Professor at the Computer Science Department at the University of Crete, Greece
Ioannis Tsamardinos, Ph.D., is a Professor at the Computer Science Department of the University of Crete, CEO, and co-founder of JADBio, a University start-up. He obtained his Ph.D. from the Intelligent Systems Program at the University of Pittsburgh in 2001. Prof. Tsamardinos’ main research directions include machine learning, bioinformatics, and artificial intelligence. More specifically his computer science work emphasizes automated machine learning, feature selection, and causal discovery. Prof. Tsamardinos has over 140 publications in international journals, conferences, and books. Distinctions with colleagues and students a Gold Medal in the Student Paper Competition in MEDINFO 2004, the Outstanding Student Paper Award in AIPS 2000, the NASA Group Achievement Award for participation in the Remote Agent team, and others. Statistics on recognition of work include more than 10000 citations (1000+ a year), and an h-index of 40 (as estimated by Google Scholar). Ioannis has been awarded the European and Greek national grants of excellence, the ERC Consolidator, and the ARISTEIA II grants respectively.
Abstract: Automated Machine Learning for Knowledge Discovery
Automated Machine Learning, or AutoML, is a newly emerging field in Machine Learning. It promises to automate predictive modeling, democratize machine learning to non-experts, boost the productivity of experts, ensure the statistical validity of the modeling process, and even surpass human experts in quality. AutoML should not only strive to produce a high-quality model, but all information, explanations, interpretations, and decision support a human expert would. In this talk, we’ll present the challenges of AutoML and the design choices we made to construct the Just Add Data Bio, or JADBio for short, AutoML platform. JADBio is particularly suited for very high dimensional data with millions of features, and low-sample datasets that present statistical estimation challenges. Particularly, JADBio focuses on Knowledge Discovery in the form of Feature Selection and identifying one or more minimal-size subsets that lead to the optimal model. Feature Selection is often the primary goal of the analysis as a first step to understanding the causal relations in our data. We’ll also discuss ongoing efforts to construct an Automated Causal Discovery engine that strives to take AutoML a step further and return the best possible Causal Model that fits the data.