Machine Learning Data Science

Chiru Anand Vaka

******@*********.*** 862-***-**** www.linkedin.com/in/chiru-anand-vaka-34a24b1a6 TECHNICAL SKILLS

• Languages & Technologies: Python (Data Structures & Algorithms, OOP, Web Development), JavaScript, C

• Web Development: Flask, Django, FastAPI, Node.js, HTML5, CSS, Tailwind CSS, three.js

• Databases: MySQL, PostgreSQL, MongoDB

• Data Science & Machine Learning: TensorFlow, Keras, OpenCV, NumPy, Pandas, Scikit-learn

• Tools & Frameworks: Git, Docker, Jupyter Notebook, VS Code, RESTful APIs EDUCATION

Montclair State University January 2024 - May 2025 Master of Science in Computer Science

Vignan University, India July 2019 - May 2023

Bachelor of Technology in Information Technology

RELEVANT COURSEWORK

Mathematics: Linear Algebra, Basic Calculus, Discrete Mathematics, Probability & Statistics Computer Science: Data Structures & Algorithms, Database Management, Cryptography & Network Security, Digital Logic

& Design, Software Engineering, Operating Systems, Computer Networks, Formal Languages & Automata Theory, Compiler Design, Data Mining Techniques

WORK EXPERIENCE

Cognizant Technology Solutions, Hyderabad, India

Programmer Analyst Trainee January 2023 – May 2023

• Engineered an API-driven banking system using Flask and ReactJS, reducing transaction processing time by 30%.

• Developed and secured RESTful APIs with JWT authentication, minimizing unauthorized access attempts by 40%.

• Optimized SQL queries with indexing, improving API response speeds by 25% and enhancing database efficiency.

• Designed and deployed microservices using Flask and Docker, accelerating deployment cycles by 50%. PROJECTS

Space Weather Emergency Simulation August 2024 - May 2025 Tools/Technologies Used: JavaScript, JSON, Node.js, Python, TensorFlow, MySql, HTML5, CSS Three.js, Git, VScode, Figma

• Designed and developed a three-level interactive simulation to educate middle school students on space-weather emergency responses and machine learning applications. Implemented a neural network model using TensorFlow/Keras (.h5 format) to classify input data and provide real-time space-weather predictions through interactive visualizations.

• Built an intuitive UI/UX interface using HTML, CSS, JavaScript, and Three.js, ensuring an engaging and seamless game- based learning experience. Integrated GitHub Copilot and ChatGPT to enhance debugging, code optimization, and feature development, reducing development time.

• Developed interactive neural network visualizations to illustrate machine learning decision processes, increasing student comprehension and engagement. Utilized the Web Speech API (speech.js) to enable voice-based accessibility, making the simulation more inclusive for students with diverse learning needs. Mood-Based Music Recommendation System January 2024 – May 2024 Tools/Technologies: HTML, CSS, JavaScript, Python, SQLite, Flask

• Developed a real-time mood-based music recommendation system leveraging facial expression analysis using CNNs and Inception architecture. Implemented a Flask-based backend for dynamic playlist updates, enabling seamless interaction between the user interface and deep learning model.

• Enhanced emotion detection accuracy by 15% through advanced preprocessing techniques, including normalization, edge detection, and data augmentation. Utilized OpenCV for real-time image capture, and trained a deep learning model on the FER-2013 dataset (38,000+ images), achieving 81.96% classification accuracy. Integrated TensorFlow and Keras to develop and optimize the neural network model, ensuring efficient mood-based music recommendations. Social Distance tracking using YOLOv3 August 2022 – May 2023 Technologies: HTML, CSS, JavaScript, Python, SQLite, Flask, OpenCV, TensorFlow, Keras

• Developed a real-time social distancing monitoring system using YOLOv3 and the COCO dataset, achieving 95% accuracy through transfer learning. Optimized the YOLOv3 object detection pipeline, enabling real-time video processing at 20 FPS

• Implemented a Euclidean distance-based algorithm to detect 6-foot social distancing violations.

• Enhanced model robustness to lighting variations and dynamic crowd movements by fine-tuning the YOLOv3 architecture and applying adaptive thresholding.

1

Contact this candidate