- Shah Muhammad Hamdi, PhD student studying Computer Science (Data Mining) at Georgia State University - Watch Recording
Dimensionality Reduction and Feature Selection in ECL-ML
Dimensionality reduction and feature selection are very important tools for any machine learning library, which help in compression and visualization of high-dimensional data, and improving the performance of supervised/unsupervised learning algorithms. In this talk, we will discuss the parallel implementation of Principal Component Analysis (PCA) using the Parallel Block Basic Linear Algebra Subsystem (PBblas) library. Additionally, we will discuss the ECL implementations of some feature selection algorithms for the HPCC Systems platform.
Shah Muhammad Hamdi is a Ph.D. student (3rd year) in the Department of Computer Science of Georgia State University. He works in Data Mining Lab (DMLab) under the supervision of Dr. Rafal Angryk. His research interests are machine learning, data mining and deep learning, more specifically, finding interesting patterns from real-life graphs and time series data. His research finds applications in the fields of solar weather analysis and neurological disease prediction. Before joining the DMLab for the Ph.D., he worked one year as a Lecturer in Computer Science in Northern University Bangladesh, Dhaka, Bangladesh. He received his Bachelor degree in Computer Science in 2014 from Rajshahi University of Engineering and Technology (RUET), Rajshahi, Bangladesh.
- Robert Kennedy, PhD student in Computer Science at Florida Atlantic University - Watch Recording
Parallel Distributed Deep Learning on HPCC Systems
The training process for modern deep neural networks requires big data and large amounts of computational power. In this talk, Robert will cover what he implemented during his summer internship. Combining HPCC Systems and Google’s TensorFlow, Robert created a parallel stochastic gradient descent algorithm to provide a basis for future deep neural network research and to enhance the distributed neural network training capabilities in HPCC Systems.
Robert Kennedy is a first 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.
- Aramis Tanelus, high school student studying at American Heritage School of Boca/Delray, Florida - Watch Recording
Developing HPCC Systems Data Ingestion APIs for Common Robotic Sensors
Aramis’s project will make it easy for anyone in robotics around the world to ingest data from common robotic sensors into an HPCC Systems platform for use in data analysis. In this Tech Talk, Aramis will be speaking about his work on the autonomous agricultural robot and implementing new packages for the Robotics Operating System to interface with HPCC Systems for big data analysis.
Aramis Tanelus is a programmer and senior at American Heritage High School where he is the lead programmer for the Advanced Robotics Team. He works with the ROS operating system collecting data from robots developed by the team and turns it into actionable output to help with robotic tasks. He currently is an intern in Boca Raton working on a project to develop software interfaces between robotic sensors and the HPCC Systems platform.
- Saminda Wijeratne, Masters student studying Computational Science and Engineering at Georgia Institute of Technology, Atlanta - Watch Recording
MPI Proof of Concept
The communication backbone of HPCC Systems connects all the different components and worker nodes in a way that each task in the system is accomplished quickly and seamlessly. The built-in "Message Passing" library in HPCC Systems is designed to handle these communications among dissimilar components and perform non-trivial communication patterns among them. In this part of the tech talk, we will explore how this library currently operates and how we can introduce a different implementation such as an existing popular library called MPI.
Saminda Wijeratne is a Masters student in the Department of Computational Science and Engineering of Georgia Institute of Technology. His area of research is High Performance Computing in Distributed clusters and AI areas such as ML and Neural networks. His adviser is Dr. Srinivas Aluru professor and co-Executive Director in Georgia Tech IRI in Data Engineering and Science. Saminda was a senior software engineer for 3 years in the industry at WSO2, an open source technology provider which offers an enterprise platform. He obtained his Bachelor of Computer Science from University of Moratuwa, Sri Lanka.