Call 2019 Partner Search Tool

The Partner Search tool offers to potential applicants to CHIST-ERA Call 2019 the opportunity to find partners, by consulting the list of Expressions of Interest (EoI) below and/or by submitting your own EoI. In the latter case, your EoI will be quality checked and published online within a few days after submission.


Submit your EoI

The following EoI have been submitted for the specific purpose of participating in the Call 2019. Filters are available to search by type of EoI, call topic or country.

Note that to widen participation throughout Europe, proposals are encouraged to include partners from the so-called Widening Countries participating in the call: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Portugal, Romania, Slovakia and Turkey.

Information: anna.ardizzoni@anr.fr


Expressions of Interest

14-11-2019

Partner looking for project
Novel Computational Approaches for Environmental Sustainability
Research areas
  • Computational Intelligence
  • Fuzzy sets and systems
  • Machine learning
  • Decision-making theory
  • Pattern recognition
  • Numerical methods
  • Application of CI methods in biological sciences
  • Statistics and data science
Comment

We have an experienced team of reserchers (also in realization of CHIST-ERA programme project) of researchers interested in co-operation in project realization.

Contact
Paweł Karczmarek
Dr
Department of Computer Science, Lublin University of Technology
Poland

14-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas
  • Computational Intelligence
  • Machine learning
  • Fuzzy sets and systems
  • Anomaly detection
  • Pattern recognition
  • Decision-making theory
  • Granular Computing
  • Numerical methods
Comment

We have an experienced team of reserchers (also in realization of CHIST-ERA programme project) of researchers interested in co-operation in project realization.

Contact
Paweł Karczmarek
Dr
Department of Computer Science, Lublin University of Technology
Poland

14-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

Deep learning, computer vision, statistical inference, deep neural networks.
Various research of deep learning applications and theory is considered in Institute of Computer Science. We working with medical applications, so the fundamental basis of explaining AI models is crutial interesting to us. Faculty website: www.mif.vu.lt/lt3/en

Selected publications:

  • Bagdonavičius V., Petkevičius L. (2020). A new multiple outliers identification method in linear regression. Metrika, doi:10.1007/s00184-019-00731-8
  • Daniušis P., Juneja S., Valatka L., Petkevičius L. (preprint) (2019). Topological navigation graph. arXiv preprint arXiv: 1910.06658.
  • J. Brusokas, Petkevičius L. (2019). Numerical analysis of SLSSIM similarity on medical X-ray image domain. Proceedings of the XXIV International Master and Phd Students Conference “Information Society”
Comment

We have plenty of research and practical experience in applying deep neural network modelling involving tools like  tensorflow, pytorch etc.
My homepage: https://klevas.mif.vu.lt/~linp/index_en.html

Contact
Linas Petkevičius
Researcher
Institute of Computer Science, Vilnius University
Lithuania

13-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

Design, implementation and evaluation of Intelligent Hybrid Algorithms for Adaptive e-Learning Systems, Data and Knowledge Mining, Bioinformatics, and applications to:

- Computational prediction of protein – protein interactions,
- Computational techniques for identifying miRNA genes,
- Human Interactome Knowledge Base,
- Modeling Financial markets with computational intelligence techniques,
- Socially-aware Virtual Environment for Inspiring Creative Learning

Comment

Use of Fusy Rules and Data Visualization Techniques to construct Explainable AI systems.

Contact
Spiros Likothanassis
Professor/ Head of Pattern Recognition Lab
University of Patras/Department of Computer Engineering & Informatics
Greece

12-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

Explainable AI interfaces
Human-centred computing, practice-based research in interaction design and user experience. Transformations in people’s behaviour, communication practices, and interaction when new technologies are introduced into a setting. empirical research using a variety of qualitative methods to identify requirements, improve usefulness and usability and identify implications for how new technologies transform social life.

Comment

Human-Computer Interaction

Contact
Grace Eden
Assistant Professor
Indraprastha Institute of Information Technology, Delhi
India

12-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

Our research areas are within the field of Evolutionary Computation and data-driven optimization in general. Using such approaches is a possible way to develop methods for increasing the explainability of complex Machine-Learning methods (e.g. neural nets). On the other hand, methods from the field of Evolutionary Computation can further be used as a hidden driver for rule creation approaches or performing learning tasks.

FH Vorarlberg (www.fhv.at) is one of the most research-intensive universities of applied sciences in Austria. The majority of research and development projects are undertaken in collaboration with regional businesses and organisations working at an international level.

Contact
Steffen Finck
Group Lead
Vorarlberg University of Applied Sciences, Research Center Process- and Product- Engineering
Austria

11-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

General research interest: Cognitive and developmental robotics, predictive robotic manipulation, learning from demonstration, affordance learnings, symbol formation and rule learning from continuous robotic interaction.

Related research interest: Symbol formation and rule learning from continuous robotic interaction. We believe that through abstracting the continuous sensorimotor experience and acquired prediction capability of the robot in symbols and rules, we can obtain high-level reasoning, planning and communication capabilities. The symbols and rules can be exploited for better explaining and communication knowledge acquired by the robot through machine learning techniques. For this, we currently investigate the suitable ML and AI techniques (particular deep learning architectures, logical inference tools and symbolic planners) that can generate grounded abstract symbols and rules.

Brief description of institution: Bogazici University is a top ranking public university that was founded in 1863. It has been ranked 186th among the Best Global Universities Ranking 2020 by US News & World Report, which places Bogazici University as “The Best Global University” in Turkey. Computer Engineering department is ranked in 200-250 range in Computer Science globally by Times Higher Education. It currently admits students from top 450 among 1.5M students through a national university exam.

Brief description of Cognition, Learning and Robotics (CoLoRs) Lab: CoLoRs lab, led by Dr. Emre Ugur is composed of 3 PhD, 5 Msc and 2 Bsc students. We are involved in H2020 IMAGINE project (www.imagine2020.eu/) and also run joint projects with Tokyo and Osaka Universities.

http://colors.cmpe.boun.edu.tr/
https://www.cmpe.boun.edu.tr/~emre/

Contact
Emre Ugur
Assistant Professor
Bogazici University, Computer Engineering
Turkey

09-11-2019

Project looking for partner(s)
Explainable Machine Learning-based Artificial Intelligence
Keywords
Fully-Automated Generator of Business Strategies and Marketing Strategies
Abstract

Combining Analytics and Big Data along with Artificial Intelligence – Adi Analytics develops a novel, breakthrough innovative, high commercial potential, fully-automated cloud-based system enabling businesses of any size and of any sector – to qualitatively plan, measure, make efficient, and capitalize on their Business Strategies and Marketing Strategies.

Expertise needed / Role in project

Digital Economy Algorithms

Preferred countries
Any
Contact
Effi Shuv
Founder and CEO
Adi Analytics Ltd.
Israel

09-11-2019

Partner looking for project
Novel Computational Approaches for Environmental Sustainability
Research areas

As a professor at the REDS institute (http://reds.heig-vd.ch ), I mainly work on the acceleration of processing thanks to heterogeneous hardware. Most of the projects end up using an FPGA in order to gain performance and energy consumption. In that context I am interested in joining a project that would need such specific hardware to reduce the ecological impact of computing.

Personal webpage: http://reds.heig-vd.ch/en/team/details/yann.thoma

Comment

Specific competences:

  • FPGA design and verification
  • Embedded systems design
  • Genomic data processing
Contact
Yann Thoma
Professor
HES-SO / HEIG-VD / REDS
Switzerland

09-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

My research area is software security, more specifically in development and application of different techniques in detection of vulnerabilities in software, namely web applications and C programs. I have been exploring machine learning techniques and NLP to improve and innovate the detection of vulnerabilities. I am the author of software security tools, being the WAP tool (http://awap.sourceforge.net/) the most popular and which uses data mining to predict false positives during the analysis of the source code of web applications. Also, the DEKANT tool uses NLP to analyze the source code of applications in the discovery of vulnerabilities.

Currently, I am the coordinator of the SEAL project (http://seal.lasige.di.fc.ul.pt/) which aims to leverage of NLP techniques to develop new techniques to process web application in discovery and identification of vulnerabilities and correction of the code of them to remove the vulnerabilities found.

I am an integrated researcher of the LASIGE - Large-Scale Informatics Systems Laboratory (https://www.lasige.di.fc.ul.pt/), and a member of the Navigators research group (https://navigators.di.fc.ul.pt/wiki/Main_Page).

Comment

I am interested in projects that aim to employ and develop explained machine learning systems for improving software, making the computer systems reliable and dependable.

My homepage: http://www.di.fc.ul.pt/~imedeiros/

Contact
Ibéria Medeiros
Assistant Professor
LASIGE, Faculty of Sciences of University of Lisboa
Portugal

07-11-2019

Partner looking for project
Novel Computational Approaches for Environmental Sustainability
Research areas

On the Alarcos research group we work on Software Sustainability. We are experts on the energy efficiency of software, measuring the consumption of a running software application, providing indications about its behaviour in terms of energy consumption and giving advices and guidelines on how to improve it. We also work on the other dimensions of Software Sustainability (the economical and the human ones). Related to this, we have experience on the inclusion of Software Sustainability aspects on the Corporate Social Responsibility actions of industries.

Although we are mainly focused on Green IN Software (how to improve the Soustainability of the Software itself), we also can contribute on Green BY Software solutions (Software developed for the sustainability on other areas).

Comment

Software Sustainability, Software Development, Software Engineering, CSR

Contact
Coral Calero
Full Professor
University of Castilla-La Mancha
Spain

06-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

High-throughput genomic technologies (RNA-seq, single cell isolations, spatial transcriptomics, and more) collect an ever-increasing amount of data about model organisms and humans. To facilitate data-driven discoveries in biology and medicine, my team develop machine learning methods, particularly focusing on unsupervised methods ranging from PCA-like appraoches to variational auto-encoders, for large-scale experimental and observational studies. We are interested in identifying underlying signatures of diseases, molecular pathways, and environmental factors by decomposing systematic patterns of variation. Our recent methodological works have been related to latent variable models (e.g., factor analysis) and unsupervised deep learning (e.g., variational autoencoders) among others. These approaches have yielded high impact projects in fundamental molecular biology such as understanding cell cycles in yeasts and dissecting cellular identities in single cell RNA-seq, as well as in translational research involving cardiovascular diseases, malaria parasites, and other complex phenotypes.

Please check more details in my research areas at http://ncc.name/

Comment

The University of Warsaw is the top-ranked institution of higher education in Poland. Our Institute of Informatics has been particularly renowed for its theoretical and practical computer science evident by our graduates being hired at premier tech companies. We further have a focus group in computational biology, where we closely collaborate with top biologists and clinicians around the world.

Contact
Neo Christopher Chung
Assistant Professor
University of Warsaw
Poland

05-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

We are looking into symbolic AI as wells game theoretical approaches to explain the predictions we make with our bioinformatics tools.  We currently focus on analysing rare diseases and try to identify with new predictive methods the origins of these diseases.  The Explainable part focusses on explaining and providing context for these predictions.  More details on the predictors and the use of game theory to explain the results can be found in the following publications.  The link with symolic AI methods is currrenty being developed. 

- Gazzo,A.M., Daneels,D., Cilia,E., Bonduelle,M., Abramowicz,M., Van Dooren,S., Smits,G. and Lenaerts,T. (2015) DIDA: A curated and annotated digenic diseases database. Nucl. Acids Res., 10.1093/nar/gkv1068.

- Gazzo A., Raimondi D., Daneels D., Moreau Y., Smits G., Van Dooren S., Lenaerts T. Understanding mutational effects in digenic diseases. Nucleic Acids Research 45(15):e140 (2017), DOI: https://doi.org/10.1093/nar/gkx557

- Versbraegen N., Fouché A., Nachtegael C., Papadimitriou S., Gazzo A., Smits G., Lenaerts T.(2019) Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases. To appear in Artificial Intelligence in Medicine.

- Papadimitriou S., Gazzo A., Versbraegen N., Nachtegael C., Aerts J., Moreau Y., Van Dooren S., Nowé A., Smits G., Lenaerts T. (2019) Predicting disease-causing variant combinations. Proceedings of the National Academy of Sciences. 116 (24), 11878-11887.

- Renaux A., Papadimitriou S., Versbraegen N., Nachtegael C., Boutry S., Nowé A., Smits G., Lenaerts T. (2019) ORVAL: A novel platform for the prediction and exploration of disease-causing oligogenic variant combinations. Nucleic Acids Research, gkz437. DOI: https://doi.org/10.1093/nar/gkz437

Contact
Tom Lenaerts
professor
Université Libre de Bruxelles/Machine Learning group
Belgium

05-11-2019

Partner looking for project
Novel Computational Approaches for Environmental Sustainability
Research areas

We are peforming AI, social learning and game theoretical research on questions that involve social dilemma's, like common-pool resource or public goods games.  The latter two problems are abstractions of many questions related to sustainability. The systems we develop allow one to understand the conditions and constraints that need to be put in place so that decision-making induces cooperation or preservation of resources.  We have currently a paper under review wherein we performed real experiments on the climate change problem (which can be represented by a collective risk dilemma or threshold public goods game) to see how people handle undertainty in that context and how it affects their decision-making. We are also examing hybrid models including humans and AI entities in this context and we developed computational models explaining the experimental observations.  The publications of me and my team can be found via website : http://tomlenaerts.be 

Comment

We have our own experimental platform and computational resources to analyse and simulate the observed results.  See http://beel.ulb.ac.be (experiments recruitment platform)

Contact
Tom Lenaerts
Professor
Université Libre de Bruxelles/Machine Learning group
Belgium

02-11-2019

Project looking for partner(s)
Explainable Machine Learning-based Artificial Intelligence
Keywords
Predictive machine learning, Network analytics, Risk Management, Sustainable fintech,
Abstract

We aim to develop explainable machine learning models, based on social network analysis, aiming at measuring and, therefore, mitigating, the risks arising from the applications of AI to finance and insurance. Network models cna fully exploit the nature of API platform based services which generate high interdependence among their users. The proposed models will be incorporated into use cases, which will be shared and improved through feedback from the relevant policy users (financial regilators, supervisors, bank and fintech associaitions)

Partners already involved

University of Pavia Fintech laboratory

Contact
Paolo Giudici
Professor of Statistics and Data Science
Fintech laboratory, University of Pavia
Italy

01-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

I am a CNRS Director of Research (Directeur de Recherche), working at the Ecole normale supérieure in Paris (https://www.ens.fr). Since Sept. 2019, I am also a Prairie fellow (= I hold a ''chair'' in the new Paris Artificial Intelligence Research Institute, https://prairie-institute.fr). I am the adjunct head of the Lattice Lab (http://www.lattice.cnrs.fr) within the ENS. 

My main research interests are: Natural language processing; Computational creativity (creativity and computers); Digital Humanities (see: http://www.lattice.cnrs.fr/en/members/direction/thierry-poibeau/ for details). 

Comment

I am interested in research about explicability in NLP domain, computer based creativity, NLP for the Humanities, which requires explainable machine leaerning techniques. 

Contact
Thierry Poibeau
CNRS Director of Research
CNRS / Ecole normale supérieure / Prairie (Paris AI Research Insittute)
France

01-11-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

My research is focused on using deep learning to model various cognitive functions, such as short term memory, visual object recognition and symbolic reasoning. My most recent contributions to the field include a novel way to represent symbolic structures in connectionist systems (Vankov & Bowers, in press), a systematic exploration of the degree to which convolutional neural networks support translation (Blything, Vankov, Ludwig,  & Bowers, 2019) and size invariance and a solution to the binding problems in recurrent neural networks (Slavov & Vankov, under preparation).

My contribution to a project within this call may consist of research related to symbolic computation in deep neural networks (i.e. relational categorization, analogical mapping), as well as in the field of visual object recognition (for example, using partial occlusion/bubbles techinque to outline the critical regions in a visual category). I supervise a number of graduate students in the Cognive Science program at New Bulgarian University, which have experience in modeling and behavioural experimentation. I also have extensive experience in analyzing the internal states (i.e. the hidden layer activations) of neural networks.

Selected publications:

  • Vankov, I. & Bowers, J. (in press). Training Neural Networks to Encode Symbols enables Combinatorial Generalization. Philosophical Transactions of the Royal Society BarXiv preprint arXiv:1703.04474
  • Blything, R., Vankov, I., Ludwig, C., Bowers, J. (2019). Extreme Translation Tolerance in Humans and Machines. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. doi: 10.32470/CCN.2019.1091-0
  • Vankov, I. & Bowers, J. (2016). Do arbitrary input–output mappings in parallel distributed processing networks require localist coding? Language, Cognition and Neuroscience, 32(3), 392–399. doi: 10.1080/23273798.2016.1256490
  • Bowers, J., Vankov, I., Damian, M., & Davis, C. (2014). Neural networks learn highly selective representations in order to overcome the superposition catastrophe. Psychological Review, 121(2), 248–261. doi:10.1037/a0035943
Comment

I have plenty of research and practical experience in neural network modelling involving tools like python, tensorflow, keras, pymc3 and tensorflow-probability, pandas, statsmodels, nltk, LENS, Matlab, etc.

My lab is equipped with relevant computing hardware (e.g. GPUs) and software.

Contact
Ivan Vankov
Assistant Professor
Department of Cognitive Science and Psychology, New Bulgarian University
Bulgaria

31-10-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas
  • Research Areas: Human Factors Engineering, Human-AI Teaming, Automation
  • University of Southampton is in the top 100 of the QS World University Rankings
  • Human Factors Engineering website: http://www.hfesoton.com/
Comment

We have expertise in the application of automation in the transportation domain, such a aviation, ground transportation and maritime. We have simulators for all three domains. Examples of our work include:

  • Griffin, T. G., Young, M. S. and Stanton, N. A. (2015) Human Factors Modelling in Aviation Accident Analysis and Prevention. Ashgate: Aldershot
  • Walker, G. H., Stanton, N. A. and Salmon, P. M. (2015) Human Factors in Automotive Engineering and Technology. Ashgate: Aldershot
  • Plant, K. L. and Stanton, N. A. (2017) Distributed Cognition and Reality: How pilots and crews make decisions. CRC Press: Boca Raton, USA
  • Banks, V. A. and Stanton, N. A. (2017) Automobile Automation: Distributed Cognition on the Road. CRC Press: Boca Raton, USA
  • Read, G., Beanland, V., Lenne, M. Stanton, N. A. and Salmon, P. M. (2017) Integrating Human Factors Methods and Systems Thinking for Transport Analysis and Design. CRC Press: Boca Raton, USA
  • Walker, G. H., Stanton, N. A. and Salmon, P. M. (2018) Vehicle Feedback and Driver Situation Awareness. CRC Press: Boca Raton, USA
  • Eriksson, A. and Stanton, N. A. (2018) Driver Reactions to Automobile Automation. CRC Press: Boca Raton, USA
Contact
Neville A Stanton
Professor of Human Factors
University of Southampton / Human Factors Engineering
United Kingdom
AttachmentSize
Microsoft Office document icon STANTON CV 2019.doc493 KB

31-10-2019

Partner looking for project
Explainable Machine Learning-based Artificial Intelligence
Research areas

Dr YILDIRIM has received the MSc degree in Mechanical Engineering in 1990 from Erciyes University, Turkey. He has received his PhD degree in 1998 from System Engineering Department, Cardiff University, UK. He was established Mechatronic Engineering Department in 2005, Kayseri, Turkey. He is head of the Mechatronic Engineering Department and head of Foreign Relations Office of Erciyes University. He is lecturing the control theory, robotics, principles of mechatronic systems, neural network applications in engineering at Erciyes University, Turkey. He has been supervised some national and international projects such as mobile nurse robot design and control, active vehicle system design and control, 2 legged walking robot control. He has been reviewed more than 200 papers. He is also author of more than 150 journal, conference papers and book chapters. He is currently supervising more than 30 PhD and Msc students and edited 4 journals. His research area is consisted of Neural Network, Mechatronic Systems, Control Theory and Applications, Mobile and Industrial Robots, Vehicle Dynamics and Control.

Keywords
Artificial neural networks,
Abstract

Test

Contact
Sahin Yildirim
Head Of Department
Mechatronic Engineering Department / Erciyes University
Turkey

31-10-2019

Project looking for partner(s)
Novel Computational Approaches for Environmental Sustainability
Keywords
Image processing, veterinary medicine, epidemiology
Abstract

Reliable monitoring of individual animals is an important solution for preventing the proliferation of dangerous epidemic diseases while improving global food safety. Traditional microchips are expensive, simply impossible in poorer developing nations. We use infrared illumination to acquire an intricate image of the structure of the cow’s iris pared with a real-time scan of each individual animals’ temperature. This system can immediately assess whether an animal is sick and to quarantine affected animals. Our project aims to be more inexpensive, quicker and much easier to use than all other current methods, especially on an industrial scale farm, creating a much-needed solution in the agricultural industry.

Partners already involved
  • University of Glasgow
  • Trinity College, Dublin
Expertise needed / Role in project
  • SME industry partner  who is willing to engage with manufacture  development.
  • Academic in Veterinary medicine in East European Country
Preferred countries
Different from those already involved
Contact
Shufan Yang
University of Glasgow
United Kingdom

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