Mohammad Ali Javidian

Purdue University, West Lafayette, IN, 47907ยท mjavidia@purdue.edu

I am a postdoctoral scholar at the School of Electrical and Computer Engineering of Purdue University. I will join Appalachian State University in Fall 2022 as an assistant professor in the Department of Computer Science.

Here is my CV.

Education

University of South Carolina

PhD
Computer Science - (Dissertation [Slides])
August 2015 - December 2019

Sharif University of Technology

Master of Science
Computer Science
September 2013

Shiraz University

Master of Science
Mathematics
September 2007

Shahid Bahonar University of Kerman

Bachelor of Science
Mathematics
July 2003

Research Interests

Research Projects

  • Quantum entropic causal inference: This project is being led by Professors Zubin Jacob and Vaneet Aggarwal. 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.

  • Causal structure learning and their applications in machine learning systems: This project is being led by Professors Pooyan Jamshidi and Marco Valtorta. 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?

  • 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.

Publications

    • Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian, Baishakhi Ray, and Pooyan Jamshidi. "Unicorn: Reasoning about Configurable System Performance through the lens of Causality." Proceedings of the European Conference on Computer Systems (EuroSys), 2022, Rennes, France. (Link of the paper)(Click here for arXiv version)(Talk by Shahriar Iqbal).

    • Mohammad Ali Javidian and Marco Valtorta. "A decomposition-based algorithm for learning the structure of multivariate regression chain graphs" International Journal of Approximate Reasoning, Volume 136, September 2021, Pages 66-85.(Link of the paper).

    • Md. Musfiqur Rahman, Ayman Rasheed, Md. Mosaddek Khan, Mohammad Ali Javidian, Pooyan Jamshidi and Md. Mamun-Or-Rashid. "Accelerating Recursive Partition-Based Causal Structure Learning Using An Improved Structure Refinement Approach" Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-2021), pages: 1028-1036, 2021. (Click here for pdf version) (Talk by Md. Musfiqur Rahman) (Click here for extended version).

    • Mohammad Ali Javidian, Vaneet Aggarwal, and Zubin Jacob. "Identification of Latent Graphs: A Quantum Entropic Approach" NeurIPS WHY-21 (Causal Inference & Machine Learning: Why now?). (Click here for pdf version), (Click here for the arXiv version).

    • Mohammad Ali Javidian, Vaneet Aggarwal, and Zubin Jacob. "Tensor Rings for Learning Circular Hidden Markov Models" NeurIPS 2021 Second Workshop on Quantum Tensor Networks in Machine Learning (QTMNL2021) (Click here for pdf version), (Click here for the arXiv version).

    • Mohammad Ali Javidian, Om Pandey, and Pooyan Jamshidi. "Scalable Causal Domain Adaptation" NeurIPS WHY-21 (Causal Inference & Machine Learning: Why now?). [Selected as Contributed Talk] (Click here for pdf version), (Click here for the arXiv version).

    • Mohammad Ali Javidian, Vaneet Aggarwal, and Zubin Jacob. "Quantum Causal Inference: An Entropic Approach" 8th Causal Inference Workshop at UAI (causalUAI-2021). (Click here for pdf version) (Click here for the poster).

    • Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "An Order-Independent Algorithm for Learning Chain Graphs" Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS-34), 2021 (Florida, USA). (Click here for pdf version)(Click here for extended version).

    • Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Learning LWF Chain Graphs: A Markov Blanket Discovery Approach" Proceedings of the Thirty Sixth Conference on Uncertainty in Artificial Intelligence (UAI-2020), pages: 1069-1078, 2020. (Click here for pdf version) (Click here for extended version) [Blog].

    • Mohammad Ali Javidian, Zhiyu Wang, Linyuan Lu, and Marco Valtorta. "On a Hypergraph Probabilistic Graphical Model." Annals of Mathematics and Artificial Intelligence, 2020. DOI: https://doi.org/10.1007/s10472-020-09701-7 (Click here for arXiv version).

    • Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms."Journal of Artificial Intelligence Research (JAIR), 2020. DOI: https://doi.org/10.1613/jair.1.12101 (Click here for arXiv version).

    • Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian, Baishakhi Ray, and Pooyan Jamshidi. "CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning." Workshop on ML for Systems at NeurIPS, 2020. DOI: (Click here for pdf version) (Click here for arXiv version).

    • Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Order-Independent Structure Learning of Multivariate Regression Chain Graphs" Proceedings of the 13th international conference on Scalable Uncertainty Management (SUM 2019, pages 324-338). (Click here for pdf version)(Click here for extended version).

    • Mohammad Ali Javidian, Pooyan Jamshidi, and Rasoul Ramezanian. "Avoiding Social Disappointment in Elections" Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2019, Montreal), May 13-17, 2019, Pages: 2039-2041. (Click here for pdf version)(Click here for extended version).

    • Mohammad Ali Javidian, Pooyan Jamshidi, and Marco Valtorta. "Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis. First AAAI Spring Symposium "Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI". March 25-27, 2019, Stanford, CA. (Click here for pdf version).

    • Zhiyu Wang, Mohammad Ali Javidian, Linyuan Lu, and Marco Valtorta. "The Causal Interpretations of Bayesian Hypergraphs. First AAAI Spring Symposium "Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI". March 25-27, 2019, Stanford, CA. (Click here for pdf version).

    • Mohammad Ali Javidian and Marco Valtorta. "Finding Minimal Separators in LWF Chain Graphs" Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM 2018, Prague), September 11-14, 2018, Pages: 193-200.(Click here for pdf version).

    • Mohammad Ali Javidian and Marco Valtorta. "On the Properties of MVR Chain Graphs" Workshop Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM 2018, Prague), September 11-14, 2018, Pages: 13-24.(Click here for pdf version of the workshop proceedings).

    • Mohammad Ali Javidian and Marco Valtorta. "Finding Minimal Separators in Ancestral Graphs." UAI Causal Inference Workshop. August 10, 2018, Monterey, CA. (Click here for pdf version).

Recent Professional Service