Languages: Python, JavaScript, TypeScript, Java, C++, C#, SQL

SUJAN DHAKAL

Monroe, LA ***********@*****.*** 318-***-**** Portfolio LinkedIn Git

Education

University of Louisiana Monroe Expected Graduation Date: December 2025 Relevant Course Work: Introduction to Computer Programming, Intermediate Programming, Applied Data Structures, Analysis of Algorithm, Object-Oriented Design and Programming, Database Management Systems, Computer Architecture, Operating Systems, Information Security Practice, Organization of Programming Languages Additional Courses: AWS Academy Cloud Foundation, CS50's Introduction to Artificial Intelligence with Python Skills

Languages: Python, JavaScript, TypeScript, Java, C++, C#, SQL, HTML5, CSS3, Bootstrap 5, SQL Tools & Frameworks: React JS, Node JS, Next JS, Git, Spring Boot, Firebase, Postman, AWS, Figma, Numpy, Tensorflow Experiences & Leadership

Technical Intern -- The MangoByte (Kathmandu, Nepal)

• Authored detailed design documentation, testing notes, and experiment protocols to guide the development of a robust database management system.

• Streamlined the UI, enhancing product experience and reducing friction for users to retrieve data.

• Implemented changes using SQL, adhering to company’s code review process and industry-leading coding practices.

• Developed comprehensive API documentation to enable seamless interaction between the frontend and backend of the system.

Skills: React JS (Typescript), AWS, SQL, Git, Program Management, Group Collaboration, Agile Methodology Teaching Assistant – University of Louisiana Monroe

• Introduced 38 freshman to Python programming comprising basic coding, debugging, and data structures.

• Helped professor by providing support during lab sessions, holding office hours, and counseling students. Skills: Python, Public Speaking, One-on-one interaction IT HELP DESK – University of Louisiana Monroe

• Provided technical support to students, faculty, and staff, resolving hardware and software issues efficiently and effectively.

• Collaborated with IT team members to improve service delivery and implement new technology solutions.

• Educated users on best practices for IT security, data protection, and effective use of university technology resources. Skills: Customer Support, Networking systems and solutions, Computer Hardware and Software Projects

MinesweeperAI GitHub -- Python, pygame

• Developed a Minesweeper game with a preset number of mines and AI-powered gameplay assistance.

• Implemented an AI model to make educated moves by leveraging a knowledge base for strategic decision-making.

• Enhanced gameplay with an AI-driven approach to improve user experience through intelligent and adaptive move. Designed AI fallback mechanism for random guessing in scenarios lacking sufficient data for calculated moves. Cozy Mart Website -- React JS, Node JS

• Worked in a team of 3 to design, develope, and deploye a full-stack web application for a furniture sales business.

• Engineered a responsive user interface, cross-browser compatibility and mobile responsiveness, implementing features such as product listings, product description and ensuring cross-browser compatibility and mobile responsiveness.

• Effectively sold furniture through the ecommerce site, driving business growth and demonstrating the effectiveness of the web application.

Student Database GitHub -- Java, Spring boot, PostgreSQL

• Developed a backend database system for efficient data management and retrieval. Optimized entity relationships and data mapping for improved database performance and data integrity.

• Configured JPA annotations to automate ORM, reducing boilerplate code and enhancing readability.

• Implemented CRUD operations and RESTful APIs to facilitate seamless interaction between the database and frontend. Applied PostgreSQL indexing and query optimization techniques to improve data retrieval speed and system scalability. SoundClassification Colab -- Python, Numpy, Matplotlib, Tensorflow, Librosa, Pandas

• Developed an audio classification model using a Feedforward Neural Network (FNN) with TensorFlow, achieving high accuracy on the UrbanSound8k dataset.

• Extracted and processed MFCC features from 8,000+ audio files, employing techniques like dropout regularization to prevent overfitting.

• Implemented real-time audio predictions, enabling automated sound classification into 10 distinct categories using a trained deep learning model..

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