• Part of the AWS AI Fundamental Research Organization.
• Working on data systems and semantic abstractions that help AI agents find, understand, and use data and organizational knowledge.

• Interned with the Learned Systems Group within Amazon Redshift.
• Developed ML models for workload tail latency prediction on Redshift Serverless, enabling improved cost-efficient scaling under production workloads.

• Interned with the Learned Systems Group within Amazon Redshift.
• Designed and evaluated techniques to accelerate recurring Redshift workloads and improve latency for enterprise data warehousing customers.

• Interned remotely with the US-based Intel Cloud Enterprise Solutions Group.
• Explored solutions for leveraging persistent memory technology for key-value stores.

• Helped a prominent organization of over 7000 employees redefine its strategic vision after a major leadership change and shifts in market conditions.
• Contributed to corporate governance restructuring recommendations presented directly to the CEO of the client company.
• Joined an international team as the domestic expert to help an organization rethink the staffing and work allocation paradigm in one of its most business-critical units.

• Contributed to a software suite which analyzed the product data of an international retail client to provide actionable business insights.
• Designed and developed a machine learning tool that facilitates sentiment analysis of intra-organizational feedback comments.

Massachusetts Institute of Technology
Doctor of Philosophy (PhD)
Electrical Engineering and Computer Science

• Research Group: Data Systems Group
• Advisor: Prof. Tim Kraska
• Thesis: Diagnosing and Managing Performance in Data Systems

Massachusetts Institute of Technology
Master of Science (SM)
Electrical Engineering and Computer Science

• Research Group: Data Systems Group
• Advisor: Prof. Tim Kraska
• Thesis: Rethinking Update-in-Place Key-Value Stores for Modern Storage

Princeton University
Bachelor of Science in Engineering (BSE), Summa Cum Laude
Electrical Engineering

• Research Group: MRM Research Group
• Advisor: Prof. Margaret Martonosi
• Thesis: Challenges and Opportunities in Heterogeneous Parallelism