Source: https://www.stdm.co/
Timestamp: 2019-04-21 02:22:31+00:00

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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.
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.
Chen, X.C., Y. Yao, S. Shi, S. Chatterjee, V. Kumar, and J.H. Faghmous. A General Framework to Increase the Robustness of Model-Based Change Point Detection Algorithms to Outliers and Noise. SIAM International Conference on Data Mining (SDM), 2016 (to appear).
Faghmous, J.H., H. Nguyen, M. Le, and V. Kumar. Spatio-temporal Consistency for Autonomous Dynamic Object Identification in Continuous Spatio-Temporal Fields. Twenty-Eighth Conference on Artificial Intelligence (AAAI) 2014.
Faghmous, J.H., M. Le, M. Uluyol, and V. Kumar. Parameter-Free Spatio-Temporal Data Mining to Catalogue Global Ocean Dynamics. Thirteenth IEEE International Conference on Data Mining (ICDM) 2013.
Faghmous, J.H., M. Uluyol, M. Le, L. Styles, V. Mithal, S. Boriah, and V. Kumar. Multiple Hypothesis Object Tracking for Unsupervised Self-Learning: An Ocean Eddy Tracking Application. Twenty-Seventh Conference on Artificial Intelligence (AAAI) 2013.
Faghmous, J.H., Y. Chamber, F. Vikebø, S. Boriah, S. Liess, M. d.S. Mesquita, and V. Kumar. A Novel Spatio-Temporal Method for Ocean Eddy Monitoring. Twenty-Sixth Conference on Artificial Intelligence (AAAI) 2012.
Faghmous, J.H, S. Shekhar, and V. Kumar. Computing and Climate: Guest Editors' Introduction to the Special Issue. IEEE Computing in Science and Engineering (CiSE), 17(6), 2015.
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.
Faghmous, J.H., A. Banerjee, A.R. Ganguly, S. Shekhar, M. Steinbach, N. Samatova, and V. Kumar. Theory-Guided Data Science for Climate Change. IEEE Computer (11):74-8, 2014.
Faghmous, J.H. and V. Kumar. A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science. Big Data 2(3), 2014.
Ganguly, A.R., Kodra, E. A., Banerjee, A., Boriah, S., Chatterjee S., Chatterjee, S., Choudhary, A., Das, D., Faghmous, J.H., et al. Toward enhanced understanding and prediction of climate extremes using physics-guided data mining techniques. Nonlinear Processes in Geophysics 2014.
Faghmous, J.H. Machine Learning. In A.M. El-Sayed and S. Galea, Eds., Systems Science & Population Health. Oxford University Press, 2016 (to appear).
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.
Faghmous, J.H. and V. Kumar. Spatio-Temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities. In W. Chu, Ed., Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenges, and Opportunities. Springer, 2013.
Karpante A., Faghmous J.H., Kawale J., et al. Earth Science Applications of Sensor Data. In C. Aggarwal, Ed., Managing and Mining Sensor Data. Springer, 2012.

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