keynote speakers
One of the highlights of the upcoming ICT conference is the lineup of esteemed keynote speakers who will be sharing their insights and experiences on the latest trends and developments in the field. These experts are at the forefront of their respective areas, and their presentations will provide attendees with valuable information and inspiration. Among the keynote speakers at the conference, you can expect to hear from..
GESINE REINERT
Professor of Statistics, University of Oxford
GESINE REINERT
Gesine Reinert, Professor of Statistics, University of Oxford
BIOGRAPHY
Prof. Gesine Reinert is a Research Professor at the Department of Statistics, Oxford, and Fellow at Keble College, Oxford (2000 – present). Her research interests cover network analysis and probabilistic approaches to machine learning, as well as approximations in probability and statistics. In 2015 she was elected Fellow of the IMS, and since 2016 she has been a Fellow of the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. Since 2020 she has been Editor-in-chief of the SpringerBriefs in Probability and Mathematical Statistics.
TOPIC: TBA
Valentina Dagienė
Professor of Informatics, Vilnius University
Valentina Dagienė
Professor of Informatics, Vilnius University
BIOGRAPHY
Title of professor: Vilnius University, 2007.
Ph.D. habilitation (in Education, Social Sciences): Vytautas Magnus University, Kaunas, Lithuania, 2005. Information culture in comprehensive schools: modelling of curricula in computer literacy and educational process. PhD in Informatics: Vytautas Magnus University, Kaunas, Lithuania, 1993. Her research interest include: Computer Science Education Research, Algorithmic Thinking, Computational Thinking, Technology Enhanced Learning, STEM education, Olympiads in informatics, contests, gamification.
DETAILS
The global pandemic has led to a ‘pivot’ to digital learning in many sectors of many countries, in schools, colleges and universities. My work with the UK Edtech Hub, British Council and Commonwealth of Learning suggests this response to the pandemic has been pedagogically conservative within those schools, colleges and universities, and furthermore may be increasing digital divides and educational disadvantage for those individuals, communities and cultures that are ignored, oppressed or poorly served by those schools, colleges and universities. My research explores in which innovative informal digital learning can help and support.
TOPIC: TBA
Thorsten Altenkirch
Professor for Computer Science, University of Nottingham
Thorsten Altenkirch
Professor for Computer Science at the University of Nottingham.
BIOGRAPHY
His research interest are in Type Theory and constructive logic and their application in proof assistants and programming languages. He has published a book on conceptual programming in Python (with Isaac Triguero) and is known for youtube videos on programming and other subjects in the Computerphile series.
TOPIC: Why dependent types matter
Aleksandar Bojchevski
Professor for Computer Science, University of Cologne
ALEKSANDAR BOJCHEVSKI
Professor for Computer Science, University of Cologne
BIOGRAPHY
Aleksandar Bojchevski is a tenured professor for Computer Science at the University of Cologne where he leads the research group on Trustworthy Artificial Intelligence. Broadly speaking his research is about models and algorithms that are not only accurate or efficient but also robust, uncertainty-aware, privacy-preserving, fair, and interpretable. One focus area of his research is (trustworthy) graph-based models such as graph neural networks. Previously he was faculty at the CISPA Helmholtz Center for Information Security. Before that he did a PostDoc and completed his PhD on machine learning for graphs at the Technical University of Munich, advised by Stephan Günnemann.
TOPIC: Machine Learning with Guarantees
Abstract: From healthcare to natural disaster prediction, high-stakes applications increasingly rely on machine learning models. Yet, most models are unreliable. They can be vulnerable to manipulation and unpredictable on inputs that slightly deviate from their training data. To make them trustworthy, we need provable guarantees. In this talk, we will explore two kinds of guarantees: robustness certificates and conformal prediction. First, we will derive certificates that guarantee stability under worst-case adversarial perturbations, focusing on the model-agnostic randomized smoothing technique. Next, we will discuss conformal prediction to equip models with prediction sets that cover the true label with high probability. The prediction set size reflects the model’s uncertainty. To conclude, we will provide an overview of guarantees for other trustworthiness aspects such as privacy and fairness.
TOPIC: Machine Learning with Guarantees