Machine Learning Series: Carsten Eickhoff, PhD

Advance-CTR & Brown DSI

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Friday, October 1, 2021:

Carsten Eickhoff, PhD: “An Introduction to Machine Learning” 

This talk will introduce the basic components of machine learning systems and research papers and introduce the notion of casting inference problems in terms of features and labels. We will discuss supervised classification and regression techniques as well as altogether unsupervised learning schemes. We will have a look at reinforcement learning, model optimization and evaluation and close with a discussion of model generality and the curse of dimensionality.

About the Speaker

Dr. Eickhoff is an Assistant Professor of Medical and Computer Science at Brown University where he leads the Biomedical AI Lab, specializing in the development of data science and information retrieval techniques with the goal of improving patient safety, individual health and quality of medical care. Prior to joining Brown, he graduated from The University of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard University. Carsten has published more than 100 articles in computer science conferences (SIGIR, EMNLP, NAACL, WWW, KDD, WSDM, CIKM) and clinical journals (Nature Digital Medicine, The Lancet - Respiratory Medicine, Radiology, European Heart Journal). His research has been supported by the NSF, NIH, DARPA, IARPA, Google, Amazon, Microsoft and others. Aside from his academic endeavors, he is a founder and board member of several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients.