A high-signal person to study if you care about the practical mechanics of adapting large models, especially where scaling theory turns into techniques that actually spread across the industry.
Researcher Profile
Editor reviewedPhillip Wallis
Parameter-efficient finetuning
Google researcher working on low-rank adaptation and agent safety
A useful profile for the path from parameter-efficient finetuning into newer agent-safety work, especially if you want people whose contributions span both model customization and tool-using systems security.
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Last reviewed
March 20, 2026
Known For
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01
LoRA
02
Agent safety and indirect prompt injection defenses
03
Practical safety methods for tool-using models
04
Parameter-efficient finetuning
05
LoRA: Low-Rank Adaptation of Large Language Models
06
Finetuning
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Related Researchers
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Useful because his work spans the older machine-comprehension era at Microsoft and the later LoRA-style adaptation line that became core infrastructure for modern finetuning.
One of the clearer people to follow if you want the bridge between deep-learning theory, practical adaptation methods like LoRA, and broader attempts to explain how large language models actually work.
A useful profile for the seam between deep-learning theory and practical large-model methods, especially if you want someone whose work spans convergence theory, small-language-model data design, and LoRA.
Co-authored LoRA: one of the core techniques behind modern fine-tuning pipelines.
Co-authored LoRA: one of the core techniques behind modern fine-tuning pipelines.