About Me

I am a PhD candidate in Computer Science at Syracuse University, advised by Prof. Kristopher Micinski. My research aims to lower the barrier to parallel computing in data-intensive domains by combining insights from modern programming languages and database theory.

At the core of my work is Datalog, a declarative query language with deep roots in database systems that has recently gained renewed attention in programming languages and AI. Prior work has shown Datalog’s effectiveness in expressing a wide range of data-intensive applications while delivering both high performance and low code complexity. I argue that by extending both the semantics and the parallel hardware support of Datalog, we can move toward a general-purpose solution for data-intensive programming on parallel hardware, including GPUs and distributed systems.

My research spans three complementary directions: (1) building high-performance Datalog engines that scale efficiently on modern HPC platforms; (2) introducing semantic extensions that enable advanced static analysis and neuro-symbolic reasoning; and (3) developing a unifying theoretical framework that reconciles fragmented Datalog features.

I have published this research in top venues including VLDB, NeurIPS, ASPLOS, CLUSTER, and AAAI.

My opinion on various different programming languages:

“A programming language that doesn’t affect the way you think about programming, is not worth knowing.” — Alan Perlis