Publications

Research across AI for complex systems, scientific machine learning, climate, energy and Earth sciences. Google Scholar maintains the live profile.

2026

2026 · EarthArXiv · Preprint

A Blueprint for Integrated Climate Intelligence

C Rodriguez-Pardo, G Ascenso, CGA Pedraza, ML Bachelery, et al.

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2026 · IJCAI

Addressing Downward Memory Loss in Hierarchical GNN Forecasters Through Memory-Buffered Decoding

T Bailie, SK Mukkavilli, V Vetrova, YS Koh

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2026 · Nature Climate Change 16, 505–507 (Comment)

Artificial intelligence to support cross-disciplinary climate change research

Y Ou, C Rodriguez-Pardo, AA Khourdajie, J Rogelj, P Zhou, SK Mukkavilli, et al.

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2026 · WSDM 2026

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

T Bailie, YS Koh, SK Mukkavilli

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2026 · 1st IJCAI Workshop on Safe Physical AI (position paper)

Position: Train Robustly and Certify at Runtime Under Partial Observability

C Zhou, SK Mukkavilli, Y Gao

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2025

2025 · EGU General Assembly Conference Abstracts, EGU25-6762 · Conference abstract / presentation

Automated disaster event extraction to understand lessons learned: A large-scale text analysis on the scientific literature of floods, droughts, and landslides.

L Stein, BM Pfitzmann, S Karthik Mukkavilli, U Ozturk, PWJ Staar, et al.

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2025 · Nature Machine Intelligence 7, 1076–1090

Model-based reinforcement learning for ultrasound-driven autonomous microrobots

M Medany, L Piglia, L Achenbach, SK Mukkavilli, D Ahmed

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2024

2024 · Nature Reviews Bioengineering

AI-driven autonomous microrobots for targeted medicine

M Medany, SK Mukkavilli, D Ahmed

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2024 · AGU24 · Conference abstract / presentation

Efficient Fine-tuning of Atmospheric Foundation Models for Wildfire Modeling

AA Rahman, L Mackey, SK Mukkavilli, S Kahou

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2024 · AGU Fall Meeting Abstracts 2024, IN51B-03 · Conference abstract / presentation

Enhancing Wind Downscaling with Foundation Models

D Salles Civitarese, JLLG Guevara Diaz, SK Mukkavilli, J Schmude, et al.

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2024 · Conference on Empirical Methods in Natural Language Processing (EMNLP), 98–112

Indus: Effective and efficient language models for scientific applications

B Bhattacharjee, A Trivedi, M Muraoka, M Ramasubramanian, et al.

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2024 · The Canadian Meteorological and Oceanographic Society (CMOS) Congress

On the development of artificial intelligence downscaling applications for medium-range forecasts of weather elements at CCMEP

C Saad, D Brunet, M Surcel, J Schmude, DS Civitarese, K Mukkavilli, et al.

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2024 · AGU Fall Meeting Abstracts 2024 (1662), A41G-1662 · Conference abstract / presentation

Site-scale methane plume simulation and validation from oil and gas facilities through advanced dispersion, atmospheric modeling, and scientific machine learning

A Fathi, JL Sousa Almeida, E Bentivegna, F Cardoso, B Elmegreen, et al.

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2024 · The Canadian Meteorological and Oceanographic Society (CMOS) Congress

Towards state-of-the-art nowcasting with a foundational AI model: an ECCC-IBM collaboration

D Brunet, D Davignon, A Mahidjiba, K Mukkavilli, DS Civitarese, et al.

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2024 · Earth's Future 12 (10), e2024EF004590

Wealth over Woe: Global biases in hydro‐hazard research

L Stein, SK Mukkavilli, BM Pfitzmann, PWJ Staar, U Ozturk, C Berrospi, et al.

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2023

2023 · AGU Fall Meeting Abstracts 2023 (6), A12H-06 · Conference abstract / presentation

A 3D spatial self-attention module on a non-uniform vertical coordinate for super-resolution wind fields

T Kurihana, K Yeo, D Szwarcman, B Elmegreen, SK Mukkavilli

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2023 · arXiv preprint arXiv:2312.13212 · Preprint

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network

T Kurihana, K Yeo, D Szwarcman, B Elmegreen, K Mukkavilli, J Schmude, et al.

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2023 · Proceedings of the AAAI Symposium Series 2 (1), 115-123

AB2CD: AI for building climate damage classification and detection

M Nitsche, SK Mukkavilli, N Kühl, T Brunschwiler

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2023 · arXiv preprint arXiv: 2309.10808 · Preprint

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

S Karthik Mukkavilli, D Salles Civitarese, J Schmude, J Jakubik, A Jones, et al.

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2023 · AGU Fall Meeting Abstracts 2023 (2), IN14B-02 · Conference abstract / presentation

ClimateHub: Deep Search for Climate, Earth and Environmental Sciences

SK Mukkavilli, T Brunschwiler, B Pfitzmann, CB Ramis, A Jones, et al.

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2023 · arXiv preprint arXiv:2310.18660 · Preprint

Foundation models for generalist geospatial artificial intelligence

J Jakubik, S Roy, CE Phillips, P Fraccaro, D Godwin, B Zadrozny, et al.

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2023 · AGU Fall Meeting Abstracts 2023 (8), A12H-08 · Conference abstract / presentation

High-Resolution WRF Wind Field Dataset for GHG Applications: Navigating Computational Challenges in Energy Research

SK Mukkavilli, W Trojak, JL Sousa Almeida, B Elmegreen, C Watson, et al.

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2023 · 2023 IEEE International Conference on Big Data (BigData), 3350-3354

Tensorbank: Tensor lakehouse for foundation model training

R Kienzler, J Schmude, N Simumba, B Blumenstiel, M Freitag, D Kimura, et al.

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2022

2022 · Fall Meeting 2022 · Conference abstract / presentation

Knowledge Graphs in Deep Search Climate-Hub

SK Mukkavilli, B Pfitzmann, A Jones, T Brunschwiler, P Fraccaro, et al.

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2022 · Hydrological Processes, e14742

Lifelines for a Drowning Science‐Improving Findability and Synthesis of Hydrologic Publications

L Stein, SK Mukkavilli, T Wagener

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2022 · ACM computing surveys (CSUR) 55 (2), 1-96

Tackling climate change with machine learning

D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, et al.

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2021

2021 · Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States …

Reliable modeling and prediction of precipitation & radiation for mountainous hydrology

D Feldman, V Chandrasekar, P Dennedy-Frank, D Dwivedi, M Mudigonda, et al.

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2021 · American Meteorological Society Meeting Abstracts 101, 14.8 · Conference abstract / presentation

Toward Generative Superparameterized Updrafts with Variational Autoencoder Interpretability

G Mooers, S Mandt, M Pritchard, T Beucler, J Tuyls, K Mukkavilli

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2020

2020 · 100th American Meteorological Society Annual Meeting · Conference abstract / presentation

AI Applications for Air Quality

SK Mukkavilli

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2020 · AGU Fall Meeting Abstracts 2020, A043-0009 · Conference abstract / presentation

Generative large eddy simulations with conditional variational autoencoders

SK Mukkavilli, MS Pritchard, KG Pressel, G Mooers, PL Ma, S Mandt

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2020 · American Meteorological Society Meeting Abstracts 100, J61. 4 · Conference abstract / presentation

Integrated Climate Extremes: Modeling Future Impacts for Visualizing Climate Change

SK Mukkavilli, Y Min, A Madanchi, VB Pacela, S Patel, Y Bengio

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2020 · American Meteorological Society Meeting Abstracts 100, 9A. 1 · Conference abstract / presentation

PMNet: Improving Aerosol Predictions Using Deep Neural Nets for Limited Ground Stations

C Hoyne, SK Mukkavilli, D Meger

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2020 · AGU Fall Meeting Abstracts 2020, A071-04 · Conference abstract / presentation

Parameterization of Autoconversion Rates in a Climate Model Using Machine Learning with Training from Large Eddy Simulations

KG Pressel, PL Ma, J Fan, JD Fast, WI Gustafson Jr, JC Hardin, C Kaul, et al.

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2020 · AGU Fall Meeting Abstracts 2020, A071-02 · Conference abstract / presentation

Towards robust operational neural network parameterizations of convection in climate models—advances in stability, credibility and software

MS Pritchard, T Beucler, G Mooers, J Ott, P Gentine, L Peng, P Baldi, et al.

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2019

2019 · Atmospheric research 215, 149-164

Assessment of atmospheric aerosols from two reanalysis products over Australia

SK Mukkavilli, AA Prasad, RA Taylor, J Huang, RM Mitchell, A Troccoli, et al.

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2019 · 99th American Meteorological Society Annual Meeting · Conference abstract / presentation

Big Data, Big Computing, Bigger Science: Earth Data for AI

TS Sliwinski, SK Mukkavilli, A Ganguly, S Collis, GJ Creager, P Tissot

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2019 · American Meteorological Society Meeting Abstracts 99, 56419 · Conference abstract / presentation

Climate Change & AI: Present and potential role of AI in assessment and response

L Joppa, V Lakshmanan, V Kumar, G Dudek, SK Mukkavilli, P Tissot

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2019 · Neural Information Processing Systems (NeurIPS), Machine Learning and the …

Deep learning for Aerosol Forecasting

C Hoyne, SK Mukkavilli, D Meger

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2019 · 18th Conference on Artificial and Computational Intelligence and its … · Conference abstract / presentation

EnviroNet: ImageNet for Environment

SK Mukkavilli, P Tissot, A Ganguly, L Joppa, D Meger, G Dudek

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2019 · 99th American Meteorological Society Annual Meeting · Conference abstract / presentation

Environet: The ImageNet for Environmental Sciences

SK Mukkavilli, L Joppa, V Lakshmanan, G Dudek, M Prabhat

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2019 · American Meteorological Society Meeting Abstracts 99, 56420 · Conference abstract / presentation

Physics guided ML: Emerging AI opportunities for weather and climate

V Kumar, V Lakshmanan, L Joppa, G Dudek, SK Mukkavilli, A McGovern

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2019 · Neural Information Processing Systems (NeurIPS), Tackling Climate Change …

Predicting ice flow using machine learning

Y Min, SK Mukkavilli, Y Bengio

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2019 · American Meteorological Society Meeting Abstracts 99, 57677 · Conference abstract / presentation

Towards Planetary Intelligence: On the Synergistic Future of AI, Weather and Climate

SK Mukkavilli

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2019 · International Conference on Learning Representations (ICLR), AI for Social …

Visualizing the consequences of climate change using cycle-consistent adversarial networks

V Schmidt, A Luccioni, SK Mukkavilli, N Balasooriya, K Sankaran, et al.

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2018

2018 · 98th American Meteorological Society Annual Meeting · Conference abstract / presentation

AI Techniques for Numerical Weather Predictions

SK Mukkavilli

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2018 · https://www.unsworks.unsw.edu.au/primo-explore/fulldisplay/unsworks_58614 …

Investigating Australian dust aerosol spatiotemporal effects on direct normal irradiance forecasts

SK Mukkavilli

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2018 · Journal of Applied Meteorology and Climatology 57 (3), 493-515

Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event

SK Mukkavilli, AA Prasad, RA Taylor, A Troccoli, MJ Kay

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2017

2017 · Penguin; edited by Paul Hawken

Drawdown: The most comprehensive plan ever proposed to reverse global warming

Research contribution by S. Karthik Mukkavilli

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2015

2015 · American Meteorological Society Meeting Abstracts 95, J6. 3 · Conference abstract / presentation

Towards a More Accurate Machine Learning Multi-Model Ensemble Method for Direct Solar Irradiance Forecasts

SK Mukkavilli, MJ Kay, AA Prasad, R Taylor, A Troccoli

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2014

2014 · AGU Fall Meeting Abstracts 2014, GC43A-0697 · Conference abstract / presentation

Sensitivity analysis of numerical weather prediction radiative schemes to forecast direct solar radiation over Australia

SK Mukkavilli, MJ Kay, R Taylor, AA Prasad, A Troccoli

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