Dept. of Computer Science
Rochester Institute of Technology
20 Lomb Memorial Dr
Rochester, NY 14623
ago AT cs DOT rit DOT edu
"The whole thinking process is still rather mysterious to us, but I believe that the attempt to make a thinking machine will help us greatly in finding out how we think ourselves."
I am an assistant professor in the RIT Computer Science Department and starting the Neural Adaptive Computing (NAC) Laboratory. I am also now the liason for the CS department's "Nature-Inspired and Evolutionary Computing" subarea. You can find a link to my curriculum vitae here and a link to my Google Scholar page as well as my Research Gate page (which is much more accurate in terms of paper citations than Google Scholar). I am also quazi-active on Quora (a Question-Answer forum/website). I fall under the RIT Computer Science Department's Artificial Intelligence Cluster (or Intelligent Systems Cluster).
Note to Ph.D. Applications: Position DetailsI am looking for motivated, talented, and enthusiastic PhD students to work in the area of machine learning, specifically on developing more neurocognitively plausible approaches to adaptation and memory formation and retention in artificial neural systems. This would entail investigating and formalizing more realistic forms of neural computation, including models of spiking neurons. A student applying to me would be interested in biologically-motivated forms of optimization and in developing models of predictive coding with a focus on continual (machine) learning. Please contact me with a short description about yourself, your interests specifically as they are related to the details of this position, and your CV. (In addition, if you are well-versed with Scala in addition to Python, certainly make that clear to me.)
Research StatementThe focus of my work is on lifelong learning--an important and challenging open problem for machine learning. I study representation learning and draw insights from cognitive neuroscience to create intelligent systems that are ultimately meant to improve their performance in online, semi-supervised, real-world environments. Statistical learning programs today perform well on very constrained, narrowly-defined tasks but struggle and fail when required to extract/aggregate knowledge across multiple tasks (consisting of data from multiple modalities) and to deal with non-stationary, one-shot, and zero-shot learning environments. My mission is to develop the learning algorithms and models needed to create such general-purpose, adaptive agents.
It is the endeavor of my research group, the Neural Adaptive Computing (NAC) Laboratory, to synthesize key aspects of models of cognition and biological neuro-circuitry, as well as theories of mind and brain functionality, to construct new learning algorithms and architectures that generalize better to unseen data and continually adapt to novel situations. Ultimately, the hope is that by building lifelong learning machines, we might gain further insight into the workings of human intelligence itself.
Current Students / Members of the NAC LabAnkur Mali, Ph.D. student (co-advised w/ Dr. C. Lee Giles at Penn State University) - neural memory systems, learning algorithms, lifelong machine learning
AbdElRahman ElSaid, Ph.D. student (co-advised w/ Travis Desell at Rochester Institute of Technology) - ant colony optimization, metaheuristics
Timothy Zee, Ph.D. student (co-advised w/ Ifeoma Nwogu at Rochester Institute of Technology) - learning algorithms, interpretable neural systems, convolutional networks
Xu Sun, MSc student - recurrent networks, time series
Michael Peechatt, MSc student - intelligent quality assurance, ant colony optimization / machine learning
Hitesh Ulhas Vaidya, MSc student - lifelong machine learning, convolutional networks
William Gebhardt, BS student - lifelong machine learning, neuroevolution
James Le, MSc student - Boltzmann machines