ShouvikGhosh

Shouvik Ghosh

My undergrad years have been crucial not only academically, but also psychologically. From being a naive student to working on numerous impactful projects, I have evolved as a student. During my high school days, my perception of "purpose in life" was to seek happiness. I used to do things that would make me happy, like purchasing the latest games or the trendiest attires or trying to score high in exams. However, I always felt a void which none of these activities could fill. Fortunately, I stumbled across Casey Neistat's Youtube channel and his definition of "purpose in life": to be useful, honourable, compassionate and keep on creating something new; instantly resonated with my conscience. With this rooted in my mind, I commenced my bachelor's degree.

During my freshman year, the AI industry boomed and terms like GAN's and CNN's flooded the student community. A brief lookup on these topics and the impact it can cause to society piqued my interest. This, combined with the relevant coursework, sparked my profound curiosity in the field of neural computing. Hence, I rigorously explored various domains and expedited this process by joining NextTech Labs, a QS award-winning student-run research lab. Here, I was involved in peer teaching and mentoring my juniors. I felt that the research environment at my university wasn't at its forefront. With the support of my peers, I started a weekly colloquium on recent trends to promote students' involvement in research. Furthermore, I conducted independent research as well as participated and won numerous hackathons and competitions. This motivated me to the next step: being useful and giving back to the community.

Over the course of the next two years, I actively searched for internship opportunities that matched my interests, intending to make the world a little bit better than the day before. The research experiences at The University of Auckland, IIIT Delhi and now at Veritas, were much more enriching compared to just reading textbooks or completing online courses. Through these experiences, I arrived at the realization that happiness is merely a byproduct of usefulness.

I believe that I would be a good addition to the hackathon environment and I would try to contribute and try to bring about a change, however small it might be.

Projects

Private {POAP}

Do you do POAP? Many expose POAPs on their wallet - also exposing lots of personal data. We mint POAPs into single-use wallets in a non-traceable way that retains control over your POAPs collection.ethers.js, Chakra UI, Dune API, POAP Api, React & Node.js

NEAR-LY CERTIFIED

Empowering Trust, One Block at a Time: Immutable and verifiable certificates powered by NEARSolidity, React, Next.js, Nodejs, NEAR Protocol, Optimism, Mantle, Scroll, TAIKO

Skills

Python
TensorFlow
Docker
Kubernetes
Machine Learning

Experience

  • Veritas - R&D Intern
    December 2019 - December 2019
    • Spearheading the project ”Sharing Docker Images
      across hosts from Veritas distributed file system”.
    • Conducting research to develop a distributed layer on top of Docker that allows multiple Docker daemons to access container images from Veritas shared file system (VxFS).
  • IIIT Delhi - Summer research intern
    May 2019 - July 2019
    • Undertook the project ”Train electricity consumption prediction” under the guidance of Dr Pravesh Biyani. Developed an LSTM forecasting model and optimised using genetic algorithm. Achieved a low RMSE value of 473kWh for every 15-minute time slot.
    • Optimised a variant of A* algorithm for public transport routing. Significantly reduced the average query time for
      source-destination pair from 27s to 0.8s.
    • Experienced first-hand academia-industry collaboration
  • The University of Auckland - Summer Research Intrer
    December 2018 - February 2019
    • Undertook the project ”Fog computing architecture for marine temperature prediction” under the guidance of Dr Aniket Mahanti. Designed a Fog-IoT architecture for real-time context-aware collaborative decision making.
    • Deployed an optimised convLSTM model on low powered fog nodes and boosted the accuracy by 11%. Conducted black box testing which showed we achieved backbone network alleviation.
    • Experienced working alongside graduate students in a research environment.