Academic Lectures
We have invited several high-level experts and research leaders to give lectures and share your knowledge with you. Here is a short overview of their background and abstracts.
- Prof. Anders Søgaard - Foundations of Trustworthy AI
University of Copenhagen, Homepage, Google Scholar
Abstract: What exactly do we mean when we talk about ’trustworthy’ AI? Or ‘fair’ AI? Or ’transparent’ AI? I sketch some of the foundational, yet still open, problems for trustworthy AI and present some empirical and theoretical work we’ve done in that space.
Bio: Anders Søgaard is consistently ranked among the most influential researchers in NLP. He has won eight best paper awards, held an ERC Starting Grant and a Google Focused Research Award, and now holds a Carlsberg Sember Ardens Grant. He previously worked for University of Potsdam, Amazon, and Google. Today, he is a Professor of Computer Science and Philosophy at the University of Copenhagen.
- Prof. Toon Calders - Fairness in Machine Learning
University of Antwerp, Homepage, Google Scholar
Abstract: Decisions made through predictive algorithms sometimes reproduce inequalities that are already present in society. Is it possible to create a data mining process that is aware of fairness? Are algorithms biased because humans are? Or is this the way machine learning works at its most fundamental level?
In this lecture, I will give an overview of some of the main results in fairness-aware machine learning, the research field that tries to answer these questions. We will review several measures for bias and discrimination in data and models, such as demographic parity, equality of opportunity, calibration, individual fairness, direct and indirect discrimination. Even though for each of these measures strong arguments in favor can be found, we will show that they cannot be combined in a meaningful way. Next to these methods to quantify discrimination we also cover several “fairness interventions” aimed at making algorithms fair that were proposed in the last decade. These techniques include pre-processing techniques such as biased sampling, in-processing techniques that deeply embed fairness constraints in learning algorithms, and post-processing techniques to make trained models fair.
Bio: Calders is a professor in the computer science department of the University of Antwerp in Belgium. He is an active researcher in the area of data mining and machine learning. He is editor of the data mining journal, and has been program chair of a number of data mining and machine learning conferences, including ECML/PKDD 2014 and Discovery Science 2016. Toon Calders was one of the first researchers to study how to measure and avoid algorithmic bias in machine learning and is one of the editors of the book “Discrimination and Privacy in the Information Society - Data Mining and Profiling in Large Databases”, published by Springer in 2013. He is currently leading a group of 6 researchers studying theoretical aspects of fairness in machine learning, as well as looking into practical use cases in collaboration with Flemish tax authorities, public welfare organizations, and an insurance company.
Abstract: Robots that are released "into the wild" are moving away from the controlled environments and laboratories in which they were developed. They are now forced to contend with constantly changing environment and uncertainty, learn on the job, and adapt continuously and over the long term. The paradigm of long-term autonomy and adaption is what enables the deployment of robots away from factory floors and warehouse where they already are omnipresent into equally challenging and promising application domains. In the summer school course, we will be looking at how robots can "survive" (operate reliably and effectively) in dynamic environments, ranging from agricultural fields to museum, experiencing slow changes due to seasons and acute uncertainty from interaction with humans. We will look at selected recent robotic developments in mapping, navigation, interaction, and perception and discuss the challenges and opportunities of deploying autonomous robots in the wild.
Bio: Marc Hanheide is a Professor of Intelligent Robotics & Interactive Systems in the School of Computer Science at the University of Lincoln, UK, and the director of the University’s cross-disciplinary research centre in Robotics, the Lincoln Centre for Autonomous Systems (L-CAS). He received the Diploma in computer science from Bielefeld University, Germany, in 2001 and the Ph.D. degree (Dr.-Ing.) also in computer science also from Bielefeld University in 2006. In 2001, he joined the Applied Informatics Group at the Technical Faculty of Bielefeld University. From 2006 to 2009 he held a position as a senior researcher in the Applied Computer Science Group. From 2009 until 2011, he was a research fellow at the School of Computer Science at the University of Birmingham, UK. Marc Hanheide is a PI in many national and international research projects, funded by H2020, EPSRC, InnovateUK, DFG, industry partners, and others, as well as the director of the EPSRC Centre for Doctoral Training (CDT) in Doctoral Training in Agri-Food Robotics (AgriFoRwArdS). The STRANDS, ILIAD, RASberry, and NCNR projects are among the bigger projects he is or was involved with. In all his work, he researches autonomous robots, human-robot interaction, interaction-enabling technologies, and system architectures. Marc Hanheide specifically focuses on aspects of long-term robotic behaviour and human-robot interaction and adaptation. His work contributes to robotic applications in care, logistics, nuclear decommissioning, security, agriculture, museums, and general service robotics. He features regularly in public media, has published more than 100 peer-reviewed articles, and is actively engaged in promoting the public understanding of science through appearances in dedicated events, media appearances, and public lectures.
- Prof. Vashek Matyáš - From ROCA (Fun & troubles with RSA keypairs) to improved security certification; Two lessons from usable security and its experiments.
Masaryk University, Czech Republic, Homepage, DBLP Scholar
Abstract:
From ROCA (Fun & troubles with RSA keypairs) to improved security certification
This lecture will start with a very brief outline of the core trouble with the Estonian (and Slovak, etc.) eID underlying cryptographic mechanism for electronic signatures. We'll then follow the path of its discovery as well as remediation, and conclude with several suggestions how to improve the fragile ecosystem of cybersecurity system certification and security evaluation.
Two lessons from usable security and its experiments
This talk will introduce the area of usable security - an interdisciplinary undertaking involving aspects of computer security (and sometimes cryptography), psychology and sociology. We'll then further explore two particular areas of research - interactions of S/W developers with TLS public-key certificates and work of developers with two-factor authentication.
Bio:
Václav (Vashek) Matyáš is a Professor at Masaryk University, Brno, CZ. His research interests are related to applied cryptography and security; he worked in the past with Red Hat Czech, CyLab at Carnegie Mellon University, as a Fulbright-Masaryk Visiting Scholar at the Center for Research on Computation and Society of Harvard University, Microsoft Research Cambridge, University College Dublin, Ubilab at UBS AG, and as a Royal Society Postdoctoral Fellow with the Cambridge University Computer Lab. Vashek also worked on the Common Criteria and in ISO/IEC JTC1 SC27. He received his PhD degree from Masaryk University, Brno, and can be contacted at matyas AT fi.muni.cz.
- Prof. Sadok Ben Yahia - Data-driven Urban mobility for CO2 emission reduction.
Scholar Homepage (TalTech)
Abstract: The transportation sector is responsible for 23% of energy-related CO2 emissions. Decarbonizing transportation is challenging, as it is still 92% dependent on non-renewable resources. However, current transport decarbonization-related policies are insufficient to decrease CO2 emissions to the expected level. Therefore, strategic approaches to reducing emissions from urban transport are critical to addressing the challenges of climate change.
In this lecture, I will give an overview of our recent research activities on a framework to build the next level of innovative data-driven traffic light strategies as the most impactful action to reduce CO2 emissions within the context of urban mobility for connected and autonomous cars. I will thoroughly go through the milestones of this framework: 1) Powerful eye-bird-view multimodal data fusion approaches feed AI models for accurate CO2 and urban noise level predictions that feed to dashboards for awareness purposes; 2) accurate predictions from time-series data for grounding the decision-making process ahead; and 3) advanced reinforcement learning techniques make use of urban noise predictions to implement the best traffic light strategy in real-time.
We will also discuss the challenges of achieving resilience by proactively detecting misbehaving entities within Vehicle-to-Everything settings
Bio: Sadok BEN YAHIA, Full Professor at the Technology University of Tallinn (TalTech) since January 2019. He obtained his HDR in Computer Sciences from the University of Montpellier (France) in April 2009 and since January 2019. He is the head of the Data Science Group in the IT School, and his research interests mainly focus on data-driven approaches for near-real-time Big Data analytics, e.g., urban mobility in smart cities (e.g., information aggregation & dissemination, traffic congestion prediction), Recommendation System. and fake content fighting.
Homepage: https://taltech.ee/en/contacts/sadok-ben-yahia DBLP: https://dblp.org/pid/20/6407.html