About
About Subhasis
Subhasis Dasgupta, Ph.D., is a database-systems researcher and scientific-platform architect at the San Diego Supercomputer Center and UC San Diego. His work spans query processing, heterogeneous databases, distributed systems, scientific workflows, HPC, knowledge graphs, and AI-enabled data discovery. Earlier in his career, he was the first employee of the U.S. cloud startup Kaavo, where he helped build its multi-cloud management platform and establish its India engineering operation.
What I build
My work centers on query processing, indexing, ingestion, and system design for heterogeneous databases and scientific platforms. I am especially interested in problems where the data layer has to span relational stores, graph systems, text indexes, APIs, and reproducible workflows without losing reliability or control.
Current work at UC San Diego and SDSC
At the San Diego Supercomputer Center and UC San Diego, I architect and implement production-oriented scientific data platforms. That includes integrating ingestion pipelines, databases, search systems, knowledge graphs, APIs, containers, workflow engines, and analytical services, as well as tracing latency, correctness, and reliability issues across the application, pipeline, database, storage, container, and compute layers.
I also work hands-on across Python, C++, Java, SQL, PostgreSQL, ClickHouse, Kubernetes, and distributed workflows, with a strong focus on performance analysis, instrumentation, and operational observability.
Startup experience
Earlier in my career, I joined Kaavo as its first employee and later served as Founding Director of the India operation. There I helped build an early application-centric multi-cloud management product, working across architecture, provisioning, configuration, monitoring, releases, and customer troubleshooting.
That experience shaped the way I think about platform work: the useful system is the one that can survive changing requirements, small-team delivery pressure, and the practical constraints of a real product environment.
Research foundations
My doctoral work at the Indian Statistical Institute, under Aditya Bagchi and Chandan Majumder of Jadavpur University, India, focused on an ontology-based access control model for digital libraries. Before and after that, I worked on grid computing, mobile computing, and data systems at Indian academic and industry organizations. Over time, the recurring thread has been the same: make complex data more usable without flattening its structure or semantics.
Selected areas
- Database internals and query processing
- Polystores and heterogeneous data systems
- Scientific data platforms and workflow systems
- Search, graph systems, and knowledge graphs
- Performance engineering, observability, and reliability
- Research-to-production platform design
Leadership, collaboration, and service
I mentor engineers and students in database internals, query processing, cloud automation, data pipelines, platform operations, and applied AI. I also collaborate across computer science, medicine, materials science, social science, and cyberinfrastructure, and I try to keep the technical tradeoffs legible for scientific and sponsor audiences.
Patents and publications
I have co-invented patented polystore ingestion and query-processing technologies, and my publications cover databases, scientific data systems, knowledge graphs, and data-intensive workflows.