Expertise and Research Interest
Artificial Intelligence, Geometric Modeling, Medical Imaging
Ming Ma, Ph.D.
Assistant Professor
Department of Graduate Computer Science and Engineering
Ming Ma, Ph.D.
ming.ma@yu.edu | 646-592-4763 | 205 Lexington Avenue, 7th FL, NYC
Ming Ma, an assistant professor, focuses his research on AI, geometric modeling and medical imaging, and he has published over 40 research papers in leading journals and conferences. Prior to joining the Katz School, he was an assistant professor in the Department of Computer Science at Winona State University.
Ma holds a Ph.D. in computer science from Stony Brook University and has worked as a postdoctoral scholar at Stanford University.
Recent Publications
- 3D vessel-like structure segmentation in medical images by an edge-reinforced network - Medical Image Analysis
- Diabetic and Hypertensive Retinopathy Classification from Retinal Images Using Dual Vision Transformer - IEEE
- MS-HyFS: A novel multiscale hybrid framework with Scalable electrodes for motor imagery classification - Biomedical Signal Processing and Control
- Computational design and additive manufacturing of periodic conformal metasurfaces by synthesizing topology optimization with conformal mapping - Computer Methods in Applied Mechanics and Engineering
- A comprehensive review on GANs for time-series signals - Springer London
- Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network - Physics in Medicine & Biology
- Dosimetric features‐driven machine learning model for DVH prediction in VMAT treatment planning - Medical physics
- TL-MSE2-Net: Transfer learning based nested model for cerebrovascular segmentation with aneurysms - Pergamon
Conference Chair/Program Committee/Organizing Committee
- Conference Session Chair: IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2025)
- Conference Registration Chair: IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2025)
- Technical Program Committee Member: International Conference on Biomedical Engineering and Technology (ICBET 2025)
- Conference Session Chair: International Conference on Digital Data Processing (DDP 2024)
- Program Committee Member: International Conference on Neural Information Processing (ICONIP 2024)
- Program Committee Member: AAAI Conference on Artificial Intelligence (AAAI 2022)
- Technical Program Committee Member: International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health (CyberLife 2019)
- Organizing Committee Member: Summer School on Embedded System Design (2011)
- Organizing Committee Member: IEEE Youth Conference on Information, Computing and Telecommunications (2010)
Teaching Experience
- Numerical Methods
- Capstone
- Algorithms and Data Structures
- Computer Systems
- Algorithms and Problem-Solving I
- Algorithms and Problem-Solving II
- Digital Image Processing
- Principles of Programming Languages
- Deep Learning
- Cybersecurity
- Computer Science Research Seminar
Student Work
Research Reflects Human-Centered Vision for Computer Science
Presentations offered a glimpse into a community of emerging engineers and data scientists.
Research Reflects Human-Centered Vision for Computer Science
At the Spring 2025 Graduate Computer Science and Engineering Research forum, the future of technology wasn’t merely on display—it was actively being built. In their research presentations, Katz School students showcased the kind of ingenuity, technical depth and ethical awareness that are not only rare in graduate education but transformative in the real world.
AI Tool Could Make Colon Cancer Detection Faster, More Reliable
Colorectal cancer remains one of the deadliest forms of cancer worldwide.
AI Tool Could Make Colon Cancer Detection Faster, More Reliable
The key to reducing its toll is early detection—specifically, the identification and removal of precancerous polyps during routine colonoscopies. However, polyp detection is an arduous task. Their varying size, shape, color and texture, along with visual obstructions like reflections and bowel contents, make them easy to miss.
To address this challenge, Katz School researchers have developed PolypSEAG-Net—a novel deep learning model that enhances polyp segmentation in colonoscopy images. Their work, recently presented at the ACM/IEEE International Conference on Connected Health, will contribute to the advancement of medical image analysis and colorectal cancer detection.
Future Scientists, Tech Leaders Take Center Stage at Graduate Research Symposium
Each project reflected the school’s mission.
Future Scientists, Tech Leaders Take Center Stage at Graduate Research Symposium
At its annual Graduate Symposium on Science, Technology and Health, the Katz School of Science and Health showcased the next generation of scientific and technological innovators, where students presented pioneering research designed to improve lives and tackle global challenges. The symposium highlighted projects across disciplines—from artificial intelligence and biotechnology to occupational therapy and digital marketing—demonstrating the Katz School’s commitment to advancing knowledge and preparing students to lead in a rapidly evolving world.
Research Reflects Human-Centered Vision for Computer Science
Presentations offered a glimpse into a community of emerging engineers and data scientists.
Research Reflects Human-Centered Vision for Computer Science
At the Spring 2025 Graduate Computer Science and Engineering Research forum, the future of technology wasn’t merely on display—it was actively being built. In their research presentations, Katz School students showcased the kind of ingenuity, technical depth and ethical awareness that are not only rare in graduate education but transformative in the real world.
AI Tool Could Make Colon Cancer Detection Faster, More Reliable
Colorectal cancer remains one of the deadliest forms of cancer worldwide.
AI Tool Could Make Colon Cancer Detection Faster, More Reliable
The key to reducing its toll is early detection—specifically, the identification and removal of precancerous polyps during routine colonoscopies. However, polyp detection is an arduous task. Their varying size, shape, color and texture, along with visual obstructions like reflections and bowel contents, make them easy to miss.
To address this challenge, Katz School researchers have developed PolypSEAG-Net—a novel deep learning model that enhances polyp segmentation in colonoscopy images. Their work, recently presented at the ACM/IEEE International Conference on Connected Health, will contribute to the advancement of medical image analysis and colorectal cancer detection.
Future Scientists, Tech Leaders Take Center Stage at Graduate Research Symposium
Each project reflected the school’s mission.
Future Scientists, Tech Leaders Take Center Stage at Graduate Research Symposium
At its annual Graduate Symposium on Science, Technology and Health, the Katz School of Science and Health showcased the next generation of scientific and technological innovators, where students presented pioneering research designed to improve lives and tackle global challenges. The symposium highlighted projects across disciplines—from artificial intelligence and biotechnology to occupational therapy and digital marketing—demonstrating the Katz School’s commitment to advancing knowledge and preparing students to lead in a rapidly evolving world.
