Media
NOAA Climate and Global Change Postdoctoral Fellowship Promo
Wildfires and Prescribed Burns
NewsWatch 12
Prescribed Burns as a Tool to Mitigate Future Wildfire Smoke Exposure
KCRA 3 Sacramento: "Cal Fire, researchers see the proven benefits of controlled burning"
Press: Press Release, Harvard Gazette, KCRA Sacramento, CBS Newspath, Missoulian
Compressed Sensing for Atmospheric Monitoring
ACCESS XVII Talk
Machine Learning for Atmospheric Chemistry Modeling
NASA GISS Seminar Series
Press: Editor’s Highlight from JAMES
Selected Conference Presentations
2023
13. M. Kelp, C. Chiu, Q. Zhu, and L.J. Mickley. Uncovering spatiotemporal drivers of urban ozone in changing NOx regimes: A data-driven case study of Los Angeles and Chicago. AGU Fall Meeting, San Francisco, CA, December 11, 2023 (Talk slides)
12. M. Kelp, C. A. Keller, K. Wargan, B.M. Karpowicz, and D. J. Jacob. Tropospheric ozone dataassimilation in the NASA GEOS Composition Forecast Modeling System GEOS-CF v2.0 including direct assimilation of thermal infra-red radiances. AMS Annual Meeting, Denver, CO, January 12, 2023 (Talk slides)
2022
11. M. Kelp, T. Liu, and L.J. Mickley. Sensitivity of population-weighted smoke exposure towildfires in the western United States: implications for prescribed burning at the state level and in rural environmental justice communities. AGU Fall Meeting, Chicago, IL, December 14, 2022 (Talk slides)
10. M. Kelp, T. C. Fargiano, S. Lin, T. Liu, J. N. Kutz, and L.J. Mickley. Data-driven placement of PM2.5 air quality sensors in the United States: an approach to target urban environmental injustice. AGU Fall Meeting, Chicago, IL, December 12, 2022 (Talk slides)
9. M. Kelp, D.J. Jacob, and H. Lin. An Online-Learned Neural Network Chemical Solver for Stable and Long-Term Global Simulations of Atmospheric Chemistry in S2S Applications. AMS Annual Meeting, January 26, 2022 (Talk slides)
2021
8. M. Kelp, D.J. Jacob, and H. Lin. A recursive neural network chemical solver for fast long-term global simulations of atmospheric composition. AMS Annual Meeting, Virtual, January 13, 2021 (Talk slides)
2020
7. M. Kelp, J. N. Kutz, J.D. Marshall, and C. Tessum. Toward stable, general machine-learned models of the atmospheric chemical system. AGU Virtual Fall Meeting, December 7, 2020 (Talk)
6. M. Kelp and D.J. Jacob. A recursive neural network chemical solver for fast long-term global simulations of atmospheric composition. Atmospheric Chemical Mechanisms Conference, Virtual, November 18, 2020 (Lightning Talk)
2019
5. M. Kelp, J. N. Kutz, J.D. Marshall, and C. Tessum. Deep Learning Emulation and Compression of an Atmospheric Chemical System using a Chained Training Regime. AGU Fall Meeting, San Francisco, CA, December 13, 2019 (Poster)
2018
4. M. Kelp, C.W. Tessum, and J.D. Marshall. Orders-of-Magnitude Speedup in Atmospheric Chemistry Modeling through Neural Network-Based Emulation. AGU Fall Meeting, Washington D.C, December 12, 2018 (Poster)
3. M. Kelp, A.P. Grieshop, C.O. Reynolds, J. Baumgartner, G. Jain, K. Sethuramanand, and J.D. Marshall. Investigating Health-Relevant Air Pollution Concentration Linkages Across Multiple Seasons During Indoor Cookstove Campaign in Rural India. ISES-ISEE Joint Annual Meeting, Ottawa, CA, August 25, 2018 (Poster)
2016
2. M. Kelp, H.O.T. Pye, E.V. Fischer, J. Brewer, and J. Fry. Global Modeling of Secondary Organic Aerosol Production from Reaction of NO3 Radical with Speciated Monoterpenes. AAAR Annual Conference, Portland, OR, October 18, 2016 (Poster)
2015
1. M. Kelp, J. Brewer, C. Keller, and E.V. Fischer. Evaluating the Potential Importance of Monoterpene Degradation for Global Acetone Production. AGU Fall Meeting, San Francisco, CA, December 16, 2015 (Poster)