• James H. Faghmous

    Visiting Assistant Professor

    Stanford University

    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)

  • Publications

    Conference Proceedings

    1. 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).
    2. Chen, X.C, J.H. Faghmous, A. Khendelwal, and V. Kumar. Clustering Dynamic Spatio-Temporal Patterns in the Presence of Noise and Missing Data. Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015
    3. 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.
    4. 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.
    5. 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.
    6. Faghmous, J.H., L. Styles, F. Vikebø, S. Boriah, S. Liess, M. d.S. Mesquita, and V. Kumar. EddyScan: A Physically Consistent Eddy Monitoring Application. Conference on Intelligent Data Understanding (CIDU) 2012. Best Student Paper Award
    7. 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.


    1. 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.
    2. 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.

    3. 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.
    4. 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.
    5. 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.

    Book Chapters

    1. Faghmous, J.H. Machine Learning. In A.M. El-Sayed and S. Galea, Eds., Systems Science & Population Health. Oxford University Press, 2016 (to appear).
    2. 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.

    3. 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.
    4. 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.
  • Selected Invited Talks

    1. "Big Science: Making Big Data Work for Science," Columbia University, New York, NY. November 2014

    2. "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

    3. "The Impact of Expert Heuristics on The Significance of Unlabelled Spatio-Temporal Patterns." Workshop on Spatial Statistics for Environmental and Energy Challenges, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, March 2014
    4. "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
    5. "Parameter-Free Spatio-Temporal Data Mining to Catalogue Global Ocean Dynamics". Institute for Mathematics and Its Applications (IMA) Special Workshop on Predictability in Earth System Processes. The University of Minnesota, Minneapolis, MN November 2013
    6. "The Impact of Spatio-Temporal Variations in Sea Surface Temperatures on Atlantic Tropical Cyclone Frequency". Expeditions in Computing Annual Workshop. Northwestern University, Evanston, IL, August 2013
    7. "Climate Change and Variability: A Spatio-Temporal Data Mining Perspective". Department of Computer Science Colloquium, The University of California at Santa Barbara, CA, June 2013
    8. "ENSO’s Spatial Warming Patterns and their Impact on North Atlantic Tropical Cyclone Activity".  Department of Earth System Science Seminar, The University of California at Irvine, CA, June 2013
    9. "Data-Driven Methods to Leverage Satellite Altimeter Observations". The Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, June 2013
    10. "Scalable Spatio-Temporal Data Mining Algorithms for Global Ocean Eddy Monitoring." IEEE SuperComputing’12 Climate Knowledge Discovery Worksop, Salt Lake City, UT, November 2012
    11. "Leveraging Statistical Machine Learning to Complement Physics-Based Hurricane Models." National Center for Atmospheric Research (NCAR), Boulder, CO, April 2012
    12. "Climate Science’s Big Data Challenge and How Artificial Intelligence Can Help" The Interdisciplinary Climate Seminar. The City College of New York, New York, NY March 2012
    13. "Tropical Cyclogenesis: A Machine Learning Approach:." NorWRF2011, Bergen, Norway, September 2011

  • Awards

    1. NSF CISE Research Initiation Initiative Award, 2015
    2. 2014 University of Minnesota Best Ph.D. Dissertation Award in Physical Sciences and Engineering
    3. MN Cup Business Plan Competition, 2nd Place ($5000 cash prize), 2013
    4. Mary A. McEvoy Award for Public Engagement and Leadership, University of MN, 2012
    5. President’s Student Leadership and Service Award, University of MN, 2012
    6. Doctoral Dissertation Fellowship, University of MN, 2012
    7. Best Student Paper Award, The NASA-IEEE Conference on Intelligent Data Understanding, 2012
    8. National Science Foundation Graduate Research Fellowship, 2008-2011
    9. NIH-Neuro-Physical-Computational Graduate Fellowship, 2006-2008
    10. Grove School of Engineering Outstanding Leadership Award, City College of New York, 2006
    11. Rhodes Scholarship nominee, City College of New York, 2006
    12. 2004 CUNYAC Scholar-Athlete Team (Men’s Basketball), City University of New York, 2004