Alex, one of the Connectionists!
Alexander G. Ororbia II
Assistant Professor
PhD, Information & Science Technology (The Pennsylvania State University), Minor in Social Data Analytics
B.S.E., Computer Science & Engineering (Bucknell University, U.S.A.), Minors in Mathematics & Philosophy
Director, Neural Adaptive Computing (NAC) Laboratory
Department of Computer Science
Rochester Institute of Technology (NY, USA)


Office: Golisano Hall Rm. 3537
Email: agovcs AT rit DOT edu (Teaching/Advising), ago AT cs DOT rit DOT edu

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Biography

Full curriculum vitae can be found here.

Alex Ororbia is an assistant professor in the RIT Computer Science Department. He is also now the liaison for the CS department's Nature-Inspired and Evolutionary Computing subarea, which falls under the RIT Computer Science Department's Artificial Intelligence Cluster. Following his studies at Bucknell University in Computer Science & Engineering, he obtained his Ph.D. in Information Science & Technology at Penn State University. There, he worked jointly for both the Intelligent Information Systems (ISS) and Applied Cognitive Science (ACS) laboratories and was appointed as an IGERT Fellow and a NACME Sloan Scholar, also receiving the Jordan-Rednor and Bunton-Waller scholarships.

Since 2018, he has been a professor at the Department of Computer Science at Rochester Institute of Technology (RIT). He is the founder and scientific director of the of the Neural Adaptive Computing (NAC) Laboratory. His research focus has been in the development of the models and learning algorithms that will give rise to the intelligent artificial agents of tomorrow -- agents that can robustly, continually learn from information across multiple modalities. In building towards the design of such agents, his work aims to bridge the fields of machine learning, computational neuroscience, and cognitive science.

Notes for Prospective Students:
  • Job Opening: I am currently looking for a new PhD student to begin in Fall 2022. Please carefully read the relevant information on the For Students page and follow the instructions for how to contact me about graduate research.
  • Students at RIT interested in doing an Independent Study or Master's thesis (seeking me as the advisor) should also consult the For Students page.
Neural generative coding.
A neural generative coding circuit (NGC) shown processing an image of a digit.

News

  • September 14, 2022 Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting accepted as full paper for publication at the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
  • July 18, 2022 Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture accepted as full paper for publication at the 15th Conference on Artificial General Intelligence (AGI 2022)
  • April 21, 2022 Our article The Neural Coding Framework for Learning Generative Models was chosen to appear in the Nature Communications Editors' Highlights for Applied Physics & Mathematics
  • April 12, 2022 CogNGen: Constructing the Kernel of a Hyperdimensional Predictive Processing Cognitive Architecture accepted as full paper for publication at the Cognitive Science Society conference (CogSci 2022), Original PsyArXiv link is: here
  • January 24, 2022 The Neural Coding Framework for Learning Generative Models accepted as full article in Nature Communications (NCOMMS 2022), Original ArXiv link is here.
  • December 1, 2021 Backprop-Free Reinforcement Learning with Active Neural Generative Coding, accepted for publication at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)
  • November 24, 2021 Hitesh Vaidya, MSc and member of NAC, has successfully passed his master's thesis defense.
  • November 18, 2021 Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay, accepted for publication as a student abstract at the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)
  • May 15, 2021 Towards a Predictive Processing Implementation of the Common Model of Cognition, accepted for publication as an extended abstract at the International Conference on Cognitive Modeling (ICCM 2021)
  • December 27, 2020 An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems accepted as full paper for publication at the 2021 Data Compression Conference proceedings (DCC 2021)
  • December 15, 2020 Michael Peechat, MSc and member of NAC, has successfully passed his master's thesis defense.
  • December 9, 2020 AbdelRhaman Elsaid, PhD and member of NAC, has successfully passed his (doctoral) dissertation defense.
  • December 8, 2020 James Le, MSc and member of NAC, has successfully passed his master's thesis defense.
  • December 3, 2020 Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units, accepted for publication at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)
  • March 20, 2020 Improving Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation accepted as full paper for publication at The Genetic & Evolutionary Computation Conference (GECCO 2020)
  • January 9, 2020 Three of my team's EvoStar papers accepted for publication to EvoStar 2020, including: The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks and An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
  • December 26, 2019 Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication accepted as full paper for publication at the 2020 Data Compression Conference proceedings (DCC 2020)
  • June 25, 2019 Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations accepted as full article in IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2019)
  • May 14, 2019 Like a Baby: Visually Situated Neural Language Acquisition accepted for publication at the 57th Annual Meeting of the Association for Computational Linguistics conference (ACL 2019)
  • March 20, 2019 Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution accepted as full paper for publication at The Genetic & Evolutionary Computation Conference (GECCO 2019)
  • March 11, 2019 A Neural Temporal Model for Human Motion Prediction accepted for publication at the Conference on Computer Vision and Pattern Recognition (CVPR 2019)
  • December 27, 2018 Learned Neural Iterative Decoding for Lossy Image Compression Systems accepted as full paper for publication at the 2019 Data Compression Conference proceedings (DCC 2019)
  • October 31, 2018 Biologically Motivated Algorithms for Propagating Local Target Representations, accepted for publication at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)