Anagha Joshi

I am an ML researcher and software engineer with over eight years of experience building large-scale applications and practical AI systems. Previously, I was a graduate research assistant at the Institute of Artificial Intelligence at the department of Computer Science at University of Georgia, Athens, USA. For my masters thesis at UGA, I focused on Interpretable AI for Image Classification and I was advised by Dr. Jaewoo Lee. My research interests lie at the intersection of machine learning and software engineering (SE), with a focus on robustness, explainability, and symbolic–neural integration. My research background and industry experience in SE and explanaiable AI have shown me that the biggest bottleneck to AI adoption is often a lack of trustworthy and secure tools.

Based in Austin, Texas, I work as a Software Development Engineer at Amazon, where I design scalable APIs, automate complex systems, and write clear technical documentation that keeps teams aligned. I am now expanding my research into projects that connect my industry background with real-world impact, especially in explainable AI and AI systems that help organizations make better decisions with limited resources.

My goal is to help build technology that is robust, understandable, and accessible — whether that means designing multi-agent AI systems, deploying scalable APIs, or improving trust in AI-powered recommendations for everyday businesses.

My research bridges software engineering and practical AI. I have developed AI-driven program analysis tools using Boolean tensor models for syntax error repair on real-world datasets.

During my master’s, I designed TPNet, an interpretable transformer-based image classifier that shows how deep learning models can remain transparent and auditable. I also built a deployed web application that automated grading tasks, combining practical software design with applied machine learning.

I am motivated by research that tackles real-world challenges at the intersection of technology, environment, and human well-being. I am interested in building AI systems that stay understandable and make a measurable difference where they are needed most.

Based on the source code from jonbarron.info.