Mohammad Ali Javidian
- Causal Bayesian Optimization: Initiated in Spring 2023, this project focuses on optimizing a key variable within a causal model, where a series of interventions can be performed. Spanning domains such as biology, operations research, and communications, this innovative method merges principles from causal inference, uncertainty quantification, and sequential decision-making. It offers an advanced variation of Bayesian optimization by incorporating causal information, optimizing system outputs in interconnected environments.
- Quantum entropic causal inference: I have been working on this project since September 2020 to develop novel algorithmic and theoretically principled methods for quantum entropic causal inference to understand causality relations between quantum particles using an entropic approach. In this project, I am collaborating very closely with Professors Zubin Jacob and Vaneet Aggarwal.
- Causal structure learning and their applications in machine learning systems: I have been working on this project since Spring 2019 to propose novel algorithms for learning the structure of causal models in order to answer the following questions: How to use the causal structure of machine learning systems for identifying and estimating causal effects of configuration options on performance? How to apply transfer learning for performance analysis of machine learning systems by means of causal models? How to use causal inference tools for performance debugging and explainability in machine learning systems? In this project, I am collaborating very closely with Professors Pooyan Jamshidi and Marco Valtorta.
- Hypergraph-Based Causal Modeling: This project was led by Professors Linyuan Lu and Marco Valtorta. I worked on this project with Zhiyu Wang to develop a novel probabilistic graphical model, which we call "Hypergraph Bayesian Network," to encode conditional independences.
- Co-Arg: Causal Argumentation System with Crowd Elicitation. Agency: Intelligence Advanced Research Project Agency (IARPA); PI: Gheorghe Tecuci. I worked on this project under the supervision of Professor Marco Valtorta to exploit the Bayesian approach in order to improve evidence-based hypothesis analysis.
