Prof. Dr. Holger Giese

Christian Medeiros Adriano (Chris)

Phone: +49 331 5509 319, Office: Building A, Room A-2.9

E-Mail: christian.adriano [at] hpi.de or [at] gmail.com

My: Linked-in and GitHub




>2021.10.27|Invited Talk: Too Big to Fail - Building Robust Intelligent Systems with Causal Machine Learning,  at the HPI Research School Retreat, Potsdam, Germany
>2021.07.14|Talk: Towards more Reliable Machine Learning-Based Systems - The Need for Methods to Discover Model Invariants,  at the HPI Research School Retreat, Potsdam, Germany
>2020.10.24|Talk: Causal and Sequential Decision Models of Software Fault Understanding,  at the HPI Research School Retreat, Potsdam, Germany
>2020.03.03|Accepted Paper on Bayesian modeling! Collective Risk Minimization via a Bayesian Model for Statistical Software Testing, Conference: SEAMS-2020, South Korea
>2019.06.19|Invited Talk: Enabling a crowd of programmers to work in parallel to identify, explain, and fix software bugs, Location: Meeting of the German Computer Science Research Training Groups 2019, in Dagstuhl
>2019.04.09|Invited Talk:Tackling the Perfect Fault Understanding Assumption with One Thousand Programmers in the Loop, Location: FutureSoc Symposium
>2018.07.18|Accepted Paper: Microtasking Fault Localization, Conference: Doctoral Symposium at the Empirical Software Engineering Conference.
>2018.07.02|Talk: Crowdsourcing the localization and fixing of software faults, Location: Software Engineering Group at the Humbolt University.
>2018.06.15|Accepted Paper: Learning Utility-changes for Rule-based Adaptation of Dynamic Architectures - Current and Future work, Conference:  ICAC-2018
>2018.03.09|Talk: Can a crowd identify the cause of a software failure and suggest valid bug fixes?, Location: University of Cape Town

Research Projects

I build causal models to predict and explain task outcomes, particularly tasks that involve subjective opinions. The models that I build concern three topics: (1) how to aggregate conflicting opinions, (2) decide if more opinions are necessary about the same or different items, and (3) who should we ask for further opinions.


  • Theory: Decision Theory, Voting, Utility theory, and Prospect theory.
  • Probabilistic Models: Causal Inference, Reinforcement Learning, Multi-Armed Bandits, Bayesian Inference, Markov Models, and Bayesian Optimization.
  • Empirical research: Quasi-experimental and Observational studies, Survival analysis, Analysis of covariance, Time series, and Sensitivity analysis.
  • Application domains: Crowdsourcing, Software Debugging, Self-Adaptive Systems, and, more recently, Comments from Q&A sites or News sites
  • Tools/Languages: R, Python, Java, Scala


I am grateful to have the opportunity to work with many brilliant graduate and undergraduate students in individual and group research projects. Follow some of recent projects that I have coordinated:


My Google Scholar - link