I am a Staff Engineer at Samsung Semiconductor (Bangalore, India), where I work in the Advanced R&D Team under the Memory Solutions Group. I’m also a member of Samsung’s Global Open eco-System Team (GOST). I currently work on CXL memory pooling and memory tiering solutions to enable the deployment of Samsung’s CMM-X hardware.

In the past I worked on data grouping problems and data placement technologies involving SSDs, specifically Flexible Data Placement (FDP) and its integration to various application stacks (ex. CacheLib). I have also worked on disaggregated storage systems and distributed systems, specifically researching the topics of data replication, state machine replication (consensus algorithms - raft), flexible node scaling and failure recovery, etc. I have also worked on workload analysis, specifically focusing on using machine learning to perform workload characterisation.

Prior to joining Samsung full time in 2020, I completed my Masters in Mathematics and Bachelors in Electrical and Electronics Engineering from BITS Pilani, Goa. During my time there I worked on the research areas of Mathematical Modelling and Disease Dynamics (stability analysis) under the guidance of Prof. Amit Setia

I enjoy teaching and I strongly believe in free access to information. In the short break I had during the pandemic between graduating from college and starting full-time at Samsung, I put up Mathematics videos on my YouTube Channel based on the knowledge I accrued in college. In the coming years, I hope to spend more time on teaching Mathematics and Computer Science!
In my free time I like to play chess, table tennis, watch football, read (mostly fiction) and indulge in art and writing as well. I also enjoy taking long walks and listening to music.

Research Interests

My research interests broadly lie in using memory and storage systems to buld high performance large scale systems. I often make use of mathematical modeling to create models and simulators to better understand the systems I work on. I am also interested in researching and improving the power efficiency and sustainability of large scale datacenter systems.