoring antibiotic resistance.Advisor: Dr. Liqing Zhang CS1114 Statistics for Social Sciences Course

      • Graduate Research Assistant

        May 2020 - May 2021
      • Graduate Teaching Assistant

        Dec 2019 - May 2020
      • Graduate Teaching Assistant

        Aug 2019 - Dec 2019
    • Microstrategy

      Jun 2021 - now
      Software engineer
  • Licenses & Certifications

  • Honors & Awards

    • Awarded to Rutwik Kulkarni
      Winner Smart India Hackathon 2019 - March 1, 2019
    • Awarded to Rutwik Kulkarni
      Department Topper (Third year engineering) PICT July 1, 2018 Rank 2 in the Computer Engineering Department of 320
    • Awarded to Rutwik Kulkarni
      Tech Mahindra Iris Hackathon Tech Mahindra Jun 2018 Bad Debts are normally due to disbursement of loans to customer who look good on paper, or the loan evaluation officer misses out on certain flags and hence passes the loan. By using an Artificial Neural Network trained to determine these bad debts on past data with financial and non financial input vectors, we can reject potential bad customers or flag and monitor already provisioned high risk loans so that bad debts can be predicted in advance and hence do not get converted into Non… Show more Bad Debts are normally due to disbursement of loans to customer who look good on paper, or the loan evaluation officer misses out on certain flags and hence passes the loan. By using an Artificial Neural Network trained to determine these bad debts on past data with financial and non financial input vectors, we can reject potential bad customers or flag and monitor already provisioned high risk loans so that bad debts can be predicted in advance and hence do not get converted into Non Performing Assets. Show less
    • Awarded to Rutwik Kulkarni
      Department Topper (Second year engineering) PICT July 1, 2017 Rank 5 in the Computer Engineering Department of 320