Software Developer and Research Assitant at UT-Arlington Research Institute

University of Texas at Arlington Research Institute (UTARI), Arlington, TX

• - Collaborating on the development of an adaptive exercise machine with research team for a game component with accessibility enhancement.
• - Implemented data visualization and dynamic scoring system for real-time performance insights.
• - Implemented a fix for heart-rate BLE device.
• - Analyzing and optimizing system mechanics to align with research goals, ensuring accuracy in data representation.

SoNIC Summer Research Workshop- Cornell

Cornell University, Ithaca, NY

• - Researched the current limitations of text and image to text multimodal AI LLaVA to use as benchmarks and implemented methods to circumvent them for applications of citizen science and aiding the visually impaired.
• - Showcased the potential of combining object detection and depth estimation in computer vision to enhance safety for visually impaired individuals.
• - Utilized monocular depth estimation to calculate and prioritize object distances for user notification.

OIT for Campus Operations-PD

Student Assistant, Arlington, TX

• - Collecting and cleansing data from multiple sources, ensuring accuracy and readiness for analysis.
• - Conducting exploratory data analysis to identify trends, patterns, and insights.
• - Created reports and dashboards using Streamlit/Chainlit, highlighting key findings and supporting data-driven decision-making.

HackUTA 2023 - Major Hacking League (MLH)

Hackathon, Arlington, TX

• - Participated in a 24-hour hackathon, demonstrating rapid prototyping and problem-solving skills under time constraints.
• - Developed Anatomy AI, an intelligent system for querying medical documents, showcasing the application of AI in healthcare.
• - Implemented a conversational interface using Chainlit, enabling natural language interactions with the AI system.
• - Leveraged the Llama LLM model for advanced natural language processing and understanding of medical terminology.
• - Utilized FAISS and Vector Database for efficient storage and retrieval of medical information.
• - Integrated HuggingFace embeddings to enhance the system's ability to understand and process medical text.
• - Demonstrated the project's capability to answer complex queries about medical conditions, as shown with the Addison's disease example.
• - Gained valuable experience in collaborative development and presenting technical solutions in a competitive environment.