Qazi, K. and Agrawal, A., 2021, December. A Context-based Crowd Sourcing Tool for Quality Assurance of SNOMED CT. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE.
Qazi, K., 2020, July. Modeling Real-World Load Patterns for Benchmarking in Clouds and Clusters. In International Journal of Advanced Computer Science and Applications. Volume 11, No. 6, 2020 (pp. 1-11).
Agrawal, A. and Qazi, K., 2020, July. Detecting modeling inconsistencies in SNOMED CT using a machine learning technique. In Methods (Elsevier). Volume 179, 2020 (pp. 111-118).
Agrawal, A. and Qazi, K., 2019, November. A Machine Learning Approach for Quality Assurance of SNOMED CT. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 792-798). IEEE.
Qazi, K., 2019, October. Vertelas - Automated User-Controlled Vertical Elasticity in Existing Commercial Clouds. In 2019 IEEE Fourth International Conference on Computing Communication and Security (ICCCS). IEEE. (Runner-up Best Paper Award).
Qazi, K. and Romero, S., 2019, October. Remote Memory Swapping for Virtual Machines in Commercial Infrastructure-as-a-Service. In 2019 IEEE Fourth International Conference on Computing Communication and Security (ICCCS). IEEE.
Qazi, K. and Aizenberg, I., 2018, August. Cloud Datacenter Workload Prediction Using Complex-Valued Neural Networks. In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) (pp. 315-321). IEEE.
Qazi, K. and Aizenberg, I., 2018, July. Towards quantum computing algorithms for datacenter workload predictions. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD) (pp. 900-903). IEEE.
Qazi, K., Li, Y. and Sohn, A., 2014, August. Harnessing Memory Page Distribution for Network-Efficient Live Migration. In 2014 IEEE Intl Conf on High Performance Computing and Communications (HPCC) (pp. 264-267). IEEE.
Qazi, K., Li, Y. and Sohn, A., 2014, June. Workload prediction of virtual machines for harnessing data center resources. In 2014 IEEE 7th International Conference on Cloud Computing (pp. 522-529). IEEE.
Qazi, K., Li, Y. and Sohn, A., 2013, October. PoWER: prediction of workload for energy efficient relocation of virtual machines. In Proceedings of the 4th annual Symposium on Cloud Computing (p. 31). ACM.