AI researcher at the Multimedia Processing Group.

• Led a pioneering AI and NLP project within the Corporate Internal Audit department to predict the risk of findings in audit reports, utilizing machine learning models, embeddings, and Azure OpenAI with a human-in-the-loop approach.• Developed a web application using Flask, Docker, and Kubernetes, integrating various programming tools to support the deployment of the project in production environments.• Collaborated closely with the Digitalization department to ensure the seamless deployment and integration of the project across corporate platforms.• Designed and implemented automation processes for monitoring user and device compliance with ISAE 3402 regulations, enhancing operational efficiency and regulatory adherence.• Conducted user testing for an online audit application, ensuring functionality met user requirements and standards.

• Implemented GPT-4 and other large language models using chain-of-thought prompting techniques, achieving F1 scores over 0.90 in a collaborative study on California SB-54 regulation, combining generative AI models with large-scale social data to explore the impact of social movements on plastics regulations.• Benchmarked sentiment classification models using the OpenAI API and Microsoft Azure, comparing performance to establish best practices for sentiment analysis applications.• Co-authored the paper titled "Generative AI and Discovery of Preferences for Single-Use Plastics Regulations", funded by Microsoft and the National Science Foundation (NSF). Presented at the AAAI 2023 Fall Symposium on Artificial Intelligence and Climate, highlighting the role of AI in climate-smart sustainable futures.