Graphene Sensors and Machine Learning
My research project focuses on building sensors which measure the effect of the changes in chemical and gas concentration on the device parameters of Graphene electrolyte-gated field-effect transistors (EGFETs) especially their minimal conduction (Dirac) point. Hence I will design analog circuits to facilitate tracking of the Dirac point through feedback mechanisms. Afterwards I will work on fabrication of these sensors and increasing their selectivity through machine learning algorithms. Finally this project aims to extend the application of EGFETs to cover areas such as smart-skin technology and novel large-scale integrated systems.

I believe that the SuperUROP program will not only equip me with skills crucial to success in graduate academic research but also increase my knowledge in circuits and their applications in material physics areas which attract my attention. In this context classes I took in MIT skills I brought from my summer internship focused on circuit design and my physics olympiad past prepared me for the research.