This paradigm shifting project advances the ability to read DNA and proteins and builds on designing nanotechnology systems using machine learning. The goal is to use computational thinking for changing the way biomolecules are designed by providing a synergistic combination of physical modeling, computational data, and adapted statistical learning methods that proactively account for the randomness. Currently all-electronic sequencing of DNA and proteins is a speculative dream that could revolutionize biochemical sciences and medicine. This project investigates theoretically and algorithmically the fundamentals of all-electronic sequencing using a computational framework. Currently, the field of machine learning treats the extraction of useful features from data as largely independent from the design of learning algorithms that act on the extracted features. This project aims at discovering whether significant performance gains can be obtained by designing learning algorithms that explicitly account for the physical variations and randomness in electronic sequencing. The underlying computational thinking can potentially lead to insights and methods leading to transformative advances on all-electronic sequencing of biomolecules.
The research direction of this project can impact biology, medicine, nanoelectronics, and the design of machine learning systems. The training of high school, undergraduate and graduate students in quantum devices and statistical theory and methods in the design and understanding of nanoscale and machine learning systems are an integral part of the activities spanned by this project.
This award is part of the Cyber-Enabled Discovery and Innovation program, and the recipients are Professors M. P. Anantram and Maya Gupta of the University of Washington.
