Amir massoud Farahmand Ph.D. Student Department of Computing Science University of Alberta 2-21 Athabasca Hall Edmonton, Alberta Canada T6G 2E8 email:

News!

• Towards Manifold-Adaptive Learning has been presented in the Topology Learning workshop at NIPS. The main message is that many conventional machine learning methods may actually be manifold-adaptive, i.e. if seemingly high-dimensional data actually come from a low-dimensional manifold, such a manifold-adaptive method behaves as if it is dealing with a low-dimensional problem. We presented two manifold-adaptivity results: one for dimension estimation and another for the traditional K-NN regressor. (Dec 2007)


• Our Global Visual-Motor Estimation for Uncalibrated Visual Servoing has been accepted and presented in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2007. (Oct 2007)

• Our Manifold-Adaptive Dimension Estimation paper has been accepted and presented in the International Conference on Machine Learning (ICML) 2007. In this paper, we provide an algorithm for estimating the intrinsic dimension of a manifold embedded in a possibly high-dimensional space. We prove a convergence rate for this method. The good news  is that the convergence rate depends only on the intrinsic dimension of the manifold and not the dimension of the embedding space. Solving the curse of dimensionality?! Not yet, but a step along that way! (;  (July 2007)

Research Interests

• Regularities in Reinforcement Learning (2007-present)
• Manifold Learning (2006-present)
• Application of Machine Learning Methods in Visual-Servoing Problems (2006-present)
• Incremental Basis Generation in Reinforcement Learning (2006)
• Hierarchical [Reinforcement] Learning in Behavior-based Systems (MS. Thesis) (2003-2005)
•  Structure Learning
•  Behavior Learning
• Concurrent Behavior and Structure Learning
• Behavior Evolution
• Concurrent Behavior Evolution and Structure Learning
• Automatic Generation of Reinforcement Signal
• Evolutionary Robotics
• Hybrid Evolution/Learning in Hierarchical Behavior-based Systems (2004-2005)
•  Evolving Neural Network-based Controllers for Robots (2001)
• Investigating the Effect of Reward Uncertainty on the Value and the Policy of the Agent. (2004-2005)
• Automatic Abstraction of Knowledge from Stimuli-Response information (2004-2005)
• Structural Abstraction
• Temporal Abstraction
• Synchronization of Chaotic Systems (2003-4)
• Stable Fuzzy Controllers (2003)
• Combinatorial Optimization Problem (Channel Assignment Problem) using Chaotically-Enhanced Hopfield Neural Network (2002-3)
• Calculating Resonant Frequencies of a Metallic Cavity using Finite Element Method (BS Thesis) (2001-2)
• Investigating the Effect of Metallic Electrode and Buffer Layer on Dielectric Waveguides (Internship) (2001)
• Several Researches on Evolutionary Computation (2000-current)
• Evolutionary Robotics
• Evolution of Hidden Markov Models (2001)
• Distributed Evolutionary Systems (2000)
• Electromagnetic Filter Design (2000)
• Analog Circuits Design (2000)