M. R. A. Jamaludin

Experience and education

Two degrees, one research post, and the awards and skills that go with them.

Education

2024 - Aug 2026

M.Sc. in Information System

Pukyong National University (부경대학교), Busan, South Korea

CGPA 4.38/4.5. Conferred Aug 2026. Global Korea Scholarship (GKS)

Thesis: Hybrid Quantum-Classical Neural Networks for Skin Lesion Classification with LLM-Powered Explainability

Supervisor: Prof. Chang-Soo Kim

2018 - 2022

B.Eng. (S.T.) in Informatics Engineering

Universitas Islam Negeri Sunan Gunung Djati, Bandung, Indonesia

CGPA 3.78/4.00, Cum Laude. Jabar Future Leaders Scholarship (JFLS)

Thesis: Implementation of the Convolutional Neural Network Algorithm in SIBI Sign Language Alphabet Classification

Research experience

Aug 2024 - Aug 2026

Graduate Researcher

uIT Lab, Pukyong National University, Busan, South Korea

  • Conducted research on trustworthy medical AI and quantum machine learning under Prof. Chang-Soo Kim.
  • Audited the HAM10000 benchmark for lesion-level data leakage and re-evaluated a hybrid quantum-classical model against a matched classical baseline (IEEE Access, 2026).
  • First author of 2 IEEE Access journal articles and 2 conference presentations (1 oral, 1 poster).

Leadership

2020 - 2021

Chief of Partnership and Programs

Google Developer Student Club, UIN Sunan Gunung Djati

  • Led a division coordinating community programs, and built partnerships with student communities and industry organizations nationally and internationally.

Awards and scholarships

2025

Best Paper Award

2025 KSII Spring Conference

For: Minimizing qubit requirements in scalable quantum models: Leveraging QECC error detection principles

2023-2026

Global Korea Scholarship (GKS)

Government of the Republic of Korea. Full graduate scholarship (M.Sc.)

2019

Jabar Future Leaders Scholarship (JFLS)

West Java Provincial Government, Indonesia. 1 year (B.Eng.)

Certifications: TOPIK Level 4 (Score 172), Korean.

Skills and languages

Machine learning

  • Deep learning (PyTorch, Keras)
  • Convolutional neural networks
  • scikit-learn
  • Hugging Face Transformers

Generative AI and LLMs

  • Large language models
  • Retrieval-augmented generation
  • Model explainability

Quantum computing

  • Quantum machine learning (Qiskit, PennyLane)
  • Noise modelling and zero-noise extrapolation

Data and evaluation

  • Pandas, NumPy, Matplotlib
  • Exploratory data analysis and visualisation
  • Data-leakage auditing and benchmark curation

Programming and tools

  • Python, SQL, C#, JavaScript
  • Git, Jupyter Notebook, LaTeX
  • Flask (REST APIs), Google Cloud Platform, Docker basics

Languages

  • Indonesian: Native
  • English: Professional Working Proficiency
  • Korean: TOPIK Level 4 (Score 172)