INNS Webinar Series  

Welcome to the INNS Webinar Series. This collection of webinars serves as a virtual learning center for students and professionals to learn and engage in research activities related to neural networks.

The portal features lectures from our Webinar Series. You are invited to attend the webinars live, held bi-monthly. 

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Abstract: AI today can pass the Turing test and is in the process of transforming science, technology, humans, and society. Surprisingly modern AI is built out of two very simple and old ideas, rebranded as deep learning: neural networks and gradient descent learning. The storage of information in neural networks by gradient descent is distributed or "holographic", and since Dennis Gabor invented holography, I am particularly honored to be a recipient of the prize that bears his name. I will describe several applications of AI to problems in biomedicine developed in my laboratory, from the molecular level to the patient level, using omic data, imaging data, and clinical data. Examples include the analysis of circadian rhythms in gene expression data, the identification of polyps in colonoscopies, and the prediction of post-operative outcomes. I will discuss the opportunities and challenges for developing, integrating, and deploying AI in the first AI-driven hospitals of the future and present two frameworks for addressing some of the most pressing societal issues related to AI research and safety.

Speaker Bio: Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Distinguished Professor in the Department of Computer Science, Founding Director of the AI in Science Institute, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines. He has made several contributions to the theory of AI and deep learning, and developed and applied AI and deep learning methods for problems in engineering and the natural sciences, for instance in physics (e.g., exotic particle detection) , chemistry (e.g., reaction prediction), and bio-medicine (e.g., protein structure prediction, biomedical imaging analysis). He has published five books, including Deep Learning in Science, Cambridge University Press (2021) and ~500 scientific articles. His honors include the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, the 2023 Dennis Gabor Award of the International Neural Network Society, and election to Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB. He serves as Associated Editor for Artificial Intelligence, Neural Networks, and the IEEE/ACM Transactions in Computational Biology and Bioinformatics. He has mentored ~100 graduate students and postdoctoral fellows and co-founded several startup companies. At UCI, he has introduced several new courses, including the course: Neural Networks and Deep Learning, and the course: AI Frontiers: Technical, Ethical, and Societal.

Catch Up on Our Latest Webinar

Joey Tianyi Zhou
Dataset Distillation and Pruning: Streamlining Machine Learning Performance   

Explore Recent Webinars

The Critical Role of AI in Learning Analytics and Assessment in the Future of Education   Irwin King  10 April 2025  Presentation Slides     Watch here

INNS Annual Lecture Nobelization of Neural Networks: Deep Roots and Insane Future of Neural Networks   Wlodzislaw Duch  17 December 2024    Presentation Slides   Watch here

AI for Finance: A Practitioner's Viewpoint on Using Neural Networks to Forecast Equity Returns Ryan Samson  12 December 2024    Presentation Slides     Watch here

 

A complete listing of all previous webinars can be found in the INNS Webinar Series Archive.

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