Haris Ghafoor
MS-PhD in Computational Science and Engineering at Yonsei University, Korea
MS-PhD in Computational Science and Engineering at Yonsei University, Korea
I am a Computational Science and Engineering researcher focused on inverse learning problem modeling, ultrasound synthesis methods, and computational methods for medical imaging.
With a background in Electrical Engineering and expertise in Digital Signal Processing and Machine Learning, I bring a multidisciplinary approach to solving complex computational problems in medical imaging and diagnostics.
Computational Methods in Medical Engineering Lab (CMME)
Working on developing advanced medical image processing methods for segmentation and enhancement using AI-inspired computational approaches.
eMoldino
Led a 5-member team to develop AI-powered tooling monitoring solutions for supply chain optimization. Developed AI models for anomaly detection in production cycles using statistical rules, deep contrastive learning, and autoencoders for robust & accurate quality detection.
Upwork
Providing freelance consulting services in machine learning and AI development. Specialized in time series forecasting, computer vision, and deep learning solutions for various clients globally.
Deep Chain Solutions
Developed global forecasting models for time series, using multitasking techniques for robust performance. Utilized pattern recognition and classification methods to identify production phases in tool-making machine data.
Exploring computational methods for medical imaging and ultrasound synthesis.
Developing mathematical models to solve inverse problems in medical imaging.
Creating novel methods for ultrasound image synthesis and enhancement.
Advancing algorithmic approaches for medical image analysis.
Physics-Guided Ultrasound Segmentation Transformer that leverages a physics-guided simulation pipeline to generate synthetic datasets for foundation model pretraining.
A gradually deeply supervised self-ensemble attention network for IoMT-integrated thyroid nodule segmentation in ultrasound images.
Development of a Network of Stacked Blocks of MLP in which each block is encoded by Recurrent Layer i.e. LSTM in order to accurately capture the different kinds of trends & seasonality in Time Series.
Internet of Things
The integration of deep learning techniques in the Internet of Medical Things (IoMT) has significantly advanced the early detection of life-threatening diseases such as thyroid cancer. Our model achieves a Dice coefficient of 79.85% and 84.27% on DDTI and TN3K datasets, respectively.
Under Submission
A framework that leverages the physics of wave propagation in tissue to supervise the training of a deep learning model for robust ultrasound segmentation. Evaluations on public breast ultrasound datasets achieved significant improvements, with Dice scores of 90.57% on BUSI and 89.82% on UDIAT.