Master thesis for the MSc. Artificial Intelligence at the Universiteit van Amsterdam, 2019
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Updated
Apr 1, 2020 - Python
Master thesis for the MSc. Artificial Intelligence at the Universiteit van Amsterdam, 2019
Adoption and impact of non-pharmaceutical interventions for COVID-19
Why And What If: Causal Inference for Everyone
The codebase for Inducing Causal Structure for Interpretable Neural Networks
biology / medicine / health papers organized in markdown files
This code is written for study "The Interaction between Population Age Structure and Policy Interventions on the Spread of COVID-19"
This robotic interactive system, TIAGoGPT is an MSc research project on "Exploring the Potentials of LLMs in TIAGo Robot-Assisted Interventions to Support Children with Social Skill Difficulties". The project implements cutting-edge technologies to revolutionise the field of child support. We employed state-of-the-art large language models in robot
Analysis and predictions about student success in online learning
CausalFlow: Causal Discovery Methods with Observational and Interventional Data from Time-series
Repository for our NeurIPS 2022 paper "Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off" and our NeurIPS 2023 paper "Learning to Receive Help: Intervention-Aware Concept Embedding Models"
[WWW 2023] Official code release for "Dynamic Interventions for Networked Contagions"
DataCurator enables you to map and understand complex systems before helping you plan, communicate and navigate successful interventions in them.
The action tracker is a management tool that helps improve effective coverage of priority interventions at district and community level, covering various interventions such as maternal, newborn, and child health (MNCH), HIV, and nutrition.
Medical action ontology
Global Health Policy Simulation model (Health-GPS)
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