Section Navigation

Mohammad Ali Javidian

MAJ


I am an assistant professor in computer science at Appalachian State University (ASU). My research interests include classical/quantum causal inference and probabilistic graphical models for decision making under uncertainty. My research seeks to develop theoretically sound, reliable, and scalable Causal AI/ML approaches with applications in computer systems and healthcare. Before joining ASU, I was a postdoctoral scholar at the Electrodynamics Lab and CLAN Lab at Purdue University, where I investigated the development of novel algorithmic and theoretically principled methods for Quantum Entropic Causal Inference. Before this position, I was a research associate at the AISys Lab at the Department of Computer Science and Engineering of the University of South Carolina, where I conducted research projects on transfer learning and performance debugging in machine learning systems using the causal inference methods. I received my PhD in Computer Science from the University of South Carolina in 2019, MS in Computer Science from the Sharif University of Technology in 2013, MS in Mathematics from the Shiraz University in 2007, and BS degree in Mathematics from the Shahid Bahonar University of Kerman in 2003.

I am the Director of Data Science Certificate Program in Computer Science.

Are you passionate about causality, probabilistic graphical models, quantum computing, or decision making under uncertainty? If you are a strong programmer with a keen interest in these fields, consider joining my research. Drop me an email to explore opportunities together..

Education:

University of South Carolina

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

Sharif University of Technology

Master of Science (September 2013)
Computer Science

Shiraz University

Master of Science (September 2007)
Mathematics

Shahid Bahonar University of Kerman

Bachelor of Science (July 2003)
Mathematics

Research Interests:

causal ai logo


Probabilistic Graphical Models: Bayesian Networks, Markov Random Fields, Chain Graphs, Ancestral Graphs
Causality
Transfer learning
Quantum Computing
Artificial Intelligence