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

speaker

Professor of Statistics, University of Oxford

Gesine Reinert

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.

ABSTRACT
Synthetic data are increasingly used in computational statistics and machine learning. Some applications relate to privacy concerns, to data augmentation, and to method development. Synthetic data should reflect the underlying distribution of the real data, being faithful but also showing some variability. In this talk we focus on networks as a data type, such as networks of financial transactions. This data type poses additional challenges due to the complex dependence which it often represents. The talk will detail some approaches for synthetic data generation, and a statistical method for assessing their quality.

TOPIC: SYNTHETIC NETWORKS

Thorsten Altenkirch

speaker

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
 
Abstract: A dependent type is a type which depends on values. Dependent types are used powerful programming languages which can express any property of a program and they are also used in interactive proof systems like Coq or Lean. I will use the agda system to illustrate the potential of dependent types. I will also highlight some issues which stop dependent types to fulfil their potential.

TOPIC: Why dependent types matter

Aleksandar Bojchevski

speaker

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

marija slavkovik

speaker

Professor for Computer Science, University of Bergen

marija

MARIJA SLAVKOVIK

Professor of Informatics, Bergen University

BIOGRAPHY

Marija Slavkovik is a full professor and chair of the Department of Information Science and Media Studies at the University of Bergen. Her research interests are: Machine ethics, Logic reasoning in social networks, Collective reasoning and decision making.

 

MORE INFO

https://scholar.google.com/citations?hl=en&user=g8UBNwUAAAAJ 

TOPIC: Why is AI a social problem in 2023? 
 
Abstract: Artificial Intelligence has been an active research area since 1956. In the same timespan AI as an area of innovation and technology has been in and out of existence. The tools we use have always played a role in shaping society, but AI has not so far  been discussed as a politically relevant topic. 
Some of the public discourse today considers topics of super intelligence and machine supremacy. The talk will discuss the reality of AI, what has changed in 2023,  the tools that we have available today and the need to decide what kind of socio-technical society do we want to live in. 

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: Why is AI a social problem in 2023?