doi.bio/william_hebgen_guss

William Hebgen Guss

Early Life and Education

William Hebgen Guss is a researcher in the field of machine learning and artificial intelligence. He received his early education at the University of California, Berkeley, where he was awarded the Regents' and Chancellor's Scholarship, the most prestigious scholarship awarded at entry. During his time at Berkeley, Guss presented proofs for the universal approximation of nonlinear operators on infinite-dimensional Banach space and generalized artificial neural networks to infinite-dimensional Banach spaces to tackle the curse of dimensionality.

Career

Guss has held positions at Neotribe, FAIR, and Meta AI. He is currently affiliated with the School of Computer Science at Carnegie Mellon University and Mistral AI.

Research

Guss's research interests include machine learning, artificial intelligence, and reinforcement learning. He has published extensively on these topics, with notable works including:

• "Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020"
• "The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors"
• "Deep Function Machines: Generalized Neural Networks for Topological Layer Expression"
• "On Characterizing the Capacity of Neural Networks using Algebraic Topology"
• "SEARCHABLE DATABASE OF TRAINED ARTIFICIAL INTELLIGENCE OBJECTS THAT CAN BE REUSED, RECONFIGURED, AND RECOMPOSED, INTO ONE OR MORE SUBSEQUENT ARTIFICIAL INTELLIGENCE MODELS"

Awards and Recognition

Guss has received recognition for his work, including the Regents' and Chancellor's Scholarship from the University of California, Berkeley.