VENKATA THAPASWINI THOTA

Driven by a deep-seated passion for innovation and a strong technical acumen, I am eager to collaborate with diverse, cross-functional teams to create transformative solutions. With a keen interest in Python, Machine Learning, Artificial Intelligence, and DevOps, I am excited to contribute my skills and expertise to tackle complex challenges and drive meaningful advancements in technology.

Parkinson's Disease Detection using ML, 04/2023

Implemented a machine learning-based appXGBoostsing the XGBoost algorithm and clinical data for early detection of Parkinson's Disease, achieving high accuracy according to results. Collaborated in a team of two to develop and implement a machine learning model, utilizing innovative skills to enhance all classifiers. Skills used include Machine Learning, Data Preprocessing, Python, and proficiency in various ML classifiers and libraries such as NumPy.

Vehicle Parking Space Counter using OpenCV, 11/2023

Collaborated with a team of two to develop a real-time vehicle parking space counter using OpenCV and Python. Contributed by actively participating in coding and providing innovative ideas to enhance project functionality and efficiency. The project aimed to analyze video feeds from security cameras, detect and track vehicles, and accurately count available parking spaces, optimizing parking management and enhancing security. Skills used include Python, OpenCV, Computer vision, and object detection.

GAT: A Graph Attention Networks for Enhanced Protein-Protein Interaction and Drug Response Prediction, 04/2024

I meticulously implemented the "TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation" paper into "GAT: A Graph Attention Networks for Enhanced Protein-Protein Interaction and Drug Response Prediction." To enhance the framework, I refined the "GAT_drug.py" and "GAT_cell.py" files to seamlessly integrate GAT functionality. This project was pivotal in advancing predictions in bioinformatics, specifically in protein-protein interaction and drug response forecasting. Skills Utilized: Proficient in Python for coding and implementation, Advanced knowledge of PyTorch for developing and optimizing neural network models, Deep understanding of graph neural networks (GNN) and graph attention networks (GAT), Ability to comprehend and implement complex research papers in the field of bioinformatics, Detail-oriented approach to modifying and improving existing codebase, Strong collaboration and teamwork skills honed in a research-intensive environment.

• Web Development Training - 2021
• Participated in Microsoft AI Classroom Series - 2021
• Certified Software Developer-Intern, TechCiti Software - 2022
• Certified AWS Developer, AWS - 2024