HIPPO Lab

AI for fair, efficient, and interpretable policy analysis

Equitable, accountable, and human-centered AI methods are important. They can be used to ensure distributive justice, interpretability and stakeholder engagement, and these are core objectives at the HIPPO Lab.

To make a real impact, AI techniques must consider all stakeholders, and that includes minimizing conflict and protecting vulnerable groups. It’s why the HIPPO Lab is developing next generation, nature-inspired and hyper-heuristic optimisation methods – the methods that can capture conflicts across multiple sectors, regions and generations. We will be using AI-based decision support for addressing complex real-world problems such as climate change mitigation and adaptation.

The HIPPO Lab is part of the TU Delft AI Labs programme.

Projects

Publications

Code & Datasets

The Team

Directors

Jazmin Zatarain Salazar

Faculty of TPM
Lab director

Pradeep K. Murukannaiah

Faculty of EEMCS
Lab director

PhD's

Damla Akoluk

PhD candidate

Shubhalaxmi Mukherjee

PhD candidate

Palok Biwas

PhD candidate

Zuzanna Osika

PhD candidate

Postdocs

Edgar Gironés

Postdoc

Interns and master students

Max Reddel

Associated faculty

Catholijn Jonker

Associated faculty

Jan Kwakkel

Associated faculty

Frans A. Oliehoek

Associated faculty

Alexander Verbraeck

Associated faculty

Education

Courses

2024/2025 

2023/2024 

2022/2023  

2021/2022  

2020/2021 

2019/2020 

 

Master projects

Openings 

  • Discussion Quality Estimation from Text, (2023/2024) 
  • Explaining NLP Classification of Human Values, (2023/2024) 
  • Active Learning NLP Value Classification, (2023/2024) 
  • Hyper-heuristi optimizaion for fair public policy design, (2023/2024) 
  • Decision support for climate policy design, (2023/2024) 
  • Interpretability of climate change policy decisions, (2023/2024) 
  • Engaging stakeholders in public policy deliberations, (2023/2024) 
  • Adaptive EMODPS, Jazmin Zatarain Salazar, (2023/2024) 
  • Climate Justice with AI: Illuminating Stakeholders' Fairness Perceptions in Climate Negotiations, Jazmin Zatarain Salazar, (2023/2024) 
  • Optimizing Adaptive Climate Policies: Exploring Nonlinear Approximating Networks Topologies in EMODPS, Jazmin Zatarain Salazar, (2023/2024) 
  • Climate Negotiation Simulation with Multiobjective Reinforcement Learning, Murukannaiah P, (2023/2024) 

Ongoing  

  • Redefining justice for Multi-objective Multi-reservoir Control Policy Optimisation, Jazmin Zatarain Salazar, Whitley Roefs (2023/2024) 
  • Policy Tree Optimization for Climate Policy Modelling, Jazmin Zatarain Salazar, Constantijn Hubert Daemen (2022/2023) 

Finished