For a complete list of publications, see my Google Scholar Page.

## Working Papers / Under Review

**A. Ghassami**, A. Ying, I. Shpitser, E. Tchetgen Tchetgen, “Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals,” under submission. arXiv preprint arXiv:2104.02929. [

__link__]

E. Mokhtarian, S. Akbari,

**A. Ghassami**, N. Kiyavash, “A Recursive Markov Blanket-Based Approach to Causal Structure Learning,” under submission. arXiv preprint arXiv:2010.04992. [

__link__]

**A. Ghassami**, S. Salehkaleybar, and N. Kiyavash, “Interventional Experiment Design for Causal Structure Learning,” under submission. arXiv preprint arXiv:1910.05651. [

__link__]

## Selected Peer-Reviewed Research Publications

I. Ng,

A. Yang,

S. Salehkaleybar,

R. Tahir, T. Khan, X. Gong,

C.Chen,

**A. Ghassami**, and K. Zhang, “On the Role of Sparsity and DAG Constraints for Learning Linear DAGs,” Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2020. [__link__]A. Yang,

**A. Ghassami**, M. Raginsky, N. Kiyavash, and E. Rosenbaum, "Model-Augmented Conditional Mutual Information Estimation for Feature Selection," Proceedings of the conference on Uncertainty in Artificial Intelligence**(UAI)**, 2020. [__link__]**A. Ghassami**, A.Yang, N. Kiyavash, and K. Zhang, “Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs,” Proceedings of the International Conference on Machine Learning**(ICML)**, 2020. [__link__]S. Salehkaleybar,

**A. Ghassami**, N. Kiyavash, and K. Zhang, “Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables,” Journal of Machine Learning Research**(JMLR)**, 2020. [__link__]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and K. Zhang, “Counting and Sampling from Markov Equivalent DAGs Using Clique Trees,” Proceedings of the Association for the Advancement of Artificial Intelligence**(AAAI)**, 2019. [__link__]**A. Ghassami**, N. Kiyavash, B. Huang, and K. Zhang, “Multi-Domain Causal Structure Learning in Linear Systems,” Proceedings of the Advances in Neural Information Processing Systems**(NeurIPS)**, 2018. [link]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and E. Bareinboim, “Budgeted Experiment Design for Causal Structure Learning,” Proceedings of the International Conference on Machine Learning**(ICML)**, 2018. [link]**A. Ghassami**, S. Khodadadian, and N. Kiyavash, “Fairness in Supervised Learning: An Information Theoretic Approach,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2018. [link]**A. Ghassami**, and N. Kiyavash, “A Covert Queueing Channel in FCFS Schedulers,” IEEE Transactions on Information Forensics and Security, 13(6), pp.1551-1563, 2018. [link]**A. Ghassami**, S. Salehkaleybar, N. Kiyavash, and K. Zhang. “Learning Causal Structures Using Regression Invariance,” Proceedings of the Advances in Neural Information Processing Systems**(NIPS)**, 2017. [link]**A. Ghassami**, and N. Kiyavash, “Interaction Information for Causal Inference: The Case of Directed Triangle,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2017.**A. Ghassami**, D. Cullina, and N. Kiyavash, “Message Partitioning and Limited Auxiliary Randomness: Alternatives to Honey Encryption,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2016.R. Tahir, T. Khan, X. Gong,

**A. Ghassami**, M. Caesar, and N. Kiyavash, “Sneak-Peek: High Speed Covert Channels in Data Center Networks,” Proceedings of the International Conf. on Computer Communications**(INFOCOM)**, 2016.C.Chen,

**A. Ghassami**, S. Mohan, N. Kiyavash, R. Bobba, and R. Pellizzoni, “Schedule-Based Side Channel Attack in Fixed Priority Real-Time Systems,” Proceedings of the IEEE Workshop on Security and Dependability of Critical Embedded Real-Time Systems, 2016.**A. Ghassami**, X. Gong, and N. Kiyavash, “Capacity Limit of Queueing Timing Channel in Shared FCFS Schedulers,” Proceedings of the IEEE International Symposium on Information Theory**(ISIT)**, 2015.