Applied Artificial Intelligence
Dr. Hardik A. Gohel is the founding director of the Applied Artificial Intelligence (AAI) laboratory at Texas A&M University-Victoria. He is also a principal investigator in a multiyear funded project from the Department of Defense (DoD) and the Department of Energy (DoE). His areas of research are Artificial Intelligence, Digital Healthcare, Cybersecurity, and Advanced Computing.
Publications
Journal Papers
- M. Mujawar, H. Gohel, S.K.Bhardwaj, S. Srinivasan, N. Hickman, A. Kaushik, "Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management", Elsevier Journal of materialstoday CHEMISTRY, doi.org/10.1016/j.mtchem.2020.100306
- H. Gohel, H. Upadhyay, L. Lagos, K. Cooper, A. Sanzetenea, "Predictive Maintenance Architecture Development for Nuclear Infrastructure using Machine Learning", Elsevier Journal of Nuclear Engineering and Technology, doi.org/10.1016/j.net.2019.12.029
- S. Bhardwaj, H. Gohel and S. Namuduri, "A Multiple Input Deep Neural Network Architecture for Solution of One-Dimensional Poisson Equation," in IEEE Antennas and Wireless Propagation Letters. doi: 10.1109/LAWP.2019.2933181
- H. Upadhyay, H. Gohel, A. Pons, L. Lagos, “Virtual Memory Introspection Framework for Cyber Threat Detection in Virtual Environment”, Advances in Science, Technology and Engineering Systems Journal Vol. 3, No. 1, 25-29, 2018
- H. Gohel, B.K. Garsondiya, A. Kothia, H. Jani, "Operational Study of Brain Reading–Neuroimaging in Human Brain Computer Interface(H-BCI)", American Research Journals, Vol. 2, Issue 6, pp. 1-6, 2017
Conference Papers
- H. Gohel, H. Upadhyay, L. Lagos, “Collaborative Trustworthy Security and Privacy Framework for Social Media” The Network and Distributed System Security Symposium (NDSS), 2019
- H. Upadhyay, H. Gohel, A. Pons and L. Lagos, "Windows Virtualization Architecture For Cyber Threats Detection," IEEE Conference on Data Intelligence and Security, 2018, pp. 119-122. doi: 10.1109/ICDIS.2018.00025
- D. Levy, H. Gohel, H. Upadhyay, A. Pons and L. E. Lagos, "Design of Virtualization Framework to Detect Cyber Threats in Linux Environment," IEEE Conference on Cyber Security and Cloud Computing, 2017, pp. 316-320. doi: 10.1109/CSCloud.2017.18
- H. Gohel, P. Sharma. Intelligent Web Security Testing with Threat Assessment and Client Server Penetration. Springer Advances in Intelligent Systems and Computing, 2016, pp. 555-567 doi:10.1007/978-981-10-0135-2_54
Books
- M. Anouncia, H. Gohel, S. Vairamuthu, "Data Visualization - Trends and Challenges Toward Multidisciplinary Perception", ISBN 978-981-15-2282-6, Springer Publications Inc.,2020
- H. Gohel "Human Brain Computer Interface (H-BCI)", ISBN 978-3-659-77990-9 Lambert Academic Publishing, 2015
Book chapter
- H. Gohel, H. Upadhyay, "Developing Security Intelligence in Big Data" Knowledge Computing and Its Applications. Springer Publications, 2018, pp. 25-50, doi: 10.1007/978-981-10-6680-1_2
Magazine Articles
- H. Gohel, H. Upadhyay, “Cyber Threat Analysis with Memory Forensics”, CSIC Communications – Knowledge Digest for IT Community, Vol 40, Issue 11, pp. 17-19, 2017
- H. Gohel, H. Upadhyay, “Developing Solutions with Big Data Technology”, CSIC Communications – Knowledge Digest for IT Community, Vol 41, Issue 2, pp. 18-21, 2017
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Research Servers
Laboratory
The NVIDIA® qualification guarantees the system's CPU, Tesla T4 and overall system performance under wide-temperature conditions is above par by putting the system through stringent tests and ensures its features can be seamlessly integrated with the NVIDIA® Tesla T4. A&M-Victoria’s AI servers proved itself worthy of our applied research solutions to amplify AI inference performances when we choose Tesla GPU qualified platforms.
As an NVIDIA® Tesla qualified GPU computing platform, PowerEdge T640 is one of the most compact systems. It is a unique industrial-grade edge AI platform supporting dual NVIDIA® Tesla T640 GPU cards. The system allows innovators to run multiple models simultaneously such as engaging advanced applications with false-fail and redundant GPU configurations or assign the two T640s to separate tasks, set one for video transcoding while setting the other for AI inference tasks. It supports 2 Intel Xeon Silver 4214 2.2Ghz 12 core CPUs with expansion capabilities. It also features compact dimensions and low power consumption characteristics. With T640 boosted AI inference processing power, it is ideal for medical image and video analysis, deep learning machine vision, autonomous machines and more.
A&M-Victoria has two PowerEdge T640 with GPU system with following configuration:
CPU: 2 Intel Xeon Silver 4214 2.2Ghz 12 core CPUs
RAM: 512 GB 3200MT RDIMM memory
STORAGE: 16 2TB 7.2KRPM 2.5” SATA Hard Drives (32TB total storage, around 28TB of usable space depending on RAID configuration)
OS DRIVE: BOSS Control Card with 240GB M.2 SSD
NETWORK: Dual port onboard 10Gb ethernet ports, second card with dual 10Gb SFP+ ports
GPU: Nvidia Tesla V100S 32GB GPU card
We have Windows Server 2022 Datacenter in both of them, with SQL server 2019 with machine learning services to perform in- memory analytics. It’s for faster AI computing and parallel processing.
Teaching Activities
Graduate Courses
- COSC 6405 : Programming for Data Science
- COSC 6315 : Data Science using Machine Learning
- COSC 6312 : Fundamentals of Cybersecurity
- COSC 6339 : Network Design Management
Undergraduate Courses
- COSC 4300 : Digital Forensics
- COSC 4339 : Telecommunication and Networks
