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James T. Lo
Ph.D., University of Southern California, USA
Professor
Office: Room MP432
Phone: 410-455-2432
Email: jameslo@umbc.edu
Personal Web Page

James Ting-Ho Lo is a Professor in the Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, Maryland, USA. He received the Ph.D. degree from the University of Southern California and was a Postdoctoral Research Associate at Stanford University and Harvard University. His research interests have included optimal filtering, system control and identification, active noise and vibration control, and computational intelligence. In 1992, he solved the long-standing notorious problem of optimal nonlinear filtering in its most general setting and obtained a best paper award.

Subsequently, he conceived adaptive neural networks with long- and short-term memories, accommodative neural network for adaptive processing without online processor adjustment, and robust and robust adaptive/accommodative neural networks with a continuous spectrum of robustness and proved that they are universal approximators of dynamical systems for adaptive, accommodative or/and robust identification, control and filtering.

He has been developing convexification and deconvexification methods for avoiding poor local-minima in data fitting (e.g., training neural networks and estimating regression models), hoping to soon remove a main obstacle in the neural network approach and nonlinear regression in statistics.

In recent years, Dr. Lo developed a functional and a low-order model of biological neural networks. The former, called the temporal hierarchical probabilistic associative memory (THPAM) and clustering Interpreting probabilistic associative memory (CIPAM), is a new paradigm of learning machines. The latter, the low-order model, comprises biologically plausible models of dendritic nodes/trees, synapses, spiking/nonspiking somas, unsupervised/supervised learning mechanisms, a maximal generalization scheme, and feedbacks with different delay durations; which integrate into a biologically plausible learning/retrieving algorithm and answer numerous fundamental questions in neuroscience.

CV as of February 2019

 
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