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Foysal Imtiaz Sabab

CS Graduate · AI Research · Full Stack Dev

foysal.sabab@gmail.com Curriculum Vitae LinkedIn GitHub

RESEARCH INTERESTS

Fields: Machine Learning, Natural Language Processing, Explainable AI, Generative AI

Topics: Large Language Models, Hallucination Detection and Mitigation, AI Reliability and Safety, Natural Language Inference, Low-Resource Language Processing, Trustworthy AI Systems

RESEARCH EXPERIENCE

Undergraduate Research

Feni University   ·   Sep 2025 – Mar 2026

Thesis: An Empirical Analysis of Hallucination Evaluation Metrics in Bangla using LLMs

  • Designed and executed an empirical evaluation framework to assess the logical and semantic stability of Generative AI models including Gemini 2.5 Flash, Gemma 3, and LLaMA 3.2.
  • Investigated hallucination behavior and model reliability using Natural Language Inference (NLI), Named Entity Recognition (NER), and consistency-based evaluation on the BanglaQA dataset.
  • Focused on trustworthiness in low-resource language settings and contributed toward building more reliable and interpretable LLM evaluation baselines.
  • Led a 3-member undergraduate thesis team, coordinated methodology design, experimental workflow, and technical reporting with faculty supervision.

EDUCATION

B.Sc in Computer Science and Engineering    (CGPA: 3.63 / 4.0)

Feni University   ·   Jan 2022 – Mar 2026

Coursework: Operating Systems, Data Structures, Algorithms, Artificial Intelligence, Networking, DBMS

PROJECTS

CV Analyzer

GitHub  ·  Live Demo

  • Developed a serverless AI-powered resume analyzer using ReactJS, React Router V7, TypeScript, and TailwindCSS.
  • Integrated Puter.js to directly access cloud storage, AI, and database capabilities from the frontend without building a separate backend.
  • Focused on clean user interaction, responsive UI, and lightweight AI-assisted resume evaluation workflow.

Facial Recognition System

GitHub

  • Built a real-time facial recognition and user authentication system using Python and OpenCV.
  • Implemented webcam-based image acquisition, face enrollment, identity verification, image preprocessing, and authentication event logging.
  • Added GUI-based interaction to improve usability and system accessibility.

SKILLS SUMMARY

Programming: Python, C++, JavaScript, TypeScript

Machine Learning & AI: Machine Learning, Deep Learning, LLMs, NLP, Model Evaluation

Data & Computing: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Data Preprocessing, EDA

Development & Tools: MERN Stack, Git, PostgreSQL, Linux, Jupyter Notebook

REFERENCE

Md Ismail Siddiqi Emon

PhD Candidate, Morgan State University

Machine Learning Associate, NIST, USA

mdemo1@morgan.edu