Goals / Deliverables

Create state of the art QF library with ML ready applications
- Easy to use and, thus, leveraging your (daily) work/research
- Flows smoothly with Tensorflow (TF), PyTorch and Python
- Applied for practical purposes in financial institutions, teaching and research
- Has common system interfaces and could re-use already libraries, eg. Prof. Fries’ FinMathLib
Aiming to become a leading modern analytics, research and teaching brand
- Teaching and research (Conferences, Papers, Books)
- Cooperation (creating models for leading banks, asset managers, insurers; leading
Establish Python/ML based solutions as industry standard – Open Source RaaS
- Financial institutions look for solutions based on “understandable”, easy to use open source software – especially for areas that do not generate revenue in the first place (risk control, middle-/ backoffice, …)
- Trend to combine classic “quant shops” with data analytics/data science units
- RaaS – Risk as a Service (Well documented, open source risk models – regulatory proof)

The QuantLab - ML aims to provide cutting edge numerical techniques that fit into the new era of combining classical QF with ML and work with the current ML set-up.
The results are applied in practice, education and research. We emphasize the work on industry best practices and industry relevance.

Create state of the art QF library with ML ready applications
- Easy to use and, thus, leveraging your (daily) work/research
- Flows smoothly with Tensorflow (TF), PyTorch and Python
- Applied for practical purposes in financial institutions, teaching and research
- Has common system interfaces and could re-use already libraries, eg. Prof. Fries’ FinMathLib
Aiming to become a leading modern analytics, research and teaching brand
- Teaching and research (Conferences, Papers, Books)
- Cooperation (creating models for leading banks, asset managers, insurers; leading
Establish Python/ML based solutions as industry standard – Open Source RaaS
- Financial institutions look for solutions based on “understandable”, easy to use open source software – especially for areas that do not generate revenue in the first place (risk control, middle-/ backoffice, …)
- Trend to combine classic “quant shops” with data analytics/data science units
- RaaS – Risk as a Service (Well documented, open source risk models – regulatory proof)

The QuantLab - ML aims to provide cutting edge numerical techniques that fit into the new era of combining classical QF with ML and work with the current ML set-up.
The results are applied in practice, education and research. We emphasize the work on industry best practices and industry relevance.

Create state of the art QF library with ML ready applications
- Easy to use and, thus, leveraging your (daily) work/research
- Flows smoothly with Tensorflow (TF), PyTorch and Python
- Applied for practical purposes in financial institutions, teaching and research
- Has common system interfaces and could re-use already libraries, eg. Prof. Fries’ FinMathLi
Aiming to become a leading modern analytics, research and teaching brand
- Teaching and research (Conferences, Papers, Books)
- Cooperation (creating models for leading banks, asset managers, insurers; leading universities)
Establish Python/ML based solutions as industry standard – Open Source RaaS
- Financial institutions look for solutions based on “understandable”, easy to use open source software – especially for areas that do not generate revenue in the first place (risk control, middle-/ backoffice, ...)
- Trend to combine classic “quant shops” with data analytics/data science units
- RaaS – Risk as a Service (Well documented, open source risk models – regulatory proof)

The QuantLab - ML aims to provide cutting edge numerical techniques that fit into the new era of combining classical QF with ML and work with the current ML set-up.
The results are applied in practice, education and research. We emphasize the work on industry best practices and industry relevance.