Alan Lockett

Alan Lockett is a prominent researcher specializing in artificial intelligence, natural language processing, and cognitive architectures, with extensive experience in neural networks, graphical models, and probability theory.

As the leader of machine learning and artificial intelligence initiatives at CS Disco, Alan focuses on developing predictive coding tools for e-discovery utilizing multimodal neural networks to analyze text, image, and metadata, catering to user-defined criteria in large datasets ranging from 1GB to 10TB.

His current research interests include salience identification in multi-modal data, text and multi-modal similarity across extensive corpora, and zero-shot/few-shot learning for fast pre-supervised deep neural network training.

Alan holds a Ph.D. in Computer Science from the University of Texas, Austin, where he conducted research on optimization theory, particularly in evolutionary computation for neural network training. He also pursued a postdoctoral fellowship in Robotics & Deep Learning at IDSIA in Switzerland under Jürgen Schmidhüber's guidance.

With four pending patents and a portfolio of over a dozen publications covering game theory, optimization theory, functional analysis, neuroevolution, graphical models, and robotics, Alan has made significant contributions to the field.

His monograph on optimization methods analysis, stemming from his thesis work, was published by Springer in 2020.

Alan Lockett has held various roles in prestigious organizations, including Vice President of Machine Learning and Artificial Intelligence at DISCO, Senior Director of Research at DISCO, Principal Data Scientist at DISCO, Postdoctoral Fellow at IDSIA, Senior Engineer at Art & Logic, Senior Software Engineer at FineTooth, Data Tools Engineer at Catalis Health, Senior Research Engineer at 21st Century Technologies, and Latin Teacher at Regents School of Austin.