“My research uses tools from statistics to make machine learning systems more robust and trustworthy — especially in complex systems such as large language models.”In their own words: thashim.github.io
Assistant Professor of Computer Science at Stanford researching how to make machine learning systems, especially large language models, more robust and trustworthy.
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Received $3.2M from 2 funders
’24
’25
Checked 25 Oct 2026
Money flows
Where their money came from and where it went. Pick years and what to show; hover a box or ribbon for details, click one to focus it, click the background to see everything.
Funded by
All grants
3 grants totalling $3.2M, newest first.
2025 2 grants · $2.3M
| Date | Funder | Amount | Purpose |
|---|---|---|---|
| Jun 2025 | Coefficient Giving | $1.9M | Robust AI Benchmarks (Tatsunori Hashimoto) source |
| 11 Feb 2025 | Science of Trustworthy AI | ~$370k | Science of Trustworthy AI — AI Safety Science (Feb 2025 cohort): Project: A Meta-analysis Approach to Understanding LM Capabilities. One of 27 AI Safety Science awards totaling $10M+; per-project amount undisclosed. — Science of Trustworthy AI — AI Safety Science (Feb 2025 cohort source |
2024 1 grant · $880k
| Date | Funder | Amount | Purpose |
|---|---|---|---|
| May 2024 | Coefficient Giving | $880k | LLM-Generated Research Ideation Benchmark (Tatsunori Hashimoto) source |