Researcher · Builder · GKS Scholar

Reyad
Hossain.

Connecting intelligent algorithms with efficient hardware for real-world vision systems.

CURRENT FOCUS
Computer Vision   /   Edge AI   /   FPGA
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M.S. Researcher · Neuromorphic Chip Design Laboratory · Inje University, South Korea

RESEARCH / SYSTEMS2026
EDGE
INTELLIGENCEAI × FPGA
VISION
PIPELINE
RTL / HDL
NEUROMORPHIC
REAL-TIME
GKS 2024Government scholarship
Inje UniversityMaster's research
Best PaperUCWIT 2025 Award
5 yearsPrior software engineering experience
01 / About me

Research at the intersection
of AI and hardware.

An interdisciplinary focus on practical intelligent systems, careful experimental design and efficient computation.

I'm Reyad Hossain, a Global Korea Scholarship (GKS-G 2024) recipient and Master's researcher in Nanoscience and Engineering at Inje University, Republic of Korea.

At the Neuromorphic Chip Design Laboratory (NCDL), I investigate computer vision, FPGA-assisted image processing, lightweight AI models and neuromorphic circuits. My current flagship work explores YOLO-based printed circuit board defect detection combined with resource-efficient streaming hardware preprocessing.

Before graduate research, I spent approximately five years working in software engineering. That experience continues to shape how I build systems, analyze results, and think about deployment constraints. I'm interested in research collaborations and future PhD opportunities.

Based in

Gimhae, South Korea

Department of Nanoscience and Engineering · Inje University

Research laboratory

Neuromorphic Chip Design Lab

Hardware/software co-design, intelligent vision and circuit architectures

Open to

Academic collaboration

PhD research discussions · Computer vision · Edge AI · FPGA acceleration

02 / Selected work

Research projects.

From dataset reliability and deep learning to streaming RTL architectures and neuromorphic computing.

03 / Scholarship

Research & publications.

Clear distinctions between accepted work, presentations, submitted thesis research and ongoing manuscripts.

04 / Background

Experience & education.

2024 — Present

Graduate Researcher · Inje University

Neuromorphic Chip Design Laboratory (NCDL) · Gimhae, South Korea

  • YOLO-based automated PCB defect detection and leakage-aware evaluation
  • Verilog RTL design, FPGA synthesis and ModelSim verification
  • Neuromorphic vision and lightweight hardware-aware AI research
2024 — Present

Master's in Nanoscience and Engineering

Inje University · GKS Graduate Scholarship

  • Master's thesis manuscript submitted September 2026
  • Focus on FPGA-assisted Edge AI, computer vision and intelligent circuits
Earlier career

Software Engineer

Bangladesh · Approximately five years of professional experience before graduate study

Undergraduate

B.Sc. in Computer Science and Engineering

World University of Bangladesh · Graduation year not provided

05 / Recognition

Awards & recognition.

★

Best Paper Award · UCWIT 2025

Recognized for research presented at the Ubiquitous Computing and Web Information Technology (UCWIT) conference, Gimhae, South Korea, December 2025. Associated with Inje University.

✦

Global Korea Scholarship · 2024

Government of the Republic of Korea scholarship for graduate study at Inje University.

07 / Capabilities

Technical toolkit.

A focused set of tools and research areas used across software, AI and digital hardware.

AI & Computer Vision

  • Deep Learning · PyTorch
  • Object Detection · YOLO
  • Machine Learning · XGBoost
  • Image Processing
  • Dataset Quality & Evaluation

FPGA & Hardware

  • Verilog HDL · Digital Design
  • Intel Cyclone IV E
  • Quartus Prime · ModelSim
  • UART Verification
  • CMOS · Neuromorphic Computing

Research & Engineering

  • Python & Research Automation
  • Experiment Reproducibility
  • Edge AI & Embedded Systems
  • Technical Writing
  • Hardware / Software Co-design
08 / Connect

Let's explore what's possible together.

I'm interested in academic collaboration, future PhD opportunities, and conversations about computer vision, FPGA acceleration, Edge AI and intelligent hardware.

Email me ↗LinkedIn ↗Google Scholar ↗GitHub ↗