Five Natural Sciences Faculty Receive NSF CAREER Awards

October 5, 2022 • by Emily Engelbart

Five faculty in The University of Texas at Austin's College of Natural Sciences have recently received distinguished Faculty Early Career Development (CAREER) Awards from the National Science Foundation.

Pictures of five faculty recipients of NSF CAREER Awards

The CAREER award recognizes junior faculty for their potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

​José Alvarado, assistant professor of physics, studies biophysics, soft matter, fluid mechanics and "active matter"—collections of agents that each move or exert forces, such as fish in a school, cells in an organism or a swarm of robots. Alvarado's CAREER award will support research that uses gels made with the proteins actin and myosin, which are found in human bodies, as actuators for soft robots. Alvarado will look for ways to optimize the robots so they can switch between optimal energy efficiency and maximum mechanical power, like a rabbit foraging versus a rabbit running from a predator.

"When an engineer designs a robot, or when a group of cells try to maximize their evolutionary fitness, there are cases where energy efficiency is an important quantity that needs to be maximized," Alvarado said.

The project will allow for the design of efficient robots, as well as a better understanding of how cells manage their energy when performing mechanical tasks. Alvarado is also developing an educational robotics project for 4th through 6th graders that aims to involve them in building a robot powered by the expulsion of a fluid. 

Gregory Durrett, an assistant professor of computer science, focuses on machine learning. Durrett is leading research that aims to develop modern question-answering systems to be able to "think through" textual evidence which would allow for more reliable answers provided to the user asking these questions. Durrett noted that systems lack a human's ability to reason about and synthesize the information presented to them; his CAREER project aims to address this shortcoming. 

Antonio Linero, assistant professor of statistics and data sciences, focuses on developing flexible and appropriate Bayesian methods for analyzing complex longitudinal data, such as medical data collected from continuous monitoring of patients at the Dell Medical School. 

"The goal of my work is to draw causal conclusions about the effect of an exposure using state- of-the-art machine learning tools, while also respecting the fundamental challenges in learning from observational data," Linero said. 

Poorly designed machine learning methods behave with biases as humans do, so Linero's objective in his research is to correct these assumptions based on Bayesian inference. The first objective of this project is to determine when this bias does and doesn't occur, advance the designs through initiatives like observational studies, and implement Bayesian nonparametric causal inference into a comprehensive computational platform. 

Joseph Neeman, assistant professor in mathematics, is doing fundamental research that could ultimately lead to better algorithms in real-world computational problems. Using a computer to solve certain math problems can be extremely hard, even when all you need is an approximate answer. Neeman is exploring problems in geometry and probability that are linked to this "hardness of approximation" challenge. A better understanding of the limits of efficient approximate computation may in turn lead to better algorithms. His CAREER award will allow for graduate and undergraduate students to participate in related research projects and fund the development of open-source software for numerical computation in addition to supporting outreach activities for K-12 students. 

Investigator Yuke Zhu, assistant professor in the department of computer science, aims to enable intelligent robots to see, think and act in real-world tasks. The main objective of this project is to build new algorithms and tools for intelligent robots that operate autonomously in everyday environments and interact with objects to perform physical tasks in ways that better allow for widespread deployment than exists with current technology. 



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