Dr. Marc Huber has over 20 years of experience performing deep research in Artificial Intelligence systems.
Greenwood Village, Colorado – – zvelo, a leading “Data as a Service” provider whose contextual categorization and malicious detection services are used to generate a myriad of data sets for text, web content, URLs and more, has announced the hiring of Dr. Marc Huber as Senior Artificial Intelligence Research Scientist.
Dr. Huber’s expertise areas are intelligent agent architectures, agent communication, language semantics, multi-agent coordination, and probabilistic plan recognition. He will leverage his background plus experience in machine learning, natural language processing, data mining, and computer vision to maintain and increase zvelo’s technical advantage.
“Marc brings an extensive background and understanding of Artificial Intelligence systems,” noted Jeff Finn, CEO of zvelo. “We anticipate that he will make an immediate and positive impact on our technology especially in the area of analyzing and gauging online user sentiment and intent.”
Dr. Huber attained his M.Sc. & Ph.D. from the University of Michigan, and has dozens of publications to his name. Prior to joining zvelo, Dr. Huber provided artificial intelligence expertise to Soar Technology and Cybernet Systems and provided AI R&D consulting to numerous government and industry partners as founder and lead research scientist of Intelligent Reasoning Systems.
About zvelo, Inc.
zvelo is a leading “Data as a Service” provider whose contextual categorization and malicious website and ad fraud detection services are used to generated a wide range of data sets, attributes and other signals including language, category values (including IAB taxonomies), named entities/sentiment, ad fraud, malicious and more for URLs, the Deep Web and unstructured data. These data sets can be accessed through simple, straightforward SDKs and APIs for integration with applications in the online advertising, data analytics, network security and endpoint security, and other use cases where contextual categorization accuracy, responsiveness, coverage, malicious detection, high performance throughput and scalability are required.