✅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
- UWOC
- Passive RF Components
- UWSN
- Visible Light Communication
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 machine learning and computer vision pipelines for biomedical applications. This includes data annotation (CVAT), image quality enhancement (SwinIR), and advanced segmentation (Mask R-CNN, Keypoint R-CNN, SAM) for analyzing complex bio-medical applications.
Ultrasound Signal Analysis & TinyML Leveraging deep learning (CNNs/Hybrid) to extract patterns, parameters, and characteristic features from complex, low-SNR ultrasound signals. Developing compact, energy-efficient, non-invasive ultrasound sensor modules augmented with Tiny Machine Learning (TinyML).
AI-Assisted Underwater IoT (UIoT) & Optical Communications Architecting and evaluating Underwater Wireless Optical Communication (UWOC) systems. Focus areas include multi-hop Underwater Wireless Sensor Networks (UWSN), relay-based diversity gain, error-correcting codes, and utilizing IMU sensor telemetry for ML-based predictive monitoring.
RF/Microwave Engineering & Hardware Prototyping Designing, simulating, and optimizing radio and microwave frequency passive components. Expertise includes Electromagnetic (EM) simulation modeling and the development of Defected Ground Structure (DGS) circuits for robust communication module integration.
Reach out to collaborate.
- 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.
1: 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.
2: ML aided Medical Image Analysis by leveraging Mask R-CNN/ SAM and Keypoint R-CNN
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 for Keypoint R-CNN. 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 (area, bounding box, MinAreaRect, PCA axes) and to compute the percentage changes between “before” and “after” images, while generating visualizations that overlay these measurements and present bar charts of the changes.
