The human brain remains the most intelligent and general information processing system on earth — so what if we built AI to match it? That’s the question driving two related fields: neuromorphic computing, which designs hardware and software modeled on the brain’s structure, and neuroAI, which applies biological principles to build more adaptable and efficient artificial intelligence. Unlike conventional computers, neuromorphic systems use spiking neural networks that fire brief bursts of electrical signals only when needed, making them far more energy-efficient and capable of handling many tasks simultaneously. These models also work in reverse, serving as tools to unlock the mysteries of how our own minds work. Researchers believe these brain-inspired approaches could help address one of AI’s biggest emerging challenges: the enormous amount of energy required to power today’s large-scale systems.
Brennen Hill x’26 is a Computer Sciences major graduating this May. He has spent his time at UW–Madison exploring the intersection of neuroscience and AI, founding a lab, contributing to award-winning robotics research, and shipping algorithms to real-world robots. Next up? He’s headed toward a PhD.
What drew you to computer science?
One reason I love computer science is the creative freedom it offers. There are so many possibilities to create and discover — and there is no single answer to any problem; each one has a seemingly infinite number of solutions. I’m also fascinated by intelligent systems. I began building them in high school, writing code for robots and complex video games. I believe in the value of studying biological intelligence in
order to best create intelligent systems, so I look at neuroscience and computational cognitive science to ground my work, and I’ve taken several graduate-level courses in machine learning.
You didn’t just join research here — you founded a lab. How did that happen?
It grew out of a curiosity that didn’t fit neatly into any existing box. Professors in AI told me that applying neuroscience to AI is still relatively uncommon. At the same time, a neuroscience professor pointed out the potential of building AI’s neurons directly into hardware, essentially describing the field of neuromorphic computing. I was pursuing something that simply didn’t have a home on campus yet.
So I searched on my own, reading textbooks and papers outside my coursework until I began to understand the active research areas and build the skills to work in them. Eventually I founded the Wisconsin Neuromorphic Computing and NeuroAI Lab, which now has over 100 members, to create both an interdisciplinary research hub and a learning community. I secured funding, dedicated space, a faculty advisor, 
and a partnership with a NeuroAI startup. I was delighted to see the collaborative nature of the environment — neuroscience students explaining the biomechanics of a neuron to engineers, and computer scientists explaining artificial neural networks to biologists.
Why UW–Madison specifically?
It has great coursework in computer science, artificial intelligence, neuroscience, and research, which was significant to me since I aimed to conduct research at the intersection of these fields. But I also came because of the Wisconsin Idea, which I firmly believe in — the principle that education should influence people’s lives beyond the boundaries of the classroom. Before college, I’d tried to live that out by tutoring students struggling in math and science. Coming to UW–Madison felt like the right place to take that further.
You’re also working at a startup that’s currently in “stealth mode,” with very little public information out there. What can you share about that?
I joined a company working on autonomous drones, where I lead state estimation research. The first project taught me a lot: state-of-the-art algorithms proved unsuited for our constrained hardware and low-accuracy sensors, so I developed a new approach that simplified the data the system needed to process. It achieved over 100 times improvement in accuracy, and it’s now deployed on all of the company’s robots. That experience taught me to navigate the entire research-to-deployment pipeline under real-world constraints.
What comes after graduation?
I’ve applied to PhD programs where faculty use neuroscience to research AI. Long-term, the career that interests me most is to be a research scientist at a place like Google DeepMind — I am very interested in discovering new knowledge but also want to apply it.
What does life outside the lab look like?
I train in dance — I’ve been focusing on ballet, contemporary, Latin, and hip-hop. And in my spare time I build video games. I’m currently developing a 3D multiplayer cooperative tower defense survival game in Unreal Engine 5. I also studied abroad at the National University of Singapore, which is commonly ranked as one of the top AI programs in the world, where a diverse, international student body exposed me to a breadth of global viewpoints.