Delivering machine learning and data solutions to basketball leagues globally through software engineering and AI expertise.

Received support from MIT Sandbox to make buildings more energy efficient in the hospitality industry. Successfully installed pilot system in MIT (FSILG) residential building to capture data for creation of MVP.

Enhancing MIT Center for Collective Intelligence's (CCI) Supermind Ideator by utilizing continuous analytics of user data to develop better organizations, processes and products that help form Superminds. Ideator encourages the formation of Superminds by utilizing LLMs during the ideation process.

MEng Thesis Focus: Develop a Machine Learning-driven recommendation system to help users in the ideation process, tailored to their specific topic, where they are in the creative process, and various other factors.

Assisted a cross-departmental team which is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings. Improved the machine learning strategy by conducting a detailed statistical analysis of temperature fluctuation and HVAC usage in MIT buildings. This analysis led to the development of complex graphical representations to be used for the team's ML system, illustrating the dynamic temperature interactions between rooms.

Improved ALS patient-ML model training efficiency by writing scripts to create functional datasets from EEG readings obtained from Brain Computer Interfaces (BCIs); the goal of the ML models was to classify when patients had a certain kind of brain activity which indicated they needed help from their care provider.