Alexander Ororbia Headshot

Alexander Ororbia

Assistant Professor

Department of Computer Science
Golisano College of Computing and Information Sciences
agovcs@rit.edu
585-475-2622
Office Location
GOL-3537

Alexander Ororbia

Assistant Professor

Department of Computer Science
Golisano College of Computing and Information Sciences

Bio

I am an Assistant Professor of Computer Science at RIT. I direct the Neural Adaptive Computing (NAC) Laboratory where we work on developing new learning procedures and computational architectures that embody various properties of biological neurocircuitry and are guided by theories of mind and brain functionality. My research focuses on predictive processing, active inference, spiking neural networks, competitive neural learning, neural-based cognitive modeling, and metaheuristic optimization.

agovcs@rit.edu
585-475-2622
Personal Links
Personal Website
Google Scholar
Areas of Expertise
Artificial Intelligence

Currently Teaching

CSCI-633
Biologically Inspired Intelligent Systems
3 Credits
There have been significant advances in recent years in the areas of neuroscience, cognitive science and physiology related to how humans process information. In this course students will focus on developing computational models that are biologically inspired to solve complex problems. A research paper and programming project on a relevant topic will be required. A background in biology is not required.
CSCI-635
Introduction to Machine Learning
3 Credits
This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. Bayesian networks, Markov models), and reinforcement learning. Programming assignments are required.
CSCI-736
Neural Networks and Machine Learning
3 Credits
The course will introduce students into the current state of artificial neural networks. It will review different application areas such as intrusion detection and monitoring systems, pattern recognition, access control and biological authentication, and their design. The students will be required to conduct research and analysis of existing applications and tools as well as to implement a course programming project on design of a specified application based on neural networks and/or fuzzy rules systems.

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