Ariful Amin
ML Engineer & Researcher
I build machine learning systems for healthcare and real-world deployment, with 6+ years of industry experience across health-tech and industrial analytics. At Malmö University, my current work focuses on interpretable learning for biomedical time-series, with broader interests in medical imaging, multimodal AI, LLMs, and generative AI.
My technical stack includes C/C++, Python, PyTorch, FastAPI, AWS, and Docker, along with experience in wearable systems and production-grade ML pipelines.
I am currently pursuing a Master’s in Applied Data Science at Malmö University, supported by the Swedish Institute Scholarship for Global Professionals, and previously completed my BSc in Computer Science and Engineering at the University of Dhaka.
Latest News
Dec 2025
Research Assistant at Malmö University
Sept 2025
Digital Bridges Program (2025) at Volvo Group Trucks Technology (Malmö)
Apr 2025
Teaching Assistant (Amanuens) at Malmö University
Mar 2025
Malmö University Stormathon Winner
Sept 2024
Started the Master's in Applied Data Science
Apr 2024
Fully Funded Scholarship - Swedish Institute Scholarship for Global Professionals
Apr 2018
Started my career as a software Engineer at Samsung R&D
Education
Master’s in Applied Data Science, Malmö University Sep 2024 - Jun 2026
Scholarship: Swedish Institute Scholarship for Global Professionals
BSc in Computer Science and Engineering, University of Dhaka Jan 2014 - Jan 2018
Experience
Coal Power Generation Company Bangladesh
Software Programmer
Apr 2022 - Aug 2024
Samsung R&D Institute Bangladesh
Lead Engineer
Jan 2022 - Apr 2022
Samsung R&D Institute Bangladesh
Software Engineer
Apr 2018 - Dec 2021
Projects
Samsung HeartWise
Remote cardiac rehabilitation and smartwatch-based health monitoring.
Interpretable Seizure Prediction
Prototype-based biomedical time-series models for more transparent seizure prediction.
Skin Disease Detection
Multimodal learning that combines dermoscopic images and clinical metadata.
Patient Health Risk
End-to-end ML pipeline with a FastAPI service for risk prediction.
Radar People Counting
Machine learning for noisy real-world radar sensing and counting accuracy.
Research
Current research
Research Assistant at Malmö University on interpretable biomedical time-series learning.
CREME VIP
Radar-based people counting using machine learning for noisy real-world sensing environments.
Focus
Biomedical AI, medical imaging, multimodal learning, LLMs, generative AI, and interpretable ML.
Teaching
Teaching Assistant, Malmö University
Supported lab teaching, problem solving, and student mentoring in programming courses.
Awards
Best Societal Impact Prize
Malmö University Stormathon, 2025
Swedish Institute Scholarship
Swedish Institute, 2024
Icon of the Month
Samsung R&D Institute Bangladesh, 2019
Blogs
Medium
Long-form technical writing and reflections.
Kaggle
Notebooks, experiments, and data storytelling.
Hugging Face
Model-oriented applied machine learning work.
I am actively looking for fully funded PhD and ML Engineer opportunities in machine learning, biomedical AI, medical imaging, and time-series learning.