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Leipzig Symposium on Intelligent Systems (LEISYS)

Thursday 22nd July 2021,14:00pm to Thursday 22nd July 2021,20:00pm

Leisys-logo

  

Leipzig Symposium on Intelligent Systems

Join us for an exciting update on current research and developments in the dynamic and diverse field of intelligent systems!

The idea of intelligent computer systems has come a long way: Originating in the realm of science-fiction writers of the early 20th century, mid-century artificial intelligence concepts developed slowly but steadily into complex advanced AI systems providing, e.g., image and speech recognition to a wide range of applications. Today, many companies and developers market such AI-based products and services as intelligent computer systems, or at least, they are often perceived as such by the public. In fact, most people are the subject of learning algorithms at least once a day, for example while browsing social media or interacting with their mobile devices.

The Leipzig Symposium on Intelligent Systems (LEISYS) aims to bring researchers from a diverse set of backgrounds together, to facilitate discussions about applications and risks of intelligent systems, as well as methods to develop such systems. The expertise of the speakers at the symposium covers, among other topics, Machine Learning, Logic, Cognitive Systems, and Formal Methods.

The symposium will take place on July 22, 2021, between 14:00 and 20:00 (CET).

We encourage listeners from all backgrounds of Computer Science to attend and participate in the discussions.

Registration to this online event is free of cost, but you need to register here. If you are a student or staff at Lancaster University, you can connect to the symposium Teams space directly at this link.

LEISYS is organised by the School of Computing and Communications at Lancaster University in Leipzig, the new branch campus of Lancaster University in the heart of Europe.

General Chairs: Thomas Schmid, Sven Linker

Local Organiser: Wiebke Lamer

Programme

EDT

BST

CEST

 

8:00

13:00

14:00

Dr Mark Brewer, Academic Dean, Lancaster University in Leipzig

 

 

 

Welcome Note

8:15

13:15

14:15

Dr Wolfgang Hildesheim, Head of Watson, Data Science & AI DACH, IBM

 

 

 

AI Factories

8:50

13:50

14:50

Break

9:00

14:00

15:00

Session 1 – Data-Driven Systems

 

 

 

Dr Vaishak Belle, Chancellor’s Fellow, University of Edinburgh

 

 

 

Principles and Practice of Explainable Machine Learning

 

 

 

Dr Hendrik Heuer, Researcher, University of Bremen

 

 

 

Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of Machine Learning-Based Systems

 

 

 

Dr Lisa Ehrlinger, Senior Researcher, Johannes Kepler University Linz

 

 

 

Automating Data Quality Monitoring

10:00

15:00

16:00

Break

10:30

15:30

16:30

Session 2 – Cognitive Systems

 

 

 

Dr Daniel Raggi, Research Associate, University of Cambridge

 

 

 

Structure and Transformations of Representations

 

 

 

Dr Hossein Rahnama, Visiting Professor, Massachusetts Institute of Technology (MIT)

 

 

 

Mental Models for AI

 

 

 

Mengting Fang, Doctoral Student, University of Pennsylvania

 

 

 

Learning to Count Visual Stimuli with a Recurrent Neural Network

11:30

16:30

17:30

Break

11:45

16:45

17:45

Session 3 – Explorative Systems

 

 

 

Dr Larisa Soldatova, Reader in Data Science, Goldsmiths, University of London

 

 

 

Automating Scientific Discovery

 

 

 

Dr Aaron Dutle, Research Computer Scientist, National Aeronautics and Space Administration (NASA)

 

 

 

Formal Methods for Unmanned Aircraft Systems

 

 

 

Alexander Immer, Doctoral Student, ETH Zürich

 

 

 

Bayesian Model Selection in Deep Learning

12:45

17:45

18:45

Get-together

 

SPEAKERS

Wolfgang Hildesheim is a high energy physicist by education and worked several years in this field, for example at the European Organization for Nuclear Research (CERN) and the German Electron Synchrotron (DESY). In 2007, Wolfgang joined IBM to lead the Automotive, Aerospace and High Tech Practice. Since 2009, he has led IBM‘s Big Data Industry Solution Business In Europe helping client enterprises to become more data driven and create business value by using Advanced Analytics. Since 2012, he is responsible for creating and growing IBM’s Watson, Data Science & Artificial Intelligence Business in Europe with a major focus on Germany, Austria and Switzerland. Wolfgang regularly presents at conferences and publications related to AI and member of the Steering Group of the German Standardization Roadmap Artificial Intelligence.

Vaishak Belle is a Chancellor’s Fellow and Faculty at the School of Informatics, University of Edinburgh, an Alan Turing Institute Faculty Fellow, a Royal Society University Research Fellow, and a member of the RSE (Royal Society of Edinburgh) Young Academy of Scotland. At the University of Edinburgh, he directs a research lab on artificial intelligence, specialising in the unification of logic and machine learning, with a recent emphasis on explainability and ethics. He has given research seminars at numerous academic institutions, tutorials at AI conferences, and talks at venues such as Ars Electronica and the Samsung AI Forum. In 2014, he received a silver medal by the Kurt Goedel Society. Recently, he has consulted with major banks on explainable AI and its impact in financial institutions.

Hendrik Heuer is a researcher at the University of Bremen. His research focuses on disinformation, plain language, and the user experience of machine learning-based curation systems like YouTube. He studied Human-Computer Interaction and Machine Learning in Bremen, Buffalo, Stockholm (KTH), Helsinki (Aalto), and Amsterdam (UvA).

Lisa Ehrlinger is senior researcher at the Johannes Kepler University (JKU) Linz and at the Software Competence CenterHagenberg (SCCH) in Austria. At JKU, she conducts research about the automation of DQ measurement and knowledge graphs and teaches courses about informationsystems, ontologies, and data modeling. At SCCH, she leads the multi-firm project SEBISTA (Secure Big Stream Data Processing), where she applies finding from her scientific work, and drives the research focus “Data Management and Data Quality”. Her research interests and publications cover the topics data quality, knowledge graphs, and information integration.

Daniel Raggi has a first degree in Mathematics, a master's degree in Cognitive Science, and a doctorate in Intelligent Systems, which he studied under the supervision of Alan Bundy at the University of Edinburgh. He has studied logic and reasoning with a focus on their computational aspects. His main interest is in understanding how we reason, what makes reasoning effective, and how creative reasoning is achieved. He believes that understanding a process means being able to encode it computationally. Thus, Daniel uses tools such as interactive theorem provers to test his ideas. Daniel is currently a research associate at the University of Cambridge. He is part of the rep2rep project which aims to understand how representations are structured and transformed for effective human use, and to use this understanding for developing tools that select representations intelligently.

Hossein Rahnama is a computer scientist and an academic entrepreneur. As a visiting professor at the MIT Media Lab and professor at Ryerson University, where he co-founded Ryerson DMZ, his research explores AI, mobile human-computer interaction, and the effective design of data-driven services. He is also the founder and CEO of Flybits Inc., a data intelligence company that is serving a global customer base. Rahnama has written more than 30 publications and received 23 patents in computer science. He served as a council member at NSERC (National Science and Research Engineering Council of Canada) and is currently serving on the board of Canadian Science Publishing and the Home Capital Group. In 2012, MIT Technology Review selected him as one of its global TR35 list - The list of top 35 Innovators under the age of 35. In 2017, he was selected as part of Canada's 40 under 40.

Mengting Fang is a doctoral student in Psychology at the University of Pennsylvania. Her research focuses on the understanding of human cognition and the creation of human-like artificial intelligence. Using a variety of approaches including computational modeling, brain imaging, and behavioral experiments, she hopes to bridge the gap between perception and conception. Previously, she worked with Dr. Jay Mcclelland at Stanford University to study children's number learning and with Dr. Stefano Anzellotti at Boston College to study multivariate brain connectivity and action prediction.

Larisa Soldatova is a Reader in Data Science at Goldsmiths University of London. Her research focuses on discovery science, reasoning, knowledge representation and semantic technologies. She leads in Goldsmiths the EPSRC-funded project ACTION on cancer aiming to develop an AI system to assist in recommending personalized cancer treatments. Larisa is also working on the Robot Scientists project, which investigates what processes of scientific discovery can be automated and how robotic and human scientists can work together. Further, Larisa is involved in a number of international projects on the development of semantic standards, e.g. Machine Learning Schema, Robotics Task Ontology Standard, Laboratory Protocols EXACT.

Aaron Dutle is a Research Computer Scientist in NASA Langley Research Center's Formal Methods group. Before joining NASA in 2014, Dr. Dutle was a mathematician, specializing in Graph Theory and Combinatorics. He currently works to advance the state of the art in Formal Methods tools and practices, and to apply these methods to problems in Air Traffic Management, Unmanned Aircraft Systems, and Urban Air Mobility. Dr. Dutle is one of the inventors of, and verification lead for, DAIDALUS, which serves as the reference implementation for Detect and Avoid capabilities for Unmanned Aircraft in the National Airspace, as described in the national standard DO-365b.

Alexander Immer is a doctoral student at ETH Zürich and fellow at the Max Planck ETH Center for Learning Systems. His research revolves around approximate inference methods and their applications. Currently, he works on algorithms for neural networks that can incorporate prior knowledge, quantify uncertainty, and automatically select the optimal model for a given problem.