Artemis Hatzigeorgiou
Artemis Hatzigeorgiou is since 2012 Professor of Bioinformatics at the University of Thessaly and since 2015 adjunct Professor at the Hellenic Pasteur Institute. From 2001 to 2007 was assistant Professor of Bioinformatics in the Department of Genetics at the Medical School of the University of Pennsylvania.
She is head of the DIANA (DNA Intelligent analysis) lab with focus on biological sequence analysis and gene regulation. The lab has long expertise on developing machine learning algorithms for characterization of noncoding RNA genes. The provided tools and databases range from microRNA target identification to pathways analysis and tools for the identification of diagnostic and therapeutic markers. They can be found under www.microrna.gr a computational gene analysis platform visited worldwide by more than 70,000 unique researchers per year.
She has published in top tier journals such as Nature, Science, Nature Methods, Nature Communication and PNAS. Her work has been cited more than 24,000 times (h-index 55) according to Google Scholar and she is for four consecutive years listed among the 1% of most cited scientists in her field according to the project Clarivate Analysis of the database Web od Science (“The Highly Cited Researchers List”) . Artemis Hatzigeorgiou is a recipient of the «Young Investigation Career Award» prize by the National Research Foundation (NSF) of USA and since 2016 president of the Hellenic Society of Computational Biology and Bioinformatics.
Bioinformatics and machine learning: a long-lasting friendship
Genomic and genetic data are growing during the last years faster than astronomical data, driven in large part by advancements in technology and the decreasing cost of DNA sequencing. The field of genomics, which studies the entire genetic makeup of an organism, has exploded in recent years as a result of these advances. Bioinformatics is the field that analyzes all this data. The integration of different form of information ranging from medical to biological data of different quality makes machine learning (ML)-based algorithms key solution for many open problems in health science. Current challenges in bioinformatics will be discussed emphasis in machine learning applications. The end of the talk will be dedicated to my decades long experience as a women in computer science.
Take me back to speakers!
