My interests are in multivariate (“big data”) analysis, data analytics, machine learning, deep learning, neural networks, and neural learning models.
I have delved into a variety of studies with both theoretical and applied value and aim to continue doing so toward solving modern applications and the necessary theory and structure that is involved.
I have taught machine learning and neural networks at both the graduate and undergraduate levels and have spearheaded and teamed on a variety of studies, including military and medical applications as well as neural learning research.
I am notably interested in testing, characterizing and validating machine learning systems with respect to general functionality as well as directly targeted goals.
My research has spanned positions at four universities as well as having many research and engineering contracts, and my accomplishments include the characterization of the ATNN neural network, the development of methodology for the detection of repeating patterns, developing a synchrony pulse-coupled network and proving its general function approximation and building a gravitational clustering model for the dynamic tracking of clusters in multi-channel data.
I am open to developing new connections in the fields of Big Data, Data Analytics, Deep Learning, Machine Learning and Neural Networks and to engaging in hands-on solutions to problems as well as collaborating with others on data analysis needs and research projects.