Systems group

Systems group

The Systems Research Group is dedicated to pushing the boundaries of computer systems through innovative and cutting-edge research in distributed computing, ensuring scalable and efficient operations across diverse networks and platforms. Our experts specialize in operating systems, developing robust and reliable infrastructures that serve as the backbone for modern software ecosystems. We are also pioneers in high-performance computing and virtualization, designing advanced solutions that optimize resource utilization, bolster system performance, and enable seamless integration across computing environments.

Students Benefits

Goals

  • High-Impact Publications: We strive to publish groundbreaking findings in top-tier journals, disseminating our innovations in distributed computing, operating systems, high-performance computing, and virtualization. By sharing our cutting-edge research, we aim to influence and guide developments in the broader systems community.
  • Global Conference Engagement: We actively participate in prestigious conferences worldwide, presenting our novel work, fostering collaborations, and exchanging insights with fellow researchers. Through these engagements, we seek to shape the future of computer systems and inspire new avenues for exploration and innovation.

 

Publications:

  • Sabr, O., Kaur, K., & Kaddoum, G. (2025). A secure multi-radio resource scheme using cooperative DRL agents for heterogeneous inter-RAN slicing under hardware impairments.[1] IEEE Internet of Things Journal.
  • Moyeen, M. A., Kaur, K., Agarwal, A., Manzano, S. R., Zaman, M., & Goel, N. (2025). Fed-Reputed: Reputation-aware client selection in hierarchical federated learning for consumer electronics. IEEE Transactions on Consumer Electronics.
  • Mecharbat, L. A., Niar, S., & Ouarnoughi, H. (2025). MARVIN: Monitoring anomalies via railway video insight and neural networks. 2025 International Conference on Smart Applications, Communications and Networking (SmartNets), 1–6.
  • Sadia, Saadat, A., Faheem, Y., Abaid, Z., & Fraz, M. M. (2024). Cloud security in the age of adaptive adversaries: A game theoretic approach to hypervisor-based intrusion detection. Journal of Systems Architecture, 156, 103281
  • Sallam, M., & Kaur, K. (2025).[1] Optimized resource forecasting for carbon-intelligent data centers with TempoSight: A hybrid deep learning approach. IEEE Internet of Things Journal, 12(23), 49884–49903
  • Islam, M. B. E., Faheem, Y., Ahmad, A., & Fraz, M. M. (2024).[1] From measured pH to hidden BOD: Quasi real-time estimation of key indirect water quality parameters through direct sensor measurements. In Lecture Notes in Networks and Systems (Vol. 1055, pp. 209–223). Springer

Stanford Top 2% (2024)

  • Kuljeet Kaur (rank 94,534)

 

Group members:

Dr. Hamza Ouarnoughi

Chair

Dr. Yasir Faheem

Member

Dr. Kuljeet Kaur

Member

Dr. Mehak Khurana

Member