I am a second-year PhD student at Stanford University advised by Professor Matei Zaharia. I am a member of the FutureData Systems research group and the Stanford DAWN group. My research is
focused on building systems and infrastructure to accelerate machine learning workloads. I earned a BS in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2017 and an MS in Computer Science from Stanford in 2019 (dual concentration in Systems and Artificial Intelligence). At UIUC I worked with Professor Indranil Gupta in the Distributed Protocols Research Group.
- Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, and Matei Zaharia. Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), Banff, Alberta, Nov. 2020. USENIX Association.
- Si Liu, Peter Csaba Olveczky, Keshav Santhanam, Qi Wang, Indranil Gupta, and José
Meseguer. ROLA: A New Distributed Transaction Protocol and Its Formal Analysis. International Conference on Fundamental Approaches to Software Engineering, Springer, 2018, pp. 77-93.
- Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, and Matei Zaharia. Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training. In 1st Workshop on Distributed Infrastructure, Systems, Programming and AI (DISPA) at VLDB 2020.
- Deepak Narayanan, Keshav Santhanam, Amar Phanishayee, and Matei Zaharia. Efficient Scheduling of DNN Training on Multitenant Clusters. Workshop on MLOps Systems at MLSys 2020.
- Deepak Narayanan, Keshav Santhanam, Amar Phanishayee, and Matei Zaharia. Accelerating Deep Learning Workloads through Efficient Multi-Model Execution. Workshop on Systems for ML and Open Source Software at NeurIPS 2018.