Seminar by Dr. Hagit Shatkay (University of Delaware)

Speaker: Dr. Hagit Shatkay (Universtiy of Delaware)                                                                                                           

Title: The Data has Its Say: Artificial Intelligence Meets Real Life

Date: Monday May 13th, 2019

Time: 10:30am

Room:  EV11.119

ABSTRACT

Almost all tasks in biology and medicine require us to analyze much data whose representation we may control, but whose meaning we do not fully – or sometimes even remotely - understand. These tasks range from elucidating the role and function of genes/proteins by utilizing sequence and observational data, to gaining insight into a patient’s disease progression from various forms of information stored in electronic health records. In the talk I will discuss work we have done within several contexts, in which we apply machine learning methods and language models – in non-traditional ways at times– toward gaining insight into biological processes and patterns of disease. The talk is based on collaborative work along with current and former graduate students within several inter-disciplinary projects.

BIO

Dr. Hagit Shatkay is a full professor at the Dept. of Computer Sciences at the University of Delaware, where she directs the Computational Biomedicine and Machine Learning Lab since 2009. She has cross- appointments at the Dept. of Biomedical Engineering and at the Delaware Biotechnology Institute. Before moving to UDel, she was an Associate Professor at the School of Computing, Queen’s University in Kingston, Ontario. She holds a PhD in Computer Science from Brown University, where she has worked on learning probabilistic models for robot navigation, and was among the first to build models for simultaneous localization and mapping (SLAM); she earned her BSc and MSc in Computer Science from the Hebrew University of Jerusalem. Prior to joining academia in 2004, she has been an IRTA postdoctoral fellow at NCBI where she was among the first to apply text mining in molecular biology (1999-2000), and an Informatics Research Scientist at Celera Genomics.

Her research is in the area of machine learning as it applies to biomedical and clinical data as well as to text and image mining. She has been an active member and a leader of the bio-text research community since its early days (1999), and one of the first to integrate text, image and sequence data within biomedical data mining. Recent major projects in her lab include research toward understanding and predicting heart and kidney disease using multiple sources of information, prediction of drug-drug interaction and protein location from text and sequence data, and document retrieval through image and text data. She is the author of numerous influential publications, and is active within the scientific community through leadership and editorial roles within international conferences, review panels and boards.