DEPARTMENT HIGHLIGHTS
( From left ) Anuj Karpatne , Department of Computer Science and Sanghani Center for Artificial Intelligence and Data Analytics ; Amrinder Nain and Sohan Kale , both in the Department of Mechanical Engineering , meet in the STEP Lab . Photo by Peter Means for Virginia Tech .
Researchers receive grant to predict the mechanics of living cells
BY BARBARA L . MICALE
With advances in deep learning , machines are now able to “ predict ” a variety of aspects about life , including the way people interact on online platforms or the way they behave in physical environments . This is especially true in computer vision applications where there is a growing body of work on predicting the future behavior of moving objects such as vehicles and pedestrians .
“ However , while machine-learning methods are now able to match — and sometimes even beat — human experts in mainstream vision applications , there are still some gaps in the ability of machine-learning methods to predict the motion of ‘ shape-shifting ’ objects that are constantly adapting their appearance in relation to their environment ,” said Anuj Karpatne , assistant professor of computer science and faculty at the Sanghani Center for Artificial Intelligence and Data Analytics .
This is a problem encountered in many scientific fields , Karpatne said . For example , in mechanobiology , cells change their shape and trajectory as they move across fibrous environments in the human body , constantly tugging or pushing on the fibers and modifying the background environment , which in-turn influences the movement of cells in a perpetual loop .
“ This is fundamentally different from mainstream applications in computer vision where changes in the background caused by pedestrians and vehicles are far less accelerated than those possible by the movement of living cells governed by the laws of mechanics and biology ,” he said .
To address this challenge , the National Science Foundation has awarded a team of Virginia Tech scientists a $ 1 million grant to create a new avenue of research in physics-guided machine learning . The project will , for the first time , systematically integrate the mechanics of cell motion available as biological rules and physics-based model outputs to predict the movement of shape-shifting objects in dynamic physical environments .
52 VIRGINIA TECH MECHANICAL ENGINEERING • ANNUAL REPORT 2020-2021