CCS/ITSRCI Seminar Series on AI in Practice
offered jointly with the IBI Center for Applied AI and UK AI/ML Hub
Artificial intelligence (AI) has been around for a long time but has only recently become a household name. Through advancements in machine learning and large language models, AI can generate high-quality content such as text, images, or audio. This capability enables exciting new products and services that enhance productivity and transform how we manage our daily tasks. In academia, AI plays an increasingly crucial role in driving groundbreaking discoveries and revolutionizing educational approaches at all levels.
The University of Kentucky community has embraced AI’s potential in research and education. Researchers from all disciplines use AI tools creatively to find a path and shorten the time to new discoveries. Likewise, educators and students at UK use AI tools to create new ways to present and internalize knowledge.
The UK Center for Computational Sciences (CCS), ITS Research Computing Infrastructure, the IBI Center for Applied AI (IBI/CAAI), and the UK Artificial Intelligence and Machine Learning (AI/ML) Hub have joined forces to offer a year-long seminar series aimed at making AI “practical” to the broad UK community. The series’ program will consist of talks by UK scholars on using AI tools and techniques to advance their research or develop new instructional material and methodologies, along with tutorial-type presentations of specific AI tools. Presentations will occur on Thursdays at 12:15 PM in the CCS conference room (McVey Hall, Room 327). The venue provides some space for in-person participation; remote participation via Zoom will also be possible. The first presentation is scheduled for September 12th. The series program can be found at https://www.ccs.uky.edu/ccs-seminar-series-on-ai-in-practice/.
THURSDAYS AT 12:15 PM
327 McVey Hall & Zoom
The next seminar:
April 24, 2025 12:15 PM – 1:15 PM
Speaker:
Xin Liang, Computer Science Department, University of Kentucky
Where:
327 McVey Hall (Virtual Speaker)
(Zoom link: https://uky.zoom.us/j/82467171189)
Title:
Advancing Extreme-scale Data Science via Trust-driven Data Reduction
Abstract:
Extreme-scale scientific simulations and experiments generate more data than that can be stored, transmitted, and analyzed. The recently delivered exascale systems and high-resolution scientific instruments are exacerbating this problem, due to the imbalanced growth between storage systems and data. My research focuses on the development of efficient algorithms and scalable software for high-performance data management on massively parallel and heterogeneous systems. In this talk, I will present how we address the scientific data challenges arising from real-world scientific applications through trust- driven data reduction. Specifically, I will talk about 1) error-controlled and feature- preserving lossy data reduction; 2) data refactoring and error-controlled progressive retrieval; and 3) throughput improvement with accelerators. Our ultimate goal is to deliver an intelligent data management system with trust-driven data reduction that enables flexible data storage, on-demand data retrieval, and adaptive data analytics, which could greatly reduce the time to obtain insights from data and thus advance scientific discoveries.
Seminar Schedule:
September 12, 2024
Dr. Brent Harrison, Computer Science, University of Kentucky
A Gentle Introduction to Modern Machine Learning
Slides
September 19, 2024
Dr. Brent Harrison, Computer Science, University of Kentucky
Practical Value Alignment Using GPT-4o
Slides
September 26, 2024
Aaron Mullen, Institute for Biomedical Informatics, University of Kentucky
CLASSify: A Self-Service Machine Learning Platform
Slides
October 3, 2024
Qiang (Shawn) Cheng, Division of Biomedical Informatics
Novel Deep Learning models and Techniques for Effective and Efficient Handling of Multiple Data Modalities
Slides
October 10, 2024
Md Atik Ahamed, Division of Biomedical Informatics
Advanced tools and packages for handling tabular data and time series data
Slides
October 24, 2024
Qiang Ye
Recurrent Neural Networks and Transformer for Sequential Data
October 31, 2024
Tony Mangino
An introduction to generative AI in Biomedical Applications
Slides
November 7, 2024
Tony Mangino
Tutorial: RGAN package and its applications
Slides
November 14, 2024
Sen-ching Samson Cheung
Challenges in building AI systems for Smart Health
Slides
November 21, 2024
Cohen Archbold and Usman Hassan
Private Machine Learning
Slides
January 16, 2025 (Temporary Timeslot – 11:45 AM – 12:45 PM)
Michael Murray
The intersection of ML, Generative AI, and Intellectual Property Law
Slides
January 23, 2025 (Temporary Timeslot – 11:45 AM – 12:45 PM)
Michael Murray and Students
Learning new subjects with Gen AI: AI tutoring and the search for grounded truth
January 30, 2025 (Temporary Timeslot – 11:45 AM – 12:45 PM)
Mahmut Gokmen
Task-Specific Adaptation of Vision Foundational Models for Coronary Artery Disease Diagnosis
Slides
February 13, 2025
Hasan Poonawala
Adding smart sensors to your data collection pipeline using deep learning on embedded computers.
Slides
February 20, 2025
Mami Hayashida and Vikram Gazula
Introduction to Retrieval-Augmented Generation
Slides
March 6
Stephen Davis
Learning to Human Rights Data: Three Use Cases from the Bitter Aloe Project
Slides
March 13 (Postponed)
William Mattingly
Topic TBA
March 27
Jeff Talbert
The Rapid Actionable Data for Opioid Response (RADOR-KY) project
April 3
Barry Farmer
Enhancing AI/ML Research with HPC Resources: A Practical Approach
Slides
April 17
Ankan Bhattacharyya
System for the 2D-3D detection of bourbon barrel features-of-interest
April 24
Xin Liang
Advancing Extreme-scale Data Science via Trust-driven Data Reduction
Links to Previous Seminar Series: