cv

PhD

Tulane University

Computer Science

• CMPS 6370 Data Visualization
• CMPS 6662 Human-Computer Interaction

Master

University of California, Irvine

Software Engineering

• SWE 211 Software Engineering
• SWE 212 Analysis Programming Language
• SWE 214 Software Analysis
• SWE 215 Dynamic Analysis, Testing, Debugging
• STATS 201 Stat Meth Data I

Bachelor

The Ohio State University

Computer Science and Engineering

• CSE 3231 Software Engineering
• CSE 3232 Software Requirement Analysis
• CSE 5236 Mobile Application Development

Breaking Strong Encapsulation: A Comprehensive Study of Java Module Abuse

IEEE/ACM

Conducted the first empirical study on Breaking Strong Encapsulation (BSE) in Java Platform Module System (JPMS). Collected and analyzed 4,079 GitHub issues to identify symptoms, abuse sources, and solutions. Our findings reveal a tension between module developers enforcing encapsulation and users attempting to break it, highlighting the need for effective mitigation strategies.

A New Vehicle Distance Measure Method Based on the License Plate with Image Edge Detection and Color Recognition in the Complex Environment

IEEE

This paper presents a method for detecting license plates and measuring the distance between vehicles using image processing techniques. It employs edge detection and color recognition, along with least square method error correction, to achieve a mean square error of 0.0428, significantly lower than the baseline.

BEAD - Breaking Encapsulation Abuse Detector

Developed an automatic detection tool leveraging static analysis to identify encapsulation abuse instances in Java projects. BEAD scans JDK source code to extract module details and analyzes Java programs to extract abuse information from reflection and compile-time invocations. The tool reports abuse instances based on combined module and package information, helping developers address Breaking Strong Encapsulation (BSE) issues in Java Platform Module System (JPMS).

• Java
• Static Analysis
• Module Systems
• Reflection Analysis
• Compile-time Analysis

Breaking Strong Encapsulation (BSE): A comprehensive Study of Java Module Abuse

Conducted an empirical study on identifying and characterizing the Breaking Strong Encapsulation(BSE) threats in Java Platform Module System(JPMS). Collected a comprehensive dataset containing 4,079 GitHub issues from various perspectives, including symptoms, abuse sources, desired functionalities, and solutions. Highlighted the need for both practitioners and researchers to develop effective strategies to mitigate BSE problems.

• Java Platform Module System
• Software Architecture
• Software Engineering

Empirical Study of Software Architectural Modularization in Java Systems

Conducted ecosystem-aware analysis within architectural decay in modules and evolution of Java modular frameworks. Rejuvenated module decay by using DARCY, an automatic architectural inconsistency detection approach, to perform a comparative study on over-exposure problems existing in Java frameworks.

• Java Platform Module System
• Static Analysis
• Software Architecture

Empirical Study of Dark Patterns Impacts on Blind Mobile Users

Conducted an empirical study with 135 Android apps and recorded 459 dark patterns existing on those Mobile apps. Categorized detected dark patterns into five types: nagging, obstruction, sneaking, interface interferences, and forced action. Analyzed Google TalkBack's mechanics and evaluated the detection issues of dark patterns from the code level. Classified dark patterns into three levels based on the impacts of blind users.

• Software Accessibility
• Mobile Software
• Human-Computer Interaction
• Software Design

Project Lily - New Architecture & Technology Stack fo Every Voice Engaged(EVE) Company

Implement EVE's new architecture and technology stack under Spring Boot framework and Twilio within AWS service to host microservices for features like invitation, registration, and reminder emails.

• Microservice Implementation
• Back-end Development

A New Vehicle Distance Measure Method Based on the License Plate with Image Edge Detection and Color Recognition in the Complex Environment

Conducted a study on developing a method to optimize existing license plate detection and vehicle distance measurement. Combined edge detection and color detection techniques for the plate detection optimization. Employed the least square method and error correction to measure the distance between vehicles, and sampled results randomly to correct errors. Paper published on EIECS 2021

• License Plate Detection
• Vehicle Distance Measurement
• Digital Image Processing