Welcome to my website!
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