Feifei Li (M.Sc.)

Doctoral candidate and research assistant

Biography

I am a PhD candidate and Research Associate at the Institute for Biomedicine Informatics (BiK), Uniklinik Köln, where my research focuses on machine learning applications in medical informatics and medical image analysis. My work includes model explainability and fairness in medical datasets (e.g., ADNI), federated learning applications in radiology, and multimodal data analysis. My broader research experience spans computer vision, reinforcement learning, and AI-driven biomedical data analysis. Additionally, I have a strong background in data-driven problem-solving and optimization methods, with prior work in industrial engineering and digital production technology. Outside of research, I am an avid snowboarder and instructor, enjoying both the technical and strategic aspects of the sport.

Contact

Academic Background

Areas of Expertise

Current Teachings

Deep Learning Basics

Bachelor Studies – Clinical Semesters – Clinicians – Doctoral Studies – Master Studies – PostDoc – Preclinical Semesters – WiSe

This lecture will introduce the principle of Deep Learning, as an important branch of machine learning methods. It covers the fundamentals of deep learning, including neural networks, backpropagation, activation functions, and optimization techniques. After covering the basics, we will dive into popular deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We will explore the architecture of these networks in depth, and learn how to implement them using popular deep learning frameworks such as TensorFlow and PyTorch. Throughout the lecture, we will also cover practical considerations for training deep learning models, such as overfitting and regularization techniques. We will also discuss techniques for evaluating the performance of a deep learning model and tuning its hyperparameters to improve its accuracy. Prior knowledge related to python basics and data science are welcome but not necessary. This lecture will act as a preliminary role for the course of medical imaging process.

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