Computer Science Machine Learning

Siddhardha Gunnam

+1-240-***-**** *****************@*****.*** Siddhardha

OBJECTIVE

Computer Science graduate from the University of Maryland with a 3.8 GPA and hands-on experience in full-stack development and machine learning. Proven ability to design scalable, production-ready software with a focus on customer impact. Seeking an SDE I role to apply technical depth, ownership, and problem-solving skills in fast-paced engineering teams.

EDUCATION

• University of Maryland Baltimore county May 2025 Master of Science in Computer Science (CGPA: 3.81) Baltimore, MD

• Gandhi Institute of Technology and Management May 2022 Bachelor of Engineering, Computer Science and Engineering (CGPA: 3.7) Visakhapatnam, India EXPERIENCE

• University of Maryland Baltimore county Feb 2024 - May 2025 Graduate Teaching Assistant Baltimore, MD

Led instruction and provided support for courses: Data Structures and Formal Languages & Automata Theory

Conducted weekly office hours and coding review sessions for 100+ undergraduate students

Assisted in designing course and exam questions to assess theoretical concepts and programming proficiency

Mentored students on recursion, dynamic programming, complexity analysis, and automata models

Collaborated with professors to monitor progress and adapt instructional strategies for diverse learning needs

Fostered a supportive learning environment, contributing to a 12% improvement in overall course performance

• Aggne Global IT Solutions June 2022 - July 2023

Software Engineer Hyderabad, India

Designed and developed fully responsive, user-centric insurance web applications using React.js, implementing over 30+ reusable UI components to streamline client interactions and policy management workflows.

Built and maintained robust backend services using Node.js and Express.js, delivering secure, RESTful APIs for handling user authentication, quote generation, and claims processing.

Integrated critical features such as JWT-based authentication, dynamic form submission, policy lookup, and claims tracking, ensuring full functionality across key insurance modules.

Collaborated directly with insurance clients to gather requirements, demo deliverables, and implement iterative feedback, improving product alignment with user expectations and industry standards.

Improved system performance by optimizing API request handling and front-end state management, reducing average page load times and increasing reliability under heavy traffic.

Led debugging sessions to resolve backend logic errors and front-end inconsistencies, resulting in the resolution of over 90% of pre-launch issues and significantly enhancing production readiness.

• Aggne Global IT Solutions Jan 2022 - May 2022

IT Software Intern Hyderabad, India

Contributed to the development of insurance web platforms using React.js, Node.js, and Express.js.

Assisted in building and testing front-end components and Backend APIs under the guidance of senior developers.

Gained hands-on experience with RESTful API integration, state management, and Git-based version control.

Participated in agile ceremonies and client discussions, gaining exposure to real-world software development practices.

PROJECTS

• Leaf Disease Advisory System Sep 2024 - Dec 2024 Tools: [Python, HTML, CSS, JavaScript, Flask, scikit-learn ]

Developed an end-to-end image classification system using Python, OpenCV, and scikit-learn to detect plant leaf diseases with 94% accuracy

Built a full-stack web application using Flask for the backend and HTML/CSS/JavaScript for the frontend to enable real-time user interaction

Designed and implemented RESTful APIs to handle image input, preprocessing, and ML model inference

Implemented data augmentation techniques (rotation, flipping, scaling) to expand the training dataset and improve model generalization, reducing overfitting and boosting test accuracy by 7%

Created a simple, responsive UI allowing users to upload images and receive disease-specific recommendations

Deployed the application on a local server with complete model integration and user interface functionality

Demonstrated full project ownership — from data preparation and model training to deployment and UI design

• Sports Management System March 2024 - June 2024

Tools: [HTML, CSS, PHP, Bootstrap]

Built a full-stack web-based College Gym and Sports Registration System using HTML, CSS, PHP, and Bootstrap

Designed a responsive and intuitive user interface to streamline student registration and gym sign-up workflows

Developed an admin panel for real-time management of student activities, sports equipment inventory, and gym schedules

Implemented dynamic event notification features to improve student engagement in sports events and gym programs

Applied modular design principles to ensure maintainability and scalability for future expansion

Enhanced user accessibility and operational efficiency, contributing to increased gym participation across the student body

• Detection of Phishing Websites Oct 2023 - Dec 2023 Tools: [Python, scikit-learn, NumPy, pandas, Flask, Jupyter Notebook]

Developed a machine learning-based classifier in Python to detect phishing websites using features such as URL structure, domain authority, and security indicators

Engineered a dataset of legitimate and phishing URLs and extracted 30+ relevant features for model training

Implemented and compared multiple algorithms (e.g., Random Forest, Logistic Regression, SVM) to evaluate detection accuracy, achieving up to 96% accuracy

Built a Flask-based web application allowing users to input URLs and receive instant phishing risk evaluations

Employed feature engineering and cross-validation to reduce false positives and improve generalization

Demonstrated end-to-end ownership including data preprocessing, model development, API integration, and UI

• Heart Disease Prediction System Jan 2022 - June 2022 Tools: [Python, scikit-learn, matplotlib, NumPy, pandas, Flask]

Built a supervised machine learning model using Python and scikit-learn to predict heart disease risk based on 13 clinical features from a dataset of 1,000+ patient records

Preprocessed and cleaned the dataset using pandas, handling missing values and normalizing inputs for model accuracy

Implemented and evaluated multiple algorithms including Logistic Regression, Random Forest, and K-Nearest Neighbors, achieving up to 92% accuracy on test data

Developed a Flask web application that allows users to input patient data and receive real-time health risk predictions

Integrated performance metrics such as precision, recall, F1-score, and confusion matrix for model evaluation and interpretability

Visualized key health risk indicators using matplotlib to assist in clinical understanding SKILLS

Language Python, HTML, CSS3, JavaScript, TypeScript, C, C++, Java, R, Go Databases MySQL, MongoDB, PL/SQL, PostgreSQL

Libraries/Environments Docker, Node.js, React.js, Express.js, Playwright, Angular Data Visualization Power BI, Tableau, Excel (Pivot tables, charts), Matplotlib Platforms Amazon Web Services, Google Cloud Platform, Microsoft Azure, Firebase, Netlify Softwares AutoCAD, MATLAB, Jupyter Notebook, MS Office Suite PUBLICATIONS

• Author : Siddhardha Gunnam, ’Leaf Disease Advisory System Using Machine Learning’, published in the International Journal for Research in Applied Science and Engineering Technology Journal, 2024 CERTIFICATIONS

• Problem Solving Through Programming in C - NPTEL

• Data Structures and Algorithms using Python - NPTEL

• Introduction to Artificial Intelligence - IBM

• Object-Oriented Programming in Java - Duke University

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