Emily Saldanha

According to our database, Emily Saldanha authored at least 15 papers between 2019 and 2025.

Collaborative distances:

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

2025

AutoLabs: Cognitive Multi-Agent Systems with Self-Correction for Autonomous Chemical Experimentation.

Gihan Panapitiya, Emily Saldanha, Heather Job, Olivia Hess

CoRR, September, 2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations.

Jessica Kong, Gihan Panapitiya, Emily Saldanha

J. Chem. Inf. Model., 2025

2024

DISHONEST: Dissecting misInformation Spread using Homogeneous sOcial NEtworks and Semantic Topic classification.

Caleb Stam, Emily Saldanha, Mahantesh Halappanavar, Anurag Acharya

CoRR, 2024

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability.

Gihan Panapitiya, Peiyuan Gao, C. Mark Maupin, Emily Saldanha

CoRR, 2024

2023

Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction.

Tianzhixi Yin, Gihan Panapitiya, Elizabeth D. Coda, Emily Saldanha

J. Cheminformatics, December, 2023

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods.

Svitlana Volkova, Dustin Arendt, Emily Saldanha, Maria Glenski, Ellyn Ayton, Joseph Cottam, Sinan Aksoy, Brett Jefferson, Karthnik Shrivaram

Comput. Math. Organ. Theory, March, 2023

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models.

Gihan Panapitiya, Emily Saldanha

CoRR, 2023

Unsupervised Keyphrase Extraction via Interpretable Neural Networks.

Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

2021

Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations.

Gihan Panapitiya, Michael Girard, Aaron Hollas, Vijay Murugesan, Wei Wang, Emily Saldanha

CoRR, 2021

VAINE: Visualization and AI for Natural Experiments.

Grace Guo, Maria Glenski, ZhuanYi Shaw, Emily Saldanha, Alex Endert, Svitlana Volkova, Dustin Arendt

Proceedings of the 2021 IEEE Visualization Conference, 2021

Extracting Material Property Measurement Data from Scientific Articles.

Gihan Panapitiya, Fred Parks, Jonathan Sepulveda, Emily Saldanha

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020

Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation.

Emily Saldanha, Aparna Garimella, Svitlana Volkova

Proceedings of the 13th International Conference on Natural Language Generation, 2020

2019

Characterizing Speed and Scale of Cryptocurrency Discussion Spread on Reddit.

Maria Glenski, Emily Saldanha, Svitlana Volkova

Proceedings of the World Wide Web Conference, 2019

ReLVis: Visual Analytics for Situational Awareness During Reinforcement Learning Experimentation.

Emily Saldanha, Brenda Praggastis, Todd Billow, Dustin Lockhart Arendt

Proceedings of the 21st Eurographics Conference on Visualization, 2019

Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation.

Dustin Arendt, Emily Saldanha, Ryan Wesslen, Svitlana Volkova, Wenwen Dou

Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019