AI projects in Europe
How is Artificial Intelligence Changing Science?
Research in the Era of Learning Algorithms
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AI Projects in Europe
Below you will find a list of projects at European universities that use different methods of artificial intelligence for their research questions.
As a starting point, we document projects in the fields of sociology, economics, medicine, geoscience and physics. The list will be extended continuously.
Adapted Home Environments for People with Dementia
This project will develop a unified monitoring framework that includes three main functionalities: warning, reminding and real-time monitoring of the home environment for assisting people with dementia.
Contact: Abdelhamid Bouchachia
Advanced Analysis of Cardiac Depolarization Using High-Frequency and Ultra-High Frequency Components of the Electrocardiogram
The essence of the project is to propose a methodology and software that will be able to automatically classify selected disorders of conduction during heart depolarization (LBBB, RBBB, LAH, LPH and ventricular hypertrophy).
Contact: Goldmann Tomáš
Advanced Robotic Breast Examination Intelligent System (ARTEMIS)
ARTEMIS aims at developing an intelligent robotic system that will help early breast cancer detection. We will develop intelligent algorithms and soft robotic system for this project.
Contact: Amir Ghalamzan
Areas of research include: AI for image acquisition, reconstruction and analysis;AI-enabled decision support for diagnosis and prognosis; Novel methodologies that address the challenges in translating AI solutions into the clinic.
Contact: Daniel Rueckert & Emma Robinson
AI Tool to Speed Up Covid-19 Diagnosis Through X-rays
DeTraC, created by computer vision and data scientists Professor Mohamed Gaber and Dr Mohammed Abdelsamea from the School of Computing and Digital Technology, uses machine learning to assess and diagnose using large datasets of images from several hospitals across the world. The technology is now publicly available for the World Health Organisation and the global medical community as an open-source program.
Contact: Mohamed Gaber & Mohammed Abdelsamea
AMR-Diag: A Novel Diagnostic Tool for Sequence Based Prediction of Antimicrobial Resistance
Taking advantage of the advances in whole genome sequencing, bioinformatics, proteomics and machine learning methods we plan to develop a decision-making tool AMR-Diag, for the detection of bacterial infection, including its resistance profile.
Contact: Rafi Ahmad
Application des Techniques d’Intelligences Artificielles à la Cohorte Cancer
Le projet a pour objet de mobiliser les données de la cohorte cancer à l’aide des techniques classiques et de celles relatives à l’intelligence artificielle, en liens avec des axes de recherche des équipes CANBIOS et QuanTIM. Différents sous objectifs généraux ont été identifiés pour différents domaines d’intérêts.
Contact: Giorgi Roch & Julien Mancini
Apprentissage Automatique pour l’Oncologie de Précision et la Conception de Médicaments
Nos recherches portent sur le développement et l’application de méthodes informatiques permettant de prédire et d’analyser la modulation de la fonction des protéines et des cellules par de petites molécules organiques. Ces problèmes peuvent être résolus en générant des modèles prédictifs à partir de données pertinentes à l’aide de l’apprentissage automatique (une approche qui a récemment été rebaptisée IA pour la découverte de médicaments).
Contact: Pedro Ballester
ARAMIS – Algorithmes, Modèles et Méthodes pour les Images et les Signaux du Cerveau Humain
L’équipe dirigée par Olivier COLLIOT & Stanley DURRLEMAN a pour objectif de construire des modèles numériques des maladies du cerveau, en particulier des pathologies neurodégénératives, à partir de bases de données multimodales issues de patients. Les principales approches utilisées sont l’apprentissage automatique (technique d’intelligence artificielle), la modélisation géométrique et statistique, et la théorie des réseaux complexes.
Contact: Olivier Colliot & Stanley Durrleman
Artificial Intelligence Breast MRI Scanning System (IntelliScan)
This project […] seeks to develop INTELLISCAN (an Artificial Intelligence, machine learning enhanced breast MRI scanning system for use as a highly efficient, more accurate breast cancer screening tool).
Contact: Tat-Hean Gan
Artificial Intelligence in Medical Imaging
We develop novel AI methods capabable of transforming rich medical imaging data in quantitative biomarkers with applications in computer aided diagnoses and patient-specific interventional planning. This provides quantitative information from individual patients so the best course of action in their care pathway can be chosen.
Contact: Alejandro Frangi, Zeike Taylor, Ali Gooya & Toni Lassila
Auswirkungen von EDV im intensivmedizinischen Umfeld
Die Daten der elektronischen Patientenakte werden systematisch auf neue und unbekannte Sachverhalte untersucht. Dazu werden neue Methoden zur Krankheitsfrüherkennung, komplexen Alarmierung und klinischen Entscheidungsunterstützung entwickelt. Das Ziel dabei ist, die Behandlungsqualität und die Patientensicherheit zu verbessern und so einen zusätzlichen klinischen Nutzen aus dem EDV-Einsatz im intensivmedizinischen Umfeld zu generieren
Contact: Ixchel Castellanos
Automatic Question Generation for Occupational Health Assessment
In this project, UH will help Heales Enterprises Ltd (HEL) to set up an automatic question generation system using advanced Machine Learning methods. This system will be used in Occupational Health Assessment.
Contact: Yi Sun & Farshid Amirabdollahian
The main objective of the Barsnes Group is to combine state-of-the-art bioinformatics research with the current biomedical knowledge, thus building a bridge between project specific high-throughput omics analyses and novel biomedical knowledge.
Contact: Harald Barsnes
Biomedical Image Analysis
Rapid advances in non-invasive neuroimaging methods have revolutionized the possibilities to study changes occurring in living brain across a variety of time-scales ranging from seconds to an entire life span. A large part of these advances can be attributed to the development of dedicated computational algorithms and applied mathematics, which are essential to extract quantitative information from images. My group develops these computational methods to analyze the brain imaging data, evaluates the methods, for example, by using advanced simulations, and together with collaborators applies these methods to study the brain.
Contact: Jussi Tohka
Bournemouth University Technology Uses Virtual Reality to Help Surgeons
Researchers at Bournemouth University (BU) have been working on technology that uses animation, special effects, artificial intelligence and virtual reality to help surgeons develop skills and prepare for surgery.
Contact: Xiaosong Yang
Causation and Novel Risk Modelling for Person-Centred Prevention and Control of Cardiovascular Diseases
This project answers the call from clinicians for a new and more accurate clinical guideline for diagnosis and treatment of CVD among asymptomatic individuals at medium or high risk, fail to be identified using existing risk models. Linking data generated from a randomised clinical trial and quality national patient and death registers, we will analyse the complex interaction between biological, behavioural, psychological and social determinants and asymptomatic carotid atherosclerosis in a subset of adult population 40+ years old in Västerbotten County in Sweden, and develop individualised risk prediction models for 3-year and 5-year cardiovascular disease morbidity and mortality. This 5-year project brings together seven multidisciplinary researchers from clinical, public health and social sciences. We will employ advanced epidemiological and statistical methods such as multilevel structural equation modelling and latent trajectory model to address the research questions. We will use employ machine learning technique, such as boosted regression tree in developing the risk prediction models. This project will contribute to innovative risk model for clinicians to tailor a person-centred cardiovascular disease prevention and control programme for individuals at risk.
Contact: Nawi Ng
Cognition, Consciousness and Brain–Computer Interfacing
We study several aspects of human cognition using MEG and EEG combined with machine learning. Many of our approaches build on brain-signal features specifically reflecting attentive and conscious processing. We also develop brain–computer interfaces both for “closed-loop” neuroscientific experimentation as well as for future clinical applications.
Contact: Lauri Parkkonen
Computational Medical Imaging and Machine Learning – Methods, Infrastructure and Applications
Our project develop, implement, disseminate and evaluate machine learning techniques in the analysis of medical images and image-related data.
Contact: Arvid Lundervold
Computational Transcriptomics and Evolutionary Bioinformatics Laboratory
The laboratory deals with computer analysis of the whole genome sequencing data obtained in the laboratory itself, by its collaborators, as well as presented in the public domain. The whole genome sequencing is reading and deciphering the sequence of DNA or RNA from any biological sample. The analysis of these data allows us to reveal the genetic programs built into the DNA of organisms, as well as their variation in pathology and environmental changes.
Contact: Elena Zemlyanskaya
Database of Functional Brain Images
Functional brain imaging methods hold promise for producing valuable information for the diagnostics of many brain disorders; however, the application of these methods is hampered by the lack of a normative database. To this end, we are aggregating a large number of magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) datasets into such a database for the application of machine-learning methods to derive biomarkers that would be indicative of brain disease states.
Contact: Lauri Parkkonen
Decision Support Systems for Diagnosis, Prognosis and Design of Clinical Trials
Based on the new representations and statistical models, we design decision support systems for diagnosis, prognosis and design of clinical trials. These systems are based on: i) personalization of statistical models to predict evolution at the patient level; ii) new machine learning approches for classification and regression on high-dimensional and structured data; iii) content-based retrieval techniques.
Contact: Olivier Colliot
Deep Learning for Volumetric Brain Analysis: Towards BigData in Neuroscience – DeepVolBrain
In the DeepVolBrain project, the final goal is to develop a new generation of quantitative MRI analysis methods to cope with the rise of BigData in neuroimaging and, ultimately, to generate new knowledge. Moreover, the proposed methods will be implemented in open access to the entire community through a web platform.
Contact: Pierrick Coupé
Deep Learning in Multi-View and Multi-Modal Surgical Videos for Improved Operating Room Management – DeepSurg
The objective of DeepSurg is to harness in an interdisciplinary manner the power of computer vision and machine learning to analyze, monitor and improve surgical workflows non-intrusively.
Contact: Nicolas Padoy
Dementia Management and Support System
DMSS main purpose is providing support for physicians in their diagnostic reasoning and choice of interventions when meeting new patients with a suspected dementia disease. Managing uncertain information and conflicting medical guidelines using state-of-the art artificial intelligence theories is one line of research, another is developing person-tailored support for reasoning and knowledge development in the medical professional.
Contact: Helena Lindgren
Development and Investigation of Machine Learning-Based Algorithm for Continuous Unobtrusive Monitoring of Bradycardic and Tachycardic Arrhythmias (BradyTachy)
The project intends to develop a machine learning-based algorithm for continuous unobtrusive monitoring of life-threatening arrhythmias and integrate it into the wrist-worn device.
Contact: Andrius Sološenko
Develop Smart, Dementia-Friendly Signage
This project aims to develop Evidence-Based Smart Dementia-Friendly Signage for health and social care environments, which can potentially lead to the development of Smart Dementia-Friendly signage to be used in health and social care settings.
Contact: Federica Pascale & Alison Pooley
Diagnosing Kidney Transplant Diseases
The group developed a pipeline for comprehensive and reproducible analysis of hundreds to thousands of proteins from kidney tissue by SWATH mass spectrometry. With this seed grant, this multidisciplinary team with expertise on pathology, proteomics and AI will determine whether a novel ensemble machine learning feature selection strategy can be used to determine robust proteomics-based patterns that are able to differentiate between different disease states with the final goal to translate these findings into practice and aid with clinical decision making.
Contact: Jesper Kers
DOLF – Death to Onchocerciasis and Lymphatic Filariasis
To enhance efforts to control and eliminate lymphatic filariasis and onchocerciasis through the optimization of drug therapies and development of strategies for mass drug administration. Our name says it all. Death to Onchocerciasis and Lymphatic Filariasis. This project, supported by the Bill and Melinda Gates Foundation, includes an ambitious set of complementary applied research projects that share the common goal of optimizing therapy to accelerate the elimination of LF and Onchocerciasis. In addition, the project aims to improve chances for LF and/or Oncho control in regions of Africa and Asia that are behind the progress of other countries. We develop strategies to ensure that areas with low acceptance, hard to reach populations, and persistent infection benefit from drug therapy research. We convene with international partners including: DNDi, the World Health Organization, the Ghana University of Health and Allied Sciences, and others to ensure timely dissemination of our results.
Contact: Achim Hoerauf
Dosisreduktion mittels neuer Algorithmen bei DSCT- und FLASH-Technologie
Die Arbeitsgruppe „Kardiovaskuläre Bildgebung“ beschäftigt sich wissenschaftlich mit Erkrankungen des Herzens und der Gefäße. Neben klinisch orientierten Fragestellungen verfolgt unsere Arbeitsgruppe das Ziel, innovative neue Bildgebungskonzepte mit zu entwickeln und diese für zukünftige klinische Anwendungen nutzbar zu machen. Dazu gehören zum Beispiel die kardiovaskuläre Ultrahochfeld-MRT und die kardiovaskuläre hybride PET/MR-Bildgebung.
Contact: Thomas Schlosser & Kai Nassenstein
Drug Design, Proteomics and Theorem Proving
Work by Sean Holden applies Bayesian inference, probabilistic programming, and computational learning theory to drug design, proteomics, and theorem proving.
Contact: Sean Holden
Épidémiologie Développementale, Promotion de la Santé Mentale et de la Réussite Éducative – HEALTHY
L’équipe Healthy est un groupe multidisciplinaire composé d’épidémiologistes, de biostatisticiens, de psychologues et de pédopsychiatres ayant pour objectif de générer des connaissances sur l’épidémiologie des problèmes de santé mentale (PSM) et leurs conséquences globales au cours de la vie (i.e. de la conception à l’âge adulte), notamment sur l’éducation et la réussite professionnelle, et de tester des interventions en promotion de la santé visant à prévenir les PSM et leurs répercussions personnels, sociales et économiques.
Contact: Sylvana Côté
Explainable Artificial Intelligence Model for Assessing the Health Risks of a Geriatric Patient (GeRiMoDIs)
The aim of the project is to propose a systematic model of explainable artificial intelligence and related algorithms for decision support systems used by geriatric providers or patients themselves.
Contact: Agnius Liutkevičius
Développement d’un algorithme d’intelligence artificielle pour l’interprétation de mammographies de dépistage.
Contact: Isabelle Thomassin
FlowCat – Automated Classification of B-cell Lymphoma Sub Types
In our project, we seek to establish an approach for automated classification of lymphoma subtypes through a deep-learning based predictive model using information from flow cytometry data thereby reducing the need for manual gating.
Contact: Peter Krawitz
Forschung am ZMNH Institut für Medizinische Systembiologie
Das primäre Ziel der Forschung am Institut ist es, menschliche Krankheiten besser zu verstehen. Der Fokus liegt dabei auf Erkrankungen des zentralen Nervensystems. Um dieses Ziel zu erreichen, müssen große Mengen heterogener biomedizinischer Daten überprüft und miteinander integriert werden. Wir entwickeln dafür unsere eigenen Systeme, die diese Aufgaben soweit wie möglich automatisieren. Auf Grundlage der integrierten Daten können wir mithilfe von statistischen Verfahren und maschinellem Lernen Informationen extrahieren, die für die untersuchten Krankheiten relevant sind. Die gewonnenen Informationen werden dann weiter ausgewertet, um unser Verständnis der Erkrankungen zu verbessern und darüber hinaus Risikofaktoren zu entdecken und möglicherweise Heilungsmöglichkeiten zu finden.
Contact: Stefan Bonn
Handicap Activité Cognition Santé – HACS
Le thème central de notre équipe est le handicap avec pour retombée attendue l’inclusion sociale des personnes en situation de handicap dans les différents lieux de vie (la maison, l’école, le travail, la cité, etc.). Nos travaux s’intéressent aux personnes avec handicaps ou maladies chroniques de l’hôpital aux lieux de vie dans une approche inclusive. Nos recherches concernent un vaste champ de pathologies neurologiques : accidents vasculaires cérébraux, maladies génétiques, inflammatoires et infectieuses, traumatismes crâniens, maladies dégénérative, troubles du spectre autistique, etc.) mais aussi le vieillissement neuro-ordinaire.
Contact: Hélène Sauzeon
High-Throughput Droplet-Based Analysis of Influenza a Virus Reassortment by Single-Virus RNA Sequencing, for Pandemic Risk Assessment and Candidate Vaccine Viruses Optimisation – Microflu-REASSORT
The aim of µFlu-REASSORT is to implement an innovative droplet-based microfluidics approach in order to provide, in an experimental setting of co-infection with two IAVs, a statistically relevant ranking of the probability of reassortant viruses to emerge, and to add a predictive component to the existing pandemic risk assessment tools of the WHO and CDC.
Contact: Nadia Naffakh
Improved Treatments of Acute Myeloid Leukaemias by Personalised Medicine
We will initially focus on a rare AML subtype, pure erythroleukaemia (PEL) to develop a methodological pipeline to elucidate pathological mechanisms and subsequently adapt it to other AML subtypes. There will be a translational approach based on AML samples as well as longitudinal data from completed clinical trials, which will provide a high quality source for big data analytics and mathematical modeling. We pursue early identification of responders to novel signaling targeted drugs. The subclonal architecture of individual AML patients and their signalling status are mapped by mass and flow cytometry and validated by quantitative proteomics. Quantitative multiomics and clinical data will be combined with methods for machine learning to develop AML classifiers. AML_PM develops pipelines for clinical decision-making that enable next generation diagnostics for AML and tailoring treatment for individual AML patients.
Contact: Bjørn Tore Gjertsen
Improving Treatments in Cerebral-Palsy Children Using Artificial Intelligence
Within this project, we propose to develop a prototype software application for automatic analysis and evaluation of 3D motion data using sophisticated technologies employing artificial intelligence.
Contact: Ladislav Plánka
Information Sciences to Support Personalized Medicine
Our group develops Artificial Intelligence (AI) methods for clinical decision. We demonstrated that ontology-based reasoning classifies atrial fibrillation alerts with results comparable to expert cardiologists.
Contact: Anita Burgun
Integrative Datenanalyse und -interpretation. Generierung einer synaptisch-integrativen Datenstrategie (SYNIDS)
Wir werden die anfängliche Analyse von transkriptomischen und proteomischen Daten bereitstellen und deren Integration, um Schlüsselproteine, Netzwerkhubs und relevante Verkettungen zu detektieren. Wir werden uns auch mit einer weiteren Herausforderung dieses Sonderforschungsbereiches beschäftigen indem wir die Synaptische integrative Datenstrategie (SynIDs) entwickeln. Diese wird beim Erstellen kausaler Zusammenhänge unterstützen. SynIDs wird den Forschern auch erlauben die Ergebnisse aus verschiedenen Bereichen abzurufen und sie mit öffentlich zugänglichen Daten zu kombinieren. Dadurch wird es vereinfacht Hypothesen aufzustellen, die dann experimentell validiert werden können.
Contact: Stefan Bonn
KÄVELI: Home Monitoring of Parkinson’s Disease
Käveli project builds a system to monitor and analyze the walking patterns of Parkinsons disease patients at home. Home monitoring is done using the sensors of the smartphone, force sensor integrated to smart insoles of shoes and wrist-worn accelerations sensors.
Contact: Jari Ruokolainen
The researchers are developing a brain-controlled hand exoskeleton that can be used in everyday life, enabling paralysed people to grasp everyday objects and thus live more independently. Dr. Surjo Soekadar, head of the Applied Neurotechnology research group at the University Hospital of Tübingen and project coordinator, is sure that this will substantially improve the quality of life for paralysed persons.
Contact: Surjo R. Soekadar
Learning Interpretable Models for Medical Diagnostics – DiagnoLearn
A central problem in practical use of statistical models is the interpretability of a model. In many applications it is quite useful to construct a scoring system which can be defined as a sparse linear model where coefficients are simple, having few significant digits, or are even integers. Ideally, a scoring system is based on simple arithmetic operations, is sparse, and can be easily explained by human experts. In this project, we challenge the problem of automated interpretable score learning purely from data.
Contact: Nataliya Sokolovska
Machine Intelligence From Cortical Networks (MICrONS)
Although neuroscience has inspired many elements of artificial neuronal networks, the mammalian visual system is still markedly different from current state-of-the-art deep neural networks in terms of its circuit architecture, robustness, and ability to learn. Two groups at the Bernstein center (Bethge, Sinz) are part of a multi-university consortium funded by the MICrONs program within the Obama BRAINinitiative, that is setting out to narrow the gap between current state-of-the-art deep learning and algorithms of the mammalian visual system by exploring circuit level functional and anatomical patterns of populations of cortical neurons.
Contact: Matthias Bethge
Machine Learning, Deep Learning & Neurosciences
Machine learning and deep learning are powerful tools for analyzing and modeling data from neuroscience experiments in order to answer specific questions. All the work to be done to push forward the research in the field of the ILCB within this QT is grouped in three axes. The first axis is about learning from data, Machine Learning and Deep Learning. The second point concerns the design of machine learning systems for brain data. The third axis focuses on the comparison of mental representations and computer representations.
Contact: Thierry Artières & Pascal Belin
Machine Learning in Biomedicine
Our team develops novel machine learning methods and models to answer key questions in biomedicine: how do mutations arise and contribute to disease? How to accurately predict cancer patient outcomes? What is the role of inherited genetic factors in diseases? Together with our collaborators, we focus on answering these questions in cancers and hematological malignancies. We create scalable and multimodal machine learning techniques utilizing genome, transcriptome, epigenome and imaging data to build clinically useful computational tools.
Contact: Esa Pitkänen
Machine Learning Modelling for AI-Guided Drug Response Prediction
We are making use of network pharmacology approaches to map target addictions and other dependency mechanisms that underlie individual drug sensitivity profiles, with the aim to identify synergistic drug-target combinations that can effectively inhibit multiple cancer driving sub-clones and other escape routes of cancer cells.
Contact: Tero Aittokallio
Machine Learning pour l’Aide au Diagnostic de l’Autisme chez l’Enfant via le « Eye Tracking »
Notre ambition : renforcer et structurer les équipes autour de la robotique chirurgicale, pour être en capacité de proposer des solutions innovantes permettant une optimisation du parcours, une médecine individualisée et des actes opératoires sécurisés et fiabilisés. Cette démarche implique de poursuivre l’évaluation des outils créés et enrichir ces systèmes en y intégrant le « Big data / intelligence artificielle ». Ces techniques auront, à coup sûr, des impacts forts sur le parcours de soin (imagerie, assistance robotisée, outils d’aide à la planification et à la réalisation de l’acte opératoire, domotique)
Contact: Michel Lefranc
Antimicrobial resistance (AMR) is increasing worldwide, and surveillance activities play a key role in informing policies to contain AMR. Moreover, resistance to new antibiotics is emerging ever quicker after their introduction onto the market, rapidly reducing the effectiveness of even last-resort antibiotics. As such, the sustainable introduction of a novel class antibiotic can only be achieved when accompanied by timely and informed surveillance and stewardship strategies.
Contact: Natacha Berbers
Maschinelles Lernen in der Laboratoriumsmedizin
Das medizinische Labor der Zukunft wird neben dem Erstellen von Befunden auf höchster Qualität eine zunehmend interaktive Ausrichtung annehmen. Dabei erhalten klinisch tätige Kolleginnen und Kollegen Empfehlungen zu Stufendiagnostik und Diagnosefindung, die auf Basis der Messwerte ermittelt werden. Um dieses Ziel zu erreichen, werden in unserer Arbeitsgruppe moderne Methoden zur Datenauswertung angewendet, wie z.B. nicht-lineare, multiparametrische Machine Learning Methoden mit deren Hilfe klinisch relevante Diagnosen vorhergesagt werden sollen. Hohe ethische Vorgaben und Datenschutzregularien (European General Data Protection Regulation GDPR / DSFVO) werden dabei berücksichtigt.
Contact: Amei Ludwig & Claas Schmidt
Medical Image Processing
We develop new algorithms for biomedical image processing. We process images from different modalities, such as magnetic resonance, ultrasound, computed tomography, or microscopy. We work in 2D, 3D and 4D. We know how to preprocess the data, how to register, segment, model, reconstruct and classify them. We use techniques from image processing, numerical mathematics, as well as machine learning.
Contact: Jan Kybic
Medical Machine Learning Lab
Mental disorders are among the most debilitating diseases in industrialized nations today. […] [Valid] predictive models would be instrumental, both, for minimizing patient suffering and for maximizing the efficient allocation of resources. Realizing this potential is the core goal of this research group. To this end, we employ state-of-the-art tools from machine learning, artificial intelligence and statistical learning such as Deep Neural Networks, Random Forests etc.
Contact: Tim Hahn & Dominik Grotegerd
The goal is to take advantage of digitalization in medicine, to link data and generate medical knowledge, and to develop and apply innovative IT solutions for a better, data-based healthcare delivery system.
Contact: Hans-Ulrich Prokosch
Mit künstlicher Intelligenz Krebs gezielt behandeln
Zuerst wollen die Forscher eine große Datenbank erstellen. Darin werden die histologischen Bilddaten und umfangreiche molekulare Daten von jeweils 1.000 Fällen häufiger Tumoren, wie Lungenkrebs, Darmkrebs und Bauchspeicheldrüsenkrebs, enthalten sein. In einem nächsten Schritt soll ein computergestütztes System darauf trainiert werden, auf Grundlage der histologischen Bildern wichtige molekulare Gruppen vorherzusagen. Falls dies mit ausreichender Genauigkeit gelingt, könnte das System in Zukunft dazu eingesetzt werden, diejenigen Tumoren schneller und kostengünstiger zu identifizieren, die besonders gut für eine bestimmte „Schlüssel-Schloss-Therapie“ geeignet sind.
Contact: Philipp Ströbel
Modeling and Development of Personalised Medicine
Work by Pietro Lio uses machine learning approaches to analyse bio-medical “big data” for disease modeling and development of personalised medicine, with integration across scales from the molecular and genomic to organ and systems levels.
Contact: Pietro Lio
Modeling for Neuroimaging Population Studies
Population imaging relates features of brain images to rich descriptions of the subjects such as behavioral and clinical assessments. We use predictive analysis pipelines to extract functional biomarkers of brain disorders from large-scale datasets of resting-state functional Magnetic Resonance Imaging (R-fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). We also built tools for automated data analysis which facilitate processing large datasets at scale. Some of our results are highlighted below.
Contact: Bertrand Thirion
Multitasking Learning Method Using Deep Neural Networks: Application to the Identification of Respiratory Suffering
The aim of this thesis is to design robust automatic facial expression analysis methods that rely on transfer learning and multitasking to identify, characterize, and monitor respiratory discomfort in the absence of direct human interaction. This study provides long-term prospects for innovative applications such as the automatic monitoring of intubated patients or the design of intelligent ventilators that can adapt to the patient’s sense of discomfort.
Contact: Gérard Biau
NemoPlast: Learning with Neurorobots: Human-Machine Interfaces for the Promotion of Motor Plasticity
In the project „NemoPlast: Learning with neurorobots: human-machine interfaces for the promotion of motor plasticity“, scientists led by Professor Alireza Gharabaghi […] are developing a novel training system for stroke patients; many of them are still significantly restricted in their motor skills years after the event. For these patients, NemoPlast is developing a training neurorobot that links an exoskeleton with a non-invasive brain stimulator.
Contact: Alireza Gharabaghi
NeuroControl: Control of Physiological Activity in Retinal Neuronal Networks
The project „NeuroControl: Control of physiological activity in retinal neuronal networks“ focuses on how activity in the neuronal networks of the retina can be controlled.
Contact: Philipp Berens & Günther Zeck
We apply machine learning models on neuroimaging data, in particular MEG. We model the visual system in the brain by analyzing the statistical structure of the natural input images. We develop the relevant theory of statistical machine learning, typically unsupervised.
Contact: Aapo Hyvärinen
Le GRC n°5 est une équipe transdisciplinaire orientée sur les cancers urologiques (prostate, rein, voie excrétrice urinaire). Ses activités reposent sur l’analyse de bases de données clinico-biologiques, génétiques et moléculaires afin d’intégrer les différentes composantes phénotypiques ou génotypiques dans des modèles prédictifs applicables aux situations cliniques de prévention, de diagnostic, de pronostic et de théranostic. Les méthodes de modélisation utilisent l’intelligence artificielle explicable (XAI) pour générer des outils d’aide à la décision applicables en clinique.
Contact: Olivier Cussenot
Patient-Centric Engineering in Rehabilitation (PACER)
In the western world, about 50% of major amputations is caused by diabetes, and the majority of these have had a long and complex medical history. They have often been too inactive, had pain and impaired general condition. It is critical that these people are motivated to take back control of their lives, and assisted to increase their quality of life. We believe that a device that could follow the patient will facilitate for a personalized and optimized rehabilitation. To the best of our knowledge we have not found any attempts to apply such scoring rules on machine learning, artificial neural network or deep learning models in a personalized and optimized device for rehabilitation of amputated patients.
Contact: Peyman Mirtaheri
The aim of the PEDIA study is to investigate the value of computer-assisted analysis of medical images and clinical features in the diagnostic workup of patients with rare genetic disorders.
Contact: Tzung-Chien Hsieh
Personalised Disease Modelling
This project aims to use state-of-the-art Artificial Intelligence techniques in order to learn the “shape” of disease as it progresses. This will enable models to be built that capture realistic progression for an individual patient, facilitating better management of disease and more appropriate interventions.
Contact: Allan Tucker
Predicting Alcohol Use Disorder through Machine Learning
We aim to solve the public health conundrum of risk-stratification for alcohol use disorder by means of the method of machine learning. Machine learning searches the best solution for a given problem in a data-set and it bypasses the need to selectively study single options. That is, machine learning is not restricted by background theory or human biases. The product of machine learning is a set of rules or contingencies that serve to implement, individual, risk stratification of alcohol use disorder. Now we have the expertise to apply this method on the rich and well defined data from the Netherlands Study of Depression and Anxiety.
Contact: Marc Molendijk
Predicting Medication Response in ADHD through Computational Modeling of the Continuous Performance Test
The current project takes a novel approach to prediction of medication response. By using computational modeling of decision making we will analyse already collected data from 250 adult ADHD patients.
Contact: Mads Lund Pedersen
Prediction and Decision Support Systems for Knee Osteoarthritis
Osteoarthritis (OA) is the most common joint disease in the world. Despite the extensive research, the etiology of OA is still poorly understood and its progression is highly difficult to predict clinically. However, large amount of accumulated clinical and research data exists, which enables new possibilities to understand OA progression when analysed with novel machine learning based methods.
Contact: Simo Saarakkala
Prediction of Epileptic Seizures from Multivariate iEEG Recordings
Using machine learning to predict and control epileptic seizures in drug-resistant patients.
Contact: Lorenzo Livi
R&D of Innovative Technology for Predicting and Early Warning of Delayed Cerebral Ischemia after Subarachnoid Hemorrhage (EWoDCI)
Project aims: to develop an innovative method for predicting and early warning of CV and DCI after aSAH, to perform clinical studies of this method and to create a software tool for forecasting vasospasm and cerebral ischemia.
Contact: Vytautas Petkus
Remote Sensing and Advanced Spectral Analysis for Coaching and Rehabilitation
This research project is part of a series of activities carried out with Cambridge Centre for Sport and Exercise Sciences, using smart sensors/wireless sensors and audio analysis in biomechanics and biomedical sciences.
Contact: Domenico Vicinanza & Jin Zhang
RESPOND3 – Responsible Early Digital Drug Discovery
Using machine learning to tackle a computational bottleneck in the drug discovery and development process.
Contact: Nathalie Reuter
The University Robotic Centre consists of a specialized room equipped with the Da Vinci S HD robot, related technology and state-of-the-art monitors for mini-invasive robot-assisted surgeries.
Contact: Vladimír Študent
SCAMPI’s primary objective is to co-design and develop a new intelligent computer-based toolkit that will help and support people affected by dementia and/or Parkinson’s in their daily living.
Contact: Neil Maiden
Massively parallel sequencing applied to single cells allows us to investigate new questions that were out of reach for classical bulk genomics. Cell-to-cell variability is central in gene regulation or cell differentiation, as it provides information on the underlying molecular networks. Consequently, single cell expression profiling has the promise of revolutionizing our understanding of genomes regulation.
Contact: Franck Picard
We are aiming to develop an intelligent MR scanner that enables fast, efficient and effective diagnostic imaging. This will be achieved by combining advances in how MR images are acquired, reconstructed and analysed with advances in Artificial Intelligence (AI) and Machine Learning (ML).
Contact: Katherine Bellenie
Sonification and Smart Sensors for Healthy Ageing
This project will investigate the design and implementation of small (possibly wearable), wireleless smart sensors with a special focus on healthy ageing.
Contact: Domenico Vicinanza & Jin Zhang
SPRING: Socially Pertinent Robots in Gerontological Healthcare
In the past five years, social robots have been introduced into public spaces, such as museums, airports, commercial malls, banks, company show rooms, hospitals, and retirement homes, to mention a few examples. In addition to classical robotic skills such as navigation, grasping and manipulating objects, i.e. physical interactions, social robots must be able to communicate with people in the most natural way, i.e. cognitive interactions.
Contact: Xavier Alameda-Pineda
SUBSAMPLE Digiteo chair
The goal of the project is to understand the neurobiological mechanisms that are involved in complex neuro-psychological disorders. A crucial and poorly understood component in this regard refers to the interaction patterns between different regions in the brain. In this project we will develop machine learning methods to capture and study complex functional network characteristics.
Contact: Bertrand Thirion
Systems Biology of Drug Resistance in Cancer
The focus of the research group is to understand and find effective means to overcome drug resistance in cancers. Our approach is to use systems biology, i.e., integration of large and complex molecular & clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets. We are particularly interested in developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information.
Contact: Rainer Lehtonen
The Artificial Intelligence Clinician Learns Optimal Treatment Strategies for Sepsis in Intensive Care
[We] developed a reinforcement learning agent, the Artificial Intelligence (AI) Clinician, which extracted implicit knowledge from an amount of patient data that exceeds by many-fold the life-time experience of human clinicians and learned optimal treatment [for sepsis] by analyzing a myriad of (mostly suboptimal) treatment decisions.
Contact: Anthony C. Gordon
The Interpretation of Physical Activity Wearable Data and its Relation with Metabolic and Brain Health in Older Adults
Because the standard interpretation of the accelerometer data does not provide enough insight about the physical activity (PA) of the study participants in free-living conditions, further analysis to combine wearable and heath data requires an experienced data scientist. In the past two years we generated labelled activity data in a validation study of 35 older adults, using accelerometers and physiological sensors. Using this dataset and state of the art machine learning algorithms, together with LIACS we created multiple activity recognition models, which can be applied to free living data collections. We are now ready to interpret free-living physical activity profiles in the LUMC studies and combine them with health parameters data, such as MRI data on brain ageing and metabolic health measured by traditional clinical parameters and metabolomics.
Contact: Eline Slagboom
Using Artificial Intelligence to Identify Biomarkers for Psychiatric Disorders
Ziel des Projekts ist es, objektive physiologische Marker für psychiatrische Störungen wie Schizophrenie-Spektrum-Störung (SSD) und Autismus-Spektrum-Störung (ASD) zu identifizieren. Mit Hilfe des Elektroenzephalogramms und der künstlichen Intelligenz wollen die Partner die gegebenen experimentellen Paradigmen optimieren, um visuelle Biomarker auf frühen Verarbeitungsstufen für SSD und ASD zu messen. Das Projekt wird von Eucor – The European Campus mit „Seed Money“ in der Förderlinie „Forschung und Innovation“ unterstützt.
Contact: Jürgen Kornmeier
Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function
This project aims to apply state-of-the-art imaging, motion analysis and machine learning techniques to characterise the motion of the heart as it beats.
Contact: Daniel Rueckert & Wenjia Bai
Weakly Supervised Learning for Accurate Annotation of Textual Clinical Documents
The extraction of medical concepts (diseases, signs, symptoms, treatments, drugs, etc.) from clinical reports is an important research topic in natural language processing. These documents, written in natural language, by humans and for humans, are still very difficult to analyse and therefore to valorise, due to the variation of language in general, but also to the technical nature of the documents, whose vocabulary varies strongly from one medical specialty to another.
Contact: Gérard Biau
A Bird’s-Eye View on Agricultural Transformation in sub-Saharan Africa: An analysis of living standards indicators combining panel data, satellite imagery and deep learning
In this project we address longstanding and unresolved questions in development research regarding the distributional effects of rural transformations, poverty and production levels. We do this through an innovative framework featuring a new application of artificial intelligence techniques. More precisely, we apply machine learning to satellite imagery along with more conventional panel survey data. This is the first study of its kind and combines expertise from distant disciplines such as physics, development research and remote sensing in a cross-disciplinary effort.
Contact: Ola Hall
A Deep Learning-Based Automated System for Seabed Imagery Recognition and Quantitative Analysis (DEMERSAL)
The project consortium brings together specialists in signal, image and video processing, and marine benthic ecologists with long-term experience in UW research. We plan to develop a user-friendly system, flexible enough to use in a variety of marine environments. To test system’s capabilities, video material collected in the Arctic Ocean, Baltic Sea, Mediterranean Sea and other world regions will be used.
Contact: Anatanas Verkikas
ADRIATIC – Advancing Diver-Robot Interaction Capabilities
The project ADRIATIC aims to advance the teaming capabilities between human divers and their robotic diving counterparties to create a synergy that would become a potential solution for many challenging diving tasks. With the paramount goal of improving the overall diving safety, the project focuses its research on improving and finding new human-robot interaction modalities in order to combine the best complementary features of both the diver and the underwater robot.
Contact: Nikola Mišković
AquaIMPACT aims to integrate the fields of fish breeding and nutrition to increase the competiveness of EU’s main aquaculture species while minimizing environmental impact.
Contact: Jochen Hemming
Artificial Intelligence and Landscape Analysis: Expanding Methods and Challenging Paradigms
In the last decade artificial intelligence has started assisting researchers in performing complex computational operations, providing scholars with the possibility to analyze datasets so far considered too large or too complex to be surveyed by humans. The exponential development and diffusion of new data-acquisition technologies, the expansion of the world wide web and the increased availability of large datasets of heterogeneous spatial geographic information, opened incredible opportunities for geo-scientists to explore and define novel investigation approaches. this project will develop an investigation approach based on the systematic training of highly intelligent machines for detecting scattered archaeological elements visible in different datase
Contact: Nicolo Dell’Unto
Artificial Intelligence for Retrieval of Forest Biomass & Structure
We present a new research project funded by Academy of Finland AIPSE program aimed at using advanced AI methods, a well-validated physically-based forest reflectance model, and EO data to map forests in the boreal zone. We will use the simulated spectra and the corresponding forest structural data to train AI algorithms; once trained, we will apply the algorithms to optical EO data from Sweden, Finland, Estonia and Russia, and hyperspectral data from Finland. The AI retrieval results will be compared against forestry data from test sites in each of these regions.
Contact: Jorma Laaksonen
Automated System of Geodetic Deformation Monitoring of Engineering Structures (ASGDM)
Automated system of geodetic deformation monitoring of engineering structures (ASGDM) is intended for the formation of a data bank, in order to ensure with the help of monitoring mode the control strain of engineering objects on the basis of integrated use of field methods of observation.
Contact: Galina Nikolayevna Tkacheva
CDE : Contrôle et Diagnostic pour l’Environnement
CDE est une équipe Toulonnaise dont les activités vont de la théorie du contrôle (contrôle optimal non linéaire, convergence des observateurs, etc.) aux aspects plus pratiques, incluant le diagnostic, en passant par différentes approches de l’automatique moderne (commande sans modèles, approches basées sur l’intelligence artificielle). Les applications concernent majoritairement l’environnement (énergies renouvelables, agriculture raisonnée, applications dans le domaine maritime).
Contact: Frederic Lafont & Nicolas Boizot
Constraining the Large Uncertainties in Earth System Model Projections with a Big Data Approach
The project COLUMBIA is an interdisciplinary project that aims to develop an innovative tool, based on the state-of-the-art machine learning technology, to efficiently analyze large amount of model data to better understand
why some models behave very differently than the others.
Contact: Jerry Tjiputra
CroMarX – Cooperative Robotics in Marine Monitoring and Exploration
CroMarX project is a project funded by the Croatian science foundation (HRZZ). It increases the efficiency of maritime research and surveillance by using cooperative marine vehicles. The project focuses on cooperative control algorithms for surface and underwater unmanned vehicles.
Contact: Nikola Mišković
Deep Neural Networks for Multi-Scale Modeling of Climate Data Dynamics
The general framework of the thesis is the development of hybrid systems combining physical modeling and statistical and neuronal modeling. The subject concerns the modeling of complex physical phenomena, which concern the dynamics of ocean circulation, which are components of climate models. The objective is to model dynamic systems, based on statistical models based on deep neural networks which integrate knowledge and constraints from the physics of the phenomenon. The subject requires in-depth skills in statistical learning and neural networks and an interest in climate modeling.
Contact: Marie Deschelle
Development and Applications of New Methods in Seismic Research
We develop and apply new methods in wide range to processing of seismic data, to seismic modeling and to interpretation and post processing of the models. Research interests include automatic seismic signal processing, classification of seismic sources and signals, improving location accuracy, combining and comparing different geophysical 3D models and extracting new information from existing models. We have a special interest in applying machine learning methods to problems in seismic research.
Contact: Timo Tiira
G2Net – A Network for Gravitational Waves, Geophysics and Machine Learning
The rapid increase in computing power at our disposal and the development of innovative techniques for the rapid analysis of data will be vital to the exciting new field of Gravitational Wave (GW) Astronomy, on specific topics such as control and feedback systems for next-generation detectors, noise removal, data analysis and data-conditioning tools.The discovery of GW signals from colliding binary black holes (BBH) and the likely existence of a newly observable population of massive, stellar-origin black holes, has made the analysis of low-frequency GW data a crucial mission of GW science. The low-frequency performance of Earth-based GW detectors is largely influenced by the capability of handling ambient seismic noise suppression. This Cost Action aims at creating a broad network of scientists from four different areas of expertise, namely GW physics, Geophysics, Computing Science and Robotics, with a common goal of tackling challenges in data analysis and noise characterization for GW detectors.
Contact: Isabel Cordero-Carrión
HEKTOR – Heterogeneous Autonomous Robotic System in Viticulture and Mariculture
The main objective of the HEKTOR project is to provide a systematic solution for the coordination and cooperation of smart heterogeneous robots/vehicles (marine, land and air) capable of autonomously collaborating and distributing tasks in open unstructured space/waters.
Contact: Nikola Mišković
High-Performance Processing Techniques for Mapping and Monitoring Environmental Changes from Massive, Heterogeneous and High Frequency Data Times Series – TIMES
The objective of the TIMES project is to produce new knowledge on the dynamics landscape objects from the massive exploitation of this big geospatial data with the objective to develop and validate novel data processing and analysis methods for environmental monitoring of landscape objects.
Contact: Anne Puissant
Impacts of DEep submEsoscale Processes on the ocEan ciRculation – DEEPER
The goals of the DEEPER project are (1) to quantify the impacts of deep-sea submesoscale processes and internal waves on mixing and water mass transformations, (2) to explore ways of parameterizing these impacts using the latest advances in machine learning, i.e. applying deep learning to the deep ocean.
Contact: Jonathan Gula
Improving Safety at Sea by Predicting Waves and Quiescent Periods
This project is using machine learning methods to model the sea’s surface from maritime radar observations, and then using the model to make predictions of that surface for up to two minutes into the future.
Contact: Jacqueline Christmas
InnovaMare – Blue Technology – Developing Innovative Technologies for Sustainability of Adriatic Sea
InnovaMare project will jointly develop and establish an innovation ecosystem model in the area of underwater robotics and sensors for purposes of monitoring and surveillance sector with a mission-oriented on the sustainability of the Adriatic Sea.
Contact: Nikola Mišković
KLIMOD – Computer Model of Flow, Flooding and Spread of Pollution in Rivers and Coastal Areas
The project carries out applied scientific research and develops a computer model for effective modelling of the flow and spread of pollution in open watercourses and in the coastal sea area, accepting river tributaries, torrents, and industrial and wastewater discharges into the coastal sea area. At the same time, a prediction model of microbiological pollution based on artificial intelligence models and the integration of microplastic models of pollution dispersion into the overall model will be developed. The computer model is adapted to the supercomputer environment, which enables high-resolution simulations to be carried out with the aim of implementing measures to mitigate the effects of climate change in priority vulnerable and transversal areas.
Contact: Vanja Travaš
Land-ATmosphere Interactions in Cold Environments – LATICE
The research group LATICE will bring a focus on cold-regions exchange processes within Earth System Sciences as an interdisciplinary initiative of collaborative research and education.
Contact: Lena Merete Tallaksen
The LEXIS project will build an advanced engineering platform at the confluence of HPC, Cloud and Big Data which will leverage large-scale geographically-distributed resources from existing HPC infrastructure, employ Big Data analytics solutions and augment them with Cloud services.
Contact: Jan Martinovic
Machine Learning and Numerical Methods in Carbonate Geology
A large part of the group’s current research focuses on using artificial neural networks to automatically recognise and quantify rock features, such as facies, diagenetic fabric, and others.
Contact: Cédric M. John
Mapping of Algae and Seagrass Using Spectral Imaging and Machine Learning
The goal of the MASSIMAL project is to develop new methods for mapping underwater vegetation (seagrass and macroalgae). Using a hyperspectral camera mounted on a drone, the seafloor will be imaged from 50-100 meters above the sea surface. By combining the hyperspectral images with manual sampling of the vegetation, machine learning algorithms can produce detailed maps of e.g. the different species distribution, vegetation density and physiological state.
Contact: Martin Hansen Skjelvareid
MATS : Machine Learning for Environmental Time Series
A huge trend in recent earth observation missions is to target high temporal and spatial resolutions (e.g. SENTINEL-2 mission by ESA). Data resulting from these missions can then be used for fine-grained studies in many applications. In this project we will focus on three key environmental issues : agricultural practices and their impact, forest preservation and air quality monitoring. Based on identified key requirements for these application settings, MATS project will feature a complete rethinking of the literature in machine learning for time series, with a focus on large-scale methods that could operate even when little supervised information is available.
Contact: Romain Tavenard
Next Generation Biomonitoring of Change in Ecosystem Structure and Function
Our vision is to develop and test a generic NGB approach that will detect ecosystem-wide change more rapidly, sensitively and cheaply than current biomonitoring. Using a unique combination of Next-Generation Sequenced DNA data and Machine Learning, NGB will reconstruct species interaction networks to identify change in ecosystem properties, revolutionising both our understanding of ecosystems and our ability to predict and mitigate global change.
Contact: David Bohan
Predictive Model of the Synergistic Effects of Environmental Pollutant Mixtures
This project proposes to develop a unique fly-based model to predict synergistic interactions between mixtures of environmental pollutants, specifically on neuroendocrine signaling. We set up a very productive fly lab, complete with a range of methodologies, enabling for high-throughput metabolic and behavioral studies.
Contact: Helgi Schiöth
There is a need to close the demand and supply gap in terms of quantity and quality of water resources. Therefore, the project “Research-based Assessment of Integrated approaches to Nature-based SOLUTIONS” (RainSolutions) aims to develop an integrated framework of methodologies to manage nature-based solutions (NBS) for the restoration and rehabilitation of urban water resources systems.
Contact: Miklas Scholz
SCAI Abu Dhabi & TOTAL Industrial Chair of Research on Artificial Intelligence
The project’s goal is to radically enhance the simulation of flow through porous media application domain in order to boost forecasting capabilities to be able to increase oil recovery and production. The modeling of fluid dynamics in porous media will be addressed by focusing on improving learning mechanisms using different perspectives, from learning the form of differential equations from data to learning dynamical models with limited computing complexity.
Contact: Gérard Biau
Seismic Imaging of the Earth Laboratory
The main research focus of the laboratory is the understanding of the processes inside the Earth by creating algorithms, collecting data and building seismic models. Identification of the geological scenarios for various structures is an important task that will help in solving many scientific and applied problems, such as mineral exploration, engineering applications, identifying sources of volcanic activation, understanding the mechanisms behind the emergence of mountains and valleys.
Contact: Ivan Kulakov
The Framework for Evaluation of Crater Detection Algorithms
The results of this project are: (1) a method for crater detection using edge detection and gradient, modified Hough transform, morphometry measurements and analysis of topography and parameter space of Hough transform, slip-tuning, and calibration; (2) an improvement of crater detection utilizing a Crater Shape-based interpolation method that proved to be efficient for the detection of very small craters from the topographies of Mars and the Moon; (3) the framework for evaluation of crater detection algorithms (FECDA) including some of the most complete publically available crater catalogues.
Contact: Sven Loncaric
Diagnostic System for Attitude Measurement Based on Psychological Distance Testing with a Use of Evolution Algorithms
The project aims to standardize and extend the innovative method of measuring pupil attitudes, interests and relationships for wide use in school and educational – psychological counseling. The project responds to the growing societal need for the development of the educational system, accompanied by the demand for current methods of diagnosing educational reality.
Contact: Lenka Skanderová
Instagram through the Prism of Artificial Intelligence
The transformations linked to digital technology are technical, but also epistemological, cognitive and finally social, economic and political. Within the framework of this doctoral research project, Instagram is the main field to apprehend Artificial Intelligence with a problematic angle questioning the calculated textualization of social practices by the digital device.
Contact: Gérard Biau
Maciej Gruszczyński, Daniel Popek, Dawid Rusiecki, and Marcin Wątroba are the team that analysed over 300 thousand tweets in terms of mood and expressing emotions during the pandemic. They categorised their content as neutral, motivating or expressing optimism, helplessness, derision, fear, or anger. The machine learning algorithms we use help to detect emotions among Twitter users. We use five models, three of which are deep neural networks – explain the students.
Contact: Przemysław Kazienko
Trust in Artificial Intelligence
To address this challenge, the current project focuses on trust—the critical building block of any society. The main goals of the project are to: (a) discern the differences between trust in humans and in AI agents; (b) identify the key psychological factors that determine the development of trust towards AIs; (c) determine the role of moral and competence components in the perception of AI (vs. human) trustworthiness; and (d) compare the relative weights of deeds and their consequences when making moral judgments about AIs (vs. humans).
Contact: Katarzyna Samson
AI Predictive Analytics
This sub-area operates across a number of fundamental fields of AI methodologies. In Natural language processing (NLP) the focus is on how to extract information from large corpus of texts (such as media and social media) to explain the economy. Methodologies include: content analysis, concept extraction, document classification: Latent Dirichlet allocation (LDA), Multi-Label Classification. Research is also carried out using Causal Inference from Machine Learning approaches. The emphasis here is on methodologies such as Causal Bayesian Networks and Causal Random Forest to draw reliable conclusions from machine learning testing approaches. There is also focus on Deep Learning through the application of deep learning methods to analyze, predict and examine risk in real systems.
Contact: Amir Sadoghi
Algorithms for Systemic Risk Measurement
The goal of the project is to develop statistical methods for modelling and measuring systemic risk in complex systems such as financial markets. By researching the methods of mathematical and computational modeling, valuable insights can be obtained. For instance, the emergence of power-law and two-phase behavior in the financial market fluctuations, or the basic mechanisms that underlie systemic risk and the stability of the complex financial systems.
Contact: Zvonko Kostanjčar
An Intelligent Assistant Providing Risk Diagnosis for Industrial Procedures – LELIE
The main goal of this project is to produce software based on language processing and artificial intelligence that detects potential risks of different kinds (health, ecological, economical, etc.) in technical documents. We will concentrate on procedural documents which are, by large, the main type of technical document. Given a set of procedures (e.g. production launch, maintenance) over a certain domain produced by a company, and possibly given some domain knowledge (ontology or terminology), the goal is to process these procedures and to annotate them wherever potential risks are identified. Procedure authors are then invited to revise these documents.
Contact: Patrick Saint-Dizier
Artificial Intelligence for Digitizing Industry
Artificial intelligence plays a central role in societies, economies and industries around the world. However, there has been a lack of AI integration in Europe. As a result, potential users are not sufficiently supported despite the benefits it can provide to all branches of the industry and its digitisation. The EU-funded AI4DI project aims to transfer machine learning (ML) and AI from the cloud to the digitising industry. It will use a seven-key-target approach to evaluate and improve its relevance within the industry. The project plans to connect factories, processes and devices within the digitised industry by utilising ML and AI. It will then collect data on their performance.
Contact: François Alin (u.a.)
Artificial Intelligence Solution for Optimizing Companies’ Social Media Campaigns (EMODI)
Given the changes in customer behaviour resulting from information and communication (ICT), it is appropriate to seek a deeper and broader understanding of the characteristics of company messages, its links to consumer engagement behaviour on social media (SM), and business performance. In addition, optimizing SM for effective corporate campaigns is an integral part of a company’s daily routine. Despite the fact, that companies collect various data on customer behaviour on SM, traditional analytical methods do not allow for complex processing and forecasting of customer behaviour and improvement of company’s performance (e.g. sales). This can be achieved through Artificial Intelligence (AI) solutions that enable companies to manage the effectiveness of SM campaign results, which can ensure their competitiveness in the marketplace.
Contact: Ineta Žičkutė
Data Driven Computational Models for Prediction and Simulation of Path Dependencies in Complex Dynamic Labour Market Systems
This project is addressing the need to understand and gain evidence based insight into complex socio-economic and environmental dynamics of Saudi Arabia’s labour market.
Contact: Faiyaz Doctor
Decision Support Systems in Digital Business
The research program „Decision Support Systems in Digital Business“ is focused to comprehensive study of complex organizational systems management be it manufacturing, service, social, ecological or virtual. The organizational system inevitably incorporates groups of people working together to achieve common goals. These systems are managed by feedback information, real time and anticipative information, which is provided by decision support systems and other information systems (IS).
Contact: Andreja Pucihar
Deep Learning for Credit Risk Assessment
Within the scope of the project deep learning models will be studied and novel deep learning methods developed specifically for the application of credit risk assessment. The goal is to build competences in applications of deep learning models for credit risk assessment for retail and SME customers, and transfer the structured knowledge to the banking industry.
Contact: Zvonko Kostanjčar
Deep Reinforcement Learning Algorithms for Risk Management
The goal of this project is to develop a novel class of risk-sensitive reinforcement learning algorithms in dynamic environments with applications in financial risk management. The objectives also include the implementation of state space representation models that extract information from time series data by exploiting latent factors and design of portfolio optimization algorithms based on proposed methods.
Contact: Zvonko Kostanjčar
Design of Machine Learning Based Algorithm for Personnel Scheduling (APTS)
Personnel scheduling problem with respect to the workforce demand and rostering has been a subject of research for a long time. Due to the high complexity of the problem, the optimal solution cannot be found in a reasonable amount of time. That is why new methods or their combinations are suggested for automatic scheduling with respect to the known set of constraints. Obviously, employers want employees to perform as many tasks as possible during their working time. On the other hand, they pay attention to the employee’s requests by suggesting flexible working hours, desirable sequence pattern of working days and work time. Optimization with a large set of constraints is not practical if a combination of evolutionary algorithms with the greedy approach is applied. It is planned to design a machine learning based algorithm and implement its prototype during this project.
Contact: Dalia Čalnerytė
Économétrie et Statistique
Le département d’économétrie mène des travaux de recherche théoriques et empiriques en économétrie, statistique et apprentissage machine. Ses membres s’intéressent à de nombreux domaines de l’économie, tels que l’environnement, la santé, la finance, le travail, l’éducation, le développement et les inégalités.
Contact: Emmanuel Flachaire
Fintech and Artificial Intelligence in Finance – Towards a Transparent Financial Industry (FinAI)
We want to facilitate interactions and collaborations between different groups of academics and industry working on Fintech and AI in Finance, to provide theoretic expertise to industrial partners, and to establish a large and vibrant interconnected community of excellent scientists across diverse fields. The key objectives are: to improve transparency of AI supported processes by developing a data-driven rating methodology.
Contact: Oleg Deev
Risk Management of the Trade Processess Involving Big Data Analytics and Artificial Intelligence
The essence of the project is to develop a model for risk assessment and management of sales processes that will help companies identify and manage risks related to customer reputation, solvency, tax avoidance through artificial intelligence and big data management systems. Research and development of the necessary data collection and evaluation algorithms would also result in creating an IT tool prototype, which will gather data from different sources, different data formats, high speed data into one large database.
Contact: Gerda Žigienė & Robertas Alzbutas
The Governance of Economic Activity in the Digital Age
Thus, the overarching research question that constitutes the liaison among the various research interests of TILEC members boils down to the following: Taking into account the growing digitalization of society, exemplified by the increasingly important role that big data and artificial intelligence play for the functioning of markets and other forms of social interaction, how should institutions and structures be designed and what types of incentives can be adopted so as to ensure that public policy objectives are attained?
Contact: Tilburg University
Machine Learning for Modelling and Control
The research activities aim at efficiently addressing modelling and control of nonlinear/time-varying behavior of systems in these domains by developing a fusion of system identification, control and machine learning methods. The resulting methods automatically construct dynamical models capturing user specified aspects of the system behavior.
Contact: Roland Toth