Welcome to my GitHub website!

• About Me
• Education
• Work Experience
• Publications
• Projects
• Skills and Technologies

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About Me

Hello! I am a Graduate Research Assistant pursuing a PhD in Computer Science with a focus on Machine Learning and Software Testing at The University of Texas at Arlington. My research sits at the exciting intersection of these fields, where I explore approaches to synthetic data generation, hyperparameter optimization, and combinatorial testing to improve machine learning models.

Education

PhD in Computer Science
The University of Texas at Arlington
Spring 2022 – Present
GPA: 4.0

BS in Software Engineering
The University of Texas at Arlington
Graduated Fall 2021
GPA: 4.0

Work Experience

Graduate Research Assistant

May 2022 - Present

• Conducting research on synthetic data generation using Variational Autoencoder and Generative Adversarial Network techniques, integrated with combinatorial testing from software testing.
• Published research findings in multiple international conferences, contributing to the advancement of knowledge in the field.
• Collaborated with fellow researchers to enhance team dynamics and drive impactful research.

Graduate Teaching Assistant - Software Testing and Maintenance

January 2022 - May 2022

• Prepared and delivered comprehensive lectures, developed course content, and guided students through complex software testing methodologies.

Machine Learning Software Developer Intern - State Farm

January 2021 - August 2021

• Designed and developed a system to generate health scores for insurance customers using Machine Learning on data from the Fitbit API.
• Improved model prediction accuracy significantly through the use of Generative Adversarial Networks.

Mobile Application Software Intern - Trimega

May 2021 - August 2021

• Developed a mobile application using React Native, collaborating with a team using Agile methodology to enhance the company’s digital presence.

Publications

2024

• Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning
  Shree, S., Khadka, K., Lei, Y., Kacker, R. N., & Kuhn, D. R.
  39th IEEE/ACM International Conference on Automated Software Engineering

• Assessing the Degree of Feature Interactions that Determine a Model Prediction
  Khadka, K., Shree, S., Lei, Y., Kacker, R. N., & Kuhn, D. R.
  2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

• A Combinatorial Approach to Hyperparameter Optimization
  Khadka, K., Chandrasekaran, J., Lei, Y., Kacker, R. N., & Kuhn, D. R.
  Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI.

  • Distinguished Paper Award Candidate

2023

• Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder
  Khadka, K., Chandrasekaran, J., Lei, Y., Kacker, R. N., & Kuhn, D. R.
  2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW).

Projects

Synthetic Data Generation

• Research project focused on generating synthetic data using combinatorial testing and Variational Autoencoder techniques, aimed at improving data availability for training machine learning models.

Health Score Prediction System

• Developed a system for State Farm to predict health scores using Machine Learning and cloud-based infrastructure, significantly improving model accuracy.

Mobile Application Development

• Designed and built a mobile application using React Native, integrating Google API and other technologies to create an interactive user interface.

Skills and Technologies

• Languages: Python, R, C, C++, Java, JavaScript, SQL, HTML, CSS
• Libraries/Frameworks: TensorFlow, PyTorch, scikit-learn, React Native, Docker, AWS
• Tools: Git, MATLAB, Tableau, Tricentis Tosca