Distinguished Speaker Series – Dr. John Gustafson

MATRIX Fall Seminar Series – Dr. Ananth Grama

Computational Methods for Analysis of Functional Brain Images

Dr. Ananth Grama

Purdue University

11/4/2022

11AM – 12PM CST

WebEx: https://utsa.webex.com/utsa/j.php?MTID=m0dd130eaf6ddc33055fb62e2e1c06923

Abstract:  Rapid advances in neuroimaging have resulted in large repositories of images with high temporal and spatial resolutions. This has motivated complex connectomic analyses aimed at understanding representation and processing of stimuli . Such analyses require novel computational tools that significantly extend the state-of-the-art in machine learning and data science. These studies have far-reaching implications for the fields of precision psychiatry, behavioural analysis, and neurodegeneration, in addition to applications in AR/VR and advanced human interfaces.

In this talk, I will discuss recent work in our group on understanding neuronal response to naturalistic visual stimulus — I present two methods based on archetypal analysis and deep learning to: (a) find interpretable representations of fMRI response, (b) predict objects in visual frames, and (c) reconstruct visual inputs. I will also briefly describe computational methods to characterize individual-level uniqueness of functional connectomes. I will present three methods based on matrix sampling, graph alignment, and linear algebraic techniques to accurately identify the identity of individuals, and the nature of the cognitive task being performed.  I will conclude my talk by highlighting the immense significance and challenges posed by problems in the field of neuroimage analyses.

MATRIX AI Fall Seminar Series – Dr. Mohamed Amer

Responsible AI: How do we get there?

Dr. Mohamed Amer

Robust AI

11/11/2022

11AM CST – 12PM CST

https://utsa.webex.com/utsa/j.php?MTID=m66e32a039daa62769074b57712455b0c

Abstract: Machine learning models are getting larger and more complex. While machine learning provides a powerful set of tools, the models fail unexpectedly, harbor biases, and are poorly understood. Nonetheless, they are deployed at scale. A machine learning-driven system requires extensive engineering & computational resources, and massive amounts of data. Currently, these resources are only available to big players, governments & corporations, and there is a lack of tools or processes to enable auditing, compliance, and evaluation of such complex systems. The models are sometimes governed by a set of rules defined by the developers, which are often subjective and not well thought out. This talk will cover a wide variety of topics around responsible AI highlighting deep learning current challenges, vulnerabilities, and possible ways to mitigate them.