Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks. - Victor Schmidt; Alexandra Luccioni; S. Karthik Mukkavilli; Narmada Balasooriya; Kris Sankaran; Jennifer T. Chayes; Yoshua Bengio (CoRR, 2019)

Deep learning for Aerosol Forecasting. - Caleb Hoyne; S. Karthik Mukkavilli; David Meger (CoRR, 2019)

Predicting ice flow using machine learning. - Yimeng Min; S. Karthik Mukkavilli; Yoshua Bengio (CoRR, 2019)

TensorBank: Tensor Lakehouse for Foundation Model Training. - Romeo Kienzler; Johannes Schmude; Naomi Simumba; Benedikt Blumenstiel; Marcus Freitag; Daiki Kimura; Zoltan Arnold Nagy; Michael Behrendt; Hendrik F. Hamann; S. Karthik Mukkavilli; Daniel Salles Civitarese (IEEE Big Data, 2023)

AB2CD: AI for Building Climate Damage Classification and Detection. - Maximilian Nitsche; S. Karthik Mukkavilli; Niklas Kühl 001; Thomas Brunschwiler (CoRR, 2023)

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation. - S. Karthik Mukkavilli; Daniel Salles Civitarese; Johannes Schmude; Johannes Jakubik; Anne Jones; Nam Nguyen; Christopher Phillips; Sujit Roy; Shraddha Singh; Campbell D. Watson; Raghu K. Ganti; Hendrik F. Hamann; et al. (CoRR, 2023)

Foundation Models for Generalist Geospatial Artificial Intelligence. - Johannes Jakubik; Sujit Roy; C. E. Phillips; Paolo Fraccaro; Denys Godwin; Bianca Zadrozny; Daniela Szwarcman; Carlos Gomes; Gabby Nyirjesy; Blair Edwards; Daiki Kimura; Naomi Simumba; et al. (CoRR, 2023)

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network. - Takuya Kurihana; Kyongmin Yeo; Daniela Szwarcman; Bruce Elmegreen; S. Karthik Mukkavilli; Johannes Schmude; Levente J. Klein (CoRR, 2023)

Tackling Climate Change with Machine Learning. - David Rolnick; Priya L. Donti; Lynn H. Kaack; Kelly Kochanski; Alexandre Lacoste; Kris Sankaran; Andrew Slavin Ross; Nikola Milojevic-Dupont; Natasha Jaques; Anna Waldman-Brown; Alexandra Sasha Luccioni; Tegan Maharaj; et al. (ACM Comput. Surv., 2023)

INDUS: Effective and Efficient Language Models for Scientific Applications. - Bishwaranjan Bhattacharjee; Aashka Trivedi; Masayasu Muraoka; Muthukumaran Ramasubramanian; Takuma Udagawa; Iksha Gurung; Nishan Pantha; Rong Zhang; Bharath Dandala; Rahul Ramachandran; Manil Maskey; Kaylin M. Bugbee; et al. (EMNLP (Industry Track), 2024)

INDUS: Effective and Efficient Language Models for Scientific Applications. - Bishwaranjan Bhattacharjee; Aashka Trivedi; Masayasu Muraoka; Muthukumaran Ramasubramanian; Takuma Udagawa; Iksha Gurung; Rong Zhang; Bharath Dandala; Rahul Ramachandran; Manil Maskey; Kaylin M. Bugbee; Mike Little; et al. (CoRR, 2024)

Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks. - Thomas Bailie; Yun Sing Koh; S. Karthik Mukkavilli; Varvara Vetrova (CoRR, 2025)