Publications

@inproceedings{vadisetty_cloud-based_2025,

title = {Cloud-Based Immersive Learning: The Role of Virtual Reality, Big Data, and Generative AI in Transformative Education Experiences},

author = {R. Vadisetty and A. Polamarasetti and M. K. Goyal and S. K. Rongali and S. K. Prajapati and J. B. Butani},

editor = {S. Mishra and H. K. Tripathy and J. R. Mohanty},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105018048438&doi=10.1109%2FASSIC64892.2025.11158636&partnerID=40&md5=6d832a0f4460d2eb93e357faba143a32},

doi = {10.1109/ASSIC64892.2025.11158636},

isbn = {9798331523022 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Immersive learning transforms education by integrating Virtual Reality (VR), Big Data, and Generative Artificial Intelligence (AI) in cloud environments. This work discusses these technologies' contribution towards increased engagement, personalized learning, and recall through flexible and interactive experiences. Realistic simulations in a secure environment, real-time analysis via Big Data, and dynamically personalized information via Generative AI make immersive learning a reality. Nevertheless, scalability, security, and ease of integration are yet to be addressed. This article proposes an integrated model for cloud-based immersive learning, comparing conventional and AI-facilitated approaches through experimental evaluation. Besides, technical, ethical, and legislative considerations and future directions for inquiry are addressed. In conclusion, with its potential for personalized, scalable, and data-intensive instruction, AI-facilitated immersive learning is a transformational technology for educational delivery. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Artificial intelligence, Big Data, Cloud analytics, Cloud environments, Cloud-based, Cloud-based learning, E-Learning, Engineering education, Generative AI, generative artificial intelligence, Immersive learning, Learning analytic, learning analytics, Learning systems, Metadata, Personalized Education, Personalized learning, Real time analysis, Realistic simulation, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jacoby_efficient_2023,

title = {Efficient Cloud Pipelines for Neural Radiance Fields},

author = {D. Jacoby and D. Xu and W. Ribas and M. Xu and T. Liu and V. Jeyaraman and M. Wei and E. D. Blois and Y. Coady},

editor = {S. Chakrabarti and R. Paul},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179765347&doi=10.1109%2FUEMCON59035.2023.10316126&partnerID=40&md5=cb75c3398a28ac80a8bc8f35da278d50},

doi = {10.1109/UEMCON59035.2023.10316126},

isbn = {9798350304138 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IEEE Annu. Ubiquitous Comput., Electron. Mob. Commun. Conf., UEMCON},

pages = {114–119},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios. © 2023 Elsevier B.V., All rights reserved.},

keywords = {Azure, Change detection, Cloud analytics, Cloud computing, Cloud-computing, Cluster computing, Containerization, Creatives, Geo-spatial, Multi-views, Neural radiance field, Neural Radiance Fields, Pipelines, User interfaces, Virtual production, Vision communities, Windows operating system},

pubstate = {published},

tppubtype = {inproceedings}

}