Intro

My name is Henok Ghebrechristos (Enock G.). I'm a graduate student at the University of Colorado Denver-Computer Science Department. My adviser is Prof. Gita Alaghband of the Parallel and Distributed Systems Lab. I'm interested in conducting research in many scientific areas including:

  • Machine Learning and AI
  • Emergent Complexity and Complex Systems Simulation
  • Theory of Computation
  • Fundamentals of the Mind and Cognition
  • Models of Reality
  • Fundamentals of Reality
My research domain is characterizing learning in deep neural network models to enable them with human-like visual acuity for image classification and object detection tasks. My primary research focus has been characterizing learning in convolutional neural networks (CNN) using existing and new techniques. This effort is to try and gain some insight as to how deep CNN models extract useful signal from large datasets. Deep learning models ( neural networks in general) are a type of artificial intelligence architectures that are modeled after the brain. Although this modeling is still at its infancy, these are promising algorithms that have demonstrated exceptional performance at dealing with complex,high volume and variety real-world data. Unfortunately, these networks (and many other deep learning or generally machine learning models) are as opaque as the brain itself. They do not store what they have learned in digital memory in raw or compressed format. Instead they defuse the information in a way that is difficult to decipher.

Projects

Deep Learning and Neural Networks

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