Md Jannatul Baki Showmik

Title: Enhancing Blockchain Efficiency via Median Rule Description: Designed and implemented a distributed blockchair architecture using a lightweight Synchronization Resource Model to improve scalability and performance. Reduced communication and storage overhead through multithreading and optimized synchronization, integrating Merkle Hash Trees for secure transaction verification. Tech Stack: Java, Python, Socket Programming, GitHub, Version Control, Blockchain, Multithreading, Merkle Hash Forest

Title: Knowledge Graph Verbalization Description: Built an enhanced knowledge graph verbalization system by extending the OWL2NL framework to generate more natural and fluent language output. Reduced redundancy through optimized triple aggregation (subjects/verbs) and developed an Angular-based UI to visualize ontology verbalizations. Tech Stack: GitHub, Java, JavaScript, Semantic Web, AngularJS, Natural Language Processing (NLP), OWL2NL Framework, Trello

At data edge, I worked on developing and maintaining ASP.NET-based enterprise banking applications, focusing on system reliability, backend performance, and database optimization. I was responsible for debugging and resolving client-reported production issues to ensure smooth application functionality. I designed and optimized SQL queries, performed CRUD operations on large-scale relational databases, and supported database maintenance to maintain data accuracy and integrity.

As a Teaching Assistant at AIUB, I supported database and programming courses by conducting and assisting in DBMS lab sessions. I guided students in SQL, database design, and normalization while helping them strengthen their problem-solving skills through hands-on exercises. I also assisted in evaluating lab assignments and provided structured technical feedback, contributing to improved learning outcomes and practical understanding of database systems.

Title: Emotion Detection from Speech Signals using Voting Mechanism on Classified Frames Description: Developed a speech emotion recognition system using Logistic Model Tree (LMT) and MFCC feature extraction, Applied majority voting for frame-level to utterance-level prediction and achieved ~70% accuracy on Emo-DB and RAVDESS datasets. Tech Stack: Python, Weka, Logistic Model Tree, Feature Extraction, Data Analysis, Data Classification

Master’s Major in Software Engineering Focused on advanced software development methodologies, system architecture, and scalable application design. Gained strong expertise in object-oriented programming, distributed systems, database management, software quality assurance, and agile development practices, with emphasis on building reliable, maintainable, and high-performance software systems.

Bachelor’s in Computer Science & Engineering Built a strong foundation in programming, algorithms, data structures, database systems, and software development. My bachelor’s thesis focused on Artificial Intelligence and data analysis, where I worked on machine learning techniques, data preprocessing, feature extraction, and model evaluation.