Intel® Student Ambassador for oneAPI
Overview
This program is focused on undergraduate and graduate students who are passionate about technology and working with developer communities to promote learning, sharing, and collaboration. It provides opportunities for students to enhance their oneAPI skills, expand their network, and learn about the cutting-edge Intel® hardware and software products.
Benefits
- Recognition from Intel as a oneAPI expert
- Support from Intel to organize local events, such as watch parties and workshops (virtual or face to face), to build the oneAPI community
- Extended access to Intel® Developer Cloud
- Early access to the latest technology developments that are under a nondisclosure agreement (NDA)
- Invitations to exclusive Intel events and training
- Reimbursement from Intel for participating in accepted conferences and events
- Opportunity to be considered for an internship at Intel
The Role of a oneAPI Student Ambassador
- Organize and deliver AI, high-performance computing (HPC), or visualization-focused training and workshops for students
- Evangelize Intel® Software Development Tools and resources
- Develop and share projects using Intel® technologies that inspire fellow students
- Post blogs and articles to share new lessons learned and best practices with peers
- Commit to act as an Intel® Student Ambassador for oneAPI for one academic year and potentially spend another year as a mentor
How to Apply
Requirements
- Enrolled in an undergraduate or graduate degree in any regionally accredited university
- Have a minimum of one year left until graduation
- Advocate for Intel technologies on campus
- Share your work at events and conferences and host meetups on campus
- Create a project that describes the work or the research you support on Intel® DevMesh
Application Process
- Sign up on Intel DevMesh.
- Create your profile, and then upload your project.
Note To access the form, you must be signed into Intel DevMesh. - Intel evaluates your application and responds to qualified candidates to continue to the next steps.
After You Submit an Application
- To join the student community for oneAPI group and showcase your projects, go to Student Developers for oneAPI.
- Go to your profile, and then select My Projects.
- Select Edit, and then select the Details tab.
- Scroll to the bottom of the form, and select the Groups drop-down menu, and then select Student Developers for oneAPI.
- Select Submit Project. Your project now appears in the Student Developers for oneAPI group.
Current Ambassadors
Aaron Masuba, BRAC University
Aaron is developing the Applied Machine Learning and Industrial IoT (AMLIIoT) Architecture. This is a state-of-the-art system control suite based on intelligent edge electronic devices and sensor fusion for machine learning applications in industrial processes, automation, and diagnostics. He uses Intel® FPGA high-performance computing (HPC) applications to accelerate and scale operations. Aaron hopes to solve some of the world's most pressing challenges such as access to efficient, sustainable carbon-neutral energy and other control system applications where optimization is required through the Intel oneAPI and AMLIIoT Architecture.
Adam Tuft, Durham University
Adam uses Intel® oneAPI toolkits to investigate the scheduling behavior of task-based parallel programming runtimes used in high-performance computing (HPC) applications. He focuses on the efficient use of OpenMP* and DPC++ (the oneAPI implementation of SYCL*) for offloading to Intel® GPUs. Working within the ExCALIBUR task-parallelism research project, Adam uses the Intel oneAPI toolkits to develop Otter, a performance analysis tool. Otter aims to help developers port existing code to use task-based parallelism effectively.
Aftab Ahmed, Sukkur IBA University
Aftab focuses on using AI techniques to build solutions for computer vision and digital image processing. He is working on a project that uses Intel® oneAPI Deep Neural Network Library (oneDNN), machine learning, deep learning techniques, and the Modified National Institute of Standards and Technology (MNIST) dataset. He is also part of multiple tech student communities on campus, and is a tech blogger and community builder.
Andrei Cozma, University of Tennessee, Knoxville
Andrei develops new ways to visualize and understand complex patterns and correlations in data. In a self-supervised manner, he uses deep neural networks to discover potentially hidden patterns and correlations, and uses them to generate meaningful visualizations about the underlying datasets. His work uses the Intel® AI Analytics Toolkit (AI Kit) and oneDNN to develop and train neural networks. He also uses the Intel® oneAPI Rendering Kit with Intel® OSPRay to create an interactive visualization application for the trained parameters.
Aneerban Saha, Manipal University Jaipur
Aneerban uses sentimental analysis and machine learning to develop an AI voice bot that listens to feelings that a user shares and then gives advice. He uses the Intel AI Analytics Toolkit for this project. His goal is to minimize problems such as depression, anxiety, and mental health issues.
Bhaskar Trivedi, Madan Mohan Malaviya University of Technology
Bhaskar uses machine learning techniques to build applications for users in finance and healthcare. He uses the Intel AI Analytics Toolkit for a project that connects stock market traders. The project provides them with a no-code interface to predict the stock prices in real time and, through HPC, make better decisions.
Deepak Joshi, Bharati Vidyapeeth's College of Engineering, Delhi
Deepak improves and optimizes existing machine learning models and develops crucial preprocessing pipelines to make them work efficiently and accurately on low-specification computers. He uses Intel oneAPI toolkits to implement new machine learning models and extract meaningful data from huge datasets.
To hasten the process of training and predicting large data, Deepak uses Intel oneAPI to take advantage of GPUs and CPUs as accelerators for his AI and natural language processing (NLP) projects.
Devesh Seethi, Northern Illinois University
Devesh builds multimodal frameworks on Intel® Developer Cloud using data from inertial sensors, vision, and audio to solve real-world problems to improve public health. He is creating a framework to track activity levels and behavioral patterns in Alzheimer patients using person re-identification, point of interest detection, motion tracking, and monocular depth estimation in the Intel® Distribution of OpenVINO™ toolkit.
Felipe Antunes, Fundação Getúlio Vargas, EMAp, Brazil
Felipe is developing a game theory model to address anxiety, depression, and social issues facing students. He uses deep neural networks to simulate the time-evolution of the model. For this work, he uses oneDNN, Intel® Optimization for TensorFlow* and Intel Developer Cloud.
Harvey Johnson, University of Nottingham
Harvey is using oneAPI to implement an efficient and timely data preprocessing pipeline for AI. This system uses GPU and accelerator resources to compress and decompress hundreds of millions of data samples in seconds. This process simplifies loading large datasets for Gavin AI to allow for training on larger datasets without I/O or storage bottlenecks.
Hunter Price, University of Tennessee, Knoxville
Hunter uses the Intel oneAPI Rendering Toolkit to build a highly performant data visualization tool. He uses the Intel OSPRay package as a ray-tracing engine along with The Visualization Toolkit* to render high-quality graphics packaged in an easy-to-use application. Hunter's goal is to provide an all-in-one application to data scientists and researchers.
James Hammer, University of Tennessee, Knoxville
James automates the deployment of Intel® oneAPI tools to Docker* in swarm mode and various scalable platforms with a focus on graphics and data visualization systems. He and his team finds novel uses for oneAPI technologies, such as developing a web-compliant front-end for Intel® OSPRay Studio. James makes automated software deployment to HPC environments and enables the deployment of other oneAPI toolkits and development.
Joshua Shiells, University of Kent, United Kingdom
Joshua is using oneAPI to take advantage of GPUs as accelerators for his AI projects and research. He is working on "Gavin," which is a transformer chatbot trained on the Reddit* Pushshift dataset. He is also focused on building a machine learning library using oneAPI to easily support multiple applications on both GPU and CPU compute. He used oneAPI to simplify the amount of code needed to handle many different devices or accelerators.
Lakshay Taneja, Shoolini University
Lakshay develops new and improves existing machine learning models and processes to make models work in a production environment. Lakshay focuses on developing machine learning algorithms to extract meaningful information from large amounts of datasets. He uses Intel® oneAPI toolkits to implement new machine learning models and improve the existing datasets.
Mohit Sharma, University of Windsor
Mohit uses Intel Optimization for TensorFlow in the Intel AI Analytics Toolkit to speed up techniques for improving machine learning classification model results. He developed a method for using a hierarchy of binary classifiers for multiclass classification. This method calculates the probability score for belonging to a class by multiplying the results of multiple binary classifiers. These binary classifiers are trained on groups of classes with a similar number of data points.
Nicholas Synovic, Loyola University Chicago
Nicholas develops new and improve existing computer vision models to run on low powered systems. He uses the AI Kit to develop and test low-powered machine learning models. This involves developing power-efficient and performant pipelines to prepare, process, and render data on devices. Nicholas focuses on developing computer vision models and finding solutions that can enable other model classes to run efficiently.
Poornima Nookala, Illinois Institute of Technology
Poornima is using Intel® VTune™ Profiler, Intel® Trace Analyzer, and Intel® Inspector in her projects in analyzing the performance bottlenecks and optimizing the runtime to scale up to hundreds of cores on modern many-core architectures. Her goal is to reduce overheads of tasking in parallel runtime systems as a step towards exascale computing.
Prajwal Kumar, Maharshi Dayanand University
Prajwal uses oneDNN and Intel Optimization for TensorFlow to optimize various charging stations for electronic vehicles (EV). His project involves developing a new EV charging network to eliminate inconveniences due to a small charging infrastructure and long charging times—the primary obstacles to mobility decarbonization.
Rachel Selina Rajarathnam, The University of Texas at Austin
Rachel works on methods and algorithms to accelerate the process of FPGA circuit placement for improving design scalability. Her DREAMPlaceFPGA, an accelerated global placement framework that's implemented using a deep-learning toolkit, uses CPUs and GPUs. The AI support in oneAPI provides a suitable platform for further development and extension of the DREAMPlaceFPGA by allowing the use of CPUs, GPUs and FPGAs in one platform.
Re'em Harel, Ben-Gurion University
Re'em develops and integrates scientific applications in HPC systems using MPI+X paradigms, focusing on MPI and OpenMP* on multicore and heterogeneous architectures. He is also developing a scalable framework benchmark named ScaleSALE and implementing OpenMP schemas to numerical schemas. In addition, Re'em is researching AI techniques for OpenMP directives that can be incorporated in the Intel® Advisor.
Rohan Sethi, Loyola University Chicago
Rohan is focused on optimizing computer vision models to run on low-power edge devices. He uses the AI support in the AI Kit to implement and test the efficiency of data preprocessing pipelines on such devices. Rohan plans on developing and comparing performance of generative and discriminative machine learning pipelines to assess optimal solutions for healthcare and nonhealthcare computer vision applications.
Shubham Luharuka, RV Institute of Technology and Management*
Shubham is developing an optimized and efficient task-oriented deep neural network to enhance the quality of videos. He uses Intel Optimization for TensorFlow and oneAPI to target the FPGA circuit for its real-time implementation. To reduce the training time of the model, he uses the Intel Developer Cloud. This implementation can be used in areas such as medical simulation, image processing, material engineering, video processing, computational materials science, optical flow estimation, computational electromagnetics, and more.
Taimur Muhammad Khan, National University of Computer and Engineering Sciences, Islamabad
Taimur develops a spell correction model for Roman Urdu using the Noisy Channel model to correct nonword errors. He uses the Intel oneAPI toolkit to implement optimized and efficient data preprocessing techniques. Taimur uses the Intel Developer Cloud to access corpus and reduce training time. This process streamlines the data flow and helps load large corpora with ease.
Taisa Calvette, Instituto Militar de Engenharia
Taisa is developing a hybrid model with recurrent neural networks to estimate river flow in hydropower plants from pluviometer and satellite precipitation data. For this work, she uses oneDNN, Intel® Optimization for TensorFlow*, and Intel Developer Cloud.
Vignesh Nagavel, VSB Engineering College, Karur
Vignesh is developing a hybrid model with recurrent neural networks to estimate river flow through hydropower plants from pluviometer and satellite precipitation data. For this work, Vignesh uses oneDNN, Intel Optimization for TensorFlow, and the Intel Developer Cloud.
Youssef Faqir, Complutense University of Madrid
Youssef uses the Intel oneAPI toolkit to develop portable algorithms without losing performance for multiarchitecture devices such as CPUs and GPUs. For that work, he developed the Non-Negative Matrix Factorization (NMF) algorithm in DPC++ (the oneAPI implementation of SYCL) and OpenMP, and the K-means algorithm focused to run over multivendor accelerators. Youssef plans to extend these works to FPGAs and compare the differences among architectures.
Become a oneAPI Student Ambassador
Get ready to inspire and influence your peers from across the globe. Demonstrate your technology leadership in AI, HPC, visualization, and rendering.
Join the Student Community for oneAPI
For more information, contact us.