Jennifer G. Dy - Institute for Experiential AI
Challenges are not barriers. They are opportunities for innovation.

– Jennifer G. Dy, Director of AI Faculty, EAI

Jennifer G. Dy is the Director of AI Faculty at the Institute for Experiential AI. She is a professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, where she first joined the faculty in 2002. She received her Master of Science and doctorate in 1997 and 2001 respectively from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, and her Bachelor of Science degree (Magna Cum Laude) from the Department of Electrical Engineering, University of the Philippines, in 1993.  

Jennifer chose to work on machine learning applications with societal impact. Her research spans both foundations in machine learning and its applications to biomedical imaging, health, science, and engineering. Multi-disciplinary research is instrumental to the growth of the various areas involved. 

Jennifer’s applied work has made research contributions in the following areas: Understanding lung disease, the diagnosis and treatment of skin cancer using images, tumor tracking in radiotherapy, the monitoring of motor fluctuations in Parkinson’s disease patients, learning individual differences of emotion, environmental health, climate informatics, and predicting disease severity in patients with retinopathy of prematurity, a leading cause of childhood blindness worldwide. She believes that working with real-world applications challenges the state-of-the-art in machine learning which leads to innovation.

Jennifer’s fundamental research has also contributed to the following fields of study: Unsupervised learning, interpretable models, explainable AI, dimensionality reduction, feature selection/sparse methods, learning from uncertain experts, active learning, Bayesian models, and deep representation learning. 

Jennifer is Director of the Machine Learning Lab and a founding faculty member of the Signal Processing, Imaging, Reasoning, and Learning Center (SPIRAL) at Northeastern. She received a National Science Foundation CAREER Award in 2004. Since 2016, Jennifer has served as Secretary of the International Machine Learning Conference Board of Directors (formerly the International Machine Learning Society) and as Associate Editor for Institute of Electrical and Electronics Engineers (IEEE) Transactions on Pattern Analysis and Machine Intelligence since 2018.

She previously served as Associate Editor for the Journal of Machine Learning Research (JMLR) from 2016 to 2019, Machine Learning Journal (MLJ) from 2007 to 2013, Data Mining and Knowledge Discovery from 2009 to 2011, and as Editorial Board Member for JMLR (2009-2016), and MLJ (2004-2007, 2014). She also served as Program Chair for the SIAM International Conference on Data Mining (SDM) 2013 and the International Conference on Machine Learning (ICML) 2018 and is currently serving as Associate Program Chair for the Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence 2022.

For the past 20 years until now, Jennifer has served and continues to serve as an organizing or a technical program committee member for numerous premier conferences in machine learning (ICML, Conference on Neural Information Processing Systems, International Conference on Learning Representations), data mining (ACM SIGKDD Conference on Knowledge Discovery and Data Mining, SDM), and artificial intelligence (the International Joint Conference on Artificial Intelligence, AAAI, International Conference on Artificial Intelligence and Statistics, and Conference on Uncertainty in Artificial Intelligence).