I’m a PhD candidate in the computer science department at Brown University studying the evaluation of AI systems, with an emphasis on real world settings. Prior to starting my PhD, I was a data scientist working with organizations such as DARPA and McKinsey, and a open source core-maintainer at Project Jupyter. My work evaluating large multilingual-language models has received the Mozilla Technology Fund 2023 Award and has also been covered in WIRED magazine.
Email me at jessica_forde at brown.edu.
News
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Our initial paper, Re-Evaluating Evaluation for Multilingual Summarization, as part of our Mozilla Technology Fund 2023 Award was recently presented at EMNLP in Miami 🌴.
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I was honored to be invited by US State Deptartment to visit New Zealand and Australia as part of their US Speakers program.
Selected Publications
- Forde JZ*, Zhang R*, Sutawika L, Aji AF, Cahyawijaya S, Winata GI, Wu, M., Eickhoff, C., Biderman, S., & Pavlick, E. Re-Evaluating Evaluation for Multilingual Summarization. EMNLP. 2024.
- Yong ZX, Zhang R, Forde J, Wang S, Subramonian A, Lovenia H, Cahyawijaya, S., Winata, G. I., Sutawika, L., Cruz, J. C. B., Tan, Y. L., Phan, L., Garcia, R., Solorio, T., & Aji, A. F. Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages. Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching. 2023.
- Lovering C*, Forde J*, Konidaris G, Pavlick E, Littman M. Evaluation beyond task performance: Analyzing concepts in AlphaZero in Hex. NeurIPS. 2022.
- Cooper AF, Lu Y, Forde JZ, De Sa C. Hyperparameter Optimization Is Deceiving Us, and How to Stop It. NeurIPS. 2021.
- Forde JZ*, Cooper AF*, Kwegyir-Aggrey K, De Sa C, Littman M. Model Selection’s Disparate Impact in Real-World Deep Learning Applications. Science and Engineering of Deep Learning Workshop, ICLR 2021. Contributed Talk.
- Paganini M, Forde J. Streamlining Tensor and Network Pruning in PyTorch. ML for Developing Countries Workshop, ICLR 2020. Contributed Talk.
- Zech JR, Forde JZ, Littman ML. Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging. ML for Healthcare Workshop, NeurIPS 2019.
- Forde JZ, Paganini M. The scientific method in the science of machine learning. Debugging Machine Learning Models Workshop, ICLR 2019. Contributed Talk
- Project Jupyter, M. Bussonnier, J. Forde, J. Freeman, B. Granger, T. Head, C. Holdgraf, K. Kelley, G. Nalvarte, A. Osheroff,M. Pacer, Y. Panda, F. Perez, B. Ragan-Kelley, and C. Willing. Binder 2.0-Reproducible, interactive, sharable environments for science at scale. Scipy. 2018.