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Subject: Conference on AI in Finance and Industry, September 3, 12:30 CEST, online
Dear all

We would like to invite you to our “5th European Conference on Artificial Intelligence in Industry and Finance”.
· When: September 3rd , 12:30 – 17:30 CEST · Where: Zoom (Please register here<zhaw.zoom.us/webinar/register/WN_FdsRHUhLTWinAQ83WVwz_w>) · Who: 19 distinguished speakers from academia and industry · Keynote: Martin Ulbrich, European Commission

Thematic sessions: · Artificial Intelligence in Industry · Artificial Intelligence in Finance · Regulatory Technology in Finance · Ethical Questions in Artificial Intelligence

Selected Speakers:
· Prof. Dr. Monica Billio, Università Ca’ Foscari Venezia Bayesian Dynamic Tensor Regression for multilayer financial networks · Dr. Paul Bilokon, The Thalesians, Toby Weston, Imperial College Optimal Execution using Reinforcement Learning · Prof. Dr. Igor Halperin, Fidelity Investments,
Inverse Reinforcement Learning: applications in Finance · Prof. Dr. Petter Kolm, New York University, Courant Institute Hedging an Options Book with Reinforcement Learning · Dr. Kay Pilz, Kinetic Minds Machine Learning for Data Generation and Hedging in Finance
· Jens Frühling, Accenture Bot and claims handling automation + automated damage assessment with AI · Prof. Dr. Helmut Hauser, University of Bristol · Evelyn Menzel, Mindfire AG On the quest for human level AI through virtual reality · Prof. Dr. Thrishantha Nanayakkara, Imperial College · Dr. Liudmila Zavolokina, Ergon Informatik/Universität Zürich Blockchain for business

Prof. Dr. Christoph Heitz, ZHAW; Francis Gross, European Central Bank; Angus Moir, Bank of England; Prof. Tim Weingärtner, HSLU; Dr. Michael Stuart, Université de Genève

Detailed schedule and more information<www.zhaw.ch/en/engineering/institutes-centres/iamp/events/artificial-intelligence-in-industry-and-finance/>

Looking forward to seeing you all online!

Best regards

Jörg Osterrieder on behalf of the entire organization committee

PS: Please also kindly sign-up for our meet-up group: https://www.meetup.com/Fintech_AI_in_Finance
************************************* ZHAW School of Engineering Jörg Osterrieder, Prof. Dr., MSc Professor of Finance Technikumstrasse 9 CH-8401 Winterthur
Tel. direct: +41 58 934 45 94 joerg.osterrieder@zhaw.ch<mailto:joerg.osterrieder@zhaw.ch>
www.linkedin.com/company/fintech-ho2020-eu/ 5th European COST Conference on AI in Finance and Industry, September 5, 2019<www.zhaw.ch/en/engineering/institutes-centres/iamp/events/artificial-intelligence-in-industry-and-finance/>

Hello,
If you have recently finished your PhD and are looking forward to pursuing a researcher career this may be of interest to you. An international tender to fulfil 2 Junior Researcher positions is open until the 20th of August 2020.
More formally, we are looking for a young PhD with stellar research track record and having been involved in R&D projects. The successful candidate should be able to integrate CISUC’s team, performing relevant research in one (or more) of its key research streams, namely: RISE — Resilient Software and Internet Systems; IS — Intelligent Systems and HCC — Human Centric Computing.
The stated position will be held under a public service employment contract with a length of three-years, which may be renewed for periods of one year up to a maximum of 6 years.
For further information please consult apply.uc.pt/IT057-20-10076 or send an email to cisuc@dei.uc.pt
Best Regards,
Penousal Machado

Special Issue on Foundations of Data Science – Machine Learning Journal
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
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Topics of Interest
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We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
*Machine Learning Foundations of Data Science
Auto-ML
Fusion of information from disparate sources
Feature engineering, Feature embedding and data preprocessing
Learning from network data
Learning from data with domain knowledge
Reinforcement learning
Evaluation of Data Science systems
Risk analysis
Causality, learning causal models
Multiple inputs and outputs: multi-instance, multi-label, multi-target
Semi-supervised and weakly supervised learning
Data streaming and online learning
Deep Learning
*Emerging Applications
Autonomous systems
Analysis of Evolving Social Networks
Embedding methods for Graph Mining
Online Recommender Systems
Augmented Reality, Computer Vision
Real-Time Anomaly, Failure, image manipulation and fake detection
*Human Centric Data Science
Privacy preserving, Ethics, Transparency
Fairness, Explainability, and Algorithm Bias
Accountability and responsibility
Reproducibility, replicability and retractability
Green Data Sciences
*Infrastructures
IoT data analytics and Big Data
Large-scale processing and distributed/parallel computing;
Cloud computing
*Data Science for the Next Digital Frontier
in: Telecommunications and 5G
Retail,
Green Transportation
Finance, Blockchains, Cryptocurrencies
Manufacturing, Predictive Maintenance, Industry 4.0
Energy, Smart Grids, Renewable energies
Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
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Submission Instructions
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Submit manuscripts to: MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
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Key Dates
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Continuous submission/review process
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021
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Guest Editors
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Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt