Mohammad Ali Javidian

Please visit My New Website

I am a research associate at the Department of Computer Science and Engineering of the University of South Carolina. Here are my ResearchGate profile, my Github profile, my CV, and my Google Scholar profile.

Storey Innovation Center
Department of Computer Science and Engineering
550 Assembly Street
University of South Carolina
Columbia, SC 29201
Office: 2212

javidian@email.sc.edu

Education

• B.S., Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran, July 2003
• M.S., Mathematics, Shiraz University, Shiraz, Iran, September 2007
• M.S., Computer Science, Sharif University of Technology, Tehran, Iran, September 2013
• Ph.D., Computer Science, University of South Carolina, Columbia, South Carolina, U.S., Fall 2015-Fall 2019 (Dissertation [Slides])

Research Interests

• Probabilistic Graphical Models (PGM): Bayesian Networks, Markov Random Fields, Chain Graphs, Ancestral Graphs.
• Causality
• Transfer learning
• Evidence-Based Reasoning
• Artificial Intelligence
• Multiagent Systems

Research Projects

• 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.
  
  

Peer-Reviewed Publications

• 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-20). (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. 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." to appear in Journal of Artificial Intelligence Research (JAIR). (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).
  
  

Papers Under Review

• Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "A Markov Blanket Discovery Approach for Learning Semi-Markovian Models."
  
  
• Mohammad Ali Javidian and Marco Valtorta. "A Decomposition-Based Algorithm for Learning the Structure of Multivariate Regression Chain Graphs." (Click here for pdf version).
  
  
• Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Learning LWF Chain Graphs: an Order-Independent Algorithm". (Click here for pdf version).
  
  

Recent Professional Service

• Program Committee member, PGM 2020, Aalborg.
• Reviewer, UAI 2020, Toronto.
• Reviewer, SEAMS 2020, Seoul.
• Reviewer, SEAMS 2019, Montreal.
• Program Committee member, PGM 2018, Prague.
• Reviewer, UAI 2018, California.
• Reviewer, UAI 2017, Sydney.

Technical Reports, Notes, and Presentations

• Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Markov Blanket Discovery in AMP Chain Graphs." (Click here for pdf version).
• Mohammad Ali Javidian and Marco Valtorta. "An Overview of the Back Door and Front Door Criteria." (Click here for pdf version).
• Mohammad Ali Javidian and Marco Valtorta. "An Illustrated Proof of The Front-Door Adjustment Theorem." (Click here for pdf version).
• Markov Properties for PGMs. (Click here for pdf version).
• LWF Chain Graphs. (Click here for pptx version).
• Multivariate Regression (MVR) Chain Graphs. (Click here for pptx version).

Teaching

• CSCE 101: Introduction to Computer Concepts

Useful Links

• International Conference on Probabilistic Graphical Models. PGM 2020, Aalborg.
• International Conference on Probabilistic Graphical Models. PGM 2018, Prague.
• Conference on Uncertainty in Artificial Intelligence (UAI). AUAI.
• CSCE 582, Bayesian Networks and Decision Graphs. (Lecture Videos).
  Instructor: Marco Valtorta
• Learning and Reasoning with Bayesian Networks. (Youtube Videos).
  Instructor: Adnan Darwiche
• Probabilistic Graphical Models. (Youtube Videos).
  Instructor: Eric P. Xing
• Bayesian Networks. (Youtube Videos).
  Instructor: Richard Neapolitan
• Evidence-Based Reasoning.
  Instructor: Gheorghe Tecuci
  • Evidence-Based Reasoning Concepts (Youtube Videos).
  • Evidence-Based Reasoning Strategies (Youtube Videos).
  • Solving an Intelligence Problem (a simple example). (Youtube Videos).
  • How to Answer More Complex Questions (a more complex example) (Youtube Videos).
  • Argument Development and Evidence Evaluation (an example) (Youtube Videos).

Gallery