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I'm a first-year Computer Science Ph.D. student at Purdue University interested in building machine learning systems that are interpretable, reliable, and able to adapt efficiently to new data and tasks. My current research interests include interpretability and reliable AI, continual and sample-efficient learning, multimodal learning, and computer vision.

Before Purdue, I earned my B.S. in Computer Science with Highest Honors from the University of Texas at San Antonio (UTSA), where I conducted research across machine learning, computer vision, AI education, distributed deep learning, and cybersecurity.

Some of my research and projects include:

• Data-Efficient Learning & Computer Vision — Investigated data pruning and sample utility across image classification and semantic segmentation, including applications to scientific defect detection.
• Multimodal & Few-Shot Segmentation — Developed segmentation pipelines using models including SAM2 for defect detection in materials fabrication.
• LLM Agents — Contributed to research studying how middle-school students identify LLM agents in live collaborative environments.
• Distributed Deep Learning — Worked on distributed DNN inference for edge environments, including pipeline-parallel inference across resource-constrained devices.
• AI Education — Developed interactive systems for teaching foundational AI concepts, including AI-See, search algorithms, decision trees, and rule-based AI.
• OOD Generalization & Cybersecurity — Investigated out-of-distribution generalization for ML-based IoT malware detection.

Programming: Python, Java, C, JavaScript, HTML/CSS

Machine Learning: PyTorch, TensorFlow, scikit-learn

ML Experience: Computer Vision, Semantic Segmentation, Data Pruning, Distributed Deep Learning, Out-of-Distribution Generalization

Tools & Computing: Git, Linux, Bash, VS Code, LaTeX, Raspberry Pi

Databases: MySQL, MongoDB

Small utilities I created to improve my research and productivity workflows. These were developed using AI-generated code based on my specifications and manually tested and iteratively refined by me.

• Word Counter Extension — Browser extension for quickly counting highlighted text, built to streamline checking word limits while writing in Overleaf.
• BibCleaner — Research utility for validating BibTeX entries and identifying preferred publication links, prioritizing authoritative conference or publisher sources over preprints when available.
• Petal — macOS productivity application combining multiple customizable timers, Pomodoro sessions, and productivity tracking to analyze how time is spent across tasks.