Mohsen Firouzi | Research


Research Interests:

    • Cognitive Science
    • Machine Learning and Soft Computing
    • Neuro-Computing; Neuroscience
    • Intelligent Control and Automation; System Identification
    • Artificial Intelligence and Robotics.
    • Computational Intelligence, Biologically Inspired Computing
    • Fuzzy Modeling
    • Intelligent Materials, Actuators, and Structures. (SMA, Piezo)
    • Digital System Design, FPGA/ASIC Design and System on the Chip, DSP.
    • Pattern Recognition and Image Classification
    Professional Research Experiences:
  • Research Group of Brain Simulation and Cognitive Science - Artificial Creatures Lab:
    • An Actor-Critic Reinforcement Learning Controller using Spike-IDS, Spiking Neuro-Fuzzy Machine, Supervisor: Prof Shouraki, EE Dept, Sharif University of Technology (Fall 2012) 
    • Developing a New Biologically inspired Spiking Neural Model for Active Learning Method (called Spike-IDS), a computational approach to human brain Learning, Supervisor: Prof Shouraki, EE Dept, Sharif University of Technology (2009-2011) PDF, 480KB
    • A new Active Approach for Pattern Analysis using a new Architecture of Active Learning Method Classifier, Supervisor: Prof Shouraki, EE Dept, Sharif University of Technology (Fall 2010), PDF - 218KB
    • Design and Implementation of an effective digital pipelined architecture for Replacing Ink Drop Spread (called PRIDS) as an effective Hardware Solution for ALM; implemented on ALTERA Cyclone II FPGA, Supervisor: Prof Shouraki & Prof Tabandeh, EE Dept, Sharif University of Technology (Fall 2009). 
  • Research Group of Intelligent Structures - Dynamic Systems Lab:
    • Proposing A new Intelligent Control Strategy for Shape Control Applications using SMA Actuators, Supervisor: Dr Hasan Saayyadi, Mechanical Engineering School, Sharif University of Technology, As Research Assistant of Mohammad R. Zakerzadeh, PhD Candidate, Mechanical Engineering School, Sharif University of Technology (Spring 2011) 
    • Developing A new Hysteresis nonlinearity identification method using Preisach based ANN Approach which is applicable in Piezo and SMA actuators, Supervisor: Dr Hasan Saayyadi, Mechanical Engineering School, Sharif University of Technology, (Spring 2010).  PDF - 2.7MB