Robert Granat, Ph.D.

Ph.D. in machine learning with domain expertise in carbon measurement, Earth science, and remote sensing

Research Expertise

Remote Sensing
Machine Learning
GHG Measurement
Geodesy
Atmospheric Science
Geochemistry and Petrology
Geophysics
Computational Theory and Mathematics
Hardware and Architecture
Software
Theoretical Computer Science
Applied Mathematics
Discrete Mathematics and Combinatorics
Statistics and Probability
Statistics, Probability and Uncertainty
Modeling and Simulation
Computer Science Applications
Astronomy and Astrophysics
Mechanics of Materials
Computational Mechanics
Space and Planetary Science
Earth-Surface Processes
Forestry
Soil Science
Aquatic Science
Oceanography
Ecology
Water Science and Technology
Geology
Geotechnical Engineering and Engineering Geology

About

Robert Granat, Ph.D. is a highly accomplished machine learning and remote sensing expert with extensive experience in research and development. He received his Ph.D. in Electrical Engineering in 2004 and his M.S. in Electrical Engineering in 1998. He has held various leadership positions in both academia and the private sector, including CTO and Head of Science at CarbonSpace Ltd, a company focused on developing novel methods for carbon measurement using satellite observations. Dr. Granat's expertise lies in the fields of machine learning, Earth science, and remote sensing technologies. He has a strong background in developing algorithms and software systems for remote sensing measurement technologies and data analysis. His work has been instrumental in advancing the understanding of climate change and its impacts on the environment. Prior to his work at CarbonSpace Ltd, Dr. Granat held several research positions at the Research Foundation of City University of New York and Jet Propulsion Laboratory (JPL). At JPL, he served as the Science Team Algorithms Lead for the OCO-2 mission, a satellite mission dedicated to measuring carbon dioxide levels in Earth's atmosphere. He also served as the Group Supervisor for the Machine Learning and Instrument Autonomy Group at JPL. Dr. Granat's contributions to the fields of machine learning, geophysics, and climate science have been recognized with numerous awards and publications. He is a member of several professional organizations, including the Institute of Electrical and Electronics Engineers (IEEE) and the American Geophysical Union (AGU). In addition to his professional achievements, Dr. Granat is also dedicated to sharing his knowledge and expertise with the next generation of engineers and scientists.

Publications

The Orbiting Carbon Observatory-2: first 18 months of science data products

Atmospheric Measurement Techniques / Feb 15, 2017

Eldering, A., O’Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R., Viatte, C., Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L., Cheng, C., Connor, B., Dang, L., Doran, G., Fisher, B., Frankenberg, C., Fu, D., Granat, R., … Yoshimizu, J. (2017). The Orbiting Carbon Observatory-2: first 18 months of science data products. Atmospheric Measurement Techniques, 10(2), 549–563. https://doi.org/10.5194/amt-10-549-2017

Real-time Earthquake Location Using Kirchhoff Reconstruction

Bulletin of the Seismological Society of America / Apr 01, 2005

Baker, T. (2005). Real-time Earthquake Location Using Kirchhoff Reconstruction. Bulletin of the Seismological Society of America, 95(2), 699–707. https://doi.org/10.1785/0120040123

iSERVO: Implementing the International Solid Earth Research Virtual Observatory by Integrating Computational Grid and Geographical Information Web Services

Pageoph Topical Volumes

Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Grant, L., Lyzenga, G., McLeod, D., Pallickara, S., Parker, J., Pierce, M., Rundle, J., Sayar, A., & Tullis, T. (n.d.). iSERVO: Implementing the International Solid Earth Research Virtual Observatory by Integrating Computational Grid and Geographical Information Web Services. In Computational Earthquake Physics: Simulations, Analysis and Infrastructure, Part II (pp. 2281–2296). Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8131-8_3

Tests and tolerances for high-performance software-implemented fault detection

IEEE Transactions on Computers / May 01, 2003

Turmon, M., Granat, R., Katz, D. S., & Lou, J. Z. (2003). Tests and tolerances for high-performance software-implemented fault detection. IEEE Transactions on Computers, 52(5), 579–591. https://doi.org/10.1109/tc.2003.1197125

A Hidden Markov Model Based Tool for Geophysical Data Exploration

Earthquake Processes: Physical Modelling, Numerical Simulation and Data Analysis Part II / Jan 01, 2002

Granat, R., & Donnellan, A. (2002). A Hidden Markov Model Based Tool for Geophysical Data Exploration. In Earthquake Processes: Physical Modelling, Numerical Simulation and Data Analysis Part II (pp. 2271–2283). Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-8197-5_7

iSERVO: Implementing the International Solid Earth Research Virtual Observatory by Integrating Computational Grid and Geographical Information Web Services

Pure and Applied Geophysics / Dec 01, 2006

Aktas, M., Aydin, G., Donnellan, A., Fox, G., Granat, R., Grant, L., Lyzenga, G., McLeod, D., Pallickara, S., Parker, J., Pierce, M., Rundle, J., Sayar, A., & Tullis, T. (2006). iSERVO: Implementing the International Solid Earth Research Virtual Observatory by Integrating Computational Grid and Geographical Information Web Services. Pure and Applied Geophysics, 163(11–12), 2281–2296. https://doi.org/10.1007/s00024-006-0137-8

Exploration of Large Digital Sky Surveys

ESO ASTROPHYSICS SYMPOSIA

Djorgovski, S. G., Brunner, R. J., Mahabal, A. A., Odewahn, S. C., Carvalho, R. R. de, Gal, R. R., Stolorz, P., Granat, R., Curkendall, D., Jacob, J., & Castro, S. (n.d.). Exploration of Large Digital Sky Surveys. In Mining the Sky (pp. 305–322). Springer-Verlag. https://doi.org/10.1007/10849171_37

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

Earth and Space Science / Nov 24, 2021

Rundle, J. B., Donnellan, A., Fox, G., Crutchfield, J. P., & Granat, R. (2021). Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning. Earth and Space Science, 8(12). Portico. https://doi.org/10.1029/2021ea001757

Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO$_2$ from Satellite Data

SIAM/ASA Journal on Uncertainty Quantification / Jan 01, 2017

Hobbs, J., Braverman, A., Cressie, N., Granat, R., & Gunson, M. (2017). Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO$_2$ from Satellite Data. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 956–985. https://doi.org/10.1137/16m1060765

Software-implemented fault detection for high-performance space applications

Proceeding International Conference on Dependable Systems and Networks. DSN 2000

Turmon, M., Granat, R., & Katz, D. (n.d.). Software-implemented fault detection for high-performance space applications. Proceeding International Conference on Dependable Systems and Networks. DSN 2000. https://doi.org/10.1109/icdsn.2000.857522

<title>Exploration of parameter spaces in a virtual observatory</title>

SPIE Proceedings / Nov 01, 2001

Djorgovski, S. G., Mahabal, A., Brunner, R. J., Williams, R. E., Granat, R., Curkendall, D., Jacob, J. C., & Stolorz, P. (2001). &lt;title&gt;Exploration of parameter spaces in a virtual observatory&lt;/title&gt; In J.-L. Starck & F. D. Murtagh (Eds.), Astronomical Data Analysis. SPIE. https://doi.org/10.1117/12.447189

QuakeSim and the Solid Earth Research Virtual Observatory

Pageoph Topical Volumes

Donnellan, A., Rundle, J., Fox, G., McLeod, D., Grant, L., Tullis, T., Pierce, M., Parker, J., Lyzenga, G., Granat, R., & Glasscoe, M. (n.d.). QuakeSim and the Solid Earth Research Virtual Observatory. In Computational Earthquake Physics: Simulations, Analysis and Infrastructure, Part II (pp. 2263–2279). Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8131-8_2

Measuring Atmospheric Carbon Dioxide from the NASA Orbiting Carbon Observatory-2 (OCO-2)

Light, Energy and the Environment 2018 (E2, FTS, HISE, SOLAR, SSL) / Jan 01, 2018

Crisp, D. (2018). Measuring Atmospheric Carbon Dioxide from the NASA Orbiting Carbon Observatory-2 (OCO-2). Light, Energy and the Environment 2018 (E2, FTS, HISE, SOLAR, SSL). https://doi.org/10.1364/fts.2018.jt1a.2

TCCON data from Jet Propulsion Laboratory, Pasadena, California, USA, Release GGG2014R0

TCCON data from Jet Propulsion Laboratory, Pasadena, California, USA, Release GGG2014R0. (n.d.). Copernicus GmbH. https://doi.org/10.14291/tccon.ggg2014.jpl01.r0/1149163

A scripting based architecture for management of streams and services in real-time grid applications

CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005. / Jan 01, 2005

Gadgil, H., Fox, G., Pallickara, S., Pierce, M., & Granat, R. (2005). A scripting based architecture for management of streams and services in real-time grid applications. CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005. https://doi.org/10.1109/ccgrid.2005.1558633

Challenges for Cluster Analysis in a Virtual Observatory

Statistical Challenges in Astronomy

Djorgovski, S. G., Brunner, R., Mahabal, A., Williams, R., Granat, R., & Stolorz, P. (n.d.). Challenges for Cluster Analysis in a Virtual Observatory. In Statistical Challenges in Astronomy (pp. 127–141). Springer-Verlag. https://doi.org/10.1007/0-387-21529-8_9

The On-Orbit Performance of the Orbiting Carbon Observatory-2 (OCO-2) Instrument and its Radiometrically Calibrated Products

Oct 04, 2016

Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O’Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., … Wunch, D. (2016). The On-Orbit Performance of the Orbiting Carbon Observatory-2 (OCO-2) Instrument and its Radiometrically Calibrated Products. https://doi.org/10.5194/amt-2016-281

Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults

Earth and Space Science / Oct 29, 2021

Granat, R., Donnellan, A., Heflin, M., Lyzenga, G., Glasscoe, M., Parker, J., Pierce, M., Wang, J., Rundle, J., & Ludwig, L. G. (2021). Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California’s Major Faults. Earth and Space Science, 8(11). Portico. https://doi.org/10.1029/2021ea001680

E-DECIDER: Using Earth Science Data and Modeling Tools to Develop Decision Support for Earthquake Disaster Response

Pure and Applied Geophysics / Apr 08, 2014

Glasscoe, M. T., Wang, J., Pierce, M. E., Yoder, M. R., Parker, J. W., Burl, M. C., Stough, T. M., Granat, R. A., Donnellan, A., Rundle, J. B., Ma, Y., Bawden, G. W., & Yuen, K. (2014). E-DECIDER: Using Earth Science Data and Modeling Tools to Develop Decision Support for Earthquake Disaster Response. Pure and Applied Geophysics, 172(8), 2305–2324. https://doi.org/10.1007/s00024-014-0824-9

Analysis of streaming GPS measurements of surface displacement through a web services environment

2007 IEEE Symposium on Computational Intelligence and Data Mining / Jan 01, 2007

Granat, R., Aydin, G., Pierce, M., Qi, Z., & Bock, Y. (2007). Analysis of streaming GPS measurements of surface displacement through a web services environment. 2007 IEEE Symposium on Computational Intelligence and Data Mining. https://doi.org/10.1109/cidm.2007.368951

A Method of Hidden Markov Model Optimization for Use with Geophysical Data Sets

Lecture Notes in Computer Science / Jan 01, 2003

Granat, R. A. (2003). A Method of Hidden Markov Model Optimization for Use with Geophysical Data Sets. In Computational Science — ICCS 2003 (pp. 892–901). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-44863-2_88

Algorithm-based fault tolerance for spaceborne computing: basis and implementations

2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484)

Turmon, M., & Granat, R. (n.d.). Algorithm-based fault tolerance for spaceborne computing: basis and implementations. 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484). https://doi.org/10.1109/aero.2000.878453

Statistical Approaches to Detecting Transient Signals in GPS: Results from the 2009-2011 Transient Detection Exercise

Seismological Research Letters / May 01, 2013

Granat, R., Parker, J., Kedar, S., Dong, D., Tang, B., & Bock, Y. (2013). Statistical Approaches to Detecting Transient Signals in GPS: Results from the 2009-2011 Transient Detection Exercise. Seismological Research Letters, 84(3), 444–454. https://doi.org/10.1785/0220130039

Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning

2009 Third IEEE International Conference on Space Mission Challenges for Information Technology / Jul 01, 2009

Granat, R., Wagstaff, K. L., Bornstein, B., Tang, B., & Turmon, M. (2009, July). Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning. 2009 Third IEEE International Conference on Space Mission Challenges for Information Technology. https://doi.org/10.1109/smc-it.2009.22

Detecting Regional Events via Statistical Analysis of Geodetic Networks

Pageoph Topical Volumes

Granat, R. (n.d.). Detecting Regional Events via Statistical Analysis of Geodetic Networks. In Computational Earthquake Physics: Simulations, Analysis and Infrastructure, Part II (pp. 2497–2512). Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8131-8_17

Improving access to geodetic imaging crustal deformation data using GeoGateway

Earth Science Informatics / Jan 18, 2021

Donnellan, A., Parker, J., Heflin, M., Glasscoe, M., Lyzenga, G., Pierce, M., Wang, J., Rundle, J., Ludwig, L. G., Granat, R., Mirkhanian, M., & Pulver, N. (2021). Improving access to geodetic imaging crustal deformation data using GeoGateway. Earth Science Informatics, 15(3), 1513–1525. https://doi.org/10.1007/s12145-020-00561-7

Building Sensor Filter Grids: Information Architecture for the Data Deluge

2005 First International Conference on Semantics, Knowledge and Grid / Jan 01, 2005

Fox, G. C., Aktas, M. S., Aydin, G., Donnellan, A., Gadgil, H., Granat, R., Pallickara, S., Parker, J., Pierce, M. E., Oh, S., Rundle, J., Sayar, A., & Scharber, M. (2005). Building Sensor Filter Grids: Information Architecture for the Data Deluge. 2005 First International Conference on Semantics, Knowledge and Grid. https://doi.org/10.1109/skg.2005.48

QuakeSim: Integrated modeling and analysis of geologic and remotely sensed data

2012 IEEE Aerospace Conference / Mar 01, 2012

Donnellan, A., Parker, J., Granat, R., De Jong, E., Suzuki, S., Pierce, M., Fox, G., Rundle, J., McLeod, D., Al-Ghanmi, R., & Ludwig, L. G. (2012, March). QuakeSim: Integrated modeling and analysis of geologic and remotely sensed data. 2012 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2012.6187219

QuakeSim: Web Services, Portals, and Infrastructure for Geophysics

2008 IEEE Aerospace Conference / Mar 01, 2008

Pierce, M. E., Fox, G. C., Aydin, G., Qi, Z., Donnellan, A., Parker, J. W., & Granat, R. (2008, March). QuakeSim: Web Services, Portals, and Infrastructure for Geophysics. 2008 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2008.4526277

QuakeSim: Efficient Modeling of Sensor Web Data in a Web Services Environment

2008 IEEE Aerospace Conference / Mar 01, 2008

Donnellan, A., Parker, J., Granat, R., Fox, G., Pierce, M., Rundle, J., McLeod, D., Al-Ghanmi, R., Grant, L., & Brooks, W. (2008, March). QuakeSim: Efficient Modeling of Sensor Web Data in a Web Services Environment. 2008 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2008.4526453

GeoGateway: A system for analysis of UAVSAR data products

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) / Jul 01, 2016

Donnellan, A., Parker, J., Glasscoe, M., Granat, R., Pierce, M., Wang, J., Ma, Y., Ludwig, L. G., & Rundle, J. (2016, July). GeoGateway: A system for analysis of UAVSAR data products. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.2016.7729046

Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2)

Earth Observing Systems XX / Sep 08, 2015

Crisp, D. (2015). Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2). In J. J. Butler, X. (Jack) Xiong, & X. Gu (Eds.), SPIE Proceedings. SPIE. https://doi.org/10.1117/12.2187291

Estimating dynamic ionospheric changes without a priori models

Radio Science / Mar 01, 2000

Granat, R. A., & Na, H. (2000). Estimating dynamic ionospheric changes without a priori models. Radio Science, 35(2), 341–349. Portico. https://doi.org/10.1029/1999rs900109

Integrating remotely sensed and ground observations for modeling, analysis, and decision support

2013 IEEE Aerospace Conference / Mar 01, 2013

Donnellan, A., Glasscoe, M., Parker, J. W., Granat, R., Pierce, M., Jun Wang, Fox, G., McLeod, D., Rundle, J., Heien, E., & Grant Ludwig, L. (2013, March). Integrating remotely sensed and ground observations for modeling, analysis, and decision support. 2013 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2013.6497163

Analysis of emergent fault element behavior in Virtual California

Concurrency and Computation: Practice and Experience / Jul 26, 2010

Glasscoe, M. T., Granat, R. A., Rundle, J. B., Rundle, P. B., Donnellan, A., & Kellogg, L. H. (2010). Analysis of emergent fault element behavior in Virtual California. Concurrency and Computation: Practice and Experience, 22(12), 1665–1683. Portico. https://doi.org/10.1002/cpe.1546

Modeling and On-the-Fly Solutions for Solid Earth Sciences: Web Services and Data Portal for Earthquake Early Warning System

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium / Jan 01, 2008

Bock, Y., Crowell, B., Prawirodirdjo, L., Jamason, P., Chang, R.-J., Fang, P., Squibb, M., Pierce, M., Gao, X., Webb, F., Kedar, S., Granat, R., Parker, J., & Dong, D. (2008). Modeling and On-the-Fly Solutions for Solid Earth Sciences: Web Services and Data Portal for Earthquake Early Warning System. IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2008.4779672

The Quakes Concept for Observing and Mitigating Natural Disasters

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium / Jul 01, 2019

Donnellan, A., Pierce, M., Wang, J., Ben-Zion, Y., Lou, Y., Padgett, C., Parker, J., Hawkins, B., Granat, R., Glasscoe, M., Rundle, J., & Ludwig, L. G. (2019, July). The Quakes Concept for Observing and Mitigating Natural Disasters. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2019.8900214

Understanding earthquake fault systems using QuakeSim analysis and data assimilation tools

2009 IEEE Aerospace conference / Mar 01, 2009

Donnellan, A., Parker, J., Glasscoe, M., Granat, R., Rundle, J., McLeod, D., Al-Ghanmi, R., & Grant, L. (2009, March). Understanding earthquake fault systems using QuakeSim analysis and data assimilation tools. 2009 IEEE Aerospace Conference. https://doi.org/10.1109/aero.2009.4839497

The Quakes Analytic Center Framework for Addressing Diverse Spatiotemporal Scales of Tectonic and Earthquake Processes

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium / Sep 26, 2020

Donnellan, A., Parker, J., Granat, R., Glasscoe, M., Hawkins, B., Rundle, J., Ludwig, L. G., Pierce, M., & Wang, J. (2020, September 26). The Quakes Analytic Center Framework for Addressing Diverse Spatiotemporal Scales of Tectonic and Earthquake Processes. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss39084.2020.9324195

Nowcasting earthquakes

Earth and Space Science / Nov 01, 2016

Rundle, J. B., Turcotte, D. L., Donnellan, A., Grant Ludwig, L., Luginbuhl, M., & Gong, G. (2016). Nowcasting earthquakes. Earth and Space Science, 3(11), 480–486. Portico. https://doi.org/10.1002/2016ea000185

Effect of temporal aggregation on multiple time series in the frequency domain

Journal of Time Series Analysis / Jun 13, 2013

Hassler, U. (2013). Effect of temporal aggregation on multiple time series in the frequency domain. Journal of Time Series Analysis, 34(5), 562–573. Portico. https://doi.org/10.1111/jtsa.12032

Coseismic gravity changes of the 2011 Tohoku-Oki earthquake from satellite gravimetry

Geophysical Research Letters / Apr 01, 2011

Matsuo, K., & Heki, K. (2011). Coseismic gravity changes of the 2011 Tohoku-Oki earthquake from satellite gravimetry: GRAVITY CHANGES BY TOHOKU-OKI EARTHQUAKE. Geophysical Research Letters, 38(7), n/a-n/a. Portico. https://doi.org/10.1029/2011gl049018

5Hz GPS seismology of the El Mayor-Cucapah earthquake: estimating the earthquake focal mechanism

Geophysical Journal International / Aug 07, 2012

Zheng, Y., Li, J., Xie, Z., & Ritzwoller, M. H. (2012). 5Hz GPS seismology of the El Mayor-Cucapah earthquake: estimating the earthquake focal mechanism: GPS seismology of El Mayor-Cucapah earthquake. Geophysical Journal International, 190(3), 1723–1732. https://doi.org/10.1111/j.1365-246x.2012.05576.x

Buried Aseismic Slip and Off-Fault Deformation on the Southernmost San Andreas Fault triggered by the 2010 El Mayor Cucapah Earthquake revealed by UAVSAR

Feb 02, 2021

Parker, J., Bilham, R., Donnellan, A., Grant Ludwig, L., Pierce, M., Wang, J., & Mowery, N. (2021). Buried Aseismic Slip and Off-Fault Deformation on the Southernmost San Andreas Fault triggered by the 2010 El Mayor Cucapah Earthquake revealed by UAVSAR. https://doi.org/10.1002/essoar.10506047.1

Coal-rock Interface Recognition Based on Time Series Analysis

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) / Oct 01, 2010

Baoping Wang, Zengcai Wang, & Shuliang Zhu. (2010, October). Coal-rock Interface Recognition Based on Time Series Analysis. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). https://doi.org/10.1109/iccasm.2010.5620422

A fast method for real-time anomaly detection using routing statistics

IEEE INFOCOM Workshops 2008 / Apr 01, 2008

Booker, G. (2008, April). A fast method for real-time anomaly detection using routing statistics. IEEE INFOCOM Workshops 2008. https://doi.org/10.1109/infocom.2008.4544604

Data mining as a foundation for science-enabling autonomy

IEEE Aerospace and Electronic Systems Magazine / Nov 01, 2008

Stolorz, P., Roden, J., & Granat, R. (2008). Data mining as a foundation for science-enabling autonomy. IEEE Aerospace and Electronic Systems Magazine, 23(11), 19–24. https://doi.org/10.1109/maes.2008.4693986

La Guardia Indígena Nasa y el Arte de la Resistencia Pacífica

Ra Ximhai / Dec 31, 2008

Salazar Zarco, A. L. (2008). La Guardia Indígena Nasa y el Arte de la Resistencia Pacífica. Ra Ximhai, 831–839. https://doi.org/10.35197/rx.04.03.2008.16.as

Using Relocatable Bitstreams for Fault Tolerance

Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007) / Aug 01, 2007

Montminy, D. P., Baldwin, R. O., Williams, P. D., & Mullins, B. E. (2007, August). Using Relocatable Bitstreams for Fault Tolerance. Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007). https://doi.org/10.1109/ahs.2007.108

Spatiotemporal filtering using principal component analysis and Karhunen‐Loeve expansion approaches for regional GPS network analysis

Journal of Geophysical Research: Solid Earth / Mar 01, 2006

Dong, D., Fang, P., Bock, Y., Webb, F., Prawirodirdjo, L., Kedar, S., & Jamason, P. (2006). Spatiotemporal filtering using principal component analysis and Karhunen‐Loeve expansion approaches for regional GPS network analysis. Journal of Geophysical Research: Solid Earth, 111(B3). Portico. https://doi.org/10.1029/2005jb003806

A community faulted-crust model using PYRAMID on cluster platforms

2004 IEEE International Conference on Cluster Computing (IEEE Cat. No.04EX935)

Parker, J., Lyzenga, G., Norton, C., Tisdale, E., & Donnellan, A. (n.d.). A community faulted-crust model using PYRAMID on cluster platforms. 2004 IEEE International Conference on Cluster Computing (IEEE Cat. No.04EX935). https://doi.org/10.1109/clustr.2004.1392656

Induced deformation during tunnel excavation: Evidence from geodetic monitoring

Engineering Geology / Jun 01, 2005

Kontogianni, V. A., & Stiros, S. C. (2005). Induced deformation during tunnel excavation: Evidence from geodetic monitoring. Engineering Geology, 79(1–2), 115–126. https://doi.org/10.1016/j.enggeo.2004.10.012

Nowcasting Earthquakes:Imaging the Earthquake Cycle in California with Machine Learning

Mar 28, 2021

Rundle, J. B., Donnellan, A., Fox, G., Crutchfield, J. P., & Granat, R. A. (2021). Nowcasting Earthquakes:Imaging the Earthquake Cycle in California with Machine Learning. https://doi.org/10.1002/essoar.10506614.1

Clustering Analysis Methods for GNSS Observations: A Data-Driven Approach to Identifying California's Major Faults

Feb 04, 2021

Granat, R. A., Donnellan, A., Heflin, M. B., Lyzenga, G., Glasscoe, M. T., Parker, J., Pierce, M., Wang, J., Rundle, J. B., & Grant Ludwig, L. (2021). Clustering Analysis Methods for GNSS Observations: A Data-Driven Approach to Identifying California’s Major Faults. https://doi.org/10.1002/essoar.10506075.1

Education

Ph.D., Electrical Engineering / June, 2004

Los Angeles, California, United States of America

M.S., Electrical Engineering / October, 1998

Los Angeles, California, United States of America

California Institute of Technology

B.S., Computation and Neural Systems / April, 1996

Pasadena, California, United States of America

Experience

CarbonSpace Ltd

CTO / November, 2013Present

Machine Learning, Remote Sensing, Carbon Measurement, Atmospheric Science

Head of Science / April, 2023October, 2023

Machine Learning, Remote Sensing, Carbon Measurement, Atmospheric Science

Science Advisor / January, 2023March, 2023

Machine Learning, Remote Sensing, Carbon Measurement, Atmospheric Science

Research Foundation City University of New York

Senior Research Staff / January, 2020March, 2023

Dept. of Earth and Atmospheric Science (Machine Learning, GNSS, InSAR, Geophysics)

Jet Propulsion Laboratory

Science Team Algorithms Lead, OCO-2 Mission / March, 2013December, 2018

Project Management, Carbon Measurement, Uncertainty Quantification, Remote Sensing, Radiative Transfer

Group Supervisor, Machine Learning and Instrument Autonomy Group / April, 2008November, 2014

Line Management, Machine Learning, Remote Sensing, Earth Science

Senior Research Staff / May, 2000January, 2020

Machine Learning, Remote Sensing, Earth Science, Fault Tolerant Computing

Research Staff / April, 1996April, 2000

Scientific Modeling, Machine Learning, Supercomputing, Imaging Systems, Fault Tolerant Computing

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At NotedSource, we believe that professors, post-docs, scientists and other researchers have deep, untapped knowledge and expertise that can be leveraged to drive innovation within companies. NotedSource is committed to bridging the gap between academia and industry by providing a platform for collaboration with industry and networking with other researchers.

For industry, NotedSource identifies the right academic experts in 24 hours to help organizations build and grow. With a platform of thousands of knowledgeable PhDs, scientists, and industry experts, NotedSource makes connecting and collaborating easy.

For academic researchers such as professors, post-docs, and Ph.D.s, NotedSource provides tools to discover and connect to your colleagues with messaging and news feeds, in addition to the opportunity to be paid for your collaboration with vetted partners.

Expert Institutions
NotedSource has experts from Stanford University
Expert institutions using NotedSource include Oxfort University
Experts from McGill have used NotedSource to share their expertise
University of Chicago experts have used NotedSource
MIT researchers have used NotedSource
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Microsoft uses NotedSource for academic partnerships
Johnson & Johnson academic research projects on NotedSource
ProQuest (Clarivate) uses NotedSource as their industry academia platform
Slamom consulting engages academics for research collaboration on NotedSource
Omnicom and OMG find academics on notedsource
Unilever research project have used NotedSource to engage academic experts