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Webinars

SAILS organises regular events such as symposia, webinars and workshops. We are organising virtual meetings until further notice.

In 2021 we will start with a biweekly lunch time seminar series, online on Mondays from 12 noon onwards.

 

13 September 21: Rob Saunders - Computational Creativity

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Rob Saunders - LIACS

Title
Computational Creativity

Abstract:
Creativity is one of the most highly prized faculties of human intelligence. The history of speculation about intelligent machines is mirrored by a fascination with the possibility of mechanical creativity. From the myths and legends of antiquity to the Golden Age of Automata in the 18th Century, the achievements of mechanical wonders were often paired with amazement at the performance of apparently creative acts. During the 20th Century the fascination with creative machines continued and at the dawn of the Computer Age the prospect of computationally modelling creative thinking was proposed as one of the “grand challenges” in the prospectus for the field of Artificial Intelligence. In the past 60 years, the field of Artificial Intelligence has seen significant progress in realising the goal of building computational creativity, from early Discovery Systems to the latest advances in Deep Learning. Like intelligence, however, the notion of creativity is an essentially social construct. Much work remains if creative machines are ever to become a reality, both in terms of technical advances and the integration of such machines into society. In addition, the development of machines capable of acting in ways that would be considered creative if performed by a human, will challenge our understanding of what it means to be creative. This talk will explore the history of creative machines and the prospects for the future of computational creativity research.

21 June 21: Marco Spruit - Natural Language Processing for Translational Data Science in Mental Healthcare

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Marco Spruit, LUMC & LIACS

Title: Natural Language Processing for Translational Data Science in Mental Healthcare
Abstract:
In this overview talk, I will first position the research domain of Translational Data Science, in the context of the COVIDA research programme on Dutch NLP for healthcare. Then, I will present our prognostic study on inpatient violence risk assessment by applying natural language processing techniques to clinical notes in patients’ electronic health records (Menger et al, 2019). Finally, I will discuss followup work where we try to better understand the performance of the best performing RNN model using LDA as a text representation method among others, which reminds us once more of the lingering issue of data quality in EHRs.

10 May: Marjolein Fokkema - A few simple rules for prediction

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Marjolein Fokkema, Psychology, FSW

Title: A few simple rules for prediction
Abstract:
Prediction Rule Ensembling (PRE) is a statistical learning method that aims to balance predictive accuracy and interpretability. It inherits high predictive accuracy from decision tree ensembles (e.g., random forests, boosted tree ensembles) and high interpretability from sparse regression methods and single decision trees. In this presentation, I will introduce PRE methodology, starting from the algorithm originally proposed by Friedman and Popescu (2008). I will show several real-data applications, for example on the prediction of academic achievement and chronic depression. I will discuss several useful extensions of the original algorithm which are already implemented in R package ‘pre’, like the inclusion of a-priori knowledge, unbiased rule derivation, and (non-)negativity constraints. Finally, I will discuss current work in which we leverage the predictive power of black-box models (e.g., Bayesian additive regression trees, deep learning) to further improve accuracy and interpretability of PRE.

26 April: Johannes Schmidt-Hieber - Towards a mathematical foundation of machine learning

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Johannes Schmidt-Hieber, Mathematical Institute, Leiden University

Title: Towards a mathematical foundation of machine learning
Abstract:
Recently a lot of progress has been made regarding the theoretical understanding of machine learning methods. One of the very promising directions is the statistical approach, which interprets machine learning as a collection of statistical methods and builds on existing techniques in mathematical statistics to derive theoretical error bounds and to understand phenomena such as overparametrization. The talk surveys this field and describes future challenges.

12 April: Mitra Baratchi - Machine learning for spatio-temporal datasets + SAILS data observatory

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Mitra Baratchi, LIACS, Leiden University

Title: Machine learning for spatio-temporal datasets + SAILS data observatory
Abstract:
Spatio-temporal datasets (e.g., GPS trajectories, Earth observations) are ubiquitous. Algorithms for effective and automated processing of such data are relevant from various applications, from crowd movement analysis to environmental modelling. These algorithms need to be designed considering the fundamental aspects of the underlying spatio-temporal processes (e.g., the existence of spatial and temporal correlations) and be robust against various ubiquitous data imperfection issues. In this talk, I will introduce the field of spatio-temporal data mining and talk about crucial open research challenges for making use of such data.

I would also like to discuss the vision of creating a “data observatory” to address various important research challenges in multi-disciplinary research. The data observatory aims to bring together datasets (the observations), AI algorithms (the tools), and expertise (the humans) in a well-equipped setting that facilitates a collaborative investigation.

29 March: Gerard van Westen - Applications of Artificial Intelligence in Early Drug Discovery

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Gerard van Westen, Professor of Artificial Intelligence & Medicinal Chemistry, LACDR, Leiden University
Title: Applications of Artificial Intelligence in Early Drug Discovery
Abstract:
Drug discovery is changing, the influence and catalytic effect of Artificial Intelligence (AI) cannot be denied. History dictates this new development will likely be a synergistic addition to drug discovery rather than a revolutionary replacement of existing methods (like the history of HTS or combichem, a new tool in the toolbox). As more and more scientific data is becoming public and more and more computing power becomes available the application of AI in drug discovery offers exciting new opportunities.

Central to drug discovery in the public domain is the ChEMBL database which provides literature obtained bioactivity data for a large group of (protein) targets and chemical structures.[1, 2] Machine learning can leverage this data to obtain predictive models able to predict  the  activity probability of untestedchemical structures  contained within the large collections of chemical vendorson the basis of the chemical similarity principle. [3, 4]

In this talk I will give an overview of research going on at the computational drug discovery group in Leiden. Central in our research is the usage of machine. I will highlight some examples we have published previously and finish with an outlook of cool new possibilities just around the corner.[5, 6]

References
1. Sun, J., et al., J. Cheminf., 2017. 9, 10.1186/s13321-017-0203-5
2. Gaulton, A., et al., Nucleic Acids Res., 2012. 40, 10.1093/nar/gkr777
3. Bender, A. and R.C. Glen, Org. Biomol. Chem., 2004. 2, 10.1039/b409813g
4. Van Westen, G.J.P., et al., Med. Chem. Commun., 2011. 2, doi:10.1039/C0MD00165A
5. Liu, X., et al., J. Cheminf., 2019. 11, 10.1186/s13321-019-0355-6
6. Lenselink, E.B., et al., J. Cheminf., 2017. 9, 10.1186/s13321-017-0232-0

15 March: Nele Mentens - Opportunities and challenges of AI in security research

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Nele Mentens, Professor of Computer Science, LIACS, Leiden University
Title: Opportunities and challenges of AI in security research
Abstract: 
Artificial Intelligence plays an important role in the protection of electronic devices and networks. Examples of domains in which AI has shown to lead to better products and protection mechanisms, are the security evaluation of embedded and mobile devices, and the detection of attacks in IoT and IT networks. Besides the added value that AI brings, there are also a number of pitfalls with respect to the privacy of users whose personal data are processed, and the confidentiality of the models that are employed. This talk will give an overview of these opportunities and challenges. 

8 March: Bart Custers - AI in Criminal Law

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Bart Custers, LAW, Leiden University
Title: AI in Criminal Law
Abstract:
AI is developed and used for many good causes, but increasingly criminals also make use of developments in AI. In this presentation, examples of crime are examined that involve AI and related technologies, including big data analytics, A/B optimization and deepfake technology. Typically such technologies can enhance the effectiveness of crimes like ransomware, phishing and fraud. Next, it is discussed how AI related technologies can be used by law enforcement for discovering previously unknown patterns in crime and empirical research on what works in sanctioning. Examples of novel patterns are presented as well as existing sophisticated risk assessment systems. From a procedural criminal law perspective, i.e., when investigating crime, AI technologies can also be used both in providing cues during criminal investigations and in finding evidence. Approaches in predictive policing are investigated as well as the potential role of existing cyber agent technology. With regard to finding evidence, advanced data analytics can prove to be helpful for finding the proverbial needle in the haystack, providing Bayesian probabilities and building narratives for alternative scenarios. For all these developments, legal issues are identified that may require further debate and academic research.

1 March: Oleh Dzyubachyk - AI-Based Quantification of Electron Microscopy Data

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Dr Oleh Dzyubachyk, LUMC, Leiden University
Title: AI-Based Quantification of Electron Microscopy Data 
Abstract: 
Electron microscopy (EM) is an imaging modality that has vast potential for becoming one of the primary beneficiaries of the advance of machine learning. In my talk I will first introduce to you this imaging modality and provide a few examples of data quantification needs. Next, I will describe our recent developments that enabled applying machine learning methodology to our in-house data and preliminary results of the mitochondria quantification project. Finally, I will share with you my ideas about potential directions for future research.

22 February: Daan Pelt - Machine Learning for Scientific Images

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Daan Pelt, LIACS, Leiden University
Title: Machine Learning for Scientific Images
Abstract: 
In recent years, convolutional neural networks (CNNs) have proved successful in many imaging problems in a wide variety of application fields. However, it can be difficult to train and apply existing CNNs to scientific images, due to computational, mathematical, and practical issues. In this talk, I will discuss newly developed methods that are specifically designed for scientific images. These methods can accurately train with large image sizes and a limited amount of training data (or no training data at all), and can automatically adapt to various tasks without extensive hyperparameter tuning. The talk will include comparisons between the new methods and existing CNNs, some recent results for real-world scientific problems, and ideas for future work.

15 February: Anne Meuwese - AI and Lawmaking: worlds apart?

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Anne Meuwese, LAW, Leiden University
Title: AI and Lawmaking: worlds apart?
Abstract: 
The intersection of 'Public Law', 'Governance' and 'Artificial Intelligence' is not limited to the question of how AI can be regulated. AI also has the potential to change certain fundamental processes of the state. This presentation looks at one such process: lawmaking. In what ways could IA change both the process and the outcome of lawmaking by legislators? Among the possible applications of AI in lawmaking discussed are 1) the use of AI in monitoring the effects of legislation ‘ex post’, in particular the potential of AI in identifying regulatory failures, 2) the possible changes in the types of norms used in legislation in sectors in which AI is used to support administrative decision-making (rules vs standards, level of abstraction, ‘granularity’ of norms), 3) the implications of AI for the idea of ‘technology neutrality’ in legislative drafting, 4) the expected increased frequency of legislative projects for which an (AI) system will need to be designed in parallel. To what extent to we see these applications emerging and what are the implications for the fields of public law and governance?

8 February: Tuna Kalayci - Reutilizing Historical Satellite Imagery in Archaeology: An AI Approach

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Tuna Kalayci, Archaeology, 
TitleReutilizing Historical Satellite Imagery in Archaeology: An AI Approach 
Abstract:
The historical imagery is invaluable in archaeological research. At the very least, an old photograph offers the first record of an object (ranging from a small pottery piece to a large ancient settlement). In some cases, these images might be the only records left due to destruction of that object. Therefore, it is beneficial for archaeologists to explore these data sources to the full extent. This talk examines one of these sources, CORONA spy-satellite and discusses the results of a CNN model for the automated documentation of ancient settlements in the Middle East. This talk will also include brief evaluations of two potential future projects: Sounds of Leiden (SouL) and Robotics in Archaeology.

25 January: Matthijs Westera - Modeling (implicit) questions in discourse

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Matthijs Westera, Assistant Professor, LUCL, Leiden University
TitleModeling (implicit) questions in discourse
Abstract: 
When we talk, we try to be clear, coherent, relevant, informative and truthful, or at least to appear that way. An audience will expect this, and this expectation constrains their possible interpretations of our utterances. How exactly this works is the topic of the linguistic field of Pragmatics, where a helpful notion has proven to be that of a Question Under Discussion (QUD): a (typically implicit) question to which an utterance must provide some kind of answer. In a coherent discourse, every utterance should address a pertinent QUD, ideally one that was evoked by the preceding discourse. Despite their centrality in the field of Pragmatics, QUDs have received only little attention in Natural Language Processing (NLP), where the vast majority of work on discourse coherence is not QUD-based but relation-based (discourse relations such as 'explanation' and 'elaboration'), and virtually any work on questions concerns, instead, either question answering (given a question, find a suitable answer to it) or 'answer questioning' (given an answer contained in a text, generate a suitable comprehension question for it). I will present my (et al.) ongoing attempts (crowdsourcing and computational modeling) to add QUDs to the NLP toolbox, hoping to receive valuable suggestions for, e.g., possible applications in the various fields represented at SAILS.

11 January  Joost Batenburg, Professor of Imaging & Visualisation, LIACS, Leiden University and programme director SAILS
Title: Explorative 3D Imaging: the Physical Search Engine
Abstract: 
Search engines and their underlying databases are becoming more and more powerful in answering a wide range of questions, driven by increasingly flexibility in formulating queries. A key limitation of such systems is that they can only answer questions based on data that is already available at the time the question is asked by the user. This conflicts sharply with the needs of exploratory research, where new questions are followed by the collection of new data, based on which the questions can be answered. This, in turn, leads to more questions, resulting in an iterative process of asking questions and collecting data. 
In this talk I will introduce the field of explorative 3D imaging, where a domain expert on a particular type of objects (products in a factory, cultural artifacts, or even medical patients) interacts with a 3D scanner that analyzes and visualizes the scan data in real-time. As a result, the scanner can adapt the data acquisition iteratively to new questions, facilitating new knowledge discovery. 
I will discuss the key challenges involved in speeding up the computational imaging pipeline that is the backbone of such a system, and the essential role of machine learning and AI in analyzing the data in real-time. 

Webinars 2020

Webinar SAILS Meets LIACS

Friday 19 June 2020 at 1 pm - Programme 

SAILS & DSO @ FSW - Webinar - Kaltura Live Room

29 May 2020 at 1 pm - Serge Rombouts
5 June 2020 at 1 pm -  Wouter van Loon
12 June 2020 at 1 pm - Bernet Elzinga

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