Abstract: Inspired by recent successes towards automating highly complex jobs like automatic programming and scientific experimentation, I want to automate the task of the data scientist when developing intelligent systems. In this talk, I shall introduce some of the involved challenges and some possible approaches and tools for automating data science.
More specifically, I shall discuss how automated data wrangling approaches can be used for pre-processing and how both predictive and descriptive models can in principle be combined to automatically complete spreadsheets and relational databases. I will argue that autocompleting spreadsheets is a simple yet highly challenging setting for the automation of data science. Special attention will be given towards the induction of constraints in spreadsheets and in an operations research context.
Bio: Luc De Raedt is full professor, now at KU Leuven, formerly at the Albert-Ludwigs-University Freiburg. He direct the KU Leuven Institute for Artificial intelligence. He is well known for his contributions on learning and reasoning, in particular, for contributions to statistical relational learning, probabilistic and inductive programming. He is a AAAI and a EurAI fellow and he received an ERC Advanced Grant in 2016 on the topic of automating data science. He is an action editor for Artificial Intelligence, Machine Learning and the Journal of Machine Learning Research. He was or will be a pc-(co)chair of flagship conferences in machine learning and in artificial intelligence (including ECMLPKDD 2001, ICML 2005, ECAI 2012, and IJCAI 2022). He was coordinator of 7 European projects (most recently FP7-FET ICON en Chist-Era ReGround), and a principal investigator in many further European and other projects. He has co-authored more than 300 publications, including 3 books, 10 papers in Artificial Intelligence, 17 in Machine Learning, and 36 at core AI conferences such as IJCAI, AAAI and ECAI. He has delivered numerous tutorials at major AI and machine learning conferences such as IJCAI, AAAI, ECCAI, ICML, NeurIPS, SIGKDD, .... According to google scholar, he is cited more than 18000 times and his h-index is 65. He has (co)advised 27 Phd students and 17 post-docs. Many of his students have won prestigious awards, including 4 ECCAI (&1 ACP) Dissertation awards for the best European thesis in AI (resp. Constraint Programming).