My research interests encompass developing fundamentally influential, principled, data-driven and model-based understanding and enablement of designs of novel systems, technologies and devices; - especially as focused on emerging solid-state storage, memory & compute architectures. With specific technical areas spanning interpretable machine learning, physical/statistical modeling & simulation, reliability science and solid-state electronics - I'm often interested in first-principles and foundational approaches. Most of my work has been at interfaces of science and engineering, and across interdisciplinary teams - while bearing meaningful practical impact on novel designs, technologies and products. With interest in real-world impact of research and pathfinding, I also enjoy detail-oriented perspectives towards enablement - in appreciation of the forest along with its trees.

Additional details on my peer-reviewed research papers and patent filings are available in the research page.

Current research interests:

  • Interpretable Machine Learning, applied on complex system design
  • Prognostics and system health
  • Resilient system design
  • Workload impacts on solid-state storage devices and systems and associated innovations, in coherence with above aspects

Past research work (at points of novelty in respective areas):

  • Phase Change (3-D Xpoint/Optane) memory fundamental device and array physics and statistics, reliability, modeling and technology
  • 3-D NAND Flash memory device physics, processing, characterization & reliability
  • Mesoscopic electron transport physics, nanostructure design
  • A disk-less Beowulf Cluster computing system design/implementation, and a MEMS display technology