S. Karthik Mukkavilli

According to our database, S. Karthik Mukkavilli authored at least 15 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling.

Thomas Bailie, Yun Sing Koh, Surya Karthik Mukkavilli

Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, 2026

2025

Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Training.

Thomas Bailie, S. Karthik Mukkavilli, Varvara Vetrova, Yun Sing Koh

CoRR, October, 2025

Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks.

Thomas Bailie, Yun Sing Koh, S. Karthik Mukkavilli, Varvara Vetrova

CoRR, April, 2025

2024

Higher Order Graph Attention Probabilistic Walk Networks.

Thomas Bailie, Yun Sing Koh, S. Karthik Mukkavilli

CoRR, 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, Elizabeth Fancher, Lauren M. Sanders, Sylvain V. Costes, Sergi Blanco-Cuaresma, Kelly E. Lockhart, Thomas Allen, Félix Grèzes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panagiotis Vagenas, S. Karthik Mukkavilli, Peter W. J. Staar, Sanaz Vahidinia, Ryan McGranaghan, Armin Mehrabian, Tsengdar J. Lee

CoRR, 2024

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, Michael Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren M. Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly E. Lockhart, Thomas Allen, Félix Grèzes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panagiotis Vagenas, S. Karthik Mukkavilli, Peter W. J. Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsengdar J. Lee

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024

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, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla P. Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer T. Chayes, Yoshua Bengio

ACM Comput. Surv., 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

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, Linsong Chu, S. Karthik Mukkavilli, Devyani Lambhate, Kamal Das, Ranjini Bangalore, Dário A. B. Oliveira, Michal Muszynski, Kumar Ankur, Muthukumaran Ramasubramanian, Iksha Gurung, Sam Khallaghi, Hanxi Li, Michael Cecil, Maryam Ahmadi, Fatemeh Kordi, Hamed Alemohammad, Manil Maskey, Raghu K. Ganti, Kommy Weldemariam, Rahul Ramachandran

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, Udaysankar Nair, Rahul Ramachandran, Kommy Weldemariam

CoRR, 2023

AB2CD: AI for Building Climate Damage Classification and Detection.

Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl, Thomas Brunschwiler

CoRR, 2023

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

Proceedings of the IEEE International Conference on Big Data, 2023

2019

Predicting ice flow using machine learning.

Yimeng Min, S. Karthik Mukkavilli, Yoshua Bengio

CoRR, 2019

Deep learning for Aerosol Forecasting.

Caleb Hoyne, S. Karthik Mukkavilli, David Meger

CoRR, 2019

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