- Paper published: 'The Call Graph Chronicles: Unleashing the Power Within', ESEC/FSE 2023
- Paper published: 'SecBench.js: An Executable Security Benchmark Suite for Server-Side JavaScript', International Conference on Software Engineering 2023
- Paper published: 'A Tale of Frozen Clouds: Quantifying the Impact of Algorithmic Complexity Vulnerabilities in Popular Web Servers', 2022
- Paper published: 'Be SMART, Save I/O: A Probabilistic Approach to Avoid Uncorrectable Errors in Storage Systems', IEEE Cluster 2022
- Paper published: 'Machine Learning for Data Transfer Anomaly Detection', SC 2020
Research Experience
- Ph.D. Candidate at CISPA Helmholtz Center for Information Security, 2021-present
- Involved in multiple research projects such as quantifying CPU-based DoS attacks in web servers, designing and implementing a probabilistic framework based on machine learning to detect uncorrectable bit errors on disk, etc.
Education
- Ph.D. in Computer Science, CISPA Helmholtz Center for Information Security, 2021-present, Advisor: Cristian-Alexandru Staicu
- M.Sc. in Computer Science and Engineering, University of Nevada, Reno, 2019-2020, Advisor: Engin Arslan
- B.Sc. in Computer Science and Engineering, Bangladesh University of Engineering & Technology, 2012-2017
Background
Research interests include program analysis, software engineering, programming languages, and machine learning. The goal is to secure the open-source ecosystem by building program analysis tools to enhance programmer productivity and uncover new security vulnerabilities. Current research focuses on developing machine learning models to improve Static Call Graph techniques for Server-side JavaScript applications.