I am an Assistant Professor in the Department of Computer Science at California State University, Fresno, where I lead the Cybersecurity & Secure AI (CySAI) Lab.

My research focuses on developing secure, privacy-preserving, and trustworthy intelligent systems at the intersection of cybersecurity, artificial intelligence, data privacy, and distributed computing.

• August 2026 — Publication: Our paper, Embedding-Based Anomaly Detection for Poisoned Client Filtering in Federated Learning, was accepted for presentation at the 2026 IEEE International Conference on Intelligence and Security Informatics (IEEE ISI 2026) in Cambridge, United Kingdom.
• 2026 — Publication: Our paper, Multi-Agent LLM Closed-Loop Optimization for NP-Hard Problems, was accepted for presentation at the IEEE SoutheastCon 2026 in Alabama, US.

• Harsharan Dhillon and Bikash Chandra Singh, “Embedding-Based Anomaly Detection for Poisoned Client Filtering in Federated Learning”, 2026 IEEE International Conference on Intelligence and Security Informatics (IEEE ISI 2026), Cambridge, United Kingdom. Accepted.

• Imran Hasan, Abdullah All Ahhad, Md Habibur Rahman, and Bikash Chandra Singh, “A Learning Framework for Smart Contract Vulnerability and Root Cause Detection.”, Blockchain: Research and Applications, 2026. View publication

• Kiran Nair and Bikash Chandra Singh, “Multi-Agent LLM Closed-Loop Optimization for NP-Hard Problems.”, IEEE SoutheastCon 2026, pp. 1–6. View publication

• Amit Chakraborty, Sandip Roy, Sayyed Farid Ahamed, Eranga Bandara, Bikash Chandra Singh, and Sachin Shetty, “A Unified Blockchain-Based Framework for Decentralized Collaborative Transfer Learning Using Adaptive Incentivization.”, IEEE International Conference on Communications (ICC), 2025, pp. 2689–2694. View publication

• Bikash Chandra Singh, Md Jakir Hossain, Rafael Diaz, Sandip Roy, Ravi Mukkamala, and Sachin Shetty, “Cooperative Local Differential Privacy: Securing Time-Series Data in Distributed Environments.”, CoRR, 2025. View publication

• Bikash Chandra Singh, Peter Foytik, Rafael Diaz, and Sachin Shetty, “TL-ConvLSTM: A Transfer-Learning-Based Convolutional LSTM to Identify and Forecast Traffic in the NextG Environments.”, IEEE Systems Journal, vol. 19, no. 2, pp. 358–369, 2025. View publication

• Privacy-preserving machine learning and federated learning
• Data privacy, local differential privacy, and privacy preferences
• AI for cybersecurity and security of AI systems
• Security and privacy of large language models
• Biometric security and cancelable biometrics
• Blockchain, distributed systems, and supply-chain security
• Big-data analytics

• Trustworthy federated learning under privacy and adversarial threats
• Adaptive privacy-noise calibration using memory-augmented LLMs
• Detection and mitigation of poisoned clients in federated learning
• Secure analysis and repair of LLM-generated code

I currently supervise student research in cybersecurity, privacy, federated learning, biometrics, and secure AI:

• Harsharan Dhillon
• Rodrigo
• Pradeepti
• Milagros
• Lizeth
• Jeremiah

At Fresno State, I teach courses in:

• CSci 158 (Applied Biometric Security)
• CSCi 159 (Systems and Cloud Security)
• CSCi 291T (Advanced Computer Security)
• CSCi 274 (Combinatorial Algorithms)
• CSCi 291T (Blockchain Systems Security)

I welcome collaborations with researchers, students, industry partners, and public agencies working on cybersecurity, secure AI, privacy-enhancing technologies, and trustworthy distributed systems.

• 🎓 Google Scholar
• 🏛️ Fresno State Faculty Profile
• ✉️ Email

Advancing cybersecurity and trustworthy AI through research, education, and collaboration.