Challenges and Opportunities in Applied Machine Learning


  • Carla E. Brodley Tufts University
  • Umaa Rebbapragada Jet Propulsion Laboratory
  • Kevin Small Tufts Medical Center
  • Byron Wallace Tufts University



Applications of Machine Learning, Active Learning, Learning from Text, supervised learning, unsupervised learning


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 reviewing two of our interdisciplinary collaborations, both of which have posed unique machine learning problems, providing fertile ground for novel research.

Author Biographies

Carla E. Brodley, Tufts University

Carla E. Brodley is a Professor and Chair of the Department of Computer Science at Tufts University. She received her PhD in computer science from the University of Massachusetts, at Amherst in 1994. From 1994-2004, she was on the faculty of the School of Electrical Engineering at Purdue University. She joined the faculty at Tufts in 2004 and became chair in September 2011. Professor Brodley's research interests include machine learning, knowledge discovery in databases, health IT, and personalized medicine. She has worked in the areas of intrusion detection, anomaly detection, classifier formation, unsupervised learning and applications of machine learning to remote sensing, computer security, neuroscience, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, chemistry, evidence-based medicine, and personalized medicine. In 2001 she served as program co-chair for the ICML and in 2004 as general chair. She serves as  associate editor of JMLR and  Machine Learning, and is on the editorial board of DKMD. She is co-chair of CRA-W, a member of the AAAI Council, and is a board member of IMLS and of CRA.

Umaa Rebbapragada, Jet Propulsion Laboratory

Principal Investigator

Kevin Small, Tufts Medical Center

Research Scientist

Byron Wallace, Tufts University

Ph.D. candidate




How to Cite

Brodley, C. E., Rebbapragada, U., Small, K., & Wallace, B. (2012). Challenges and Opportunities in Applied Machine Learning. AI Magazine, 33(1), 11-24.