Dr Raj Mani Shukla

Raj conducts research in the areas of Trustworthy and Responsible AI, as well as the safety and security of AI applications, with an interest in cross-disciplinary applications such as sustainability, IoT, healthcare, and cybersecurity.

[email protected]

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Background

Raj’s research focuses on the intersection of trustworthy and responsible AI, privacy-preserving AI, safety and security of AI application, Internet of Things, and Cloud/Edge computing. Additionally, Raj engages in interdisciplinary research, utilizing AI for various applications such as healthcare, sustainable and smart cities, cybersecurity, and Internet of Things.  Raj has published many research papers in these areas. To view the updated list of publications, please refer to Raj’s homepage or Google Scholar profile. Raj would be pleased to talk with the students and researchers to work and collaborate on innovative projects.

Prior to joining ARU, Raj worked as a KTP Associate - Data Scientist at the University of Bristol. He completed PhD in Computer Science and Engineering at University of Nevada, Reno, USA. Raj worked as Junior Research Fellow (JRF) at IIT Kanpur. He received a master's degree from NIT Kurukshetra and completed bachelor's from BIET Jhansi.

Raj would be happy to consider applications from prospective PhD students. Visit Computing and Information Science PhD to know more about our PhD program.

Spoken Languages

• English
• Hindi

Research interests

• Large Language and Vision Models
• Trustworthy and responsible AI, Ethical AI
• Privacy-preserving AI
• Safety and Security of AI applications
• AI for Healthcare applications, IoT-based healthcare
• Cyber Security, Networking, anomaly detection, and Software-defined networks
• Internet of Things, Smart Cities, and Industry 4.0
• Cloud and Edge-based application development
• Sustainable cities and societies, Smart Grid, and EV charging

Areas of research supervision

Raj would be happy to consider applications from prospective PhD students. Find out more about our Computing and Information Science PhD program.

Teaching

• Cyber Security and AI Case studies
• Machine Learning and Data Engineering bootcamp
• Machine Learning Techniques
• Neural Computing and Deep Learning
• Principle of Data Mining and Machine Learning
• Cloud Computing
• Machine Learning
• Deep Learning and application

Qualifications

• PhD in Computer Science and Engineering, University of Nevada, Reno, USA
• M.Tech in Instrumentation, National Institute of Technology, Kurukshetra, India
• B.Tech in Electronics and Communication, Bundelkhand Institute of Engineering and Technology, Jhansi, India
• Post Graduate Certificate in Teaching and Learning, (PGCert), Anglia Ruskin University, UK.

Current PhD supervision

• Farah Al-Adwan
• Donnie Mcleod

Memberships, editorial boards

TPC, IEEE International Conference on Consumer Electronics, 2025

TPC, IEEE International Conference on Consumer Electronics – Taiwan, 2024

Member, IEEE Consumer Technology Society’s (CTSoc), Internet of Things (IoT) Technical Committee membership

Member, BCS, The Chartered Institute for IT

Member, IEEE Consumer Technology Society’s (CTSoc) Application-Specific CE for Smart Cities (SMC) Technical Committee membership

TPC, IEEE Consumer Communication and Networking Conference, 2021-Present

TPC, International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2023)

Program Committee member, Digital Theme UK-Ukraine Research Twinning Conference, 2023

TPC, EmergencyComm 2020: The International Workshop on Security, Privacy, and Trust for Emergency Events, co-hosted with SecureComm 2020

Guest Editor, Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System, MDPI Sustainability, September 2022 - January 2024

Guest Editor, Machine Learning for Sustainable Planning and Modelling in Future Smart Transportation System, MDPI Future Transportation, September 2022 - January 2024

Regular reviewer of top-tier journals and conferences: IEEE Transactions on Advanced Topic in Computational Intelligence, IEEE Transactions on Industrial Informatics, IEEE Communication Magazine, IEEE Consumer Communication and Networking Conference

Research grants, consultancy, knowledge exchange

PI, SIROCCO: A Distributed, Trustworthy, and Resilient AI Platform for Wind Farm Cyber Security, Innovate UK, CyberASAP 2024, phase 2

PI, ZEPHYR: Robust and Trustworthy AI Platform for Enhanced Wind Farm Cybersecurity, Innovate UK, CyberASAP 2024, phase 1

Co-I, OTRAND: An AI-powered solution to detect ransomware targeting OT networks, Innovate UK, CyberASAP 2024, phase 1

Co-I, Using observations to predict distress in psychiatric inpatients, ARU QR fund with University of Cambridge

PI, p-CTI – Privacy-Aware Cyber Threat Intelligence Information-Sharing Platform, Innovate UK, CyberASAP 2023, phase 1

PI, Development of Privacy Preserving Techniques for AI-Enabled Applications, ARU QR fund

Selected recent publications

Check the latest publications at: Raj's Google Scholar profile.

Das, T., Shukla, R.M., Rath, S. and Sengupta, S., 2024. Bringing to light: adversarial poisoning detection in multi-controller software-defined networks. IEEE Transactions on Network Science and Engineering, November.

Al-Adwan, F., Shukla, R.M., Abroshan, H., Islam, S. and Al-haddad, R., 2024. From spatial to frequency domain: defending medical image classification against steganography-based adversarial attacks. In: IEEE International Conference on Big Data, Washington DC, USA, December.

Das, T., Shukla, R.M. and Sengupta, S., 2024. Poisoning the well: adversarial poisoning on ML-based software-defined network intrusion detection systems. IEEE Transactions on Network Science and Engineering, November.

Kumar, A., Shukla, R.M. and Patra, A.N., 2024. Fortifying SplitFed learning: strengthening resilience against malicious clients. In: Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA, November.

Das, T., Shukla, R.M. and Sengupta, S., 2022. What could possibly go wrong?: identification of current challenges and prospective opportunities for anomaly detection in Internet of Things. IEEE Network, October.