About Us

The Rational Intelligence (RI) Lab is dedicated to understanding the underlying principles that enable autonomous agents to acquire knowledge effectively from their experiences. Our primary objective is to utilize this understanding in designing novel machine learning (ML) algorithms capable of engaging in rational interactions with complex environments.

Our research endeavors encompass the following focal areas:

  1. Prediction: We aim to develop ML algorithms that demonstrate resilience in the face of distribution shifts. Our focus lies in domain adaptation (DA), domain generalization (DG), out-of-distribution (OOD) generalization, and robustness. To tackle these challenges, we employ kernel methods, with particular emphasis on the kernel mean embedding of distributions, as a robust mathematical framework.

  2. Causation: Our research investigates the utilization of causal relationships to enhance ML models. Furthermore, we explore the application of sophisticated ML algorithms to aid in causal inference within complex environments. Topics of interest include unobserved confounders in causal inference, spurious correlation in machine learning, distributional treatment effects, counterfactual inference, and algorithmic decision-making. We leverage modern quasi-experimental designs, such as instrumental variable (IV), proxy variables, and regression discontinuity design (RDD), as valuable tools for addressing these issues.

  3. Regulation: We focus on the regulation of ML model deployment in the real world to ensure generalizability, equity, trustworthiness, and democracy. Additionally, we investigate how emerging challenges, including feedback loops, strategic manipulations, and adversarial attacks, fundamentally impact the training of ML models. To gain deeper insights into these problems, we incorporate techniques from algorithmic game theory, mechanism design, social choice theory, and other relevant sub-fields of economics.

We envision that the advancement of next-generation machine learning models, distinguished by their reliability, trustworthiness, safety, and security, calls for collaborative efforts involving diverse stakeholders. These stakeholders encompass not only individual citizens and governments but also intergovernmental organizations. Our array of technical solutions, functioning at different levels of granularity, strive to pave the path towards the democratization of artificial intelligence (AI).

For further information about our research, please visit the publication page and explore our Github repository.

CISPA        Helmholtz       ELLIS

Our group is currently affiliated with the CISPA–Helmholtz Center for Information Security in Saarbrücken, Germany and the ELLIS Unit Saarbrücken. The Helmholtz Association is a union of 18 scientific-technical and biological-medical research centers, making it the largest scientific organisation in Germany.

Latest News


Siu Lun

Siu Lun Chau



Junhyung Park

PhD student

Huynh Quang

Huynh Quang Kiet Vo

PhD student


Anurag Singh

PhD student


Muneeb Aadil

Research assistant


Jake Fawkes



Renan Gadoni



  • Simon Föll  (Intern)
  • Jonas Kübler  (PhD student) — Now Applied Scientist at Amazon AWS Tübingen
  • Rui Zhang  (Intern)
  • Si Kai Lee  (Intern) — Now PhD student at Yale University
  • Xiaohan Chen  (Intern) — Now at Alibaba Group
  • Korrawe Karunratanakul  (Intern) — Now PhD student at ETH Zürich
  • Hamed Shirzad  (Intern) — Now PhD student at UBC
  • Prabhu Pradhan  (Intern)
  • Chirag Gupta  (Intern) — Now PhD student at CMU
  • Sorawit Saengkyongam  (Intern) — Now PhD student at ETH Zürich, Seminar for Statistics
  • Uzair Akbar  (Intern) — Now Software Engineer at OMMAX
  • Purin Klunklar  (Undergraduate) — Now Data Analytic Audit at Krung Thai Bank (KTB)
  • Weerapatra Charoenkitsupat  (Undergraduate) — Now Customer Data Analyst at United Overseas Bank Limited (UOB)
  • Siraporn Tongurai  (Undergraduate)