bio

Marlan McInnes-Taylor holds an M.S. in Computer Science from The University of Texas at Austin, where his research focused on continual learning, biologically inspired machine learning, and deep learning. His master’s thesis, Deep Generative Similarity-Weighted Interleaved Learning, explored methods for reducing catastrophic forgetting in neural networks by drawing inspiration from theories of human learning and memory. His broader interests include artificial intelligence, generative modeling, computer vision, and the development of more adaptable and data-efficient learning systems. He also holds B.S. degrees in Computer Science and Applied Mathematics from Florida State University and a B.A. in Philosophy from the University of Florida.