His research focus is a part of the Artificial Intelligence (AI) field, specifically through machine learning (ML).

Three Carnegie Mellon University researchers in the School of Computer Science recently earned Faculty Early Career Development Program (CAREER) awards from the National Science Foundation. The awards are the foundation's most prestigious for young faculty researchers. An assistant professor in the Computer Science and Electrical and Computer Engineering departments, Weina Wang received $500,000 to develop algorithms that guarantee ultra-low latency in edge computing, which supports emerging applications such as autonomous driving, augmented reality and automated mobile robots. This work will establish algorithms to optimize the time it takes for data to travel from one point to another and for the corresponding computation to be done without lag, even with a high volume of users in those systems. In addition to this research, Wang will also use the grant to continue expanding STEM outreach activities for K-12 students -- mentoring students from underrepresented groups, promoting the visibility of researchers from underrepresented groups and initiating online outreach seminars for the general public.

Four Carnegie Mellon University researchers in the School of Computer Science recently received Faculty Early Career Development Program awards from the National Science Foundation. The nearly $2.5 million will further research in deep learning, the safety of robots and autonomous systems, software engineering, and machine learning for healthcare. The NSF Faculty Early Career Development Program, commonly known as CAREER awards, is the foundation's most prestigious for young faculty members. Changliu Liu, an assistant professor in the Robotics Institute, was awarded nearly $745,000 for research to improve the safety of autonomous systems operating closely with humans. The work will develop a new algorithmic framework to assure the safety of robotic systems that optimizes performance when safety can be managed, anticipates and compensates for inevitable failures when it cannot, and learns from past mistakes.

The MIT Stephen A. Schwarzman College of Computing announced its first two named professorships, beginning July 1, to Frédo Durand and Samuel Madden in the Department of Electrical Engineering and Computer Science (EECS). These named positions recognize the outstanding achievements and future potential of their academic careers. "I'm thrilled to acknowledge Frédo and Sam for their outstanding contributions in research and education. These named professorships recognize them for their extraordinary achievements," says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. Frédo Durand, a professor of computer science and engineering in EECS, has been named the inaugural Amar Bose Professor of Computing.

A Rochester Institute of Technology professor has earned a prestigious National Science Foundation award to use computers to better understand human behavior and social interaction. Ifeoma Nwogu, an assistant professor of computer science, received an NSF Faculty Early Career Development (CAREER) award and grant for her five-year project. She aims to study human behavior in a new way, by using machine learning techniques to analyze and find patterns in the many signals that individuals display during social interactions. Her work will specifically look at groups working in science, technology, engineering and math (STEM), with the aim of supporting underrepresented groups in STEM. "In a conversation, people are constantly displaying and processing different non-verbal signals, such as how fast someone is talking or the facial expressions they are making," said Nwogu.

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system.