✅About Me
With a Ph.D. in AI Convergence and an M.S. in Electrical Communication Systems, I build intelligent systems that connect the physical world with machine learning. I bring a unique, dual-focused expertise to my work:
- AI & Data Science: Designing end-to-end ML pipelines for medical image processing, geometric analysis, engineer ML pipelines capable of extracting robust features from high-dimensional, low-SNR temporal data, and ultrasound signal classification.
- Hardware & Communications: Prototyping next-generation IoT/UIoT modules (VLC, LoRa, UWOC) and architecting AI-assisted networks, grounded by a strong foundation in RF/microwave design and fabrication.
- AI assisted IoT
- Applied AI for Biomedical Applications
- Visible Light Communication/UWOC
- Passive RF Components
PhD Artificial Intelligence Convergence
Pukyong National University, Korea
M.S. Electrical and Electronic Engineering
Soonchunhyang University, Korea
B.S Telecommunication Engineering
University of Engineering and Technology, Pakistan
Applied AI & Medical Image Processing Designing end-to-end ML and computer vision pipelines (CVAT, SwinIR, Mask/Keypoint R-CNN, SAM) for biomedical applications.
Ultrasound Signal Analysis & TinyML Leveraging deep learning to extract patterns from low-SNR ultrasound signals and developing energy-efficient TinyML sensor modules.
AI-Assisted UIoT & Optical Comm. Architecting UWOC systems, multi-hop UWSNs, relay-based diversity gain, and ML-based predictive monitoring via IMU telemetry.
RF/Microwave Engineering Designing, simulating, and optimizing radio/microwave passive components, including EM simulation and DGS circuit development.
- Non-invasive drug-release monitoring via AI-enhanced ultrasound in gelatin-magnesium gallate hydrogels. Y. S. Park, M. Salman, B. Sridharan, and H. G. Lim. Submitted, 2025.
- Development of ultrasound-based driver alcohol detection system for safety (UDADSS). J. H. Park, M. Salman, and H. G. Lim. Submitted, 2025.
DUI Detection via ML and Ultrasound
This study proposes a new, accurate, and low-cost method for detecting driver intoxication using ultrasonic sensors and machine learning, aiming to improve upon existing Driver Alcohol Detection System for Safety technologies. This research uses a simple, durable 40kHz ultrasonic transducer to detect varying concentrations of alcohol. The resulting signals are then analyzed using five different CNN-based ML models.
Tech Stack: Ultrasonic Sensors, Python, CNNs, Signal Processing
ML-aided Medical Image Analysis (Mask/Keypoint R-CNN & SAM)
I design and implement an end-to-end analysis pipeline for red blood cell imaging using machine learning. I use CVAT to annotate images with bounding boxes, pixel-perfect segmentation masks, and keypoints. I incorporate SwinIR to enhance image quality and fine-tune a Mask R-CNN (and Keypoint R-CNN when needed) on the custom dataset. I also leverage the SAM tool (Segment Anything Mask) for robust segmentation tasks. Additionally, I develop scripts to calculate geometric dimensions and compute statistical percentage changes between “before” and “after” images.
Tech Stack: PyTorch, OpenCV, CVAT, SwinIR, SAM, Data Visualization
