LWDA 2021 - Program - Hosted by MCML

Lernen. Wissen. Daten. Analysen. - Learning. Knowledge. Data. Analytics.

LWDA 2021 hosted by the Munich Center for Machine Learning (MCML), Munich, 01.09–03.09.2021.

LWDA 2021 in Munich

Quick Links: Schedule | Workshop Programs | Keynote Speakers

We thank you for your patience today regarding the track sessions. After collecting feedback, 30 mins should be enough to get back to our program. Accordingly, due to our delay:

Schedule of LWDA 2021

Time Wednesday
(Sep 1)		 Thursday
(Sep 2)		 Friday
(Sep 3)
09:00
10:00 Opening Keynote 3
Prof. Dr. Mykola Pechenizkiy
Keynote 1
Prof. Dr. Michael Leyer
11:00 Track Session Track Session
Joint Session
12:00
Lunch Break Lunch Break Lunch Break
13:00
Track Session Track Session Track Session
14:00
15:00 Closing Session
16:00 Community Meetings
Keynote 2
Prof. Dr. ir. Arjen P. de Vries
17:00
Social Event (Open End)
18:00
19:00

It is planned to have a short break of 5-10 minutes between the sessions.

Joint Session

Wednesday, Sep 1st, 11:30-12:30

Program of each Workshop

FG Datenbanksysteme - Data Engineering for Data Science

Wednesday, Sep 1st, 13:30-16:00

Thursday, Sep 2nd, 11:00-12:30

FG Knowledge Discovery und Machine Learning

Wednesday 2021-09-01: 11:30-16:00

Thursday 2021-09-02: 11:00-12:30

Thursday 2021-09-02: 13:30-16:00

Friday 2021-09-03: 11:00-12:30

Friday 2021-09-03: 13:30-15:00

FG Business Intelligence und Analytics

Donnerstag, 2.9.:

FG Knowledge Management

Wednesday, Sep 1st, 13:30-16:00

Thursday, Sep 2nd, 13:30-16:00

(Break / “get-together” until official community meeting at 16.00)

FG Information Retrieval

Wednesday, Sep 1st, 13:30-16:00

Keynote 2

Thursday, Sep 2nd, 13:30-16:00

Community Meeting

FG Grundlagen von Datenbanken

Donnerstag, 02.09.2021

Freitag, 03.09.2021

LWDA Keynote Speakers

Prof. Dr. Michael Leyer

How our brain reacts to and interacts with data, information and knowledge

Michael Leyer

Abstract: Individuals decide and act on the basis of perceived data, information and knowledge. What happens in the brain with this input and which influencing factors lead to which decisions and actions is targeted in different disciplines. In addition, there are algorithms and systems that generate data, information and knowledge, make it available to individuals and with which individuals interact. The complexity is increased with different systems related to artificial intelligence. But even if artefacts are generated that process input without humans, there are many interactions. The keynote gives an overview of different theories that explain cognitive processes of people from different angles. It also takes a closer look at how visualizations of data and information are processed and influence cognitive processes. In addition, the interaction of humans with applications based on artificial intelligence is considered as well as how these are accepted by humans.

Bio: Prof. Dr. Michael Leyer holds the Chair of Service Operations at the University of Rostock and Adjunct Professor in the School of Management of the Queensland University of Technology in Brisbane, Australia. He conducts research on the effects of new technologies, the design of new forms of work (future of work), the integration of customers into business processes and the design of service networks. A central aspect is the consideration of behavior, cognitive processes and decisions of people in processes. The topics are examined from a theoretical perspective in order to be able to derive well-founded, practical implications. He has published his research results in over 100 scientific publications. In addition to his research activities, he is active in various positions. He is President of the Council of the University of Rostock, in the scientific management board of the Center for Entrepreneurship and a member of the board of the Information and Communication Network at the University of Rostock. In addition, he is involved in the Association of University Teachers in Business Administration with the establishment of innovative concepts at the central scientific association conference and is a member of the steering committee of the Knowledge Management Group (FGWM) in the Gesellschaft für Informatik.

Prof. Dr. Mykola Pechenizkiy

The origins and future of AI fairness, accountability and transparency

Mykola Pechenizkiy

Abstract: Modern machine learning techniques contribute to the massive automation of the data-driven decision making and decision support. Multiple examples from different industries, healthcare, education, and government illustrate the challenges of developing and making use of trustworthy and human-centered AI. It becomes better understood and accepted that employed predictive models may need to be audited. Disregarding whether we deal with so-called black-box models (e.g. deep learning) or more interpretable models (e.g. decision trees), answering even basic questions like “why is this model giving these answers?” and “how do particular features affect the model output?” is nontrivial. In reality, auditors need tools not just to explain the decision logic of an algorithm, but also to uncover and characterize undesired or unlawful biases in predictive model performance, e.g. by law hiring decisions cannot be influenced by race or gender. In this talk I will give a brief overview of the different facets of comprehensibility of predictive analytics and reflect on the current state-of-the-art and further research needed for gaining a deeper understanding of what it means for predictive analytics to be truly transparent, fair and accountable. I will also reflect on the necessity to study utility of the methods for interpretable predictive analytics.

Bio: Mykola Pechenizkiy is Professor of Data Mining at the Department of Mathematics and Computer Science, TU Eindhoven. His main expertise and research interests are in predictive analytics and its application to real-world problems in industry, healthcare and education. He leads Trustworthy AI interdisciplinary research studying foundations of robustness, safety, trust, reliability, scalability, interpretability and explainability of AI; developing novel techniques for informed, accountable and transparent predictive and prescriptive analytics; and demonstrating their ecological validity in practice in collaboration with industrial parters. He has co-authored several publications and served on the program committees of the leading data mining and AI conferences, including IJCAI, ECMLPKDD, AAAI, and ICML among others.

Prof. Dr. ir. Arjen P. de Vries

You will want to rank your text data with a database too!

Arjen P. de Vries

Abstract: My research has always focused on the question how to integrate databases and information retrieval technology. Why would you even want to do that? Are the join operations necessary to solve information access problems not way too expensive to run ranking queries on a database engine? In this talk I will argue that, yes, it is technically feasible and desirable to bring the benefits of “the database approach” to the field of information retrieval, to enable the field to tackle the challenges posed by the next generation of search systems. Addressing complex information needs that span multiple, heterogeneous information sources and match the relevance criteria to the personal or work context where they arise calls for a higher level of abstraction than the inverted file, and adoption of the separation of concerns and data independence that are the de facto standard for developing business applications. I will discuss the basic building blocks drawn from my prior research and experience with the integration of IR and databases, and conclude with a brief introduction to GeeseDB, a research toolkit being developed in my group to explore the benefits of graphs as a representation and express search solutions in a graph query language.

Bio: Arjen P. de Vries is professor of Information Retrieval and research director of the Institute of Computing and Information Sciences.at Radboud University Nijmegen in the Netherlands. His research aims to resolve the question how users and systems may cooperate to improve information access, with a specific focus on the value of a combination of structured and unstructured information representations. He is a founding member of Spinque, a company that integrates databases and information retrieval to develop search solutions with and for information specialists.

Edgar Meij

Search and Discovery for Finance

Abstract: Finance professionals face a myriad of tasks in their day-to-day workflows, including finding relevant information, generating trade ideas, staying up to date with breaking news or general trends in the world, and coming up with novel ways to generate “alpha”. Historically, this has centered mainly around traditional sources of data such as stock exchange ticks, news stories, company filings, etc. An increasing amount of data is being generated in textual form on social media and elsewhere, however, and given the simultaneous increase in more machine-readable forms of “alternative data” such as web site usage, credit card transactions, and mobile app analytics, we face a unique opportunity to identify, score, rank, suggest, filter, and alert to financially relevant information and events. In this talk, Edgar describes typical information needs of finance professionals and how Bloomberg uses techniques such as search, summarization, entity linking, and natural language understanding & generation to address those needs.

Bio: Edgar Meij is the head of the Artificial Intelligence (AI) Discovery group in Bloomberg’s Engineering department. He leads several teams of researchers and engineers who develop systems that provide question answering capabilities, smart contextual suggestions with severe latency constraints, as well as the Bloomberg Knowledge Graph with its advanced machine learning-based analytics that is used to generate accurate, timely, and contextual financial insights. Edgar holds a PhD in computer science from the University of Amsterdam and has an extensive track record in information retrieval, natural language processing, and machine learning. Before joining Bloomberg, Edgar worked at Yahoo! Labs on all aspects related to entities in the context of web search.