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Hi there 👋

Hi, I'm Daniel, a researcher and data scientist focused on applications in global public health and development policy.

My interests lie in modern Bayesian statistics and machine learning for epidemiological research and public program evaluation, with a focus on causal inference and leveraging disparate data sources, especially in developing countries.

I earned a master's degree in statistics from the University of Munich after completing a bachelor's degree in mathematics with a minor in economics, also at the University of Munich. Prior to joining C4ED, a global research non-profit focused on impact evaluation projects, I worked as a research assistant and interned at UNDP and in the private sector.

I'm a German-Colombian dual citizen and currently based in Islamabad, Pakistan and Frankfurt am Main, Germany.

If you'd like to chat about research, don't hesitate to get in touch!

You can find me on LinkedIn or write me at dseussler at outlook dot com.

Daniel Seussler's Projects

costmboost icon costmboost

Cost-sensitive loss in the component-wise boosting framework.

docintegrity icon docintegrity

A small python package for plagiarism checks in docx files.

horseshoe icon horseshoe

A repo to test different sampling algorithms related to sparse bayesian regression models and the horseshoe prior specifically.

pymc-examples icon pymc-examples

Examples of PyMC models, including a library of Jupyter notebooks.

rossmann_sales icon rossmann_sales

Test bench for Stan w/ ADVI and Pathfinder for scalable Bayesian inference.

socialnetworks_amen icon socialnetworks_amen

Replication Code for the Seminar on Statistical Modelling of Social Networks: The Additive and Multiplicative Effects Network Model. Application Case: Interstate Defence Alliances in 2000.

ssahealthriskfactors icon ssahealthriskfactors

Replication files for my master's thesis. Component-wise boosting for identification of health risk factors.

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