Sanmi Koyejo to give November 11 Frontiers Lecture

Sanmi KoyejoThe MBM Program continues its Frontiers in Miniature Brain Machinery lecture series with Sanmi Koyejo. He is an Assistant Professor of Computer Science at the University of Illinois.

Koyejo will speak November 11, 2020 at 4pm in Zoom on “Synthesizing brain images using machine learning: applications, promises, and pitfalls.” The lecture is open to the public.

Abstract:

Generative models, a class of statistical machine learning models, provide promising solutions for modeling and synthesis in applications where precise mechanisms are not well developed. Modern generative models synthesize high-dimensional structured data by combining standard statistical methods with deep neural network models. This talk will outline new brain image synthesizers based on generative models, then will summarize the promises and pitfalls of functional brain image synthesis for supporting a variety of analyses, including data augmentation and image denoising. A particular focus is on brain data synthesis for power analysis. In the experimental sciences, the sample size is determined using statistical power analysis. However, traditional power analyses can be costly when data are difficult or expensive to collect. To this end, synthetic power analyses provide a framework for estimating statistical power by leveraging image synthesis. I will illustrate how synthetic power analyses result in an effective alternative to classic techniques.

Bio:

Koyejo’s research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards, including a best paper award from the conference on Uncertainty in Artificial Intelligence (UAI), a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization. He completed his PhD in Electrical Engineering at the University of Texas at Austin and postdoctoral research at Stanford University with a focus on developing machine learning techniques for neuroimaging data.

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