Daniel P. Miranker

I have embarked on research in neuro-symbolic systems. An intent of these systems is to combine the sharp accurate logic of symbolic systems with the contemporary AI methods that hallucinate. The goal is to create more reliable and verifiable AI systems. Upon becoming faculty at the University of Texas I did a series of projects that would, today, be recognized as the neuro-symbolic design pattern.  Since these projects predated the emergence of neural-network solutions other statistical methods were used. Though hybrid, these results necessarily predate the adoption ot the term  "neuro-symbolic". 

My primary research and teaching interest always concerns advances in the intersection of artificial intelligence, scaling up database management systems and parallel computing,  and the challenges of creating systems that integrate all three.   From time to time technology developments in other discaplines yield the next source of order-of magnitude larger amounts of data.  The result is the set of open problems is continually renewed and the pleasure of making interdisciplinary contributions is a never ending cycle of exciting opportunity.

I received a Bachelor of Science in Mathematics from MIT ‘79 and a Ph.D. in Computer Science from Columbia University ‘86,'87. My dissertation concerned the creation of the DADO machine, a pioneering 1023-core parallel AI computer that became operational in 1985.  My dissertation results included that DADO achieved  its primary goal, high-performance inference of rule-based intelligent systems.  Core architectural concepts from the DADO computer have reappeared: at least one in Nvidia products and a distinct one in a recently announced Broadcom product. I have led projects on the development of graph-based semantic databases, AI search and pattern-matching and indexing large collections of non-traditional data types.  Application areas have included finance, life science, evolutionary biology and network security.

Twice my research results made it to commercial practice by starting companies, Capsenta Inc. and Liaison Technology Inc. Both companies’ provided solutions to data integration problems by applying AI methods. After repeated acquisitions, intellectual property licensed from the University of Texas to launch Capsenta is now part of ServiceNow’s Data Fabric offering. Liaison Technology was acquired by Forest Express which rebranded itself as Liaison Technology and was later aquired by OpenText. 

 
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