James H. Faghmous
Visiting Assistant Professor
My work leverages advanced machine learning and artificial intelligence along with design and domain expertise to address growing global health inequities. Specifically, I use novel ML/AI techniques to understand how social, environmental, and economic factors interact to create health disparities. I believe that the greatest source of precision and impact will come from these contextual factors and not from genetics.
Slightly Longer Bio
I am a computer scientist leading a global health research institute at the Icahn School of Medicine at Mount Sinai. I pursued this wonderful opportunity because of a fundamental misalignment in incentives in mainstream academia: as a computer scientist, I was rewarded for creating new methods, while the world needs new solutions to momentous problems (global change, growing inequality, water-food-energy shortages, etc.). Instead, I realigned my incentives and joined an endeavor that encourages and rewards large-scale problem solving over methods for methods' sake. My long-term research goal is to develop data science tools that accelerate scientific discovery and yield actionable insights that cannot be attained using traditional modes of discovery (e.g. experimentation or simulations). We are hiring in several data science positions including tenure-track faculty.
I received my Ph.D. in computer science from the University of Minnesota under Prof. Vipin Kumar (ACM SIGKDD 2012 Innovation Award winner and author of "Introduction to Data Mining"), where I was part of a 5-year $10M Expeditions in Computing project to understand climate change from data. My Ph.D. thesis at the intersection of data mining and global climate change received the 2014 Best Dissertation Award in Science and Engineering at the University of Minnesota. While at Minnesota, I have had the privilege to mentor over 20 students, and I look forward to continuing this tradition in NYC. My research has been generously funded by the US National Science Foundation (NSF) and the National Institutes of Health (NIH)
Faghmous, J.H., I. Frenger, Y. Yao, A. Lindel, R. Warmka, and V. Kumar. A Daily Global Mesoscale Ocean Eddy Dataset From Satellite Altimetry. Scientific Data, 2, 2015.
X. Chen, A. Khandelwal, S. Shi, S. Boriah, J.H. Faghmous, and V. Kumar. An Unsupervised Method for Global Water Extent Monitoring. In V. Lakshmanan, E. Gilleland, A. McGovern, and M. Tingley, Eds., Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the Fourth International Workshop on Climate Informatics, Springer, 2015.
Selected Invited Talks
"Big Science: Making Big Data Work for Science," Columbia University, New York, NY. November 2014
"Spatio-Temporal Data Mining Methods to Identify Mesoscale Ocean Eddies from
Satellite Altimeter Data" Workshop on Clouds, Convection and Data. NYU Abu Dhabi, Abu Dhabi UAE, March 2014