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Himanshu Upadhyay

Himanshu Upadyay, Program Director (Artificial Intelligence & Cyber), Associate Professor (ECE)

Himanshu Upadhyay
Program Director (Artificial Intelligence & Cyber), Associate Professor (ECE)

Program Director (Artificial Intelligence & Cyber)
Associate Professor (ECE)

upadhyay@fiu.edu  305-348-6603

Google Scholar Link: https://scholar.google.com/citations?user=mREbKMkAAAAJ

Dr. Himanshu Upadhyay is serving Florida International University’s Applied Research Center as Program Director for the past 23 years, overseeing the Artificial Intelligence /  Cybersecurity / IT research group. He is working as Associate Professor in the College of Electrical and Computer Engineering teaching Artificial Intelligence/Machine Learning and Cybersecurity courses.

He has extensive experience in artificial intelligence/machine learning, big data, cybersecurity, quantum machine learning, information technology, management and engineering, leading multimillion dollar cybersecurity and artificial intelligence/machine learning projects for the Department of Defense – Test Resource Management Center, Director, Operation Test & Evaluation and Defense Intelligence Agency. He is also responsible for knowledge/waste management, artificial intelligence/machine learning and big data research projects for the Department of Energy’s Office of Environmental Management. He has published multiple papers in the area of cybersecurity, machine learning, deep learning, big data, knowledge / nuclear waste management and service-oriented architecture.

His current research focuses on artificial intelligence, machine learning, deep learning, big data, quantum machine learning, cyber analytics/visualization, cyber forensics, malware analysis and blockchain. He has architected a range of tiered and distributed application to address strategic business needs, managing a team of researchers and scientists building secured enterprise information systems and mentoring undergraduate and graduate students.

Background Education

  • Nagpur University, Mechanical Engineering, BS, 1987
  • IGNOU, Operations Management, MBA, 1996
  • VBSP University, Business Administration, Ph.D., 2014

Certifications

  • Certified Project Management Professional (PMP)
  • Microsoft Certified Professional Developer (MCPD)
  • Microsoft Certified Information Technology Professional (MCITP)
  • Microsoft Certified Solution Developer for .Net (MCSD.Net)
  • Microsoft Certified Database Administrator (MCDBA)

Research and Professional Experience

  • Associate Professor
    Florida International University, Electrical & Computer Engineering
    08/2022 – Till Date

  • Principal Scientist
    Florida International University, Applied Research Center
    04/2019 – 08/2022

  • Adjunct Professor
    Florida International University, Electrical & Computer Engineering
    08/2017 – 08/2022

  • Senior Research Scientist
    Applied Research Center, Florida International University, Miami, FL
    09/2016– 04/2019
  • Research Scientist
    Applied Research Center, Florida International University, Miami, FL
    04/2006 – 09/2016
  • Coordinator, Computer Applications
    Applied Research Center, Florida International University, Miami, FL
    12/2001 – 04/2006

Executive Summary

  1. Principal Investigator on various research projects with total funding of $3.136 Million from the Department of Defense – Director, Operational Test & Evaluation, Test Resource Management Center, National Science Foundation, National Nuclear Security Administration, Department of Energy’s Office of Environmental Management., Savannah River National Laboratory, Lawrence Berkeley National Laboratory and Google focused on AI/ML, Generative AI, Cybersecurity, Hardware Virtualization, Big Data & Cyber Analytics, Advanced Machine Learning Automation Systems etc.
  2. Co-Principal Investigator on various research projects with funding of $11.140 Million from the Department of Energy’s Office of Environmental Management, Department of Defense –Test Resource Management Center, National Science Foundation focussed on AI/ML, long term environmental monitoring, hypervisor instrumentation, cyber, virtualization, memory forensics & cyber analytics etc.
  3. Led the Cyber research (CTAM- Cyber Threat Automation & Monitoring) for DOD with 6.4 million dollars funding to study the impact of the malware threats on mission running in virtual environment using system under test. This involved instrumenting the Xen & KVM hypervisor, virtual machine introspection, smart memory acquisition to extract data structures like process list/system calls and anomaly detection in real time using machine learning & deep learning algorithms & models.
  4. Leading the hardware virtualization research for DOD emulating the physical devices and processors like STM-32, commercial LRUs etc and automating the emulation process using memory map.
  5. Working in the field of Generative AI – Large Language models to build algorithms, develop small language models and domain specific models for Nuclear Decommissioning for DOE-EM supporting AI based Knowledge inferencing including document summarization, chatbots and Q&A.
  6. Research with DOE National labs focused on design & development of Computer Vision algorithm architecture for object detection, segmentation & classification for nuclear waste identification and segregation and its integration with Robotics Arm.
  7. 23 years of research experience in Artificial Intelligence, Machine Learning, Deep Learning, Generative AI, Bigdata, Cybersecurity, Information Technology and Engineering, serving as Program Director for AI & Cyber Center of Excellence, established at Applied Research Center supporting various federal agencies and private industry.
  8. Published multiple papers in the area of artificial intelligence, machine learning, big data, virtualization, hypervisor instrumentation, cyber analytics/visualization, memory forensics, knowledge management, nuclear waste management, service-oriented architecture and cybersecurity. Served as Technical Chair for Artificial Intelligence track in ITEA conference and Co-chair-AI/ML Applications in Radioactive Waste Management track in International Conference on Nuclear Waste Management 2024.
  9. Designed & developed Artificial Intelligence & Bigdata concentration in ECE at the undergraduate (6 courses) and graduate (6 courses) levels focused on machine learning, deep learning, cloud, big data, and visualization. Designed Generative AI courses at undergraduate & graduate level and Quantum machine learning course at the graduate level.
  10. Leading the program in the ECE to provide professional Microsoft certifications to the students in the AI & Bigdata track leading to the student success, bridging the gap between academia and industry. The certifications being offered are AZ900 – Azure Fundamentals, AI900 – AI Fundamentals, DP900 – Data Fundamentals and SC900 – Security Fundamentals. 285 students have passed the certifications.
  11. Served as member of FIU’s Presidential Artificial Intelligence Council and Quality Enhancement Plan (QEP) program focused on educating students in Artificial Intelligence area. The QEP committee is responsible for introducing Foundation AI and Data Science badge program for all FIU students. Member of the CyberFlorida advisory council – consortium of 12 Florida’s public universities enabling active collaboration in the cyber research field. Member of the FIU’s emerging pre-eminent cybersecurity and CIETA program.
  12. Received ASME best conference paper award in Waste Management 2022 conference for research paper titled “Mobile platform for structural health monitoring using Convolution Neural Network”. In Waste Management 2021 conference, paper titled “Waste Information Management System with 2020-21 waste streams and in Waste Management 2019 conference paper titled “Big data framework with machine learning for D&D applications” have received recognition of best in track presentation and distinguished contributions to the advancement of radioactive waste and material management.

Publications

Peer-Reviewed Journals 

  1. Banerjee, B., Chakravarthy, K., Fisher, W., Riley, R., Sabile, E., Sabino, J., Scully, T., Silvas, J., Sinclair, A. E., Soberanis, P., Upadhyay, H., Wells, D., & Werner, J. (2024). Digital Twin: A Quick Overview. ITEA Journal, 45(1). Retrieved from https://itea.org/journals/volume-45-1/digital-twin-a-quick-overview/   Impact Factor: NA 
  2. Aldyaflah, I. M., Zhao, W., Upadhyay, H., & Lagos, L. (2023). The Design and Implementation of a Secure Datastore Based on Ethereum Smart Contract. Applied Sciences, 13(9), 5282. Impact Factor: 2.7 , Citations: 4 
  3. Zhao, W., Aldyaflah, I. M., Gangwani, P., Joshi, S., Upadhyay, H., & Lagos, L. (2023). A Blockchain-Facilitated Secure Sensing Data Processing and Logging System. IEEE Access, 11, 21712-21728. Impact Factor: 3.9, Citations: 14 
  4. Gangwani, P., Joshi, S., Upadhyay, H., & Lagos, L. (2023). IoT Device Identity Management and Blockchain for Security and Data Integrity. International Journal of Computer Applications, 184(42), 49-55. Impact Factor: 3.12, Citations: 4 
  5. Meray, A. O., Sturla, S., Siddiquee, M. R., Serata, R., Uhlemann, S., Gonzalez-Raymat, Denham M., Upadhyay H., Lagos L., Eddy-Dilek C., & Wainwright, H. M. (2022). Pylenm: A machine learning framework for long-term groundwater contamination monitoring strategies. Environmental Science & Technology, 56(9), 5973-5983. Impact Factor: 11.4, Citations: 14 
  6. Gangwani, P., Perez-Pons, A., Bhardwaj, T., Upadhyay, H., Joshi, S., & Lagos, L. (2021). Securing environmental IoT data using masked authentication messaging protocol in a DAG-based blockchain: IOTA tangle. Future Internet, 13(12), 312. Impact Factor: 3.4 , Citations: 49 
  7. Bhardwaj, T., Reyes, C., Upadhyay, H., Sharma, S. C., & Lagos, L. (2021). Cloudlet-enabled wireless body area networks (WBANs): a systematic review, architecture, and research directions for QoS improvement. International Journal of System Assurance Engineering and Management, 1-25. Impact Factor: 2.0, Citations: 12 
  8. Gohel, H. A., Upadhyay, H., Lagos, L., Cooper, K., & Sanzetenea, A. (2020). Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nuclear Engineering and Technology, 52(7), 1436-1442. Impact Factor: 2.7,  Citations: 118 
  9. Gangwani, P., Soni, J., Upadhyay, H., & Joshi, S. (2020). A deep learning approach for modeling of geothermal energy prediction. International Journal of Computer Science and Information Security (IJCSIS), 18(1). Impact Factor: NA , Citations: 43 
  10. Peddoju, S. K., Upadhyay, H., Soni, J., & Prabakar, N. (2020). Natural language processing based anomalous system call sequences detection with virtual memory introspection. International Journal of Advanced Computer Science and Applications, 11(5). Impact Factor: 0.9, Citations: 15 
  11. Peddoju, S. K., Upadhyay, H., & Lagos, L. (2020). File integrity monitoring tools: Issues, challenges, and solutions. Concurrency and Computation: Practice and Experience, 32(22), e5825. Impact Factor: 2.0, Citations: 12 
  12. Upadhyay, H., Gohel, H., Pons, A., & Lagos, L. (2018). Virtual memory introspection framework for cyber threat detection in virtual environment. Adv. Sci. Technol. Eng. Syst. J, 3, 25-29. Impact Factor: NA, Citations: 3 
  13. Upadhyay, H., & Gohel, H. (2017). Design of Advanced Cyber Threat Analysis Framework for Memory Forensics. International Journal of Innovative Research in Computer and Communication Engineering, 5(2), 132-137. Impact Factor: 8.16 , Citations: 6 

Book 

  1. Bhardwaj, T., Upadhyay, H., Sharma, T. K., & Fernandes, S. L. (Eds.). (2023). Artificial Intelligence in Cyber Security: Theories and Applications (Vol. 240). Springer Nature. 

Book Chapters 

  1. Soni J., Sirigineedi S., Vutukuru K. S., Sirigineedi S. C., Prabakar N., & Upadhyay H. (2023). Learning-Based Model for Phishing Attack Detection. In Artificial Intelligence in Cyber Security: Theories and Applications (pp. 113-124). Cham: Springer International Publishing. Citations: 1 
  2. Das, S., Gangwani, P., & Upadhyay, H. (2023). Integration of machine learning with cybersecurity: applications and challenges. Artificial intelligence in cyber security: theories and applications, 67-81. Citations: 2 
  3. Soni J., Gangwani P., Sirigineedi S., Joshi S., Prabakar N., Upadhyay H., & Kulkarni S. A. (2023). Deep Learning Approach for Detection of Fraudulent Credit Card Transactions. In Artificial Intelligence in Cyber Security: Theories and Applications (pp. 125-138). Cham: Springer International Publishing. 
  4. Gangwani P., Perez-Pons A., Joshi S., Upadhyay H., & Lagos L. (2023). Integration of Data Science and IoT with Blockchain for Industry 4.0. In Blockchain and its Applications in Industry 4.0 (pp. 139-177). Singapore: Springer Nature Singapore. Citations: 10 
  5. Gangwani P., Joshi S., Upadhyay H., & Lagos L. (2023). AI-Based Anomaly Detection on IoT Data-Driven Thermal Power Plants for Condition Monitoring and Preventive Maintenance. In Artificial Intelligence in Cyber Security: Theories and Applications (pp. 83-97). Cham: Springer International Publishing. 
  6. Soni, J., Prabakar, N., & Upadhyay, H. (2023). Convolutional Neural Network-Based Cancer Detection Using Histopathologic Images. In Innovations in Machine and Deep Learning: Case Studies and Applications (pp. 287-303). Cham: Springer Nature Switzerland. 
  7. Soni, J., Prabakar, N., & Upadhyay, H. (2023). Deep Learning-Based Efficient Customer Segmentation for Online Retail Businesses. In Benchmarks and Hybrid Algorithms in Optimization and Applications (pp. 147-164). Singapore: Springer Nature Singapore. 
  8. Soni, J., Prabakar, N., & Upadhyay, H. (2023). Quantum Computing-Enabled Machine Learning for an Enhanced Model Training Approach. In Quantum Computing: A Shift from Bits to Qubits (pp. 201-216). Singapore: Springer Nature Singapore. 
  9. Gangwani, P., Bhardwaj, T., Perez-Pons, A., Upadhyay, H., & Lagos, L. (2023). On the Convergence of Blockchain and IoT for Enhanced Security. In Artificial Intelligence in Cyber-Physical Systems (pp. 35-49). CRC Press. Citations: 3 
  10. Gangwani, P., Perez-Pons, A., Joshi, S., Upadhyay, H., & Lagos, L. (2023). Integration of Data Science and IoT with Blockchain for Industry 4.0. In Blockchain and its Applications in Industry 4.0 (pp. 139-177). Singapore: Springer Nature Singapore. 
  11. Soni, J., Prabakar, N., & Upadhyay, H. (2022). Towards Detecting Fake Spammers Groups in Social Media: An Unsupervised Deep Learning Approach. In Deep Learning for Social Media Data Analytics (pp. 237-253). Cham: Springer International Publishing. Citations: 2 
  12. Bhardwaj, T., Mittal, R., Upadhyay, H., & Lagos, L. (2022). Applications of swarm intelligent and deep learning algorithms for image-based cancer recognition. Artificial Intelligence in Healthcare, 133-150. Citations: 17 
  13. Bhardwaj, T., Upadhyay, H., & Lagos, L. (2022). Deep learning-based cyber security solutions for smart-city: application and review. Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems, 175-192. Citations: 9 
  14. Bhardwaj, T., Upadhyay, H., & Sharma, S. C. (2020). An autonomic resource allocation framework for service-based cloud applications: a proactive approach. In Soft Computing: Theories and Applications: Proceedings of SoCTA 2019 (pp. 1045-1058). Springer Singapore. Citations: 11  
  15. Peddoju, S. K., & Upadhyay, H. (2020). Evaluation of IoT data visualization tools and techniques. Data visualization: Trends and challenges toward multidisciplinary perception, 115-139. Citations: 29 
  16. Soni, J., Prabakar, N., & Upadhyay, H. (2020). Visualizing high-dimensional data using t-distributed stochastic neighbor embedding algorithm. Principles of data science, 189-206. Citations: 25 
  17. Gohel, H. A., & Upadhyay, H. (2018). Developing Security Intelligence in Big Data. Knowledge Computing and Its Applications: Knowledge Manipulation and Processing Techniques: Volume 1, 25-50. Citations: 2 

In Press 

  1. Upadhyay H., Soni, J., Quintero W., Joshi S., Lagos L. (2024, March). Artificial Intelligence Based Nuclear Decommissioning Document Summarization. Accepted for publication in the Proceedings of the Waste Management 2024 Conference. Number of typewritten pages: 8, Single – Spaced 
  2. Soni, J., Wainwright H., Upadhyay H., Xu Z., Lagos L. (2024, March). ALTEMIS: Long Term Ground Water Monitoring Using LSTM Algorithm for Anomaly Detection. Accepted for publication in the Proceedings of the Waste Management 2024 Conference. Number of typewritten pages: 7, Single – Spaced 
  3. Noval A., Gutierrez D., Soni J., Upadhyay H., Pons A., Lagos L. (2023, December). Methodologies for Email Spam Classification using Large Language Models. Accepted for publication in the Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE. Number of typewritten pages: 7, Single – Spaced 
  4. Tripathi S., Upadhyay H., Soni, J. (2023, December). Quantum Neural Network Classification Based Cyber Threat Detection in Virtual Environment. Accepted for publication in the Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE. Number of typewritten pages: 6, Single – Spaced 

Works In Progress 

  1. Novel A., Upadhyay H., Lagos L., Soni J., Prabakar N. (2024). Spatial-Temporal Analysis of Groundwater Well Features from Neural Network Prediction of Hexavalent Chromium Concentration. ACS ES&T Water. Impact Factor: 5.3 
  2. Tripathi S., Upadhyay H., Soni J. (2024). Advanced Cardiovascular Health in a Quantum AI-driven Healthcare Framework. IEEE Journal of Biomedical and Health Informatics. Impact Factor: 7.7 
  3. Rojas A., Lagos L., Upadhyay H., Soni J., Prabakar N. (2024). Detection and Identification of Low-Level Nuclear Waste through Computer Vision: A Comparative Analysis. Neural Computing and Applications. Impact Factor: 6.0 
  4. Tripathi S., Upadhyay H., Soni J. (2024). A Quantum LSTM Based Approach to Cyber Threat Detection in Virtual Environment. The Journal of Supercomputing. Impact Factor: 3.3 
  5. Soni J., Prabakar N., Upadhyay H. (2024). DYNA-B: An Enhanced And Dynamic Batch Size Tuning For LSTM Neural Network. The Journal of Supercomputing. Impact Factor: 3.3 
  6. Soni J., Perez-Pons A., Upadhyay H., Tripathi S., Jansen A. (2024). Enhancing Military Cyber Defense: Email Summarization and Classification via LLMs and LSTMs. Military Cyber Affairs. 

Conferences 

  1. Soni, J., Prabakar, N., Upadhyay, H. (2023, November). Vision Transformer-Based Emotion Detection in HCI for Enhanced Interaction. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_8 
  2. Gangwani, P., Perez-Pons, A., & Upadhyay, H. (2023, August). A Comparative Analysis of Trust Management Models for Wireless Sensor Networks. In 2023 IEEE International Flexible Electronics Technology Conference (IFETC) (pp. 1-3). IEEE. 
  3. Gutierrez, D., Ocampo, W., Perez-Pons, A., Upadhyay, H., & Joshi, S. (2023, May). Virtualization and Validation of Emulated STM-32 Blue Pill Using the QEMU Open-Source Framework. In 2023 11th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE. Citations: 2 
  4. Soni, J., Prabakar, N., & Upadhyay, H. (2022, December). Ada-Thres: An Adaptive Thresholding Method To Mitigate The False Alarms. In 2022 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 916-921). IEEE. 
  5. Soni, J., Prabakar, N., & Upadhyay, H. (2022, November). EA-NET: A Hybrid and Ensemble Multi-Level Approach For Robust Anomaly Detection. In Proceedings of 31st International Conference on Software Engineering and Data Engineering (Vol. 88, pp. 18-27). Citations: 3 
  6. Valle, S., Prabakar, N., & Upadhyay, H. (2022, November). Building a Distributed System for Live Virtual Machine Introspection. In Proceedings of 35th International Conference on Computer Applications in Industry and Engineering (Vol. 89, pp. 72-80). Citations: 1 
  7. Soni, J., Prabakar, N., & Upadhyay, H. (2022, October). A Multi-layered Deep Learning Approach for Human Stress Detection. In International Conference on Intelligent Human Computer Interaction (pp. 7-17). Cham: Springer Nature Switzerland. 
  8. Wainwright, H., Meray, A., Xu, Z., Dafflon, B., Gonzalez-Raymat, H., Siddiquee, M., Uhlemann S., Upadhyay H., Denham M., Quiter B., & Eddy-Dilek, C. (2021, December). Advanced Long-term Environmental Monitoring Systems (ALTEMIS) for Sustainable Remediation. In AGU Fall Meeting Abstracts (Vol. 2021, pp. H44C-02). 
  9. Zhao, W., Upadhyay, H., & Lagos, L. (2021, October). Design and implementation of a blockchain-enabled secure sensing data processing and logging system. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 386-391). IEEE. Citations: 3 
  10. Soni, J., Peddoju, S. K., Prabakar, N., & Upadhyay, H. (2021, October). Comparative analysis of LSTM, one-class SVM, and PCA to monitor real-time malware threats using system call sequences and virtual machine introspection. In International Conference on Communication, Computing and Electronics Systems: Proceedings of ICCCES 2020 (pp. 113-127). Springer Singapore. Citations: 14 
  11. Meray, A., Wainwright, H. M., & Upadhyay, H. (2020, December). pyLEnM: Machine learning and analytics toolkit for long term water quality monitoring using a remote sensing network. In AGU Fall Meeting Abstracts (Vol. 2020, pp. A216-0009). 
  12. Lahade, S. V., Namuduri, S., Upadhyay, H., & Bhansali, S. (2020, May). Alcohol sensor calibration on the edge using tiny machine learning (Tiny-ML) hardware. In Electrochemical Society Meeting Abstracts 237 (No. 26, pp. 1848-1848). The Electrochemical Society, Inc. Citations: 5 
  13. Sirigineedi, S. S., Soni, J., & Upadhyay, H. (2020, March). Learning-based models to detect runtime phishing activities using URLs. In Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis (pp. 102-106). Citations: 16 
  14. Bhardwaj, T., Upadhyay, H., & Sharma, S. C. (2020, January). Framework for quality ranking of components in cloud computing: regressive rank. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 598-604). IEEE. Citations: 10 
  15. Bhardwaj, T., Upadhyay, H., & Sharma, S. C. (2020, January). Autonomic resource provisioning framework for service-based cloud applications: a queuing-model based approach. In 2020 10th international conference on cloud computing, data science & engineering (confluence) (pp. 605-610). IEEE. Citations: 13 
  16. Thejas, G. S., Soni, J., Boroojeni, K. G., Iyengar, S. S., Srivastava, K., Badrinath, P., Sunitha BR., Prabakar N., & Upadhyay, H. (2019, December). A multi-time-scale time series analysis for click fraud forecasting using binary labeled imbalanced dataset. In 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS) (pp. 1-8). IEEE. Citations: 18 
  17. Soni, J., Prabakar, N., & Upadhyay, H. (2019, December). Behavioral analysis of system call sequences using LSTM Seq-Seq, cosine similarity and jaccard similarity for real-time anomaly detection. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 214-219). IEEE. Citations: 28 
  18. Bhardwaj, T., Upadhyay, H., & Sharma, S. C. (2019, October). Autonomic resource allocation mechanism for service-based cloud applications. In 2019 international conference on computing, communication, and intelligent systems (ICCCIS) (pp. 183-187). IEEE. Citations: 19 
  19. Soni, J., Prabakar, N., & Upadhyay, H. (2019, July). Feature extraction through deepwalk on weighted graph. In Proceedings of the 15th international conference on data science (ICDATA’19), Las Vegas, NV. Citations: 17 
  20. Peddoju, S. K., Upadhyay, H., & Bhansali, S. (2019, June). Health monitoring with low power IoT devices using anomaly detection algorithm. In 2019 Fourth international conference on fog and mobile edge computing (FMEC) (pp. 278-282). IEEE. Citations: 11 
  21. Soni, J., Prabakar, N., & Upadhyay, H. (2019, May). Deep learning approach to detect malicious attacks at system level: poster. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks (pp. 314-315). Citations: 11 
  22. Joshi, S., Upadhyay, H., Lagos, L., Akkipeddi, N. S., & Guerra, V. (2018, April). Machine learning approach for malware detection using random forest classifier on process list data structure. In Proceedings of the 2nd International Conference on Information System and Data Mining (pp. 98-102). Citations: 35 
  23. Upadhyay, H., Gohel, H. A., Pons, A., & Lagos, L. (2018, April). Windows virtualization architecture for cyber threats detection. In 2018 1st International Conference on Data Intelligence and Security (ICDIS) (pp. 119-122). IEEE. Citations: 6 
  24. Gohel, H. A., Upadhyay, H., Pons, A., & Lagos, L. E. (2017, June). Design of virtualization framework to detect cyber threats in linux environment. In 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 316-320). IEEE. Citations: 5 
  25. Gohel, H., & Upadhyay, H. (2017). Cyber threat analysis with memory forensics. CSI CommunICatIonS, 5. Citations: 7 
  26. Lagos, L., Upadhyay, H., & Shoffner, P. (2013, September). D&D Knowledge Management Information Tool: A Web Based System Developed to Share D&D Knowledge Worldwide. In International Conference on Radioactive Waste Management and Environmental Remediation (Vol. 56024, p. V002T05A004). American Society of Mechanical Engineers. Citations: 2 
  27. Upadhyay, H., Lagos, L., & Roelant, D. (2012). Application of Knowledge Business Framework to Nuclear Decommissioning. Management, 2(5), 171-179. 
  28. Geisler, T., Shoffner, P., Upadhyay, H., & Quintero, W. (2007). Waste information management system: a web-based system for DOE waste forecasting. Citations: 7 
  29. Upadhyay, H., Quintero, W., & Lagos, L. (2020, July). Waste Information Management System with 2019-20 Waste Streams-20491. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  30. Upadhyay, H., Lagos, L., Joshi, S., & Szilagyi, A. (2020, July). Artificial Intelligence Application to D and D-20492. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). Citations: 4 
  31. Quintero, W., Upadhyay, H., & Lagos, L. (2020, July). D and D Research on KM-IT Platform-20494. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  32. Upadhyay, H., Lagos, L., Joshi, S., & Abrahao, A. (2019). Big Data Framework with Machine Learning for D and D Applications-19108. United States. Citations: 8 
  33. Upadhyay, H., Quintero, W., Lagos, L., & Shoffner, P. (2019, July). Waste Information Management System with 2018-19 Waste Streams-19106. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  34. Quintero, W., Upadhyay, H., Lagos, L., & Shoffner, P. (2019, July). Robotics on KM-IT platform-19107. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  35. Upadhyay, H., Quintero, W., Lagos, L., & Shoffner, P. (2018, July). Waste Information Management System with 2017-18 Waste Streams-18302. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  36. Upadhyay, H., Lagos, L., Quintero, W., & Shoffner, P. (2018, July). KM-IT Mobile Platform for D and D-18300. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  37. Upadhyay, H., Lagos, L., Quintero, W., Shoffner, P., & Roelant, D. (2017, July). Waste Information Management System with 2016-17 Waste Streams-17246. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  38. Upadhyay, H., Lagos, L., Quintero, W., Shoffner, P., & DeGregory, J. (2017, July). Application of Robotics Technology to D and D-17249. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  39. Upadhyay, H., Lagos, L., Quintero, W., Shoffner, P., & DeGregory, J. (2016, July). Robotics Technologies on Knowledge Management Information Tool (KM-IT) Platform–16465. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  40. Upadhyay, H., Lagos, L., Quintero, W., Shoffner, P., & Roelant, D. (2016, July). Waste Information Management System with 2015-16 Waste Streams-16463. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  41. Deshon, J., & Upadhyay, H. (2015, July). Best practices mobile application for D and D KM-IT-15712. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). 
  42. Joshi, S., Upadhyay, H., & Lagos, L. (2015, July). Deactivation and decommissioning web log analysis using big data technology-15710. WM Symposia, Inc., PO Box 27646, 85285-7646 Tempe, AZ (United States). Citations: 4 
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