Dr. K Naveen Kumar is a dedicated researcher in the field of Artificial Intelligence (AI) and Machine Learning (ML). Currently, he is a Postdoctoral Research Associate in Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Masdar City, Abu Dhabi, UAE. He has graduated with PhD in the Department of Computer Science and Engineering at the prestigious Indian Institute of Technology Hyderabad (IIT Hyderabad). Previously, he was a Master's student in Computer Science and Engineering from IIT Hyderabad. He holds a Bachelor's degree in Computer Science and Engineering from the renowned Indian Institute of Information Technology Vadodara (IIIT Vadodara) Gujarat in 2018. His research interests encompass several critical areas, including security for federated learning, adversarial machine learning, AI for healthcare, scene perception, and path planning for autonomous vehicles, particularly in adverse weather conditions. His contributions to the field are evident through his publications in reputable venues, such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computer Vision Patter Recognition (CVPR) conference, Elsevier Pattern Recogniton, IEEE Transactions on Information Forensics and Security, Elsevier Artificial Intelligence in Medicine, IEEE Transactions on Intelligent Transport Systems, among others. He is the receipient of Excellence in Research Award from the Deptartment of CSE, IIT Hyderabad in 2024. He also served as an external reviewer for esteemed conferences like IEEE S&P, and USENIX and journals like IEEE Transactions on Information Forensics and Security, Neurocomputing, IEEE Neural Networks and Learning Systems. His dedication extends to the practical deployment of AI solutions by developing end-to-end AI solutions and ensuring their effective deployment on edge devices, contributing significantly to the advancement of AI technologies.
1. Security for Privacy-Preserving Machine Learning (Federated Learning) with Adversarial and Defender Perspectives
2. Developing Secure and Private Vision LLMs for Medical AI Applications
3. Ensuring Trustworthy Federated Learning through Verifiability, Auditability, and Mitigability
4. Enhancing Autonomous Vehicle Technology in Transitional Weather Conditions
5. Traffic Congestion Forecasting and Estimation using Aerial Video Analysis
- [Feb 2025] : Our work on Fortifying
Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client
Contribution accepted at CVPR 2025.
- [Jan 2025] : Invited as Guest of Honor at the International Conference on Intelligent Systems and Computational Networks (ICISCN 2025) at Bidar, Karnataka, India.
- [Dec 2024] : Successfully Defended my PhD thesis titled Navigating Adversarial Attacks and Defense Mechanisms in Federated Learning: A Dual Perspective
Approach.
- [Dec 2024] : Our two papers accepted at IEEE Transactions on Information Forensics and Security & Elsevier Artificial Intelligence in Medicine.
[Oct '23 - Mar '24]. Worked on optimized defense against poisoning attacks in federated learning for medical image classification.
[Jan '23 - July '23]. Worked on optimized model poisoning attack in federated learning.
[May '22 - Sep '22]. Worked on mitigating the data poisoning attacks in federated learning using a precision-guided approach.
[Jan 2022 - Dec 22]. Worked on a non-convex optimization approach to mitigate data poisoning attacks in federated learning.
Datasets Developed
Developed by Dr. K Naveen Kumar
Last updated in April 2025
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