Daniel D'souza
Daniel D'souza is a Research Engineer at Cohere whose work spans multilingual AI, machine-learning fairness, and coding-agent teams. He studied electrical and communications engineering at KLS Gogte Institute of Technology, pursued graduate study at the University of Michigan, and worked at ProQuest. His research includes African-language entity recognition, subgroup performance in neural-network ensembles, the Aya model family, and multilingual inference-time scaling. As of October 2026, he studies how coding-agent teams can solve software tasks.
Timeline
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2009–2018
Engineering and graduate study
He earns a Bachelor of Engineering in Electrical, Electronics and Communications Engineering at KLS Gogte Institute of Technology in 2013. At the University of Michigan, he pursues an MS in 2016–2017 and works as a research assistant in the Language and Information Technologies Lab in 2018.
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2017–2024
ProQuest data science
At ProQuest, he is a software engineering intern in 2017 and a data scientist in 2018. He serves as a senior data scientist from 2022 to 2024.
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2021–2023
African-language NER and fairness
He coauthors MasakhaNER, a dataset for named entity recognition in ten African languages, in 2021. In 2023, he coauthors FAIR-Ensemble, which finds that homogeneous deep-neural-network ensembles can improve minority-group performance as the number of models grows.
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2024
Building multilingual Aya models
He coauthors Aya Model, a massively multilingual generative language model that follows instructions in 101 languages, and Aya Dataset, a human-curated instruction collection spanning 65 languages. He also coauthors Aya Expanse, a family of 8B and 32B parameter multilingual language models.
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2025
Scaling inference across languages
His 2025 paper studies inference-time compute for open 8B and 111B models across seven languages. The team proposes language- and task-aware sampling strategies that improve performance across languages and tasks.
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As of October 2026
Research Engineer at Cohere
As of October 2026, he is a Research Engineer at Cohere. With coauthors Keenan Samway and Nouha Dziri, he studies teams of coding agents. On ProgramBench, the team finds that assigning workers to exploration and planning matches the performance of delegating planning, implementation, and verification together at half the cost.
Career
- Since 2024CohereResearch Engineer
- 2018University of MichiganResearch Assistant
- 2017–2024ProQuestSoftware Engineering Intern, Data Scientist
Education
- 2017University of MichiganMS
- 2013KLS Gogte Institute of TechnologyBachelor of Engineering in Electrical, Electronics and Communications Engineering
Most-cited works
- MasakhaNER: Named Entity Recognition for African LanguagesWith Sebastian Ruder
- Estimating Example Difficulty using Variance of GradientsWith Sara Hooker
- sourmash v4: A multitool to quickly search, compare, and analyze genomic and metagenomic data sets
- Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelWith Niklas Muennighoff, Phil Blunsom and Sara Hooker
- Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningWith Niklas Muennighoff, Sara Hooker and Sebastian Ruder
Sources and credits
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- Daniel D'souza — Lacuna
- MasakhaNER: Named Entity Recognition for African Languages — ACL Anthology
- FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling
- Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model — ACL Anthology
- Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning — ACL Anthology
- Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
- When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs — ACL Anthology
- Lessons from scaling teams of coding agents: reach the frontier at 30% of the cost


