Building A Future Worth Living

Engineer/Data Scientist

• Led end-to-end engineering and manufacturing of a 3D hollow-tube bioreactorsystem using CNC machining, 3D printing, and Arduino-based sensor integration, improving throughput and efficiency by over 10%.
• Developed real-time computer vision models for natural killer (NK) cell detection inmicroscopy images, enabling semi-automated classification.
• Designed and implemented closed-loop sensor feedback systems to monitor andcontrol environmental variables (temperature, oxygen, pH).
• Holder of patent: Device for testing a sample of cells.
• Collaborated across hardware, software, biology, and business functions; preparedinvestor presentations and communicated technical value to non-technical stakeholders.

Data Scientist/ML Eng

• Designed and delivered a production-ready ML application for body composition analysis, achieving 95% model accuracy through hyperparameter optimization, cross-validation, and feature engineering.

DevOps Engineer

• Implemented a recommendation ML model coupled with a DevOps approach to orchestrate container deployment on AWS via Terraform.

• Established CI/CD pipeline and utilized Terraform to automate and have reusable code to automate lifecycle.

• Implemented autoscaling, monitored with AWS CloudWatch, and collaborated on container security, resulting in an efficient, scalable, and reliable system.

• Developed a facial recognition system using Siamese neural networks with TensorFlow for application authentication.

Data Eng / ML Eng

• Developed Foodie-AI, an ML-powered application for preventative medicine that analyzes dietary and health data to deliver personalized recommendations using SQL, NoSQL (MongoDB), and vector databases for efficient semantic search and retrieval-augmented generation.

• Integrated LiDAR, MediaPipe, and Three.js systems to resolve landmark scan inaccuracies, enabling precise 3D body modeling, improved pose estimation, and reliable anatomical landmark detection for accurate health metrics.

• Built scalable stream data pipelines to process unstructured datasets for ML-driven applications helping process TB of data.

• Implemented autoscaling, monitored with AWS CloudWatch, and collaborated on container security, for the purpose of scalable, and reliable systems