AI Agent for ML-Business Alignment

Developed an AI agent to improve alignment between ML development teams and business stakeholders. The agent facilitates communication, provides crucial business context, and ensures ML models directly support strategic goals and KPIs. Key features include:

• Real-time context provision to ML teams
• Alignment of model evaluation criteria with business impact metrics
• Automated flagging of potential conflicts with business rules or market realities
• Streamlined access to relevant past models and business insights
• Significant reduction in knowledge transfer time and misalignment issues

UC Berkeley ML/AI - What drives the patient's length of stay?

Developed a machine learning model to predict patient length of stay in hospitals, optimizing resource allocation and improving patient care. Utilized neural networks and ensemble methods to achieve high accuracy in predictions. Integrated an advanced AI chatbot, powered by a transformer-based neural network, to autonomously analyze the findings and provide concise decision-making guidance for stakeholders.

MOSAIC - AI Immigration Chatbot

Developed an AI-powered chatbot with SFU Blueprint for MOSAIC to assist Canadian Immigration Consultants, significantly enhancing user experience by providing personalized recommendations and real-time information in multiple languages. Leveraging technologies such as Flask for backend services, Neo4j for graph database integration, and OpenAI models for natural language processing, the chatbot efficiently processes user queries and retrieves relevant information. The project was shortlisted in the Top 4 of the SFU CS Diversity Award, recognizing its innovative approach to improving accessibility and efficiency for newcomers, immigrants, and refugees.

Voronoi Health Analytics - AI Medical Translation & Retrieval Engine

Developed a retrieval augmented generation software that translates doctors’ natural language requests into JSON, streamlining access to CT and MRI scan analytics through the DAFs application. Eliminated the need for medical professionals to memorize complex codes for report retrieval, potentially saving hours of manual reference time per week. Utilized Python and frameworks such as Llama Index and the OpenAI API to develop the software, enhancing efficiency and accuracy in processing natural language to JSON conversion.