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Find out more about the HPCC Systems Summer Intern Program including how to apply and read this blog introducing the students and their projects.

13 students joined our intern program in 2024. Our students presented about their projects to the team during the year and 12 of them entered our 2024 Poster Contest held at the virtual HPCC Systems Community Day Summit in October 2024.

Meet the Class of 2024

Name

Project Title

Description

Mentor(s)

Resources

Aryaman Gautam

Bachelor of Tech Data Science
Mukesh Patel School of Technology, Management and Engineering, India

HPCC Systems local deployment on K3D cluster

The goal of this project was to establish an initial setup for a local deployment of HPCC Systems on K3D. K3D is a lightweight wrapper to run K3S (Rancher Lab's minimal Kubernetes distribution) in docker which makes it very easy to create single and multi-node K3S clusters in docker.

Xiaoming Wang

Godji Fortil

Chinmay Desai

Sidharth Ganesan 

View Poster 

2023 Community Day recording

Boqiang Li

Ph.D. in Computer Science, Clemson University, USA

Convert Generalized Neural Network bundle (GNN) to native Tensorflow 2.0 

Neural Networks have emerged as a powerful tool for analyzing complex datasets like images, video, and time-series data, surpassing classical methods in their effectiveness. To leverage this potential, HPCC Systems offers the Generalized Neural Network Bundle (GNN), which combines the parallel processing capabilities of HPCC Systems with the robust Neural Network functionalities of Keras and TensorFlow. This project upgraded the GNN bundle to utilize the native Tensorflow 2 interface. The upgraded GNN with Tensorflow 2 demonstrated several significant advantages over its previous version.

Lili Xu

Roger Dev

View Poster

View Blog

Carlos Caceres 

High School Student American Heritage School Delray, FL, USA

Practical Application of Generative AI Technology 

During this project a generalized interface was created for HPCC Systems to access GPT and ChatGPT. From there the steps were taken to use HPCC Systems to train a neural network model capable of classifying faces into different emotions. These emotions would then be processed by the interface to create a call to OpenAI’s API from which an appropriate response would be generated.

Lili Xu

Roger Dev

View Poster

View Blog

2023 Community Day recording

Charvi Dave

Bachelor of Tech Data Science
Mukesh Patel School of Technology, Management and Engineering, India

Resume analyzer in NLP++

A Resume Analyzer is the implementation of an approach to apply various techniques for analyzing the resumes a company receives and retrieving the main sections. This project has leveraged the NLP++ plugin to process resumes and extract the main headers and sections of the resume, such as skills, work experience, email, and education. 

David de Hilster

Umesh Mahind

Nandhini Velu

View Poster

2023 Community Day recording

Elizabeth Lorti       

Bachelor of International Development,
King's College, UK

HPCC Systems Marketing and Branding

As a returning HPCC Systems intern and one that has worked year-round on maintaining social media, this year, I completed a review of my own social media contributions and strategy to see what could be done to improve, as well as will conducted interviews among stakeholders and recorded minutes to best understand and communicate the needs of the Technology Summit and Community Day stakeholders.  

Jessica Lorti

View Poster

View Blog

Hiroki Sato            

Masters in Computer Science University of Indiana, USA

Automation of HPCC Systems Cloud Native Deployment to AWS with Terraform

This project leveraged Terraform to explore the deployment of the HPCC Systems containerized application onto AWS Elastic Kubernetes Service cluster (EKS). During the internship, we developed a hpcc-aws-terraform module. This consisted of building a necessary AWS infrastructure such as virtual private cloud (VPC), subnets, necessary security group, EKS cluster and node group. 

Wayne Carty

Godson Fortil

View Poster

View Blog

Jessie Mao                 

High School Student Lambert High School Suwanee, GA, USA

HPCC Systems Deployment with Various Helm Chart Configurations

This project provided two solutions for HPCC Systems deployments. The overrides solution utilizes the default values.yaml file while using other files to modify it. Overrides can be used to make small changes to the values.yaml, and mainly concentrates on Roxie and Thor. The HPCC-lite, on the other hand, does not require a custom values.yaml file, so can be used with other files to create more scenarios. 

Xiaoming Wang

Godson Fortil

View Poster

View Blog

Johnny Huang        

Bachelor of Computer Science
University of Toronto, Canada

Improve Error Handling and Reporting for Automated Test Systems

This project concentrated primarily on refining the GitHub Actions scripts, a vital tool for automated testing within the HPCC Systems environment. These scripts analyze the logs generated from tests, providing a granular breakdown of the executed tests. I also introduced enhancements to the scripts to improve the fault tolerance of our testing systems. These included adding logic to retry failed actions, increasing the resilience of the system to transient issues, reducing test failures, and decreasing the need for manual interventions.

Attila Vamos

View Poster

View Blog

K Dheemonth                    

Bachelor of Computer Science and Engineering 
RVCE, India

Sentiment Analysis in English

During my internship we created a number of parsers and an analyzer using NLP++(Visual Text). To do this, we defined the different rules that map to a very generic manner of supplying the sentiments rather than having for specific ones. NLP++ assisted in constructing the parsers for assigning different sentiments depending on user, cricket terms, player and team interests and team supports. The second phase centered on the sentiments that were given to emojis. Emojis in the dictionary, a capability offered by NLP++, were used to assign sentiments to the cricket tweets. 

David de Hilster

View Poster

View Blog

2023 Community Day recording

Kruthika Pinnada            

Bachelor of Computer Science and Engineering 
RVCE, India

Resume Analyzer

The project "Resume Analyzer" leverages the power of NLP++ programming language to build a digital human reader that parses the resume text in the same we humans do. The system has made use of the “zoning” of a resume (done by a previous intern) and aims at doing an in-depth analysis of text and extracting valuable information in the way a human does. 

David de Hilster

View Poster

View Blog

2023 Community Day recording

Logan Patterson          

Masters in Data Science
New College of Florida, USA

Designing Test Algorithms for Causal Model Discovery Within the HPCC Systems Causality Framework

The discovery model testing algorithm was used on four different algorithms, one of which was already implemented within Because: PC (Peter-Clark), GES (Greedy Equivalence Search), IGCI (Information Geometric Causal Inference), and RCC (Randomized Causation Coefficient. Each of the models were compared to one another based on performances with various datasets to determine viability of both the testing algorithm and the models themselves. This algorithm hopefully paves the way for easier integration and implementation of causal discovery algorithms for future developments within the HPCC Causality Framework

Roger Dev

Lili Xu

View Poster

View Blog

Narayan Kandel            

Ph.D. in Computer Science, Clemson University, USA

Enhancing Performance of Distributed Neural Network with GNN Bundle

Our work addresses the challenges of parallelizing neural network training, recognizing that, in certain scenarios, superior results can be achieved by training on a single high-powered node (e.g., with GPU) or a limited number of nodes. To enhance network performance and accuracy, we pursued two distinct approaches. Firstly, we optimized neural network training by strategically setting a limit on the number of nodes used for training, thereby reducing communication overhead. 
Secondly, we investigated an alternative multi-node approach to network training, varying the starting points across multiple nodes and averaging the results. This technique shows promises yielding improved predictions compared to a single-network setup. 

Lili Xu

Roger Dev

View Poster

View Blog

Nisha Bagdwal              

Kennesaw State University, USA

Develop an Automated ECL Watch Test Suite

Attila Vamos

Chris Lo

View Poster

View Blog

Rohith Surya Podugu                

California State University, USA

Refactoring and Releasing PyHPCC

Amila de Silva

View Poster

View Blog

Sabrina Harris                             

New College of Florida, FL, USA

HPCC Systems Machine Learning Tutorials

Bob Foreman

View Poster

View Blog

Scarlett Huang 

Dreyfoos School of the Arts West Palm Beach, FL, USA 

Investigate Third-Party Environments (Google Big Query)

Ming Wang

Terrence Asselin

View Poster

View Blog

Shounak Joshi 

University of Florida, USA    

Investigate Third-Party Environments (Azure Synapse Analytics)

Ming Wang

Michael Gardner

View Poster

View Blog

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