The University of Georgia (UGA) Data Science and AI Seminars are monthly online seminars that cover interdisciplinary research topics in data science (DS), artificial intelligence (AI), statistics, engineering, biomedical informatics, and public health. We aim to bring together researchers from these fields to discuss exciting topics on DS/AI with interdisciplinary applications.

Upcoming Talks
  • Speaker: Christos Davatzikos (Wallace T. Miller Sr. Professor of Radiology, University of Pennsylvania)
  • Title: Machine Learning in Neuroimaging: applications to brain aging, Alzheimer’s Disease, and Schizophrenia
  • Date/Time: Friday, September 24, 2021, 10:00AM – 11:00AM
  • Zoom Link:
  • Abstract: Machine learning has deeply penetrated the neuroimaging field in the past 15 years, by providing a means to construct imaging signatures of normal and pathologic brain states on an individual person basis. In this talk, I will discuss examples from our laboratory’s work on imaging signatures of brain aging and early stages of neurodegenerative diseases, brain development and neuropsychiatric disorders. I will discuss some challenges, such as disease heterogeneity and integration of data from multiple sites in order to achieve sample sizes required by deep learning studies. I will discuss the integration of these methods and results in the context of a dimensional neuroimaging system and its contribution to integrated, precision diagnostics.
  • Bio: Christos Davatzikos is the Wallace T. Miller Sr. Professor of Radiology at the University of Pennsylvania, and Director of the Center for Biomedical Image Computing and Analytics. He holds a secondary appointment in Electrical and Systems Engineering at Penn as well as at the Bioengineering an Applied Mathematics graduate groups. He obtained his undergraduate degree by the National Technical University of Athens, Greece in 1989, and his Ph.D. degree from Johns Hopkins, in 1994, on a Fulbright scholarship. He then joined the faculty in Radiology and later in Computer Science, where he founded and directed the Neuroimaging Laboratory. In 2002 he moved to Penn, where he founded and directed the section of biomedical image analysis. Dr. Davatzikos’ interests are in medical image analysis. He oversees a diverse research program ranging from basic problems of imaging pattern analysis and machine learning, to a variety of clinical studies of aging and Alzheimer’s Disease, schizophrenia, brain cancer, and brain development. Dr. Davatzikos has served on a variety of scientific journal editorial boards and grant review committees. He is an IEEE fellow, a fellow of the American Institute for Medical and Biological Engineering, and member of the council of distinguished investigators of the US Academy of Radiology and Biomedical Imaging Research.
  • Speaker: Jun Liu (Professor of Statistics, Harvard University)
  • Title: TBA
  • Date/Time: Friday, October, 2021 (TBD)
  • Zoom Link: TBA
  • Abstract: TBA
  • Bio: TBA
  • Speaker: Xia Hu (Associate Professor of Computer Science, Rice University)
  • Title: Towards Effective Interpretation of Deep Neural Networks: Algorithms and Applications
  • Date/Time: Friday, October 15, 2021
  • Zoom Link: TBA
  • Abstract: While Deep neural networks (DNN) have achieved superior performance in many downstream applications, they are often regarded as black-boxes and are criticized by their lack of interpretability, since these models cannot provide meaningful explanations on how a certain prediction is made. Without the explanations to enhance the transparency of DNN models, it would become difficult to build up trust among end-users. In this talk, I will present a systematic framework from modeling and application perspectives for generating DNN interpretability, aiming at dealing with two main technical challenges in interpretable machine learning, i.e., faithfulness and understandability. Specifically, to tackle the faithfulness challenge of post-hoc interpretation, I will introduce how to make use of feature inversion and additive decomposition techniques to explain predictions made by two classical DNN architectures, i.e., Convolutional Neural Networks and Recurrent Neural Networks. In addition, to develop DNNs that could generate more understandable interpretation to human beings, I will present a novel training method to regularize the interpretations of a DNN with domain knowledge.
  • Bio: Dr. Xia “Ben” Hu is an Associate Professor at Rice University in the Department of Computer Science. Dr. Hu has published over 100 papers in several major academic venues, including NeurIPS, ICLR, KDD, WWW, IJCAI, AAAI, etc. An open-source package developed by his group, namely AutoKeras, has become the most used automated deep learning system on Github (with over 8,000 stars and 1,000 forks). Also, his work on deep collaborative filtering, anomaly detection and knowledge graphs have been included in the TensorFlow package, Apple production system and Bing production system, respectively. His papers have received several Best Paper (Candidate) awards from venues such as WWW, WSDM and ICDM. He is the recipient of NSF CAREER Award. His work has been cited more than 10,000 times with an h-index of 44. He was the conference General Co-Chair for WSDM 2020.
  • Speaker: Yiran Chen (Professor of Electrical and Computer Engineering, Duke University)
  • Title: TBA
  • Date/Time: Friday, November 2021 (TBD)
  • Zoom Link: TBA
  • Abstract: TBA
  • Bio: TBA
  • Speaker: Gari Clifford (Professor of Biomedical Informatics and Biomedical Engineering, Emory University and Georgia Institute of Technology)
  • Title: The PhysioNet Challenges as a Platform for Better ML: From Noisy Labels to Reducing Overtesting
  • Date/Time: Friday, December 3, 2021, 12:00PM – 1:00PM
  • Zoom Link: TBA
  • Abstract: TBA
  • Bio: TBA
Past Talks


  • Tianming Liu, Distinguished Research Professor, Department of Computer Science, UGA
  • Ping Ma, Distinguished Research Professor, Department of Statistics, UGA
  • WenZhan Song, Georgia Power Mickey A. Brown Professor, College of Engineering, UGA
  • Yuan Ke, Assistant Professor, Department of Statistics, UGA
  • Zhong-Ru (Paul) Xie, Assistant Professor, College of Engineering, UGA
  • Sheng Li, Assistant Professor, Department of Computer Science, UGA