Schedule

Title: AI, BI & SI -Artificial, Biological and Statistical Intelligences

Speaker: Dennis Lin

Abstract: Artificial Intelligence (AI) is clearly one of the hottest subjects these days. Basically, AI employs a huge number of inputs (training data), super-efficient computer power/memory, and smart algorithms to perform its intelligence. In contrast, Biological Intelligence (BI) is a natural intelligence that requires very little or even no input. This talk will first discuss the fundamental issue of input (training data) for AI. After all, not- so-informative inputs (even if they are huge) will result in a not-so-intelligent AI. Specifically, three issues will be discussed: (1) input bias, (2) data right vs. right data, and (3) sample vs. population. Finally, the importance of Statistical Intelligence (SI) will be introduced. SI is somehow in between AI and BI. It employs important sample data, solid theoretically proven statistical inference/models, and natural intelligence. In my view, AI will become more and more powerful in many senses, but it will never replace BI. After all, it is said that “The truth is stranger than fiction, because fiction must make sense.” The ultimate goal of this study is to find out “how can humans use AI, BI, and SI together to do things better.”

Title: Machine-Precision Neural Networks for Multiscale Flows

Speaker: Ching-Yao Lai

Abstract: Deep learning techniques are increasingly being applied to scientific problems where network precision is crucial. Despite being considered universal function approximators, neural networks often struggle with optimization issues in practice. We developed an algorithm to tackle this issue and achieve machine-precision representation (Wang and Lai, JCP 2024). I will discuss two examples in fluid dynamics using neural networks (NN) to approximate partial differential equations (PDEs) solutions and solve inverse problems. The first example concerns the search for singularities of the Euler equations (Wang-Lai-Gómez-Serrano-Buckmaster, PRL 2023). The second application uses PDE-constrained neural networks to infer the viscosity model of geophysical complex fluids at the planetary scale.

Data Science for the Biological Sciences - Workshop 1

Speakers: Kathleen Lois Foster & Alessandro Maria Selvitella

Abstract: This Workshop is aimed at students and practitioners in the biological sciences who are interested in developing coding and data science skills to solve concrete problems emerging in their biological field of study. By the end of the workshops, the participants will have gathered technical and theoretical skills in data science. They will have learned how to install R and R-studio, use the statistical software R to perform basic statistical analysis of a biological question, and visualize the biological information hidden in the data under study. Furthermore, they will have gained knowledge about the structure of different types of data, descriptive statistics, including mean, standard deviation, confidence intervals, and probability distributions, how to perform hypothesis tests, including t-test and ANOVA, and the difference between statistical and biological significance.

Slides

R-code

[In person only or specific request must be sent to the organizers for online participation]

Data-Driven Dynamical Systems - Workshop 1

Speakers: Alessandro Maria Selvitella

Abstract: This Workshop is aimed at graduate students with some background in differential equations and machine learning. The workshop will cover topics such as Runge-Kutta methods, Physics Informed Neural Networks, and System Identification of Nonlinear Dynamics. These tools will be illustrated on a toy problem, a dynamical system modeling the central pattern generator of the lamprey.

[In person only or specific request must be sent to the organizers for online participation]

Title: Topological Data Analysis of Spatial Systems

Speaker: Mason Porter

Abstract: I will discuss topological data analysis (TDA), which uses ideas from topology to quantify the "shape" of data. I will focus in particular on persistent homology (PH), which one can use to find "holes" of different dimensions in data sets. I will briefly introduce these ideas and then discuss a series of examples of TDA of spatial systems. The examples that I'll discuss include voting data, the locations of polling sites, and the webs of spiders under the influence of various drugs.

Data Science for the Biological Sciences - Workshop 2

Speakers: Kathleen Lois Foster & Alessandro Maria Selvitella

Abstract: This Workshop is aimed at students and practitioners in the biological sciences who are interested in developing coding and data science skills to solve concrete problems emerging in their biological field of study. By the end of the workshops, the participants will have gathered technical and theoretical skills in data science. They will have learned how to install R and R-studio, use the statistical software R to perform basic statistical analysis of a biological question, and visualize the biological information hidden in the data under study. Furthermore, they will have gained knowledge about the structure of different types of data, descriptive statistics, including mean, standard deviation, confidence intervals, and probability distributions, how to perform hypothesis tests, including t-test and ANOVA, and the difference between statistical and biological significance.

Slides

R-code

[In person only or specific request must be sent to the organizers for online participation]

Data-Driven Dynamical Systems - Workshop 2

Speakers: Alessandro Maria Selvitella

Abstract: This Workshop is aimed at graduate students with some background in differential equations and machine learning. The workshop will cover topics such as Runge-Kutta methods, Physics Informed Neural Networks, and System Identification of Nonlinear Dynamics. These tools will be illustrated on a toy problem, a dynamical system modeling the central pattern generator of the lamprey.

[In person only or specific request must be sent to the organizers for online participation]

Title: Embodied Intelligence and NeuroAI

Speaker:  Bing W. Brunton

Abstract: TBA