Stack Developer Machine Learning

Dinesh Devanaboina

Kansas City, MO 816-***-**** ******.******@*****.*** ********@**.*** Linkedin EDUCATION

Master of Science (MS) in Computer Science University of Kansas, Lawrence Dec, 2024 Masters & Bachelors of Technology (M. Tech + B. Tech) in Computer Science Vellore Institute of Technology July, 2022 TECHNICAL SKILLS

Languages: Python, Java, JavaScript, HTML/CSS

Databases: MySQL, PostgreSQL, MongoDB, Oracle

Cloud Platforms: Amazon Web Services, Google Cloud Platform, Azure Frameworks: Angular, React, Node, Express, RESTful APIs, Spring Boot Tools: Git, Visual Studio, OpenCV, Deep & Machine Learning, NumPy TECHNICAL EXPERIENCE

Java Full Stack Developer Wipro, India Jan, 2022 – July, 2022

● Spearheaded the development and maintenance of scalable Java Full Stack applications, leveraging Spring Boot for backend services and React for dynamic, user-centric frontend interfaces.

● Engineered and optimized RESTful APIs, improving system performance by 25% and ensuring real-time data aggregation for enhanced responsiveness.

● Designed and managed MongoDB databases, reducing query response time by 30% and enabling efficient handling of personalized user preferences and large datasets.

● Delivered a full-stack news application with customizable settings and personalized feeds, increasing user engagement by 30% and retention rates significantly.

● Ensured 95% uptime for real-time news aggregation, guaranteeing timely and relevant content delivery to users.

● Conducted code reviews and performance tuning, maintaining high-quality standards and achieving seamless integration of frontend and backend components.

● Enhanced application usability and scalability through performance optimization and adherence to software engineering best practices.

Java Full Stack Developer Prograd, India March, 2021 – Dec, 2021

● Architected and developed a high-performance full-stack invoicing application using the MERN stack (MongoDB, Express.js, React, Node.js), streamlining invoice management for efficiency.

● Implemented robust user authentication mechanisms, ensuring secure access to financial data and delivering personalized user experiences.

● Designed and built responsive, user-friendly interfaces with React, improving usability and increasing user engagement by 20%.

● Engineered and integrated RESTful APIs, enabling seamless communication between frontend and backend and optimizing data handling speed by 30%.

● Delivered critical features such as invoice tracking and PDF generation, enhancing operational efficiency and simplifying record-keeping for users.

ACADEMIC PROJECT EXPERIENCE

COVID-19 Detection Using Cough Sound

● Developed a COVID-19 detection tool using log-mel spectrogram features from cough audio signals, leveraging customized deep learning algorithms for accurate and automated classification.

● Enabled a non-invasive, cost-effective, and scalable solution for early COVID-19 diagnosis, offering a potential alternative for mass screening during pandemics. Achieved high classification accuracy i.e., >90% in testing datasets, reducing diagnostic time significantly compared to traditional methods and demonstrating real-world feasibility for deployment in healthcare systems. Vehicle Tracking and Speed Estimation From Traffic Videos

● Developed a real-time vehicle tracking and speed estimation system leveraging OpenCV, Haar cascades, and NumPy for vehicle detection and tracking. Utilized Python for algorithm development and integrated video processing frameworks to analyze traffic videos efficiently. Provided a scalable solution to enhance urban traffic management and road safety, enabling efficient vehicle monitoring and proactive identification of traffic violations.

● Enabled actionable insights for traffic authorities, such as identifying speeding vehicles in real-time and optimizing traffic flow patterns, contributing to a potential 20% reduction in traffic congestion and enhanced road safety outcomes. Grocery Demand Prediction

● Built a demand forecasting model using historical sales data and external factors (e.g., holidays, oil prices). Performed data cleaning, feature engineering, and EDA to uncover key trends, seasonal patterns, and demand drivers.

● Implemented SARIMA to enhance forecast accuracy, addressing challenges like stockouts and overstocking, leading to more efficient supply chain operations. Improved forecast accuracy by 20%, optimizing inventory levels and reducing operational costs through better demand predictions.

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