Guest Speakers and subjects:
- Vannel Zeufack - HPCC Systems Intern 2019, Kennesaw State University
HPCC Systems Intern Project: Developing and Assessing Unsupervised Anomaly Detections Methods using HPCC Systems
Vannel is a student of Kennesaw State University, studying a Masters in Computer Science.
Vannel has been researching various log analysis techniques to detect abnormal activities on computing/network systems alongside his professor at KSU. His aim is to adopt a number of machine learning and big data analysis techniques, to implement an algorithm that has the ability to detect unknown cybersecurity threats. The ideas for his project are based on this paper: Experience Report: System Log Analysis for Anomaly Detection by Shilin He, Jieming Zhu, Pinjia He and Michael R. Lyu.
- Farah Alshanik - HPCC Systems Intern 2019, Clemson University
HPCC Systems Intern Project: Domain Based Common Words List Using High Dimensional Representation of Text
Farah Alshanik is a Ph.D. student of computer science in Clemson University. She received her B.S from Jordan University of Science and Technology. She is working with Dr.Amy Apon as a Research Assistance in Data Intensive Computing Ecosystems (DICE) Lab. Her interest is focused on applying high performance computing to machine learning problems.
The aim of Farah's internship project is to use a text vectors bundle (CBOW) with HPCC Systems to find the common words for any datasets. Her project is based on the hypothesis that eliminating domain based common words will enhance the performance of the classification methods used as well as improve the results of topic modeling. The ability of HPCC Systems to massively scale-up and its fast distributed data storage will enhance the performance of the methodology.
- Robert Kennedy - HPCC Systems Intern 2019, Florida Atlantic University
HPCC Systems Intern Project: Expanding HPCC Systems Deep Neural Network Capabilities - Create HPCC Systems VM on Hyper V
Robert Kennedy is a second year Ph.D. student in CS at Florida Atlantic University with research interests in Deep Learning and parallel and distributed computing. His current research is in improving distributed deep learning by implementing and optimizing distributed algorithms.
Robert has been researching and developing GPU accelerated Deep Learning algorithms on HPCC Systems. In his proposal, Robert talks about how GPU acceleration vastly improves Deep Learning training time. His work will produce the first GPU accelerated library (to his knowledge) and expand our deep neural network capabilties. Creating an HPCC Systems VM on Hyper V as part of this project, will increase the number of configurations on which HPCC Systems can be deployed and provide the building blocks needed for the possible future development of different distributed configurations that we don't currently provide, such as model parallelism and enabling HPCC Systems to Deep Learn using asynchronous algorithms.
- Russ Whitehead - Architect, LexisNexis Risk Solutions
Security and HPCC Systems - Cryptographic ECL Standard Library
Russ Whitehead (William) is an HPCC Systems Software Architect and has a BS degree in Computer Science from University of Florida, and an MBA from the University of Miami. His background is operating systems development, network management and voice recognition, and he has been a LexisNexis employee and a platform team member since May 2008. His top responsibility is the HPCC security framework, which he has been contributing to since 2012.