Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Focuses
AI-Powered Curation for the Modern Mind. Harnessing prompt engineering and chain-of-thought reasoning to turn information overload into structured insight. 
Semantic Search
Uncover hidden connections across scientific literature.
publications
Identifying an Optimal Machine Learning Generated Image Marker to Predict Survival of Gastric Cancer Patients
Published in Proceedings of SPIE, Medical Imaging 2022: Computer-Aided Diagnosis, 2022
This study investigates the feasibility of identifying and applying a novel quantitative imaging marker, generated through machine learning techniques, to predict the survival of gastric cancer patients. The approach aims to enhance prognostic accuracy and assist in personalized treatment planning.
Recommended citation: Pham, H., Jones, M., Gai, T., Islam, W., Danala, G., & Zheng, B. (2022). "Identifying an Optimal Machine Learning Generated Image Marker to Predict Survival of Gastric Cancer Patients." In Proceedings of SPIE, Vol. 12033, Medical Imaging 2022: Computer-Aided Diagnosis. https://doi.org/10.1117/12.2611788
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Deep Learning-Based Rectum Segmentation on Low-field Prostate MRI to Assist Image-guided Biopsy
Published in Proceedings of SPIE, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 2023
This study presents a deep learning approach for automatic rectum segmentation in low-field (58–74 mT) prostate MRI scans. Utilizing a U-Net architecture, the model achieved a Dice similarity coefficient of 0.89, demonstrating its potential to enhance the accuracy and efficiency of image-guided prostate biopsies in low-resource settings.
Recommended citation: Pham, H., Le, D. B. T., Sadinski, M., Narayanan, R., Nacev, A., & Zheng, B. (2023). "Deep Learning-Based Rectum Segmentation on Low-field Prostate MRI to Assist Image-guided Biopsy." In Proceedings of SPIE, Vol. 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling. https://doi.org/10.1117/12.2654511
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Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency
Published in Journal of Electronic & Information Systems, 2023
This study introduces a reinforced active learning framework for semantic segmentation, integrating techniques such as Dueling Deep Q-Networks, Prioritized Experience Replay, and Noisy Networks. The approach addresses dataset imbalances and annotation efficiency, demonstrating improved segmentation performance with reduced labeling efforts.
Recommended citation: Han, D., Pham, H., & Cheng, S. (2023). "Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency." Journal of Electronic & Information Systems, 5(2), 45–60. https://doi.org/10.30564/jeis.v5i2.6063
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Non-Iterative Cluster Routing: Analysis and Implementation Strategies
Published in Applied Sciences (MDPI), 2024
This paper introduces a non-iterative cluster routing technique for capsule networks, offering enhanced performance with fewer parameters compared to traditional iterative methods. The approach maintains the part–whole relationship and demonstrates robust generalization to novel viewpoints.
Recommended citation: Pham, H., & Cheng, S. (2024). "Non-Iterative Cluster Routing: Analysis and Implementation Strategies." Applied Sciences, 14(5), 1706. https://doi.org/10.3390/app14051706
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Circuit Lab
Undergraduate course, University of Oklahoma, Electrical and Computer Engineering Department, 2020
Teaching Assistant — Assisted students in hands-on electronic circuit lab sessions covering topics such as op-amps, logic gates, transistors (BJT), and oscilloscope usage. Guided experiments, clarified theoretical concepts, ensured adherence to lab procedures, and graded lab reports.
Linear Algebra
Graduate course, University of Oklahoma, Electrical and Computer Engineering Department, 2023
Teaching Assistant:
- Conducted recitations and office hours, clarifying problem-solving techniques and theoretical foundations.
- Graded assignments, quizzes, and exams; provided detailed feedback to improve student understanding.
Microcontroller
Undergraduate course, University of Oklahoma, Electrical and Computer Engineering Department, 2024
Teaching Assistant — Assisted students in programming and debugging embedded systems using the ARM Cortex-M3 based LPC1762 microcontroller.