Published/Accepted (see my Google Scholar page)

– M. Imani, S. F. Ghoreishi, “Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design”, IEEE Transactions on Neural Networks and Learning Systems, 2021.

– M. Imani, S. F. Ghoreishi, “Two-Stage Bayesian Optimization for Scalable Inference in State Space Models”, IEEE Transactions on Neural Networks and Learning Systems, 2021.

– M. Imani, S. F. Ghoreishi, “Scalable Inverse Reinforcement Learning Through Multi-Fidelity Bayesian Optimization”, IEEE Transactions on Neural Networks and Learning Systems, 2021.

– 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.