Gitika Thakur

Jagdalpur

Summary

Computer Science student with expertise in Python, C++, and data structures. Proficient in statistical analysis and machine learning, focused on addressing quantitative trading challenges. Experienced in managing large datasets and creating efficient solutions within collaborative team settings.

Overview

years of professional experience

Certification

Work History

Project Intern

IBM Phemsoft

Virtual

06.2024 - 07.2024

Developed cloud-based compliance management tool using Python and C++ with PaaS and SaaS methodologies.
Implemented secure SFTP for data transfer, enhancing data security.
Leveraged AWS Lambda for efficient data handling and processing.

Research Intern

GiftAbled Foundation

Virtual

06.2022 - 07.2022

Conducted data analysis to support empowerment initiatives for underemployed individuals during job searches.

Researched economic challenges in underdeveloped communities, emphasizing women's empowerment issues.

Led a team throughout internship to enhance project outcomes and collaborative efforts.

Education

Bachelor of Technology - Computer Science Engineering

University of Petroleum And Energy Studies

Dehradun

01-2025

Skills

Python and Java programming
C and JavaScript development
HTML and CSS design
Nodejs and MySQL integration
MongoDB and data management
Data structures and algorithms

Object-oriented programming (OOP)
CI/CD pipelines and Docker
Cloud computing and AWS
Version control with Git/GitHub
Machine learning techniques
Familiarity with Python libraries: Pandas, NumPy, Keras

Projects

Image Cryptography System (Tech Stack: Python, C++, HTML, CSS, JavaScript)

Designed an encryption system for securing image transmissions by transforming pixel data into secure formats.
Integrated image steganography for enhanced security and developed a user-friendly interface for encryption/decryption.

Fake News Detection System (Tech Stack: Python, NLP, LSTM, TensorFlow)

Built a Fake News Detection System using NLP techniques and LSTM for text classification.
Applied text preprocessing methods like tokenization and stopword removal using pre-trained word embeddings.

Medical Image Segmentation using Deep Learning (Tech Stack: Python, PyTorch, OpenCV, nnU-Net, NumPy)

Built a deep learning model using nnU-Net to segment the left atrium from cardiac MRI scans with high accuracy.
Performed data preprocessing on DICOM images and applied augmentation to improve model robustness.