Abbreviated curriculum vitæ; an up-to-date professional copy can always be found here.

Work Experience

Google Research

Staff Software Engineer (Bungee), 2023

Google Research

Anthromet - AI for Weather and Climate


Technical Lead - Atmospheric Science, 2021-2023


Waymo Via - Perception - Staff SWE

Chief Scientist, 2017-2021

Tomorrow Weather - Director of Meteorology


Postdoctoral Research Associate, 2016-2017

Massachusetts Institute of Technology

Institute for Data, Systems, and Society

Advisors: Noelle Selin, Susan Solomon



Ph.D., 2017

Massachusetts Institute of Technology

Department of Earth, Atmospheric and Planetary Sciences

Advisors: Dr. Chien Wang and Professor Ron Prinn


B.S., magna cum laude, 2010

Cornell University

Department of Earth and Atmospheric Sciences

Thesis Advisor: Professor Natalie Mahowald

Awards and Honors


Outstanding Student Presentation Award

American Meteorological Society, 2015


Graduate Research Fellowship

National Science Foundation, 2012


Father James B. Macelwane Award in Meteorology

American Meteorological Society, 2011


National Defense Science and Engineering Fellowship

ASEE (declined), 2012

Highlighted Publications and Talks

Please refer to Google Scholar, ORCID or my CV for a full listing of relevant material.

Refeered / Peer-Reviewed

  1. Eastham, S., Monier, E., Rothenberg, D., Paltsev, S., Selin, N.: Rapid estimation of climate‐air quality interactions in integrated assessment using a response surface model, ACS Environ. AU, 3, 3, 153‐ 163. doi:10.1021/acsenvironau.2c00054, 2023
  2. Silva, S., Ma, P.‐L., Hardin, J., Rothenberg, D.: Physically Regularized Machine Learning Emulators of Aerosol Activation, Geosci. Model Dev., 14, 3067–3077, doi:10.5194/gmd‐14‐3067‐2021, 2021.
  3. Jin, Q., Grandey, B. S., Rothenberg, D., Avramov, A., Wang, C.: Impacts of Ship Emission Regulations, DMS Emissions, and Aerosol Mixing States on Cloud Radiative Effects of International Shipping Emissions, Atmos. Chem. Phys., Discuss., doi:10.5194/acp-2018-416, 2018.
  4. Rothenberg, Daniel and Chien Wang: An aerosol activation metamodel of v1.2.0 of the pyrcel cloud parcel model: development and offline assessment for use in an aerosol–climate model, Geosci. Model Dev., doi:10.5194/gmd-10-1817-2017, 2017.

Invited Talks, Panels, and Seminars

  1. Texas A&M; Atmospheric Sciences Seminar, Invited Speaker, College Station, TX, April 17, 2024
  2. University of Albany Atmospheric Sciences Research Colloquium, Invited Speaker, Albany, NY, October 2023 (to be rescheduled)
  3. Sustainable Science Techniques for Artificial Intelligence in Weather Applications, Panelist, AMS Washington Forum, Washington, D.C., 2023
  4. Using ML/AI for Data‐Driven Decision‐making. Presenter/Panelist, National Academies of Science and Engineering, Machine Learning and Artificial Intelligence to Advance Earth System Science: Opportunites and Challenges, Washington, D.C., 2022 | PDF
  5. Status of AI in the Atmospheric Sciences. Panelist, 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, AMS Annual Meeting, Phoenix, AZ, 2019.

Conference Talks

  1. Rothenberg, Daniel. Enabling Scalable, Serverless Weather Model Analyses by ”Kerchunking” Data in the Cloud. AMS Annual Meeting. Baltimore, MD, 2024. | PPTX
  2. Rothenberg, Daniel. Driving Hyper‐Local Weather Information with Autonomous Vehicles. AMS Annual Meeting. Denver, CO, 2023.
  3. Biryukov, S., Minsk, J., and Rothenberg, D. Physics-Informed Downscaling, Bias Correction, and Bayesian Probabilistic Ensembling of Weather Forecast Models. AMS Annual Meeting. Houston, TX / Virtual, 2022
  4. Rothenberg, Daniel. Rapidly Prototyping High-performance Meteorological Data Systems Using Xarray and Numba. Ninth Symposium on Advances in Modeling and Analysis Using Python, AMS Annual Meeting. Phoenix, AZ, 2019. | PDF
  5. Rothenberg, Daniel. A Python-based Parcel Model Framework for Studying Aerosol-Cloud Processes. Sixth Symposium on Advances in Modeling and Analysis Using Python. New Orleans, 2016. | PDF
  6. Rothenberg, Daniel and Nick Barnes. Lessons From Deploying the USHCN Pairwise Homogenization Algorithm in Python. 92nd Annual Meeting of the American Meteorological Society, Second Symposium on Advances in Modeling and Analysis Using Python. New Orleans, LA. 2012 | PDF


  1. Rothenberg, Daniel. Improved Forecasting Method with Machine Learning. US Patent US20200132884A1. August 2, 2022.
  2. Elkabetz, S. et al. Real-time weather forecasting for transportation systems. US Patent US10962680B2. March 3, 2021.
  3. Elkabetz, S. et al. Improved real-time weather forecasting system. World Patent WO2019126707A1. June 27, 2019.

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