We are a systems science group advancing methodology for modeling, observation, and inference to improve prediction and discovery in Earth, Planet, Climate, and Life applications. Our approach exploits feedbacks between theory, data, experts, and analogs for informative solutions to nonlinear high-dimensional problems in stochastic system settings, which includes estimation, control, learning, model reduction, uncertainty quantification, adaptive observation, detection, and decision-making. Some examples of our work include new statistical-physical approaches to model natural hazard risk in a changing climate, building autonomous stratospheric observatories to study extreme events, and using machine learning to discover governing fluid dynamical equations.

 

ESSG is strongly interdisciplinary and comfortable developing both methodology and application. You can find us tinkering with "what works in the field" while stretching the methodological contours of systems science. At our heart, we are motivated to bring systems science to investigations of the earth and environment.

 

Participants come from many pedagogical areas, including EAPS, EECS, Mathematics, and Mechanical and Aerospace Engineering. 

As a graduate student studying "Artificial Intelligence," in 1996, Sai Ravela realigned his work to become centered in Sustainability and Systems Science (read personal journey here). ESSG's ethos, captured by this ee cummings poem, is to understand Nature using knowledge scientifically acquired in the process of doing systematic work to gather information for meaningful inferences.  

  • Truth Seekers: We are here to learn the Truths of our existence. No field is out of bounds for us to pursue (recall: Truth Alone Prevails).
  • Freedom of Expression: As a corrolary to the seeking truth and in light of the controversies at the institute, we add that we firmly defend your right to the freedom of expression and the right to hold a different viewpoint. We would rather that you be slightly cocky and fiercely independent rather than be utterly polite and always agreeable. 
  • Do great Work1: An internal marching band exhorts us to advance! Diligently, intuitively, creatively, methodically, and energetically. Use all fair means of Right Action (e.g., see Dharma) to produce great work.
  • Service before Self: In academia, the universal demand to be "visible" and "standout" often leads to cut-throat competition and zero-sum games that decouples ends from means and the common good for individual reward. For example, seeking questionable funding has become a mode; see a re-evaluation here. We believe that the "service before self" principle encourages a focus on process over the anticipation of reward (e.g., see Bhagavad Gita 2:47) which, in the long run, we posit is a more sustainable basis for knowledge gain.
  • Observe All, Disrupt None: The rate of scaling new technology is now so rapid that there is barely a contemplation of deep consequences. We posit that the ESSG "observe all, disrupt none" theme promotes a minimal intervention model  for  Sustainability and is consistent with the Native American wisdom that envisioning utility several "generations" out is an excellent test for scaling "disruptive Tech," especially where "doing good" motivates the speed; e.g. batteries everywhere to stave off climate change.  In a sense, our "anti-disruption" systems science also promotes a dynamic and data driven approach to Sustainability.
  • The World is One Family: ESSG is one of the most diverse groups in our department and, we dare say, the institute. We are people of various religions, races, identities, and cultures and, without doubt, committed to excellence. We believe that the world is one family (e.g., see Vasudaiva Kutumbakam) and we find it a calling to help the disdvantaged scholar avail better opportunities. Come join us to enhance our culture! 

1: This slogan is borrowed from GoSpotCheck Inc. 

 

Scientists  |  Graduate  |  Undergraduate | High School |  Admin | Collaborators |  Degree Committee  |  Alumni

Sai Ravela, Director --
Nonlinear Dynamics and Chaos, Stochastic Processes,  Estimation, Control, Optimization, Learning and Inference, Autonomous Observatories, Computational Intelligence -- with application to Earth, Planets, Climate, and Life

Visiting Scientists and Postdoctoral Associates

Dr. ​William Rodi -- Seismology, Uncertainty, and Inversion.

Dr. Michael Barbehenn, Ecological Informatics, and Statistical Inference.
Dr. Anamitra Saha -- Flood Extremes and Climate, Physically-driven Machine Learning
Dr. Jiangchao Qiu  – Hydrological and Hydrodynamic Simulation, Compound Hydrometeorology Extremes Analytics, Natural Hazards, and Climate
Dr. Marzieh Mirhoseini -- ML-Physics approaches to Model Reduction and Uncertainty Quantification

Dr. Samiya Alkhairy, Estimation & Control, Analytic Methods.

Graduate Students

Lubna AlBarghouty (Course XII), Nuclear Explosion Monitoring and Informative Inference
Goran Zivanovic (Course VI), Exoplanet Detection and Machine Learning
Jae Deok Kim  (Course XII, MIT-WHOI Joint Program), Electromagnetic Geophysics, Geothermal Energy and Machine Learning
Madeline Loui (Course XVI/VI) Computer Vision, Machine Learning, Remote Sensing, Inundation Mapping

Undergraduate Students

Thelonious (Theo) Cooper (Course VI), Embedded Systems for Autonomy, Music Recognition
Ziwei (Grace) Jiang (Course VI), Image Recognition Architectures, Distributed Image Recognition
Tyrin-Ian Todd (Course VI) -- Informative Learning for Mapping 

​High-School Students

 

 

Administrative Staff

  • Jaga Purevsuren, Senior Financial Assistant
  • Alma Pellecer, Administrative Assistant

Collaborators

  • Andreas Prein, NCAR
  • Delaine Reiter, Applied Research Associates
  • Dagoberto Pulido IPN, Mexico
  • Dennis Bernstein, Adaptive Control, University of Michigan
  • Auroop Ganguly, Machine Learning and Sustainability, NEU
  • Sara Seager, Machine Learning and Exoplanets, MIT
  • Dirk Smit, Machine Learning and Super-resolution, Shell
  • Brent Minchew, Glacier/Ice Observation
  • Paul O Gorman, Atmospheric Dynamics, and Learning
  • Kerry Emanuel, Hurricane Risk
  • John Marshall, Ocean Modeling and Fluid Laboratory
  • Tom Reynolds and Fulvio Fabrizi, Machine Learning and Long-Range Forecasting, Lincoln Laboratory
  • Balu Nadiga, Dynamics and Machine Learning, Los Alamos National Laboratory
  • Ziwei Li  -- Predictability and Learning

Phd Committee

  • Ammar Alali (EAPS)
  • Aarti Dwivedi (EAPS)
  • Shiqing Liu (CEE)
  • Rachel Price (Aero-Astro)
  • Lubna AlBarghouty -- Research Advisee
  • Goran Zivanovic (EECS) -- Research Advise
  • Gregory Ely (EAPS) 
  • Zhuchang Zhan (EAPS)
  • Elizabeth Basha (EECS)
  • Nishant Yadav (Northeastern U)

Alum

  1. Prof. Joaquin Salas -- Computer Vision and Sustainability.
  2. Adebekun, Fiyifolu O.;  Learning Dynamics from Data
  3. Ali, Salem J.
  4. Diego Luca Gonzalez Gauss, Machine Learning and Extreme Floods
  5. Amro Alshareef, Barrier Films, Medical Devices, Unmanned Aircraft Systems
  6. Vivian Ngyuen, Image Recognition for Animal Biometrics and Conservation
  7. Bowman, Scott G.
  8. Tasmeem Reza (Course XVIII-C), Analytic Methods for Estimation and Control
  9. Chen, Ashley
  10. Chew, Juliana L. -- Flood Detection
  11. Clarke, Shanelle G.
  12. Cooper, Thelonious A.
  13. Edison, Jacob C.-- Hybrid Engine Design
  14. Feldman, Rose G.
  15. Gomez-Paz, Paco J.
  16. Horne, Kyra
  17. Ismail, Elyud S.
  18. Jamee, Mehrab S.
  19. Jiang, Ziwei
  20. Margolis, Gabriel B.-- Bayesian Deep Learning; MIT
  21. Marks, Boaz J.-- Tiltrotor Dynamics and Control
  22. Mloduchowski, Tomasz B.
  23. Mohamed, Amy I.
  24. Mohammed Salim, Abdulazeez O.
  25. Reilly, Sonia M.
  26. Reza, Tasmeem
  27. Suarez, David R.
  28. Sun, Tingxiao
  29. Thakur, Nandini S.
  30. Tian, Grace Y.; (Course XVIII),  Analytic Methods for Estimation and Control
  31. Trivedi, Mihir
  32. Wheatley, Caitlin K.
  33. Wolfenberger, Katelyn M.; Mechanical Engineering, Research Associate
  34. Xu, Iris
  35. Yamaguchi, Erina
  36. Yang, Christopher M.
  37. Yang, Emily P.
  38. Aarti Dwivedi-- Full Waveform Inversion and Machine Learning
  39. Nishant Yadav (NEU, CEE)
  40. Zhuchang Zhan (EAPS)
  41. Dongjoon Lee, Research Assistant  -- Hybird UAS Design, Dynamics 
  42. Abdelazeez Salim, Research Assistant -- Adaptive Control
  43. Ammar Alali, Research Assistant  -- Energy Systems and Learning
  44. Elizabeth Basha, EECS => University of the Pacific
  45. Kshitij Bakliwal, Purdue University
  46. Timothy Langlois, Lincoln Laboratory, Cornell, Adobe
  47. Clea Denamiel, Post-Doctoral Research Associate
  48. Sara Davila, Research Intern -- Climate Risk
  49. Emily Pitts, EECS
  50. Joshua Runge, EECS
  51. Fernando Shao, Mathematics=> Stanford, Oxford
  52. John Williams, EAPS
  53. Jeff Castillo, Aero Astro, Research Associate
  54. James Duyck, Math and Computer Science (Research Associate) => CMU => Google
  55. Chelsea Finn, EECS (Research Associate)=> Berkeley=> Google=>Stanford
  56. Alex Grossman, Research Assistant  -- Learning Long-Range Weather Prediction and Causal Discovery
  57. Elyud Ismail, Mechanical Engineering, Research Associate
  58. Samiya Al Khairy, Postdoctoral Researcher -- Information Theoretic Inference
  59. Amira Malik, Research Assistant  -- Stratoro Dynamics and Control
  60. Joshua Mitteldorf, Visiting Scientist
  61. Meghana Ranganathan, Graduate Advisee
  62. Nick Roberts, Aero Astro, Research Associate
  63. Christoph Schultheiss, Research Assistant => ETH
  64. Yuvraj Sehgal, Pilot 
  65. Hans Seybold, Post-Doctoral Research Associate => ETH
  66. Piyush Tagade, Post-Doctoral Research Associate => Samsung India
  67. Navi Tansaraviput, Researh Assistant
  68. Margaret Trautner, Research Assistant  -- Neural System Dynamics and Optimization; Caltech
  69. Mihir Trivedi, Research Assistant  -- SkyCandy Ensemble Control 
  70. Randy Westlund, Systems Engineer

Thanks

The people who directly or indirectly have influenced the development of ESSG
  • Prof. Maria Zuber, VP of Research
  • Prof. Rob van der Hilst, Chair of the Department
  • Prof. Kerry Emanuel, Atmospheric Dynamics
  • Prof. John Marshall, Oceanography
  • Prof. Jon How, Aero-Astro
  • Prof. Dennis McLaughlin, Hydrology
  • Prof. Youssef Marzouk, Director, Center for Computational Engineering
  • Prof. Laurent Demanet, Director, Earth Resources Laboratory
  • Prof. Bill Freeman, Computer Vision
  • Prof. Alan Willsky, Stochastic Systems
  • Prof. Allen Hanson, Computer Vision 
  • Prof. Bruce Croft, Information Retrieval
  • Prof. Rod Grupen, Robotics
  • Prof. M J S Rangachar, Electrical and Communication Engineering

Adminitrative Assistamt: Alma Pellecer
77 Massachusetts Avenue 54-512, Massachusetts Institute of Technology, Cambridge MA 01239
TEL: (617) 324- 1960 
pellecer at mit dot edu

Senior Financial Assistant: Jaga Purevsuren

 

Director: Sai Ravela

77 Massachusetts Avenue 54-1818, Massachusetts Institute of Technology, Cambridge MA 01239
TEL: (617) 253-0997 
ravela at mit dot edu

Acknowledgment

ESSG is funded in part by the Massachusetts Institute of Technology's Climate Grand Challenges program, Liberty Mutual, Office of Naval Research (ONR), the AFOSR Data-Driven Dynamic Application Systems (DDDAS) Program, the National Science Foundation (NSF), Lincoln Laboratory, Naval Undersea Warfare Center Division (NUWC), the Seaver Intitute, MIT's MISTI and ESI programs.