Marlan McInnes-Taylor

I hold an M.S. in Computer Science from The University of Texas at Austin, where my research focused on machine learning, continual learning, and biologically inspired approaches to artificial intelligence. At UT Austin, I was a member of the Brain-Behavior-Computation Lab advised by Xue-Xin Wei and the Neural Networks Research Group advised by Risto Miikkulainen.

My master’s thesis, Deep Generative Similarity-Weighted Interleaved Learning, investigated continual learning through deep generative similarity-weighted interleaving, with the goal of enabling neural networks to acquire new knowledge while limiting catastrophic forgetting. The work was motivated in part by complementary learning systems theories of human learning and memory and explored how ideas from biological learning can inform more effective learning paradigms for artificial neural networks.

More broadly, my interests lie at the intersection of artificial intelligence, machine learning, and neuroscience. I am particularly interested in continual and lifelong learning, generative modeling, computer vision, and approaches that translate insights from biological intelligence into more capable and adaptable artificial systems. I enjoy both the research side of these problems and the engineering involved in turning ideas into working machine learning systems.

Previously, I earned a B.S. in Computer Science and a B.S. in Applied Mathematics from Florida State University, as well as a B.A. in Philosophy from the University of Florida.

Outside of research and engineering, my interests include running, strength training, homelabbing and following single-seat motorsport—particularly Formula 1, Formula 3 and Formula Regional.