Hi Tim, I'm not sure what role would suit me best- titles haven't fit me well over a 15+ year career, but "data science and adjacent" wouldn't be inaccurate. With a focus on translating technical findings into strategic input for leadership decision & policy making- neutral or opinionated per the context's needs. The former has usually intersected with operations in coordination with IT, the later to the c-level.
By training: dusty philosophy & cog sci, less dusty linguistics, translation theory, NLP. More recent work w/ London School of Economics on AI Law, Policy, & Governance. LLMs come naturally, so I'm looking for a change. (Location: NYC area, USA)
For what I can offer:
Over the past few months I've built a toolkit for model interpretability that operates below black-box prompt-output observation and raw numeric activations, collapsing a lot of complexity into something more discrete and tractable, with accuracy that appears, at least from outside the core AI industry, to be harder to come by with many current methods. This includes monitoring and inference-time intervention without retraining or weight modification.
I've used it to improve benchmark performance across modalities- DeBERTa mini boosted overall > 10% in the adversarial HANS dataset through fewer false positives, no retraining or degradation in other performance. Needs testing in any deployment of course. Similarly, MedGemma, MMFLD, Whisper, a few others, with some of their standard benchmarks. Same methodology of exploration and inspection.
Utility tooling along the way includes an intuitive REPL interface for token-by-token exploration of model internals during inference, or optionally post-inference with data capture by SQLlite & LanceDB, analysis with UDF's and python. Other tools for pre-token-gen semantic monitoring and intervention intra inference. I've observed some things that are more speculative, though still promising, for understanding model behavior. All generally grounded in classical Linguistics areas of study that seem less mined for insights than industry has had opportunity to pursue in-depth with but actionable nonetheless.
I'd love to talk further. These are mainly my personal time interests though, so a resume through a job posting doesn't generally cover things adequately, if a different option is available, though I can go that route if you'd prefer.
By training: dusty philosophy & cog sci, less dusty linguistics, translation theory, NLP. More recent work w/ London School of Economics on AI Law, Policy, & Governance. LLMs come naturally, so I'm looking for a change. (Location: NYC area, USA)
For what I can offer:
Over the past few months I've built a toolkit for model interpretability that operates below black-box prompt-output observation and raw numeric activations, collapsing a lot of complexity into something more discrete and tractable, with accuracy that appears, at least from outside the core AI industry, to be harder to come by with many current methods. This includes monitoring and inference-time intervention without retraining or weight modification.
I've used it to improve benchmark performance across modalities- DeBERTa mini boosted overall > 10% in the adversarial HANS dataset through fewer false positives, no retraining or degradation in other performance. Needs testing in any deployment of course. Similarly, MedGemma, MMFLD, Whisper, a few others, with some of their standard benchmarks. Same methodology of exploration and inspection.
Utility tooling along the way includes an intuitive REPL interface for token-by-token exploration of model internals during inference, or optionally post-inference with data capture by SQLlite & LanceDB, analysis with UDF's and python. Other tools for pre-token-gen semantic monitoring and intervention intra inference. I've observed some things that are more speculative, though still promising, for understanding model behavior. All generally grounded in classical Linguistics areas of study that seem less mined for insights than industry has had opportunity to pursue in-depth with but actionable nonetheless.
I'd love to talk further. These are mainly my personal time interests though, so a resume through a job posting doesn't generally cover things adequately, if a different option is available, though I can go that route if you'd prefer.