Aurora leverages the strengths of the foundation modelling approach to produce operational forecasts for a wide variety of atmospheric prediction problems, including those with limited training data, heterogeneous variables, and extreme events
We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. Published at ICML 2023 (Spotlight).
We present PDEArena, a modern PyTorch Lightning-based deep learning framework for neural PDE modeling. Published at TMLR 07/2023.
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms. Published at Transactions on Large-Scale Data-and Knowledge-Centered Systems XLVIII.
In this work, we present combined measurements of the production and decay rates of the Higgs boson, as well as its couplings to vector bosons and fermions. Published at Eur. Phys. J. C 79 (2019) 421.
This paper reports a search for a singly produced third-generation scalar leptoquark decaying to a $\tau$ lepton and a bottom quark. Published at Journal of High Energy Physics (2018).
My PhD thesis on neutral Higgs bosons and Z bosons. Published at CERN-THESIS-2018-066.
This paper reports a search for additional neutral Higgs bosons in the $\tau\tau$ final state in proton-proton collisions at the LHC. The search is performed in the context of the minimal supersymmetric extension of the standard model (MSSM). Published at Journal of High Energy Physics (2018).
In this paper, we report a measurement of the the $Z/ \gamma^* \rightarrow \tau \tau$ cross section and validation of the fake factor background estimation method. Published at European Physical Journal. C, Particles and Fields (2018).
We report the first direct observation of the Higgs boson decaying into a pair of fermions. Published at Eur. Phys. Lett. B 779 (2018) 283.