I build causal models to predict and explain task outcomes, particularly tasks that involve subjective information (opinions, knowledge). The models that I build concern three topics: (1) how to aggregate conflicting information (opinions, knowledge), (2) decide if more information is necessary for making a decision, and (3) who should ideally provide the information. I am particularly interested in the cases where software engineers and autonomous agents are both providers and consumers of information. More recently, I have been investigating these concerns under the topic of transfer learning and combining symbolic and learning-based methods (neuro-symbolic) to determine how fragments of information/knowledge can be reused, updated, extended, or forgotten.

Topics

• Theory: Decision Theory, Voting, Utility theory, Prospect theory, and Measurement 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, Traffic-Signal Control, Multi-Agent Systems.
• Tools/Languages: R, Python, Java, Scala, Rust.

Projects

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 research projects that I have coordinated:

• Cross-Snapshot Attention for Identifying Graph Propagation Patterns in Dynamic Real-World Networks
• Robust Multi-Agent Reinforcement Learning for Self-Adaptive Systems
• Machine Learning-Based Control for Self-Adaptive Systems
• Digital Assistant for Design Thinking Artifact Repository

Courses that I have been teaching:

Advanced Topics in Software Engineering: Automation and AI  (Winter 2024)

Software Engineering with Machine Learning: Tools and Methods (Winter 2024)

Meta-Reinforcement Learning for Self-Adaptive Systems (Winter 2023)

Graph Neural Networks for Knowledge Graph Systems (Winter 2023)

Probabilistic Models: Modeling, Learning and Analysis (Winter 2023)

Multi-Agent Reinforcement Learning on Self-Adaptive Systems (Summer 2022)

Machine Learning on Spatio-Temporal Graphs (Summer 2022)

Graph Neural Networks (Winter 2022)

Adversarial Self-Supervised Learning with Digital Twins (Winter 2022)

AI Ethics Engineering (Winter 2022)

Quantum Programming with Applications to Machine Learning (Summer 2021)

Machine Learning-Based Control of Dynamical Systems (Summer 2021)

Highly Scalable Systems (Summer 2021)

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 (Summer 2019)

Predictions based on Graphs and Machine Learning (Summer 2018)

My Google Scholar - link