Inverted PbS CQD architecture can cause damage to the CQD absorbing layer due to harsh solvents used in the ETL solution. The goal is to replace solution-phase ZnO nanoparticle ETL layer with sputtered ZnO with using the AJA Magnetron Sputterer at the Materials Characterization and Processing Center (MCP). This method deposits the ETL without a solvent, protecting the absorbing layer from damage and improving overall power conversion efficiency (PCE).

India faces major healthcare access challenges, especially in rural areas with limited doctors and long travel times. Telemedicine bridges this gap, but current clinical history-taking at the community level is inefficient—CHWs often collect lengthy, unfocused histories before connecting with hub doctors. Our project leverages AI and Large Language Models (LLMs) to create an adaptive, efficient history-taking tool that guides CHWs to ask relevant questions based on patient responses. This system reduces consultation time, improves information quality, and helps doctors make quicker, more accurate decisions. We are developing and optimizing this tool using agent-based simulations and prompt engineering, and will evaluate its performance with both expert clinical reviewers and in a real-world pilot in rural Nashik, India. This innovation aims to enhance care quality, reduce delays, and ultimately scale across India’s public telemedicine ecosystem, including eSanjeevani.

We are developing a personal finance and budgeting app designed for college students to help them track expenses, create personalized budgets, and improve financial literacy. The app will feature an AI-powered assistant to answer finance-related questions and provide budgeting insights. Our goal is to create a one-stop solution for students to budget, save, and manage their money effectively.

Design, build, and test a means to detect and prevent an autonomous forklift from tipping. Use the device to inform performance improvements to the forklift to safely improve speed of operation.