Mahdi Imani

Photo of Mahdi Imani
Title:
Assistant Professor
Office:
SEH 5580 | Office Hours: By appointment
Phone:
202-994-0410
Email:
[email protected]
Website:
https://web.seas.gwu.edu/imani/

 

Professor Imani is a director of the Machine Learning and Bayesian Inference (MLBI) Lab. His research interests are focused on machine learning, Bayesian statistics and decision theory, with a wide range of applications from computational biology to cyber-physical systems. He is especially interested in scalable and risk-based decision making in complex uncertain systems. 

  • Ph.D., Electrical and Computer Engineering, Texas A&M University, 2019

  • M.Sc., Electrical and Computer Engineering, University of Tehran, 2014

  • B.Sc., Mechanical Engineering, University Tehran, 2012

  • Machine Learning and Data Analytics
  • Bayesian Optimization and Statistical Learning
  • Decision Theory and Reinforcement Learning
  • Statistical Signal Processing and Integrated Sensing
  • 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", AAAI, 2019. 

  • 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.

  • M. Imani, and U.M. Braga-Neto, "Point-Based Methodology to Monitor and Control Gene Regulatory Net-works 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.

  • 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), in press, 2018.

  • E. Hajiramezanali, M. Imani, U.M. Braga-Neto, X. Qian, and E.R. Dougherty "Scalable Optimal BayesianClassification of Single-Cell Trajectories under Regulatory Model Uncertainty", BMC genomics, 20(6), 435.

  • 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), in press, 2018.

  • 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): 1–10.

  • M. Imani, and U.M. Braga-Neto, "State Estimation of Partially-Observed Gene Regulatory Networks withArbitrary 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 andUncertain 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. R. 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.

  • 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 DynamicalSystems 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.

  • Recipient of the Association of Former Students Distinguished Graduate Student Award for Excellence in Research-Doctoral, Texas A&M University, 2019.

  • Finalist nominee for the Outstanding Graduate Student Award, Texas A&M University, 2018.

  • Recipient of the Best PhD Student Award, Department of Electrical and Computer Engineering, Texas A&M University, 2015.

  • Recipient of the Best Paper Finalist Award, the 49th Asilomar Conference on Signals, Systems, and Computers, 2015.