Bhavya Kailkhura
About Bhavya Kailkhura
Bhavya Kailkhura is a Research Staff member at Lawrence Livermore National Laboratory, specializing in deep learning models for material discovery and machine learning systems for UAV drones. He holds a Ph.D. in Electrical and Computer Engineering from Syracuse University and has been engaged in research since 2018.
Work at Lawrence Livermore National Laboratory
Bhavya Kailkhura has been employed at Lawrence Livermore National Laboratory as Research Staff since 2018. In this role, Kailkhura engages in advanced research focused on creating reliable and interpretable deep learning models specifically for material discovery. This position allows for the application of machine learning techniques to solve complex problems in scientific research.
Education and Expertise
Kailkhura holds a Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering from Syracuse University, which was completed from 2012 to 2016. Prior to this, Kailkhura earned a master's degree in electrical engineering from the same institution, studying from 2010 to 2012. This educational background provides a strong foundation in engineering principles and machine learning methodologies.
Research Focus
Kailkhura's research primarily concentrates on developing safe and robust machine learning systems for autonomous collaborative UAV drones. This work involves creating deep learning models that are interpretable and reliable, which is crucial for the deployment of UAV technology in various applications. Additionally, Kailkhura specializes in uncertainty-aware small data deep learning, aiming to improve sample efficiency in machine learning models.
Background
Kailkhura's academic journey includes significant time spent at Syracuse University, where foundational knowledge in electrical engineering was established. The transition from a master's program to a Ph.D. program reflects a commitment to advancing expertise in the field of electrical and computer engineering, particularly in machine learning applications.