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Dan has been with LexisNexis for five years and is a Senior Architect in the Solutions Lab Group. He has worked for Apple and Dun & Bradstreet, and he ran his own custom programming shop for a decade. He's been writing software professionally for over 35 years and has worked on a myriad of systems, using a lot of different programming languages.

He extended the ECL Std.Date library module to include time support and also authored our Kafka plugin. Dan is also available to to mentor students who apply to complete a summer internship with HPCC Systems for projects involving adding interfaces to ECL for other languages or external data stores. Dan is a regular contributor to the HPCC Systems Open Source Project. He recently contributed

his Data Patterns ECL bundle, which is available in

the HPCC Systems GitHub repository. Dan spoke about this bundle at

our 2018 HPCC Systems Community Day Summit. This ECL bundle includes a set of data profiling capabilities to help identify the shape of data as part of the data discovery cycle.

Lili Xu is in the final stages of completing her PhD in Computer Science. She has worked in the DICE lab directed by Dr. Apon in the school of computing at Clemson University in the USA. Lili has completed three internships with the HPCC Systems team, working on machine learning applications. Her research area is machine learning, natural language processing and high performance computing. 

More information about her intern projects is available and also posters illustrating her work which she entered into our HPCC Systems poster contest. She spoke about her most recent project during one of our Tech Talk webcasts and presented about her work at

our 2018 HPCC Systems Community Day Summit, alongside our LexisNexis colleague, Gus Reyna. We are pleased that Lili has join the team as a LexisNexis employee.

• Implementation of the Yin Yang K Means machine learning algorithm

•  and the extension and improvement of this algorithm
• Clustering LDA
• HPCC Systems Tech Talk 17 - Using HPCC Systems ML to Map Thousands of Violation Descriptions to Standard Violation Codes