Education

Yashbir Singh completed his Bachelor’s in Pharmaceutics and Drug Design from M.J.P. Rohilkhand University, Bareilly, India, followed by a Master’s in Bioinformatics from the Indian Institute of Information Technology, Allahabad. He then earned his PhD in Biomedical Engineering from Chung Yuan Christian University, Taiwan. After completing his postdoctoral work in AI Radiology at Mayo Clinic, he continued his research at the same institution as a Postdoctoral Fellow in AI Radiology. 🎓🔬

Experience

Yashbir Singh has held various prestigious positions, including visiting faculty at Harvard Medical School and a medical scientist at WVU Medicine, West Virginia. He has also been a junior research fellow at IIT Mandi, India, and a teaching assistant at IIIT Allahabad, India, and Chung Yuan Christian University, Taiwan. His diverse experiences across academia and clinical environments have significantly shaped his research in medical imaging and AI. 🌍👨‍⚕️

Research Interest

Dr. Singh’s research interests lie in the intersection of artificial intelligence, medical imaging, and oncology. He has pioneered the use of algebraic topology-based machine learning for predicting clinical outcomes, specifically in liver diseases like primary sclerosing cholangitis. His current work focuses on developing deep learning frameworks for early cancer detection, integrating machine learning with topological data analysis, and improving patient-specific diagnostic tools. 🔍💻🧬

Award

Yashbir Singh has received several prestigious awards, including the 2024 Research Award from RSNA and Best Paper Presenter at IEEE CCWC 2022. His work has been recognized for its innovation in AI-driven medical imaging and clinical applications. In addition, he has received Best Reviewer Awards from ICSPIS and the 2020 Medical Imaging Best Paper Award from SPIE. These accolades highlight his leadership in the field of AI-based medical research. 🏆📜

Publication Top Notes:

Yashbir Singh has contributed to over 20 high-impact publications. Some notable works include:

Decoding ChatGPT: a taxonomy of existing research, current challenges, and possible future directions

Mitigating bias in radiology machine learning: 1. Data handling

Mitigating bias in radiology machine learning: 2. Model development

The future of gpt: A taxonomy of existing chatgpt research, current challenges, and possible future directions

A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Identification of Novel Abiotic Stress Proteins in Triticum aestivum Through Functional Annotation of Hypothetical Proteins

Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis

His work is highly cited, demonstrating a significant impact on the field of AI and medical imaging, especially in the context of liver disease. 📖📈