STSM

Short-term scientific missions support our COST Action.

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Applications Grant Period 1

If you are an evaluator for the STSM and ITC conference grant applications, you can find them here (protected).

Ahmad Amine Loutfi, PhD Fellow, Norwegian University of Science and Technology (NTNU), Norway.[1]

• Host institution: Institute of Wealth & Asset Management, Zurich University of Applied Sciences (ZHAW), Switzerland.

• Supervisor at ZHAW, Switzerland: Prof. Dr. Peter Schwendner, Director of Institute[2]

• Supervisor at NTNU, Norway: Prof. Dr. Per Bjarte Solibakke.[3]

• STSM project: In this project, we propose to study the extent to which alternative data (News/reports) can predict electricity spot prices. This will allow us to assess the backtesting of relevant investment strategies. The results of this project will also be used to extend an ongoing research project where we study electricity spot price prediction based only on conventional data. We aim to augment the conventional data set with a new feature which reflects the alternative data, and then run the newly augmented dataset through the same neural network model and then compute the new loss function results in order to assess the models’ performance with and without alternative data.

Miller Janny Ariza Garzón, PhD student at Data Science [4].Research member of Project FINTECH-EU Ho2020 [5]. Facultad de Informática [6]. Universidad Complutense de Madrid (UCM) [7]. Spain.

• STSM title: Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry (FinAI).

• Host institution: Zurich University of Applied Sciences (ZHAW) School of Engineering, Winterthur, Switzerland.

• Home institution: Universidad Complutense de Madrid (UCM), Madrid, Spain.

• Supervisor at ZHAW, Switzerland: Dr. Branka Hadji Misheva, Scientific Employee, Zurich University of Applied Sciences.

• Supervisor at UCM, Spain: Prof. Dr. Javier Arroyo, MC Member and Associate Professor at Facultad de Informática, Universidad Complutense de Madrid.

• STSM project: Being part of the working group 2 - Transparent versus Black Box Decision-Support Models in the Financial Industry [8], we will investigate the tradeoff between explainability and predictive performance of different black-box models as they apply to financial problem sets, primarily in risk management-credit risk. Specifically, we will study the elements that must be evaluated for a black-box model to be considered interpretable and explainable to take advantage of its predictive potential.

Stjepan Picek, assistant professor, Delft University of Technology, The Netherlands.[9]

• STSM title: Genetic Programming for the Fraudulent Activity Detection: Performance and Transparenct Perspectives.

• Host institution: University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia.

• Start date: 2021-01-11

• End date: 2021-01-26

• STSM project: In this project, we will explore available datasets for fraudulent behavior classification and evaluate their common characteristics. Based on it, we will start a series of experiments with techniques from the machine learning domain and genetic programming to compare their performance. Finally, we will investigate what are the transparency considerations when using genetic programming and what kind of interpretability/explainability one can hope to achieve with such techniques.