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

COGS-621
Foundations of Scientific Computing
3 Credits
This course will introduce students to foundational concepts in numerical computation that are useful for engineering and the mathematical, computational, and physical sciences. Topics will include floating-point arithmetic, error analysis, linear and nonlinear equations, numerical solution of systems of algebraic equations, constrained and unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, and basic methods for sampling stochastic processes. Implementation of various numerical methods and solvers will be done in Python, MATLAB, and R. Connections to computational modeling of cognition will be made throughout the course as motivating examples for various key concepts and tools.
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.
MATH-790
Research & Thesis
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

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