he/him/his
Brett Daley is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Christopher Amato. He specializes in a subfield of machine learning called deep reinforcement learning, which combines approximate models of the brain (neural networks) with an incentive mechanism analogous to a dopaminergic reward system. Brett’s research seeks to make this type of learning more efficient in the presence of rich sensory information, such as high-resolution images. For his experiments, he tests new algorithms by measuring how quickly they can learn to play arcade games or control simulated robots without any prior knowledge.
Brett completed his undergraduate degree at Northeastern and then studied abroad at Tsinghua University in Beijing as a Schwarzman Scholar. He found that China’s unique cultural differences, rapid economic development, and enormous manufacturing sector made it it a fascinating case study for machine learning applications. Brett began his career in traditional engineering fields, like computer architecture, circuit design, and firmware development. After working at Tesla and witnessing its swift progress on Autopilot and its highly automated Gigafactory in Nevada, he acquired a strong interest in machine learning, which led to his current research path.
he/him/his
Brett Daley is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Christopher Amato. He specializes in a subfield of machine learning called deep reinforcement learning, which combines approximate models of the brain (neural networks) with an incentive mechanism analogous to a dopaminergic reward system. Brett’s research seeks to make this type of learning more efficient in the presence of rich sensory information, such as high-resolution images. For his experiments, he tests new algorithms by measuring how quickly they can learn to play arcade games or control simulated robots without any prior knowledge.
Brett completed his undergraduate degree at Northeastern and then studied abroad at Tsinghua University in Beijing as a Schwarzman Scholar. He found that China’s unique cultural differences, rapid economic development, and enormous manufacturing sector made it it a fascinating case study for machine learning applications. Brett began his career in traditional engineering fields, like computer architecture, circuit design, and firmware development. After working at Tesla and witnessing its swift progress on Autopilot and its highly automated Gigafactory in Nevada, he acquired a strong interest in machine learning, which led to his current research path.
