Name: Rajesh Kumar Jat
Profile: Artificial Intelligence Engineer
Email: 2021kpad1001@iiitkota.ac.in
Current Location: New Delhi
Programming Languages
- Python
- R
- Sql
- C#
- C++
- C
Libraries/Frameworks
- PyTorch
- TensorFlow
- SpeechBrain
- Whisper
- OpeSeaDragon
- Qiskit
- nltk
- RegEx
- Flask
- React
- Express
- JavaScript
- PyQt5
- Scikit-Learn
- Seaborn
- Plotly
- Matplotlib
- Pandas
- NumPy
Tools
- R Studio
- Power BI
- Apache Hadoop
- Qt-Designer
- LabelImg2
- AWS
- GitHub
- Jupyter Notebook
- Google Colab
- Moodle (LMS)
Professional Experience
I am currently employed as a AI Research Engineer at WESEE, the Research & Development Lab of the Indian Navy. I have been with WESEE since July 2023 and have been actively involved in various projects, including:
Object Detection, Face Recognition, Large Language Models (LLMs), Generative Adversial Networks (GANs) for Image Translation, Chatbots and Speech Processing.
Internship
I did an internship at Object Automation System Solutions Pvt. Ltd., Chennai, from September 2022 to January 2023, as an Artificial Intelligence Intern.
During this internship, I gained practical experience in:
- Developing recommendation systems
- Managing Virtual Private Servers (VPS)
- Setting up and configuring MOODLE (Learning Management System)
- Implementing various machine learning algorithms
Education
Master's Degree: Indian Institute of Information Technology, Kota (MNIT Campus, Jaipur)
Major: Computer Science and Engineering
Specialization: Artificial Intelligence & Data Science
Completed: July 2023
Bachelor's Degree: Vivekananda Institute of Technology, Jaipur.
Major: Computer Science and Engineering.
Year of Graduation: 2021
Projects
"All models are wrong but some are useful." ~ George E. P. Box
Image Processing
Face Detection Attendance System
This project automates the attendance process. By leveraging the power of computer vision, it accurately identifies and recognizes faces in real-time. The user-friendly interface developed using PyQt5.
Quantum Computing
Quantum Gates Visualizer
This project offers a user-friendly interface that simplifies concepts of q-Gates, making it easy to Visualize & understand quantum gates and their potential applications. Let's Dive into the World of Quantum Computing.
ML + NLP
Movie Recommendation System
A user-friendly interface that suggests movies/series based on user's unique tastes and preferences. With advanced algorithms and machine learning, this system analyzes user's interest & provides tailored recommendations.
Analytics
Data Analysis
Exploring and analyzing the vast collection of movies and TV shows available on Netflix.
In this, I dived deep into the data exploration to uncover hidden trends, insights, and patterns that can inform about user choices and preferences.
Data Minig
Cluster Analysis
Using advanced algorithms and statistical techniques, tried to identify groups, or clusters, of data points that share similar characteristics.
One can use cluster analysis to discover relationships, segment customers, group products, and more.
Data Mining
Association Rules Analysis
Used Statistical techniques to identify co-occurring items and rules that provide insights into customer behavior, product recommendations, and more. One can identify cross-selling opportunities, optimize pricing strategies, and improve customer satisfaction.
Machine Learning
ML Algos Implementation
Implemented some popular ML algorithms such as Naive Bayes, K-Nearest Neighbors, and Decision Trees. Mainly, training and testing of these algorithms is performed using libraries like NumPy, Pandas, Matplotlib and Scikit-Learn.
Data Science
Exploratory Data Analysis
Intended to visualize and analyze the data to identify patterns, relationships, and insights. So one can detect outliers, missing values, and other data quality issues and apply data preprocessing techniques to prepare the data for further analysis.
Data Science
Data Handling
It covers, Data uploading, cleaning, and transforming the data for analysis purposes. We can easily detect and correct data quality issues, such as missing or duplicate values, using data cleaning techniques, normalization etc.
