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




>2020.10.24|TalkCausal and Sequential Decision Models of Software Fault Understanding at HPI Research School Retreat, Potsdam, Germany
>2020.03.03|Accepted Paper on Bayesian modeling! Collective Risk Minimization via a Bayesian Model for Statistical Software TestingConference: 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 LoopLocation: FutureSoc Symposium
>2018.07.18|Accepted Paper: Microtasking Fault LocalizationConference: 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?, LocationUniversity of Cape Town

Research interests

I build statistical 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.

While pursuing these research problems, I rely on a set of models and technology:

  • Theory: Decision Theory, Multi-Armed Bandits, Voting, Utility theory, and Prospect theory.
  • Probabilistic Models: Causal Inference, Bayesian Inference, Markov models, and Bayesian Optimization.
  • Empirical research: Quasi-experimental and Observational studies, Survival analysis, Analyzes 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, Spark


My Google Scholar - link