Published/Accepted (see my Google Scholar page)

– M. Alali, and M. Imani, “Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models”, IEEE Transactions on Artificial Intelligence, 2025.

– Z. Zhang, H. Zhou, M. Imani, T. Lee, T. Lan, “Learning to Collaborate with Unknown Agents in the Absence of Reward”, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2025). (Acceptance Rate: 23.4%)

– A. KazemiNajafabadi, D. Aksaray, M. Imani, “Defense Policy Optimization with Linear Temporal Logic Specifications for Interconnected Networks”, 2025 AIAA SciTech Forum.

– A. KazemiNajafabadi, M. Imani, “Robust Defense Strategy for Network Security Against Unknown Attack Models”, 2025 AIAA SciTech Forum.

– A. Ravari, G. Jiang, Z. Zhang, M. Imani, R. Thomson, A. Pyke, N. Bastian, and T. Lan, “Adversarial Inverse Reinforcement Learning Conditioned on Human Factor Modeling in Cyber Defense”, 57th Asilomar Conference on Signals, Systems, and Computers, 2024.

– Y. Lin, S. F. Ghoreishi, T. Lan, and M. Imani, “High-Level Human Intention Learning for Cooperative Decision-Making”, IEEE Conference on Control Technology and Applications (CCTA), 2024.

– S. H. Hosseini, and M. Imani, “Dynamic Intervention in Gene Regulatory Networks: A Partially Observed Zero-Sum Markov Game”, IEEE Conference on Control Technology and Applications (CCTA), 2024.

– M. Alali, and M. Imani, “Bayesian Optimization for State and Parameter Estimation of Dynamic Networks with Binary Space”, IEEE Conference on Control Technology and Applications (CCTA), 2024.

– Y. Mei, M. Imani, T. Lan, “Bayesian Optimization for Gaussian Process Modulated Inhomogeneous Poisson Process”, International Conference on Learning Representations (ICLR), 2024.

– A. KazemiNajafabadi, M. Imani, “Optimal Joint Defense and Monitoring for Networks Security Under Uncertainty: A POMDP-Based Approach”, IET Information Security, 2024.

– M. Alali, and M. Imani, “Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024.

– A. Ravari, S. F. Ghoreishi, M. Imani, “Optimal Inference of Hidden Markov Models Through Expert-Acquired Data”, IEEE Transactions on Artificial Intelligence, 2024.

– M. Alali, A. KazemiNajafabadi, and M. Imani, “Deep Reinforcement Learning Sensor Scheduling for Effective Monitoring of Dynamical Systems”, Systems Science & Control Engineering, 2024.

– A. Ravari, S. F. Ghoreishi, M. Imani, “Implicit Human Perception Learning in Complex and Unknown Environments”, American Control Conference (ACC), 2024.

– A. KazemiNajafabadi, S. F. Ghoreishi, M. Imani, “Optimal Detection for Bayesian Attack Graphs under Uncertainty in Monitoring and Reimaging”, American Control Conference (ACC), 2024.

– N. Asadi, S. H. Hosseini, M. Imani, D. P. Aldrich, S. F. Ghoreishi, “Privacy-preserved federated reinforcement learning for autonomy in signalized intersections”, International Conference on Transportation and Development, Atlanta, GA, 2024.

– S. H. Hosseini, and M. Imani, “Modeling Defensive Response of Cells to Therapies: Equilibrium Interventions for Regulatory Networks”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024.

– S. H. Hosseini, and M. Imani, “An Optimal Bayesian Intervention Policy in Response to Unknown Dynamic Cell Stimuli”, Information Sciences, 2024.

– M. Alali, M. Imani, “Kernel-Based Particle Filtering for Scalable Inference in Partially Observed Boolean Dynamical Systems”, 20th IFAC Symposium on System Identification, (SYSID 2024).

– Y. Mei, T. Lan, M. Imani, S. Subramaniam, “A Bayesian optimization framework for finding local optima in expensive multi-modal functions”, 26th European Conference on Artificial Intelligence (ECAI), 2023.

– S. H. Hosseini, M. Imani, “Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective”, IEEE Conference on Artificial Intelligence, 2023.

– A. Ravari, S. F. Ghoreishi, M. Imani, “Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge”, IEEE Conference on Artificial Intelligence, 2023.

– A. KazemiNajafabadi, M. Imani, “Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs”, Cybersecurity, 2023.

– M. Alali, M. Imani, “Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks”, American Control Conference (ACC), 2023. [Finalist Paper Award]

– A. Ravari, S. F. Ghoreishi, M. Imani, “Optimal Recursive Expert-Enabled Inference in Regulatory Networks”, IEEE Control Systems Letters, 2023.

– M. Alali, M. Imani, “Inference of Regulatory Networks Through Temporally Sparse Data”, Frontiers Control Engineering, 2023.

– M. Imani, S. F. Ghoreishi, “Scalable Inverse Reinforcement Learning Through Multi-Fidelity Bayesian Optimization”, IEEE Transactions on Neural Networks and Learning Systems, 2022.

– M. Imani, S. F. Ghoreishi, “Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design”, IEEE Transactions on Neural Networks and Learning Systems, 2022.

– M. Imani, S. F. Ghoreishi, “Two-Stage Bayesian Optimization for Scalable Inference in State Space Models”, IEEE Transactions on Neural Networks and Learning Systems, 2022.

– Y. Ni, M. Issa, M. Imani, X. Yin, M. Imani, “HDPG: Hyperdimensional Policy-based Reinforcement Learning for Continuous Control”, Proc. 59th ACM/IEEE Design Automation Conference (DAC), 2022.

– S. Rescsanski, M. Imani, F. Imani, “Heterogeneous Sensing and Bayesian Optimization for Smart Calibration in Additive Manufacturing Process”, ASME International Mechanical Engineering Congress and Exposition, 2022.

– Z. Zou, H. Chen, P. Poduval, Y. Kim, M. Imani, E. Sadredini, R. Cammarota, M. Imani, “BioHD: an efficient genome sequence search platform using HyperDimensional memorization”, Proceedings of the 49th Annual International Symposium on Computer Architecture (ISCA ’22:), 2022.

– M. Imani, M. Imani, S. F. Ghoreishi, “Bayesian Optimization for Expensive Smooth-Varying Functions”, IEEE Intelligent Systems, 2022.

– M. Imani, M. Imani, S. F. Ghoreishi, “Optimal Bayesian Biomarker Selection for Gene Regulatory Networks under Regulatory Model Uncertainty”, 2022 American Control Conference (ACC), 2022.

– M. Imani, S. F. Ghoreishi, “Partially-Observed Discrete Dynamical Systems”, 2021 American Control Conference (ACC), 2021.

– M. Imani, S. F. Ghoreishi, “Adaptive Real-Time Filter for Partially-Observed Boolean Dynamical Systems”, 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

– S. F. Ghoreishi and M. Imani, “Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems,” Journal of Computing and Information Science in Engineering, Vol. 21, August 2021.

– M. Imani, and S. F. Ghoreishi, “Optimal Finite-Horizon Perturbation Policy for Inference of Gene Regulatory Networks,” IEEE Intelligent Systems, Vol. 36, 2021.

– M. Imani, and S. F. Ghoreishi, “Bayesian Optimization Objective-Based Experimental Design,” 2020 American Control Conference (ACC), 2020.

– S. F. Ghoreishi and M. Imani, “Bayesian Optimization for Efficient Design of Uncertain Coupled Multidisciplinary Systems,” 2020 American Control Conference (ACC), 2020.

– M. Imani, E.R. Dougherty and U.M. Braga-Neto, “Boolean Kalman Filter and Smoother Under Model Uncertainty”, Automatica, Vol. 111, January 2020.

-M. Imani, and U.M. Braga-Neto, “Control of Gene Regulatory Networks using Bayesian Inverse Reinforcement Learning,” IEEE Transactions on Computational Biology and Bioinformatics (TCBB), 16.4 (2019): 1250-1261.

– A. Bahadorinejad, M. Imani and U.M. Braga-Neto, “Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems”, IEEE Transactions on Computational Biology and Bioinformatics (TCBB), 2020.

– S. F. Ghoreishi and M. Imani, “Offline Fault Detection in Gene Regulatory Networks using Next-Generation Sequencing Data”, 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2019.

– M. Imani, S. F. Ghoreishi, D. Allaire, and U.M. Braga-Neto, “MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models”, In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7858-7865, 2019. (acceptance rate: 16.2%)

– M. Imani, S. F. Ghoreishi, and U.M. Braga-Neto, “Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments”, Advances in Neural Information Processing Systems, pp. 8146-8156, 2018. (acceptance rate: 20.8%)

– M. Imani, and U.M. Braga-Neto, “Point-Based Methodology to Monitor and Control Gene Regulatory Networks via Noisy Measurements,” IEEE Transactions on Control Systems Technology (TCST), 27.3 (2019): 1023-1035.

– M. Imani, and U.M. Braga-Neto, “Finite-Horizon LQR Controller for Partially-Observed Boolean Dynamical Systems”, Automatica, 95, p. 172-179, 2018.

– M. Imani, and U.M. Braga-Neto, “Particle Filters for Partially-Observed Boolean Dynamical Systems,” Automatica, 87, p. 238-250, 2018.

– E. Hajiramezanali, M. Imani, U.M. Braga-Neto, X. Qian, and E.R. Dougherty “Scalable Optimal Bayesian Classification of Single-Cell Trajectories under Regulatory Model Uncertainty”, BMC genomics, 20.6 (2019): 435.

– M. Imani, R. Dehghannasiri, U.M. Braga-Neto and E.R. Dougherty, “Sequential Experimental Design for Optimal Structural Intervention in Gene Regulatory Networks Based on the Mean Objective Cost of Uncertainty,” Cancer Informatics, 17 (2018): 110.

– M. Imani, and U.M. Braga-Neto, “State Estimation of Partially-Observed Gene Regulatory Networks with Arbitrary Correlated Measurement Noise,” EURASIP Journal on Advances in Signal Processing, 2018(1), p.22, 2018.

– M. Imani, and U.M. Braga-Neto, “Optimal Control of Gene Regulatory Networks with Unknown Cost Function,” 2018 American Control Conference (ACC) (pp. 3939-3944), 2018.

– M. Imani, and U.M. Braga-Neto, “Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs,” IEEE Transactions on Control of Network Systems (TCNS), 5.2, 2018.

– M. Imani, and U.M. Braga-Neto, “Maximum-Likelihood Adaptive Filtering for Partially-Observed Boolean Dynamical Systems,” IEEE Transaction on Signal Processing, 65.2 (2017): 359-371.

– M. Imani, and U.M. Braga-Neto, “Optimal Finite-Horizon Sensor Selection for Boolean Kalman Filter,” 51st Asilomar Conference on Signals, Systems, and Computers (pp. 1481-1485), Pacific Grove, CA, 2017.

– S. Xie, M. Imani, E. Dougherty, and U.M. Braga-Neto, “Nonstationary Linear Discriminant Analysis,” 51st Asilomar Conference on Signals, Systems, and Computers (pp. 161-165), Pacific Grove, CA, 2017.

– M. Imani, and U.M. Braga-Neto, “Multiple Model Adaptive Controller for Partially-Observed Boolean Dynamical Systems,” 2017 American Control Conference (ACC) (pp. 1103-1108), 2017. [Invited Paper]

– L.D. McClenny, M. Imani, U.M. Braga-Neto, “Boolean Kalman Filter with Correlated Observation Noise,” 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 866-870), 2017.

– Levi D. McClenny*, M. Imani* (*equal contribution), and U.M. Braga-Neto, “BoolFilter: an R package for estimation and identification of Partially-Observed Boolean Dynamical Systems,” BMC bioinformatics, 18.1 (2017): 519.

– M. Imani, and U.M. Braga-Neto, “Point-Based Value Iteration for Partially-Observed Boolean Dynamical Systems with Finite Observation Space,” 55th IEEE Conference on Decision and Control (CDC) (pp. 4208-4213), 2016.

– M. Imani, and U.M. Braga-Neto, “State-Feedback Control of Partially-Observed Boolean Dynamical Systems Using RNA-Seq Time Series Data,” 2016 American Control Conference (ACC) (pp. 227-232), 2016.

– M. Imani, and U.M. Braga-Neto, “Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother,” 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 972-976). IEEE.

– M. Imani, and U.M. Braga-Neto, “Optimal Gene Regulatory Network Inference using the Boolean Kalman Filter and Multiple Model Adaptive Estimation,” 49th Asilomar Conference on Signals, Systems and Computers (pp. 423-427), 2015, IEEE. [Finalist Paper Award]

– M. Imani, M. A. Tajeddini, and H. Kebriaei, “Bidding Strategy in Pay-as-Bid Markets by Multi-Agent Reinforcement Learning,” 28th International Power System Conference (PSC2013).

– M. A. Tajeddini, H. Kebriaei, and M. Imani, “Multi-Agent Reinforcement Learning for Strategic Bidding in Electricity Markets,” 5th Iranian Conference on Electrical and Electronics Engineering (ICEEE2013).

– M. Imani, S. F. Ghoreishi, and M. Shariat-Panahi, “An innovative method based on the fuzzy Actor-Critic and the proof of existence of its stationary points,” 9th International Industrial Engineering Conference (IIEC2013).

– M. Imani, S. F. Ghoreishi, and M. Shariat-Panahi, “A new approach to transfer non-Markov to Markov Environments and its application in mobile robots,” 9th International Industrial Engineering Conference (IIEC2013).

Software

– BoolFilter Package: “Estimation and Identification of Partially-Observed Boolean Dynamical Systems,” R package library [Link].

Workshop

– M. Imani, and U.M. Braga-Neto, “Partially-Observed Boolean Dynamical Systems: Estimation and Control,” 9th Annual Winedale Workshop, October, 2016.

– M. Imani, and U.M. Braga-Neto, “Adaptive Estimation and Control of Boolean Dynamical Systems,” 8th Annual Winedale Workshop, October, 2015.

– M. Imani, and U.M. Braga-Neto, “Control and Inference of Gene Regulatory Network,” 1st Annual Symposium for Genome Sciences & Society (TIGSS), October, 2015.