Short Bio

Short Bio Dr. Meisam Mohammady is an Assistant Professor in the Department of Computer Science at Iowa State University (ISU). Prior to joining ISU, he was a Research Scientist at CSIRO Data61, which is Australia’s leading digital research network, helping various partners across business, government, and industry solve a wide range of data-centric problems. Meisam obtained his Ph.D. from Concordia University, Montreal, QC, Canada. Before that, he received his M.Sc degree from the Electrical Engineering Department of Ecole Polytechnique Montreal, Canada, and B.Sc degree from the Electrical Engineering Department of Sharif University of Technology, respectively. Meisam is the recipient of the Distinguished PhD Dissertation Award among all Engineering and Natural Science PhD dissertations and was selected as Concordia University's nominee for both Canada-wide CAGS and ADESAQ competitions. Meisam's research focuses on developing responsible (Private, Adversarially Robust, and Fair) Machine Learning techniques including Federated Learning with provable accuracy, Differential Privacy, Learning Theory and Optimization. His research contributions are published in top CS conferences and journals, such as IEEE S&P, ACM CCS, IEEE TDSC, ACM TOPS, and IEEE TKDE. Access my CV here.

I am always looking for motivated students, visiting scholars/students, and undergraduate researchers. Please email your application materials to Dr. Meisam Mohammady if you are interested in our research. Graduate admission information can be found here.

Recent News

• March 2024: One paper on "VLIA: Navigating Shadows with Proximity for Highly Accurate Visited Location Inference Attack against Federated Recommendation Models" accepted by ASIACCS'24 (Acceptance rate: 55/284~19%). Congrats Thirasara!
• Feb 2024: One paper on "User GPS Trajectory Reconstruction from Federated Route Recommendation Models" accepted by ACM Transactions on Intelligent Systems and Technology (ACM TIST'24) (Impact Factor: 10.489). Congrats Thirasara!
• December 2023: One paper on "Mitigating Distributed Backdoor Attack in Federated Learning Through Mode Connectivity" accepted by ASIACCS'24 (Acceptance rate: 55/284~19%). Congrats Kane!
• December 2023: One (first-co-author) paper on "Guaranteeing Differential Privacy over Infinite Disclosure" accepted by IEEE Symposium on Security and Privacy 2024 (Acceptance rate: 22/1389~17.8%).
• January 2023: One paper on "Optimally Mitigating Backdoor Attacks in Federated Learning" accepted by IEEE TDSC (Impact Factor: 7.3). Congrats Kane!
• December 2022: One paper on "Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis" accepted by VehicleSec'23