Junsung Hwang
I'm a Computer Science and Applied Mathematics student at Stony Brook University. My interests include algorithm design, computational complexity, and applied mathematics.
Academic Background
A theoretical computer scientist specializing in algorithm design and computational complexity.
Research Statement
My work lies at the intersection of theoretical computer science and applied mathematics. I focus on analyzing algorithmic efficiency and computational complexity with an emphasis on distributed computing systems and parallelization methodologies.
I am particularly interested in developing new mathematical models for understanding the fundamental limitations of computation, drawing from both classical complexity theory and modern algebraic approaches.
My research philosophy centers on theoretical rigor and mathematical precision, with the goal of advancing our understanding of computational boundaries and contributing to the foundational theories that underpin computer science.
Education
B.S. in Computer Science & Applied Mathematics
[Stony Brook University] • [2023 - 2026]
Research Areas
- Algorithmic Complexity Theory
- Distributed Computing Models
- Computational Efficiency Analysis
- Mathematical Optimization
- Theoretical Foundations of Machine Learning
- Formal Methods and Verification
Research Focus
Theoretical exploration at the intersection of algorithm design, computational complexity, and mathematical modeling.
Research Areas
Algorithmic Efficiency in Distributed Systems
Theoretical analysis of algorithmic efficiency in large-scale distributed systems.
Formal Verification of Parallel Algorithms
Developing mathematical models for verifying the correctness of complex parallel algorithms.
Computational Complexity of Optimization Problems
Investigating theoretical boundaries of computational complexity for optimization algorithms.
Algorithmic Efficiency in Distributed Systems
This research develops new mathematical frameworks for analyzing algorithmic efficiency in distributed computing environments. I examine the fundamental limitations of distributed algorithms and establish theoretical bounds on their performance across varying network topologies. My work introduces novel complexity measures that account for communication constraints, asynchronicity, and fault tolerance—extending classical complexity theory to better characterize distributed computation.
Selected Publications
On the Computational Complexity of Distributed Task Allocation
Sample, & Johnson, A. (2023). Journal of Theoretical Computer Science, 45(3), 112-128.
This paper presents a comprehensive analysis of the computational complexity boundaries for distributed task allocation problems. We prove several novel lower bounds and provide matching algorithms that achieve these bounds under various network models and communication constraints.
Formal Verification of Asynchronous Consensus Protocols
Sample., et al. (2022). Proceedings of the International Symposium on Distributed Computing, 234-249.
In this work, we develop a novel formal verification framework for asynchronous consensus protocols. Our approach combines model checking with theorem proving to establish correctness guarantees even in the presence of arbitrary message delays and partial system failures.
My Projects
Practical applications of computer science and mathematics concepts.