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

Graduate-level Lectures:

Graph Neural Networks (Winter 2021)

Markov Models: Modeling, Learning, and Analysis (Winter 2021)

Online Learning for Self-Healing and Self-Optimization (Summer 2020)

Advanced Topics in Self-Adaptive Systems: Online Machine Learning (Winter 2019)

Predictions based on Graphs and Machine Learning (Winter 2018)

My Google Scholar - link