SOCR News HDDA 2024

Overview

Data analysis methods are rapidly evolving due to the significant expansion of the size and complexities of data and the proliferation of new technologies. From social media networks to public health, bioinformatics to personalized medicine, environmental studies to nanoscience, and even financial analysis, diverse domains are facing new challenges. This surge has not only been confined to academic research; rather, it has permeated the practical spheres of businesses and governmental entities. As a response to this evolving landscape, there is an imperative to craft novel algorithms that can effectively scale with the dimensions of these datasets. In parallel, the development of new theoretical tools is essential to comprehend the statistical properties inherent to these algorithms. Promising breakthroughs in this realm encompass techniques such as variable selection, penalized methods, and variational inference, marking the frontier of advancements in data analysis and interpretation.

Since its inception in 2011 at the Fields Institute in Toronto, HDDA gathers leading researchers in the area of high-dimensional statistics and data analysis. The objectives include: (1) to highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advance of both mathematical and statistical sciences, (2) to identify important directions for future research in the theory of regularization methods and variational inference, in algorithmic development, and in methodology for different application areas, facilitate collaboration between theoretical and subject-area researchers (econometrics, finance, social science, biostatistics), and (3) to provide opportunities for highly qualified personnel to meet and interact with leading researchers in the area.

Session Logistics

• Title: Data Science, Artificial Intelligence, and High-Dimensional Spatiotemporal Dynamics (Organizer: Ivo Dinov (Michigan))
• Date/Time: August 28-30, 2024 (Singapore Time Zone)
• Venue: ESSEC Business School, Asia-Pacific campus, Singapore
• Registration: ... coming up ...
• Conference: 2024 High-Dimensional Data Analysis (HDDA-13) (Organizer: S. Ejaz Ahmed (Brock University)).
• Session Format: Three talks (details coming up).

Session Presenters

• Eric Tatt Wei Ho, CAPE and Department of Statistics, Universiti Teknologi Petronas, Malaysia.

Title: A survey of opportunities through case studies of Generative AI, adaptation of Large Foundation Models and Physics Informed Neural Networks for high dimensional data analysis

Abstract: Deep neural networks have demonstrable ability to learn effective feature representations for state-of-art classifiers and regressors from data. From the perspective of data analytics, the task of extracting salient features from data shares similarities with the signal processing task of learning parsimonious and descriptive feature representations. When paired with sensitivity analysis methods from the explainable AI community, these form a powerful new toolbox to explore the complex associations embedded in high dimensional data. However, training deep neural networks on high dimensional data remains challenging with no guarantee of convergence to a good solution,presumably stymied by the curse of dimensionality. Deep neural network models tend to be overparameterized to facilitate convergence towards a good solution via gradient descent optimization. Often, it is also challenging practically to acquire sufficient high-quality labeled training data. This results in a sparse sampling of the high dimensional data space which introduces challenges to generalization. Training deep neural networks via supervised learning can be conceived as solving an under-determined system of nonlinear equations so model overparameterization and paucity of constraints from training data can be understood as limitations to converging the training of an accurate neural network model. In linear algebra, under-determined systems are solved by imposing additional constraints via regularization. Drawing inspiration from this, I discuss how recent advances in generative AI, adaptation of large foundation models and physics informed neural networks can be conceptualized as imposing additional constraints to ameliorate the challenge of sparse sampling in high dimensional data space. While generative AI attempts to learn additional constraints directly from the training data, transfer learning from large foundation models such as Low Rank Adaptation of Large Models attempt to borrow generalizable constraints from a different data domain whereas physics-informed neural networks impose constraints expressed as differential equations directly in the gradient descent training. Through case studies, I propose some practical approaches to apply these concepts and conclude with brief sharing on a method to reduce overparameterization of deep neural networks.

• ...TBD...

• Ivo D. Dinov, Statistics Online Computational Resource, University of Michigan.

---

---

---

Translate this page:

(default)	Deutsch	Español	Français	Italiano	Português	日本語	България	الامارات العربية المتحدة	Suomi	इस भाषा में	Norge
한국어	中文	繁体中文	Русский	Nederlands	Ελληνικά	Hrvatska	Česká republika	Danmark	Polska	România	Sverige