I am Manjit,
an aspiring
data scientist,
from India!

Conducted an in-depth research project on the application of machine learning in data security, analyzing multiple approaches for integrating ML into secure communication systems, and produced a report titled “Cognitive V2X Communication: A Machine Learning Framework for Secure, Context-Aware Message Encoding.”

In the past year, I have worked on several projects with national and international clients, primarily collaborating with R&D teams. My responsibilities included extracting valuable insights from data, making predictions, building models, and optimizing them. I have worked on various topics, contributing to various industry-specific solutions.

I am currently pursuing my Master's degree, focusing on a range of projects that include Advanced Statistical Analytics, Natural Language Processing, Computer Vision and Recommender Systems. These projects are aimed at applying cutting-edge techniques and methodologies to solve real-world problems and advance the state of research in these fields.

With a Bachelor's degree in Electronics and Communication Engineering (ECE), I was introduced to the field of TinyML (Embedded Machine Learning), which holds great promise for the future. Throughout my academic journey, I have completed several projects in pure ECE and have also applied TinyML to tackle real-world challenges.

A research paper was published on smart agriculture technology that focuses on the application of machine learning for suggesting the best crops using embedded systems without the use of the internet - TinyML (EdgeAI).

Contributed to a publication on natural language processing pipelines for sentiment analysis, covering preprocessing strategy, model benchmarking, and practical evaluation metrics.

Presented research from my 3rd Semester M.Tech (Data Science) in collaboration with Udayaditya Technologies on developing a balanced emotion-labeled text dataset and a Transformer-based model for accurate emotion classification.

Received the 2nd Best Paper Award at MICRO 2025 – the 12th International Conference on Microelectronics Circuits and Systems for presenting research on deploying a CatBoost-based ASD detection model on embedded systems for individuals across all age groups.