I am an experienced IT professional with a Master's degree in Computer Science, equipped with extensive experience and expertise across various domains. My proficiency lies in Data Analytics, Machine Learning, Deep Learning, and Software Engineering. I excel in utilizing a wide range of tools and technologies, including Python, Java, Spring Boot, Microservices, JavaScript, SQL, Docker, Scikit-Learn, TensorFlow, Power BI, Azure, and MlFlow.
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JavaScript
HTML
CSS
Bootstrap
React
Redux
Python
Java
C++
C
Spring Boot
Hibernate
SQL
Postgresql
MongoDB
AWS
MYSQL
Sequelize
Numpy
Pandas
Scikit-Learn
Tensorflow
Power BI
Data Factory
Data Bricks
Visual Studio
Eclipse
NetBeans
MacOS
Linux
Windows
GitHub
Gitlab
Git
Azure
Docker
JenKins
I developed machine learning models using Python, Scikit-Learn, and Tensorflow to detect anomalies in product data. I managed version control with Git, utilized Azure for deployment, and tracked models with MlFlow. Additionally, I created ETL pipelines with Python and SQL for data processing and utilized Power BI for data visualization and reporting.
In this role, I played a pivotal role in the backend development of Java-based projects. My responsibilities encompassed project development, rigorous testing, and implementation of the Spring framework. Leveraging technologies commonly employed in Java backend development, such as Hibernate, Spring Boot, JPA (Java Persistence API), and RESTful APIs, I ensured the seamless functioning and robustness of the projects. I also developed and maintained microservices using Spring Boot and Docker.
During my internship at Infosys Limited, I underwent comprehensive training in a range of technologies, including Python, AngularJS, Java, Hibernate, Spring, and SQL. Additionally, I successfully designed and developed a web application for a third-party railway ticket booking system. This project involved the integration of AngularJS, Java, Hibernate, Spring, Bootstrap, and HTML to deliver a robust and efficient solution.
The project is primarily Python-based, leveraging the MNE library for EEG signal processing and Scikit-learn for implementing machine learning models. Furthermore, it incorporates a streamlined approach to creating automated machine learning pipelines through YAML.
The project encompasses mini-projects focused on various aspects, including argument mining from natural language text, evaluating argumentation quality, and retrieving arguments from online searches. These endeavors are rooted in core techniques derived from natural language processing.
This project entails a comprehensive data analysis initiative utilizing the Covid-19 dataset to examine the pandemic's impact on diverse countries. Furthermore, it involves the development of machine learning models aimed at forecasting the short and long-term trends of Covid-19 cases.
It was a team project which involved the creation of multi hoping network using C++, Contiki OS, and Cooja.
This is a Java Based Project that is used to create a web drive for the storage and synchronization of files and folders over cloud. Front end is developed using HTML, CSS, and JavaScript. The backend is developed using Java.
This paper was written as an extension of my master thesis in IT Security group at Paderborn University, Germany. It was supervised by Patricia Arias Cabarcos, who also leads the group. Further, the implementation aspects of this benchmarking tool was advised by M.Sc Matin Fallahi, a research associate at Karlsruhe Institute of Technology, Germany. The paper was submitted to the Journal of Information Security and Applications on 31st January 2024, and is currently under review process.