Joerg Osterrieder is Associate Professor for Artificial Intelligence and Finance at University of Twente (Netherlands), through a joint appointment with ING and Kickstart AI, a Dutch National Initiative on Artificial Intelligence.
He has been working in the area of financial statistics, quantitative finance, algorithmic trading, and digitisation of the finance industry for more than 15 years. Joerg is the Action Chair of the European COST Action 19130 Fintech and Artificial Intelligence in Finance, an interdisciplinary research network combining 200+ researchers and 49 countries globally. He was the director of studies for an executive education course on "Big Data Analytics, Blockchain and Distributed Ledger", co-director of studies for "Machine Learning and Deep Learning in Finance" and has been the main organizer of an annual research conference series on Artificial Intelligence in Industry and Finance since 2016. He is a founding associate editor of Digital Finance, an editor of Frontiers Artificial Intelligence in Finance and frequent reviewer for academic journals. In addition, he serves as an expert reviewer for the European Commission on the "Executive Agency for Small & Medium-sized Enterprises" and the "European Innovation Council Accelerator Pilot" programmes. Previously he worked as an executive director at Goldman Sachs and Merrill Lynch, as quantitative analyst at AHL as well as a member of the senior management at Credit Suisse Group. Joerg is now also active at the intersection of academia and industry, focusing on the transfer of research results to the financial services sector in order to implement practical solutions.

European COST (Cooperation in Science and Technology) Action 19130 Fintech and Artificial Intelligence in Finance

I am the Action Chair of the COST Action Fintech and AI in Finance. With a network of 49 countries and 200+ researchers, we are working on a substantial number of research topics, including, but not limited to: Reinforcement learning for trading, Sentiment analysis for Finance, Machine learning for Finance, Fintech applications, Blockchain and Cryptocurrencies.

Global reseearch cooperations

I have close research cooperations with academics from around the globe

• Professor Ali Hirsa, Columbia University, US, jointly working on synthetic data generation, reinforcement learning for finance, explainable artificial intelligence, co-supervising MSc and PhD students
• Professor Stephan Sturm, Worcester Polytechnic Institute, US, working on financial mathematics, including reinforcement learning for Finance, co-supervising MSc and PhD students
• Dr. Alex Posth, Zurich University of Applied Sciences, Switzerland, working on self-play algorithms for Finance
• Professor Stephen Chan, American University of Sharjah, UAE, working on blockchain and cryptocurrencies
• Professor Saralees Nadarajah, Manchester University, UK, working on statistical properties of cryptocurrencies
• Professor Codruta Mare, Babes-Bolyai University, Romania, working on sentiment analysis for Finance
• Professor Ioana-Florina Coita, University of Oradea, Romania, working on sentiment analysis for Finance
• Professor Branka Hadji Misheva, Bern University of Applied Sciences, Switzerland, working on reinforcement learning for finance and explainable AI for Finance
• Professor Ronald Hochreiter, Vienna University of Business and Economics, Austria, working on AI and financial technology

PhD Co-supervision and PhD committees

I am involved in the PhD Co-Supervision and PhD committees of several universities in Europe and the US.

• Patchara Santawisook, August 2022, "Price Impact of VIX Futures and Two Order Book Mean-Field Games", member of the PhD Committee, main supervisor: Prof. Dr. Stephan Sturm, Worcester Polytechnic University (WPI), US. Dissertation Committee: Dr. Stephan Sturm, WPI (Advisor), Dr. Marcel Y. Blais, WPI, Dr. Jörg Osterrieder, University of Twente, Dr. Andrew Papanicolaou, North Carolina State University, Dr. Qingshuo Song, WPI Dr. Frank Zou, WPI
• Sebastian Singer, 2021 - 2025, co-advisor and member of the PhD Committee, main supervisor: Prof. Dr. Ronald Hochreiter, WU Vienna, Austria
• Dr. Piotr Kotlarz, 2019 - 2023, local advisor, PhD at University of Liechtenstein
• Dr. Branka Hadji Misheva, 2019 - 2023, local advisor, PhD at University of Pavia, Italy
• Dr. Rui Li, 2020, PhD examiner, main supervisor: Saralees Nadarajah, University of Manchester, UK
• Dr. Idika Okorie, 2019, PhD examiner, main supervisor: Saralees Nadarajah, University of Manchester, UK
• Dr. M. Weibel, 2019, PhD examiner, main supervisor: Juri Hinz, University of Technology, Sydney, Australia

ING Group - University of Twente Cooperation - Associate Professorship Finance and Artificial Intelligence

I am working closing with ING Group, the Global Analytics team, on advanced, quantitative, data-driven research projects, relevant both for academia and industry.

1. Applications of synthetic data generation for Finance

•Testing trading strategies robustness, comparing portfolio construction methods, estimating the risk of a portfolio or a strategy, alternative pricing and hedging of options and other derivatives, generating trading signals, detecting anomalies in fundamental data, with a particular focus on using generative adversarial networks.

•Synthetic generator for (arbitrage-free) volatility surfaces

•Synthetic data generators that are differentially private, i.e. do not leak information about the original data, and still have enough features

2. Research on risk management related topics

3. Privacy-enhancing techniques for storing and analysing confidential data

4. Federated Learning. This is a machine learning technique that trains an algorithm across multiple servers holding local data samples, without exchanging them. Research is needed into how this can be used in Finance applications, especially those that use confidential data.

5. Applications of Reinforcement learning in Finance. Existing applications include portfolio optimization and optimal trade execution. Further research is needed to extend this technique to other areas in finance.

6. The value of innovation projects in Finance. Innovative projects have a high-risk of failure and are often also focused on cost reduction and loss-avoidance topics. Therefore the impact on the P&L of the company is not immediately clear. The project is supposed to find ways of measuring the cost/benefit ratio and provide a conceptual approach.

7. The use of "meta labeling" technique (tailored to non-HFT strategies). The approach consist in building a secondary ML model that learns how to use a primary exogenous model. It can help build an ML system on top of a white box (like a fundamental model founded on economic theory). The advantages of the approach is that it uses a way higher signal to noise ratio than when applying ML directly to (very noisy) traditional financial data. 

8. Early warning systems for credit risk. Despite many years of research into credit risk, large and unexpected losses still happen frequently. Research on the causal relationships between market prices and external ratings as well as applying machine learning techniques and using new datasets for predicting downgrading and default  of loans is beneficial to reduce credit losses.

• Reinforcement Learning for Finance (MSc)
• Information Systems for the Financial Services Industry (MSc)
• Applications of Artificial Intelligence in Business (MSc)

Since 2015, I have worked on more than 30 research projects, mainly as project lead or principal investigator, funded by Europe Horizon 2020, Horizon Europe, Swiss National Science Foundation, Innosuisse and the Finance industry. 
The topics cover many aspects of quantitative, data-driven topics for Finance, ranging from trading strategies, efficient markets to machine learning and artificial intelligence in Finance, including latest developments such as blockchain, virtual currencies, Fintech and sustainable Finance.

Most notable international projects:
* Cooperation ING Group - University of Twente
* Action Chair COST Action 19130 Fintech and Artificial Intelligence, Horizon Europe
* FIN-TECH – Financial Supervision and Technology Compliance Training Programme, EU Horizon 2020 
* Network-based credit risk models in P2P lending markets, Swiss National Science Foundation