Ian Abraham, PhD

Ian Abraham's research focuses on the development of learning and control algorithms that enable robust and curios behaviors in robotic systems. These behaviors allow robots to learn new skills and capabilities that adapt to their environment. A key aspect of his group's research is developing mathematical principles for learning and control that guarantee reproducibility and reliability in the robot behaviors. Interestingly, these principles serve a dual purpose that provides insight towards understanding how animals and humans adapt to their environments. Already, his prior work has connected how rats use the sense of touch via their whiskers to navigate and explore their world which can help inform how humans use their sense of touch. In advancing a robot's ability to learn and adapt to their environment through principled mechanisms, Abraham's group hopes to uncover similar principles that generalize across human perception and cognition.

deep learning ;  neural networks ;  optimization  

algorithms ;  artificial intelligence ;  machine learning ;  motor control ;  robotics ;  sensory systems  

Abraham is an Assistant Professor in Mechanical Engineering at Yale University with a courtesy appointment in Computer Science, where he focuses on developing methods for robotic control and learning. Before joining Yale, he was a postdoctoral researcher at Carnegie Mellon University's Robotics Institute, in the Biorobotics Lab. He received his PhD and MS degrees in Mechanical Engineering from Northwestern University and his BS degree in Mechanical and Aerospace Engineering from Rutgers University. During his PhD, he also worked at the NVIDIA Seattle Robotics Lab, where he investigated GPU-accelerated robust control.