Build domain-aware agents that automate routine work and reduce engineering overhead, delivering transparent reasoning for everyone.

• Developed web-based applications for genetic variants analysis, visualization, and detection at the University of Utah School of Medicine.

• Contributed to open source projects like https://gene.iobio.io/ and https://bam2.iobio.io/.

• Deployed a centralized monitoring system (Grafana, Prometheus, Loki, Docker-compose) to improve backend stability by 70% and reduce costs by 30% through auto-scaling and proactive alerting.

• Developed a 3D printer ticket system using Python, FastAPI, and MongoDB to automate print request workflows and monitor printer operational status, integrating with Jira for ticket management.

• Provided consultation to students and faculties on 3D modeling and assisted with 3D designing, scanning, and printing.

• Automated bio-lab data processing and enhanced the AI-aided drug discovery workflow by building ETL pipelines with a CI/CD process (GitHub Actions, Docker, ECR), improving data delivery efficiency for scientific teams.

• Built event-driven, serverless ETL pipelines using AWS Lambda, AWS Glue and DynamoDB creating a scalable and cost-efficient data processing foundation for the ML platform.

• Deployed a machine learning model inference service and Streamlit web application with Python and RDKit, empowering biologists to perform real-time chemical compound prediction analysis