Elizabeth A. Barnes

20 papers A* 1Journal 18Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	AI-Informed Model Analogs for Subseasonal-to-Seasonal Prediction. CoRR Jacob B. Landsberg, Elizabeth A. Barnes, Matthew Newman
2025	J	`jnl`	ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation. J. Mach. Learn. Res. Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Helge Heuer, Benjamin R. Hillman, Andrea M. Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth A. Barnes, Noah D. Brenowitz, Christopher S. Bretherton, Veronika Eyring, Savannah L. Ferretti, Nicholas J. Lutsko, Pierre Gentine, Stephan Mandt, J. David Neelin, Rose Yu, Laure Zanna, Nathan M. Urban, Janni Yuval, Ryan Abernathey, Pierre Baldi, Wayne Chuang, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Po-Lun Ma, Sara Shamekh, Guang Zhang, Michael S. Pritchard
2025	J	`jnl`	Forecasting the Future with Yesterday's Climate: Temperature Bias in AI Weather and Climate Models. CoRR Jacob B. Landsberg, Elizabeth A. Barnes
2025	J	`jnl`	Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework. CoRR M. A. Fernandez, Elizabeth A. Barnes
2025	J	`jnl`	Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network. CoRR M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes, Mark DeMaria, Marie McGraw, Galina Chirokova, Lixin Lu
2025	J	`jnl`	Turning Up the Heat: Assessing 2-m Temperature Forecast Errors in AI Weather Prediction Models During Heat Waves. CoRR Kelsey E. Ennis, Elizabeth A. Barnes, Marybeth C. Arcodia, Martin A. Fernandez, Eric D. Maloney
2024	J	`jnl`	AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. AI Mag. Amy McGovern, Imme Ebert-Uphoff, Elizabeth A. Barnes, Ann Bostrom, Mariana G. Cains, Phillip Davis, Julie L. Demuth, Dimitrios I. Diochnos, Andrew H. Fagg, Philippe Tissot, John K. Williams, Christopher D. Wirz
2024	J	`jnl`	Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models. CoRR Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon, Shiheng Duan, Joshua Elms, Jiwoo Lee, L. Ruby Leung, Dan Lu, Maria J. Molina, Travis A. O'Brien
2023	A*	`conf`	ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard
2023	J	`jnl`	ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.
2022	J	`jnl`	Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. CoRR Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
2022	J	`jnl`	Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. CoRR Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
2021	J	`jnl`	Controlled abstention neural networks for identifying skillful predictions for classification problems. CoRR Elizabeth A. Barnes, Randal J. Barnes
2021	J	`jnl`	Controlled abstention neural networks for identifying skillful predictions for regression problems. CoRR Elizabeth A. Barnes, Randal J. Barnes
2021	J	`jnl`	Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset. CoRR Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
2021	J	`jnl`	Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner
2021	J	`jnl`	Will Artificial Intelligence supersede Earth System and Climate Models? CoRR Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner
2020	—	`conf`	Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. xxAI@ICML Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes
2019	J	`jnl`	Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. CoRR Benjamin A. Toms, Elizabeth A. Barnes, Imme Ebert-Uphoff
2017	J	`jnl`	Working with Daily Climate Model Output Data in R and the futureheatwaves Package. R J. G. Brooke Anderson, Colin Eason, Elizabeth A. Barnes

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.