好色先生TV

Saurabh Vyas wants to learn how people think, and he鈥檚 using a combination of engineering, cognitive science and artificial intelligence to find out.

The brain can absorb new information and use it to navigate unfamiliar situations. In school, people learn skills like multiplying numbers, and later, in everyday life, they can apply those same methods to solve problems 鈥� such as calculating the tip on a restaurant bill.

Vyas, who joined 好色先生TV鈥檚 Neuroscience Institute and Department of Biomedical Engineering as an assistant professor in January 2026, aims to understand how the brain encodes information during learning and later retrieves it in new contexts.

鈥淚 want to study how the brain takes knowledge about some topic then applies it to solve a totally new problem,鈥� Vyas said. 鈥淚 want to try to understand how neural circuits actually represent that knowledge, acquire that knowledge through learning and ultimately use that knowledge to solve problems that the brain has essentially never seen before.鈥�

Vyas investigates how the brain learns and recalls skills through an iterative cycle, beginning with artificial intelligence models and gradually integrating insights from biological experiments. He starts by training and testing AI models to solve new tasks. As these models learn, Vyas analyzes their behavior and refines their structure until they accurately reflect current understanding of the brain.

鈥淚n an artificial system, you have access to everything; you know exactly what that system is trying to do, and critically, how exactly it鈥檚 doing it. That gives us an unusually good starting hypothesis for what to look for in real brain data,鈥� Vyas said.

After using the AI model to better understand how the brain might respond in a specific and novel situation, Vyas tests those ideas in a biological model. He uses advanced electrodes capable of recording activity from thousands of neurons simultaneously across multiple brain regions, allowing him to observe how they work together in a network. He then uses the results from those tests to refine the AI model, so it more accurately reflects natural behavior.

鈥淩epeatedly cycling between the theory and experiment loop, sharpens the models and our understanding of the neural mechanisms behind intelligent behavior,鈥� Vyas said. 鈥淲e get to make sense of neural events that are happening on single individual moments, and this is so important and meaningful for neuroscience because now we can interpret how a subject solves a problem in a way that they may have never solved in the past and may never solve again in the future.鈥�

Vyas joined Carnegie Mellon after completing a postdoctoral fellowship at Columbia University. He said he looks forward to collaborating with colleagues in Carnegie Mellon鈥檚 Neuroscience Institute and engage with the Center for the Neural Basis of Cognition, a joint effort between Carnegie Mellon and the University of Pittsburgh.

鈥淭here鈥檚 a really strong collaborative atmosphere across different departments and different labs,鈥� Vyas said. 鈥淚t will really enable me to do the kinds of research I鈥檓 excited about.鈥�

Matthew Smith, co-director of the CNBC and professor of biomedical engineering in the Neuroscience Institute and the Department of Biomedical Engineering, was part of the hiring selection. He said that Vyas鈥� work will bring a whole brain perspective to the Neuroscience Institute and the CNBC.

鈥淪aurabh is focused on one of the most important mysteries in cognition 鈥� how does the brain engage in abstract reasoning to flexibly solve new problems?鈥� Smith said. 鈥淪aurabh鈥檚 work is at the core of the neural basis of cognition, and I鈥檓 excited for how he advances the mission of the CNBC.鈥�

Vyas said he is eager to help future researchers gain experience and achieve their own breakthroughs. He has started building his lab at Carnegie Mellon 鈥� the Laboratory of General Intelligence and Computation (LOGIC) 鈥� and will begin teaching graduate computational neuroscience courses in Spring 2027.

鈥淚 really value training the next generation of scientists,鈥� Vyas said. 鈥淚鈥檓 looking forward to engaging students with new ideas that are still fresh in the labs and not in the textbooks yet.鈥�