Challenges and Opportunitites in Applied Machine Learning

Speaker:  Carla E. Brodley; Professor and Dean, College of Computer and Information Science, Northeastern University

Machine learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (such as accuracy or AUC) to that of existing classification models on publicly available datasets. In terms of advancing machine learning as an academic discipline, this approach has thus far proven quite fruitful. However, it is our view that the most interesting open problems in machine learning are those that arise during its application to real-world problems. We illustrate this point by presenting the results of several of our interdisciplinary collaborations, which have posed unique machine learning problems, providing fertile ground for novel research.   Applications areas will include systematic reviews for evidence-based medicine, generating maps of global land cover of the Earth, and finding lesions in the MRI’s of treatment resistant epilepsy patients.  Our machine learning contributions span active learning, both supervised and unsupervised learning, and their combination.

Bio:

Carla E. Brodley is the Dean of the College of Computer and Information Science. Prior to joining Northeastern, she was a professor of the Department of Computer Science and the Clinical and Translational Science Institute at Tufts University (2004-2014). Before joining Tufts she was on the faculty of the School of Electrical Engineering at Purdue University (1994-2004).

A Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Dean Brodley’s interdisciplinary machine learning research has led to advances not only in computer and information science, but in many other areas including remote sensing, neuroscience, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, chemistry, evidence-based medicine, and predictive medicine.

Dean Brodley’s numerous leadership positions in computer science and her chosen research field of machine learning and data mining include serving as program co-chair of ICML, co-chair of AAAI, and serving as an associate editor of the Journal of AI Research, the Journal of Machine Learning Research, and Machine Learning. She has served on the Defense Science Study Group, the Computer Research Association Board of Directors and the AAAI Council. Currently she is a board member of the International Machine Learning Society and of DARPA’s Information Science and Technology (ISAT) Board.