The efl cordially invites you to its Annual Conference 2022 on “Democratization of Data Science and AI”. Participants will gain insights from leading researchers, practitioners, and regulators. The talks will cover a broad range of topics including automation of typical tasks in machine learning (ML) such as data cleaning, making ML more accessible, or state-of-the-art visualization techniques, which are important for the user experience and transparency of data science applications. Moreover, ethical aspects of AI will be discussed and a perspective from Deutsche Bundesbank on the topic of data democratization will be provided.
The event will take place on October 11, 2022 at the Casino Building of the Goethe University Frankfurt. The event is planned as a pure in-person event. The registration is possible until Oct 3rd.
Due to the current covid situation, we recommend wearing face masks during the conference and additionally recommend that each attendee take a quick test on the day of the conference.
The annual conference in 2022 is organized by Prof. Carsten Binnig (Technical University of Darmstadt) and his team: Nadja Geisler, Benjamin Hättasch, Benjamin Hilprecht, Adrian Lutsch, Meghdad Mirabi.
14:30 REGISTRATION & COFFEE
Prof. Dr. Carsten Binnig, TU Darmstadt
Prof. Dr. Oliver Hinz, Goethe University Frankfurt
15:10 EXAMPLE-DRIVEN DATA CLEANING
Prof. Dr. Ziawasch Abdejan, Research Director, Leibniz University Hannover and Visiting Academic at Amazon
15:40 CURRENT DEVELOPMENTS IN FOUNDATION MODELS (LLMs) AND HOW THIS RELATES TO AI DEMOCRATIZING EFFORTS
Dr. Markus Schmitz, AI Consulting, Aleph Alpha GmbH
16:10 KEYNOTE: DESIGNING AI SYSTEMS WITH EVERYONE IN MIND
Fernanda Viégas, Principal Scientist, Google Research, Gordon McKay Professor, Harvard University
Martin Wattenberg, Principal Scientist, Google Research, Gordon McKay Professor, Harvard University
16:50 LAUDATIO: 20 YEARS EFL
Prof. Dr. Joachim Würmeling, Chairman of the Board of Trustees, Deutsche Bundesbank
17:10 REFRESHMENT BREAK
17:40 CHALLENGES IN DATA DEMOCRATIZATION AT BUNDESBANK
Stefan Bender, Head of Research Data and Service Center, Deutsche Bundesbank
18:10 DATA CONSIDERATIONS FOR RESPONSIBLE DATA-DRIVEN SYSTEMS
Prof. Dr. Asia Biega, Max Planck Institute for Security and Privacy (MPI-SP)
18:40 GET TOGETHER
All times in CEST.
Ziawasch Abedjan is Professor for “Databases and Information Systems” at Leibniz Universität Hannover and Visiting Academic at Amazon Search. He is Junior Fellow of the German Computer Science Society, Fellow of the Berlin institute on Foundation of Learning and Data and member of the L3S Research Center. He has published more than 50 peer-reviewed papers in the area of data integration and data analytics. Ziawasch Abedjan received his PhD at the Hasso-Plattner-Institute in Potsdam and received the best dissertation award of the University of Potsdam in 2014. After his PhD, he was a postdoctoral associate at MIT and Junior Professor at the TU Berlin. He is further recipient of the SIGMOD 2019 most reproducible paper award, SIGMOD 2015 best demonstration award, and CIKM 2014 best student paper award. His research is funded by the DFG and the German Ministry of Research and Education.
Markus Schmitz is a consultant at Aleph Alpha in Heidelberg and Guest Lecturer at the University of Erlangen-Nuremberg. Schmitz recieved his PhD in Information Systems from the University of Erlangen-Nuremberg working on the Design and Integration of Machine Learning in Information Systems. Working for BMW as an AI expert and AI Advocate, he worked on designing, implementing, and communicating Computer Vision and Reinforcement Learning Systems. He joined Aleph Alpha in 2022 to enable others in using the next generation of AI. His main areas of focus are AI Enablement, Innovative Problem Solving with Large Language Models, and AI Use Case Architectures.
Fernanda Viégas and Martin Wattenberg are Principal Scientists at Google, where they co-lead the PAIR (People+AI Research) initiative. Viégas and Wattenberg are also Gordon McKay Professors of Computer Science at Harvard, where Fernanda is also Sally Starling Seaver Professor at the Radcliffe Institute for Advanced Study. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by major media companies around the world to display the weather.
Stefan Bender is Head of the Data and Service Center of the Deutsche Bundesbank and – since 2018 - Honorary Professor at the School of Social Sciences, University Mannheim. With his position at Deutsche Bundesbank he is chair of INEXDA (the Granular Data Network) and member of the German Data Forum (https://www.konsortswd.de/en/). Before joining the Deutsche Bundesbank Bender was head of the Research Data Center (RDC) of the Federal Employment Agency at the Institute for Employment Research (IAB), which he has interna-tional established a research data centre including access to IAB data in the US (for example Berkeley, Harvard). His research interests are data access, data quality, merging administra-tive, survey data and/or big data, record linkage and establishment data. He has published over 100 articles in journals including the American Economic Review or the Quarterly Journal of Economics. For more details: https://www.bundesbank.de/en/bundesbank/research/rdsc/staff-members/stefan-bender-604504
Asia J. Biega is a tenure-track faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) leading the Responsible Computing group. Her research centers around developing, examining and computationally operationalizing principles of responsible computing, data governance & ethics, and digital well-being. Before joining MPI-SP, Asia worked at Microsoft Research Montréal in the Fairness, Accountability, Transparency, and Ethics in AI (FATE) Group. She completed her PhD in Computer Science at the MPI for Informatics and the MPI for Software Systems, winning the DBIS Dissertation Award of the German Informatics Society. In her work, Asia engages in interdisciplinary collaborations while drawing from her traditional CS education and her industry experience including stints at Microsoft and Google.