Collaborating with Caterpillar to:"Develop an AI model using Caterpillar insights (current supplier performance trends and future indicators), leveraging cloud data storage, dashboards, and static inputs to predict supplier constraints for Caterpillar."Keywords: Data Analytics, Data Science, LangChain, Vector and Raster DataTools: Data Visualization, Gradio, Hugging Face, Large Language Models, Pandas, Python, Pytorch, Snowflake, SQL, Streamlit

1) Mentoring students on data science projects in collaboration with Sandia National Labs 2) Providing guidance to 18 hybrid students (13 in-person and 5 online) from Purdue and Florida A&M on technical skills, project development, and inter-university coordination

1) Developing an AI model for the Autonomous Karting Series, optimizing race strategies for both a pre-mapped track and real time track navigation2) Engineered and integrated a Battery Management System on Raspberry Pi3) Configured LiDAR and camera systems on Nvidia Jetson 4) Overcame challenges in documentation inconsistencies and debugged missing packages to achieve seamless interface and functionality between LiDAR, Camera, and Nvidia Jetson.

As part of a student research team at The Data Mine, working in collaboration with Sandia National Labs, I helped develop a model that predicts where a plane is headed if it suddenly loses contact with ground control. We used historical flight data and some pretty advanced tools to figure out the most likely destination in these situations.Key Responsibilities:Data Analysis: Worked with a huge amount of flight data using Python and Tracktable, focusing on flight paths to build a model that predicts destinations.Model Development: Created a grid system that tracks where flights have historically landed based on their routes, then used this data to predict where a missing flight might be going.Accuracy Improvement: Managed to increase the accuracy of predicting the correct airport to 31%, and found that the right destination was in the top 20 airports 75% of the time.Visualization: Developed visual tools to show prediction accuracy and flight paths, using a color-coded map of the U.S. to display the top predicted destinations.Technologies Used:1) Python2) Git and Github3) Jupyter Notebooks4) Tracktable5) Anvil for processing large datasetsImpact:This project was a great learning experience in motion analysis and aviation safety. It could potentially help in emergency situations by predicting where lost flights are headed.

* Collaborated with Sacramento ACM Professional Chapter to host the 5th Future Worlds Symposium;* Coordinated the 2 day event as the Master of Ceremonies;* Curated the symposium’s participant-facing website;

Developed User authentication system from website to database;Setup and Deployed IOT devices;Developed a Scheduler that presents relevant train data to Railway companies;Improved UI and UX of website;Cleaned and did Quality Assurance on data provided by 3rd party services;