Training & Curriculum

Imaging AI seeks to advance innovative diagnostic and prognostic algorithms in Radiology and Pathology, equipping you with the competencies to develop and validate AI / deep learning workflows for biomedical image analysis and translate theoretical knowledge into clinical solutions. Through hands-on learning, you will master AI-driven image analysis for disease biomarker identification, diagnostic and prognostic modeling, and progression tracking and monitoring. The course will also emphasize appropriate statistical validation (e.g., multilevel regression modeling) and evaluation of the AI models. Special topics include graph-based methods, spatial multimodal analysis, and user interface design.  

Designing and inventing new biomedical devices and wearables in any area of healthcare requires a comprehensive clinical and physics-based understanding of the human body integrated with the art of engineering design. This course focuses on developing devices and wearables for the neuromuscular system. We will start with a brief introduction to the human anatomy, the neuromuscular system, and the behavior of different types of signals, such as electrical, acoustic, and optical waves, that can be used to understand human tissue condition and behavior, along with examples of the current state of the art. We will then delve deep into 2 to 3 clinical problems medical providers face in musculoskeletal medicine, where biomedical devices could improve screening, diagnosis, or assessment and, therefore, improve clinical care.  We will focus on pathophysiology, the clinical workflow, and constraints inherent in human subject studies, and the engineering limitations, before exploring potential pathways to develop a biomedical device or wearable. The second part of the class will focus on developing a working prototype of a wearable device. You will gain hands-on experience with prototyping tools and devices such as high-end 3D printers, Computer-Aided Design (3D design), programming microcontrollers and sensors, and transducers. 

AI algorithms for personalized medicine require multi-modal data to capture the interactions between our genes and the environment in order to understand disease conditions. This course will cover algorithms and methods used to analyze complex biomedical data, including DNA sequences, genetics, epigenetics, proteomics, single-cell genomics, and molecular image data. A mentored term project will provide you with hands-on experience for carrying out independent research, highlighting the importance of interdisciplinary collaborations and the value of incorporating diverse perspectives in research.

Computational Biomedicine, a rapidly growing discipline at the intersection of biology, medicine, statistics, and computer science, offers exciting opportunities for real-world impact in healthcare. In the dynamic landscape of biomedical research, where data plays an increasingly crucial role, understanding scientific inquiries and developing quantitative skills for data analysis and interpretation are essential. This course, serving as an introduction to Computational Biomedicine, will focus on modeling health and disease systems. We will cover computational modeling principles, apply modeling techniques, analyze model performance and limitations, and explore innovative computational frameworks, algorithms, and architectures. These tools are not just theoretical concepts, but practical solutions to address unmet needs and open problems in biomedical research and clinical practice. The course will use project-based and hands-on learning experiences to enhance students’ understanding and application of the subject matter, preparing them for the exciting challenges of the field. 

This course explores the ethical challenges and considerations involved in developing and deploying artificial intelligence (AI) systems in healthcare and public health contexts, including responsible use, patient consent, bias of AI algorithms, and fairness in models. You will critically examine predictive models and AI applications used for making important health decisions, addressing factors that lead to trustworthy AI. Through a reverse classroom approach, students will engage in active learning activities to analyze the potential for bias, risk, and social inequity in AI systems. The course will emphasize project-based learning, allowing students to learn and apply ethical AI principles and practices to real-world healthcare scenarios. 

This course provides comprehensive coverage in machine learning, covering both theoretical foundations and practical applications. Students will learn concepts, algorithms, and techniques used in machine learning. Emphasis will be placed on real-world applications, particularly in biological and clinical sciences. Students will gain hands-on experience through practical exercises and projects and learn the theory and practice of machine learning from a variety of perspectives. Topics include supervised learning (classification, regression); unsupervised learning (clustering, dimensionality reduction); reinforcement learning; and computational learning theory. 

The significant advance of natural language processing (NLP) approaches in the last few years, with the advent of chatbots that seem to hold conversations and even express ‘chain-of-thought’ reasoning behind their answers, sets the bar high for what these systems can accomplish within the healthcare setting, facilitating patient-physician interaction and improving diagnostic accuracy. This course will take a hands-on approach to explore the boundaries of NLP and Artificial Intelligence, enabling deep understanding of cutting-edge technologies that could help address the hardest problems currently faced by clinicians and patients.

AI algorithms for personalized medicine require multi-modal data to capture the interactions between our genes and the environment in order to understand disease conditions. This course will cover algorithms and methods used to analyze complex biomedical data, including DNA sequences, genetics, epigenetics, proteomics, single-cell genomics, and molecular image data. A mentored term project will provide you with hands-on experience for carrying out independent research, highlighting the importance of interdisciplinary collaborations and the value of incorporating diverse perspectives in research.