Welcome to my portfolio!

• About Me
• Research Interests
• Education
• Publications
• Experience
• Technical Skills
• Honors & Awards

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About Me

Hello! I am a PhD candidate in Machine Learning at The University of Texas at Arlington with 6 first-authored publications (46+ citations) in top-tier venues including KDD and ASE. My research focuses on explainable AI, adversarial robustness, and trustworthy ML systems, conducted in collaboration with NIST. I am also the co-founder of Bhasha Tech, an AI-powered language learning platform serving 50,000+ users across 130+ countries. I combine deep research expertise with proven ability to build and scale real-world ML products.

Research Interests

Explainable AI (XAI), Trustworthy Machine Learning, Adversarial Robustness, Combinatorial Testing for ML, Synthetic Data Generation, Deep Learning Interpretability

Education

Ph.D. in Computer Science – Machine Learning and Artificial Intelligence The University of Texas at Arlington Expected August 2026 GPA: 4.0/4.0

B.S. in Software Engineering The University of Texas at Arlington Graduated Fall 2021 GPA: 4.0/4.0 (Summa Cum Laude)

Publications

6 peer-reviewed papers, 46+ citations

2026

• ABLE: Adversarial Boundary Local Explanations for ML Predictions K. Khadka, S. Shree, P. Budhathoki, Y. Lei, R. Kacker, D.R. Kuhn ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2026 (Top-tier) Achieved state-of-the-art fidelity on local explanations using novel adversarial bracketing technique

• A Combinatorial Approach to Synthetic Data Generation for ML K. Khadka, J. Chandrasekaran, Y. Lei, R. Kacker, D.R. Kuhn SN Computer Science, vol. 7, no. 59, 2026

2024

• Constructing Surrogate Models Using Combinatorial Testing S. Shree, K. Khadka, Y. Lei, R.N. Kacker, D.R. Kuhn 39th IEEE/ACM International Conference on Automated Software Engineering (Top-tier)

• A Combinatorial Approach to Hyperparameter Optimization – Distinguished Paper Award Candidate K. Khadka, J. Chandrasekaran, Y. Lei, R.N. Kacker, D.R. Kuhn IEEE/ACM 3rd International Conference on AI Engineering, 22 citations

• Assessing Feature Interactions in Model Predictions K. Khadka, S. Shree, Y. Lei, R.N. Kacker, D.R. Kuhn IEEE International Conference on Software Testing, Verification and Validation Workshops, 5 citations

2023

• Synthetic Data Generation Using Combinatorial Testing and VAE K. Khadka, J. Chandrasekaran, Y. Lei, R.N. Kacker, D.R. Kuhn IEEE International Conference on Software Testing, Verification and Validation Workshops, 16 citations

Experience

Co-Founder & CTO

Bhasha Tech, Inc. – Bhasha Language Learning App January 2025 – Present | Dallas, TX

• Co-founded and launched Bhasha, an AI-powered language learning platform serving 50,000+ users across 130+ countries
• Architected and deployed iOS and Android applications leveraging Large Language Models for personalized language instruction and adaptive learning paths
• Built end-to-end ML pipeline for speech recognition, pronunciation feedback, and conversational AI tutoring
• Scaled platform infrastructure to handle rapid user growth while maintaining low latency and high availability

Guest Lecturer – Introduction to Machine Learning

Winston-Salem State University November 2025 | Winston-Salem, NC

• Delivered a 2-week lecture series on Introduction to Machine Learning for undergraduate students
• Covered foundational ML concepts including supervised/unsupervised learning, neural networks, and practical applications

Graduate Research Assistant

The University of Texas at Arlington May 2022 – Present | Arlington, TX

• Leading research on Explainable AI methods, developing novel techniques for interpreting deep learning model predictions with applications to CNNs and Vision Transformers
• Pioneered ABLE (Adversarial Boundary Local Explanations), a novel method using adversarial pairs to construct interpretable local surrogate models – accepted to KDD 2026
• Developed combinatorial testing approaches for synthetic data generation using VAEs and GANs, improving data diversity while maintaining distributional fidelity
• Collaborating with NIST researchers (Dr. Rick Kuhn, Dr. Raghu Kacker) on AI standards and trustworthy ML systems

Graduate Teaching Assistant – Software Testing

The University of Texas at Arlington January 2022 – May 2022 | Arlington, TX

• Delivered lectures on software testing methodologies, including static analysis (SonarQube) and automation (Selenium)
• Mentored 40+ students on testing frameworks, code quality, and CI/CD best practices

Machine Learning Software Developer Intern

State Farm – Life Fit Project January 2021 – August 2021 | Richardson, TX

• Designed ML pipeline generating health risk scores using K-Means clustering on Fitbit API data for insurance underwriting
• Improved model accuracy from 78% to 93% by implementing GAN-based data augmentation for imbalanced classes
• Deployed scalable backend using AWS (Lambda, SageMaker, EC2, S3, Cognito) serving 10K+ daily predictions

Technical Skills

• Machine Learning: PyTorch, TensorFlow, scikit-learn, XGBoost, Transformers, CNNs, GANs, VAEs, SHAP, LIME
• Programming: Python, R, C/C++, Java, JavaScript, SQL, Shell Scripting
• Cloud & MLOps: AWS (SageMaker, Lambda, EC2, S3), Docker, Git, MLflow, Weights & Biases
• Data & Visualization: Pandas, NumPy, Matplotlib, Seaborn, Tableau, MySQL, PostgreSQL

Honors & Awards

• Distinguished Paper Award Candidate – IEEE/ACM CAIN 2024
• MavPitch Competition Winner – $15,000 Award (2025)
• Summa Cum Laude – B.S. Software Engineering, UTA (2021)