I am a Schmidt AI in Science postdoctoral fellow in the Department of Computer Science at the University of Toronto. I received my Ph.D. in Computer Science from the University of Toronto, where I was advised by Steve Easterbrook. My research interests lie at the intersection of machine learning and climate modelling. In particular, I combine statistics and numerical simulations to improve our understanding of meteorology and climate, and guide the development of the next generation of climate models.

I spent Winter 2020 as a research intern at Microsoft Research New England, under the supervision of Lester Mackey. Before joining the University of Toronto, I obtained my MSc in Bioresource Engineering from McGill University.

2023:

• Our paper SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking got accepted at NeurIPS 2023 this December. See you in New Orleans!
• I graduated! I am now a Schmidt AI in Science fellow at the University of Toronto.
• I'm presenting progress on our Adaptive Bias Correction for Improved Subseasonal Forecasting work at ICML 2023 syn-ml workshop in Honolulu this july. Join us!
• Our paper Adaptive Bias Correction for Improved Subseasonal Forecasting is out!
• I received the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship!

2022:

• I'm presenting our work on identifying windows of forecast opportunity using our Adaptive Bias Correction framework at the Neurips 2022 CCAI and AI4Science workshops in New Orleans this December. Join us!
• Our preprint Adaptive Bias Correction for Improved Subseasonal Forecasting is on arXiv.
• I'm presenting progress on our Adaptive Bias Correction for Improved Subseasonal Forecasting work at EGU 2022 in Vienna this May. Join us!
• Our subseasonal_data python package providing an API for loading and manipulating the SubseasonalClimateUSA dataset is out!
• Our subseasonal_toolkit python package providing implementations of our subseasonal forecasting toolkit models and baselines is out!.
• I'm presenting our work on adaptive bias correction at the S2S Water Workshop this March. Join us!

2021:

• We received the CCAI 2021 Innovation grant!
• I'll be presenting our subseasonal forecasting toolkit at the NeurIPS 2021 CCAI workshop this December. Join us!
• Our preprint Learned Benchmarks for Subseasonal Forecasting is on arXiv.
• Our team placed in the top 5 in the Forecast Rodeo 2 organized by NOAA and the U.S. Bureau of Reclamation!
• Our paper Online Learning with Optimism and Delay got accepted at ICML 2021 this July. See you then!
• I'm presenting our work on subseasonal climate forecasting at the 2021 Google Flood Forecasting Workshop this October. Join us!

2020:

• I was awarded a Microsoft Research PhD Award!
• Our paper on developing a daily multilayer cropland soil moisture dataset using Machine Learning is out.
• Our WeatherBench work got the best paper award at the ICLR 2020 CLimate AI workshop!
• Our paper WeatherBench: a benchmark data set for data-driven weather forecasting is out!
• Judah Cohen will be presenting our work on machine learning for subseasonal forecasting at AMS 2020 in Boston this January. Stop by!
• I'm interning at Microsoft Research New England in Cambridge, this winter. I will be working with Lester Mackey on sub-seasonal forecasting .
• I'm invited to the NASA Radiant Earth ML workshop in Washington, DC this January. I will be giving a talk on accelerators and obstacles to emerging AI-enabled research fields.

2019:

• I'm attending the Tackling Climate Change with Machine Learning workshop at NeurIPS in Vancouver this December.
• I'm visiting Mike Pritchard's Computational Clouds and Climate Lab at University of California, Irvine this Fall.
• I'm part of the organizing committee of the climate informatics workshop. CI2019 will be held in Paris in October 2019. Join us!
  
• I'm presenting a poster for our paper on recovering parameters of strange attractors at the ICML AI for climate workshop in Long Beach this June. Stop by!
  
• I'm attending the CAM Future Physics for Global Atmospheric Models tutorial and workshop at NCAR in Boulder this June.
  
• Our paper on coupling the maximum overlap discrete wavelet transform with LSTMs is out.
  
• I'm visiting Pierre Gentine's lab at Columbia University in NYC this February.
  

2018:

• I was selected as a finalist for the Microsoft Research PhD Fellowship.
  
• I'm presenting a poster for our paper on accelerators and obstacles to emerging ML-enabled research fields at the CRACT Neurips workshop in Montreal this December. Stop by!
  
• I'm organizing the ML4Climate session at the Google Self-Organizing Machine Learning Conference in Toronto this November. Join us!
  
• I'm part of the organizing committee of the 8th Climate Informatics workshop. CI 2018 will be held in Boulder, Colorado in September 2018. Come and join us!
  
• I'm part of the organizing committee of the 5th International Conference on Information and Communication Technologies for Sustainability. ICT4S 2018 will be held in Toronto in May 2018. Join us!
  

2017:

• Math on a boat interview from the 2017 Heidelberg Laureate Forum held in beautiful Heidelberg, Germany.
  
• Our climate downscaling work discussed in this episode of the Science Inside podcast.
  

As a teaching assistant at University of Toronto:

• CSC165: Mathematical Expression and Reasoning for Computer Science. Winter 2017, Fall 2018 & Winter 2018.

As a teaching assistant at Akhawayn University:

• MTH2303: Linear algebra. Fall 2013.
• CSC3326: Database systems. Winter 2012.

• My sister's work in graph theory and algorithms.
• Other interests in robotics from my undergrad days:
  RFID-based Location System for Forest Search and Rescue Missions
• À mon père, Sidi M. Mouatadid (1949-2019).