Category: Announcements

January 30, 202511:45 AM - 12:45 PM (Temporary Timeslot) Speaker:Mahmut Gokmen, Institute for Biomedical Informatics, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:Task-Specific Adaptation of Vision Foundational Models for Coronary Artery Disease Diagnosis Abstract: This presentation will demonstrate the application of vision foundational models in detecting, grading, and understanding the real-world implications of Coronary Artery Disease. During the session, the general architecture of a foundational model will be explained, along with how it is trained using a self-supervised technique (DINO) and how it can be adapted to task-specific structures when necessary. Additionally, the current pace of advancements in foundational models and their future potential will also be discussed. Click here to see the complete list of speakers.

January 23, 202511:45 AM - 12:45 PM (Temporary Timeslot) Speaker:Michael Murray, Rosenberg College of Law, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:Learning new subjects with Gen AI: AI tutoring and the search for grounded truth Abstract: This talk and demonstration will provide an overview of research into the use of gen AI to assist learning at undergrad, graduate school, and professional school levels. It will discuss experiments with a growing number of gen AI systems using custom instructions, priming, retrieval-augmented generation (RAG) techniques, and prompt engineering to test Gen AI's functionality in learning new areas of study and reviewing prior learning, and in minimizing confabulations (hallucinations) that deviate from grounded truth. Click here to see the complete list of speakers.

January 16, 202511:45 AM - 12:45 PM (Temporary Timeslot) Speaker:Michael Murray, Rosenberg College of Law, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:The intersection of ML, Generative AI, and Intellectual Property Law Abstract: This talk will discuss several of the leading intellectual property law issues involving gen AI. It will discuss the two primary copyright law issues: whether the training of large language models (LLMs) and the outputs of gen AI systems constitute copyright infringement, and whether work created with the assistance of gen AI systems should be copyrightable and owned by the human end-user. It will also raise legal issues raised by deepfakes under right of publicity (name, image, likeness) law, privacy, trademark, and various criminal laws. Finally, the talk will discuss ethical considerations in law and general…

The seminar speaker for the Fall 24 Statistics Seminar Series isDr. Hongyi Wang, from Rutgers University/CMU.  Dr. Wang will give apresentation in MDS 220 at 3:00pm on Friday, December 6.Additional information about Dr. Wang can be found here and a copy of the abstract is here.

November 14, 202412:15 pm - 1:15 pm Speaker:Samson Cheung, Electrical and Computer Engineering, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:Challenges in building AI systems for Smart Health Abstract: Artificial intelligence is touted as the next frontier in healthcare, promising to revolutionize medical research and deliver equitable and low-cost care to all. However, there are many significant challenges to apply and develop AI for medical applications. Deep-learning based systems are at the forefront of AI but they are notorious at demanding large amounts of carefully labeled and annotated data. While simple labeling tasks can rely on crowdsourcing, medical data labeling requires expertise that could be rare and costly. In addition, there are usually significant bias and class imbalance issues with medical data. Expanding the knowledge base and diversity of…

November 7, 202412:15 pm - 1:15 pm Speaker:Tony Mangino, Department of Biostatistics, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:Using the RGAN Package for Implementing Generative Models for Biomedical Applications Abstract: While generative adversarial networks (GANs) are widely implemented using a variety of software packages and programming languages, many applied researchers make use of the R Statistical Software Package to conduct their analyses. The development of the RGAN package (Neunhoeffer, 2022) now allows for a user-friendly command line interface for training GANs on tabular or image data in the R environment. This tutorial guides attendees through the process of training and evaluating a GAN trained using the RGAN framework through a real clinical example dataset. Attendees who wish to follow along must have the following software installed: The R…

October 31, 202412:15 pm - 1:15 pm Speaker:Tony Mangino, Department of Biostatistics, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:An introduction to generative AI in Biomedical Applications Abstract: Generative AI has become ubiquitous since the advent of ChatGPT, though the underlying mechanisms are far from novel. Goodfellow’s 2014 introduction of generative adversarial networks (GANs) was a watershed moment for the generative modeling domain and has since been widely used in a wide variety of contexts. This presentation and accompanying tutorial introduce participants to a lesser-used application of GANs: Their ability to generate synthetic patient records with data largely mirroring the same data obtained from real patients. This presentation illustrates an example using data from patients admitted to UK hospitals for either Acute Coronary Syndrome (ACS) or Takotsubo Syndrome (TTS),…

October 24, 202412:15 pm - 1:15 pm Speaker:Qiang Ye, Department of Mathematics, University of Kentucky Where:327 McVey Hall(Zoom link: https://uky.zoom.us/j/82467171189) Title:Recurrent Neural Networks and Transformer for Sequential Data Abstract: Many machine learning problems involve sequential data. Recurrent neural networks (RNNs) and Transformer are neural network architectures designed to efficiently model temporal connections within a sequence and handling variable sequence lengths in a dataset. However, RNNs suffer from the so-called vanishing or exploding gradient problems, which also reduces its ability to pass information in a long sequence. Transformer solves this problem through a self-attention mechanism but faces challenges in efficiently scaling to long sequences because the self-attention computation is quadratic with respect to the sequence length. We will present several orthogonal RNN models that we have developed to address the vanishing/exploding…

October 17, 202412:15 pm - 1:15 pm Speaker:Aaron Mullen, Institute for Biomedical Informatics, University of Kentucky Where:VIRTUAL!(Zoom link: https://uky.zoom.us/j/82438134047) Title:Forecasting Opioid Incidents for Rapid Actionable Data for Opioid Response in Kentucky Abstract: We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future opioid overdose incidents around the state of Kentucky. If successful, state governments could use forecasts to properly prepare and distribute resources effectively. The approach taken primarily uses county and district level aggregations of EMS opioid overdose encounters and forecasts future counts at a monthly level. A variety of additional covariates were also tested to determine their impact on the model’s performance. Models with different levels of complexity were evaluated as well to optimize training time and accuracy. The results…