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An open-source, local, read-only MCP server that checks your request before Google quietly hands back nothing Ask the Google Search Console API for clicks filtered by country us and you get zero rows. No error, no warning, just nothing. It's not the only one.
I spent the past few years working on AI for health. Starting with custom multimodal encoders, post-training, and sophisticated multi-agent architecturs, I now see modeling work becoming less and less important for domain-specific applications.
Having worked on robustness problems during my PhD, I see many of the characteristics appearing in the evaluation of LLMs and AI systems. Adversarial attacks such as jailbreaks are becoming more relevant, edge cases finally become relevant, test sets and benchmarks are not static anymore but…
Recently, I attended the Responsible AI for Social and Ethical Healthcare 2025 “2.0” Symposium organized by, among others, Harvard Medical School. The symposium featured various panels on topics surrounding generative AI, in particular multimodal LLMs, in healthcare.
Writing and responding to reviews is the bread and butter of any academic and especially in AI research, PhD students are confronted with both rather early compared to other displicines.
Watermarking AI-generated content has the potential to address various problems that generative AI threatens to aggravate — misinformation, impersonation, copyright infringement, web pollution, etc.
With the goal of deploying generative AI systems, rater studies are becoming increasingly common and important. This means more and more researchers and engineers face the challenge of actually planning and conducting rater studies for AI systems.
Dealing with rater disagreement is becoming more important in AI, especially for LLMs and in specialized domains such as health. In the past year, I helped open source two datasets allowing to study rater disagreement in the health domain: a relabeling of MedQA, a key benchmark for evaluating…
In this article, I want to share some thoughts on the difference between research ideas and technology, particularly in machine learning. This distinction is have been contemplating since starting my PhD.
Engineering and running experiments are a key component of most PhDs in AI. While there are plenty of more theoretical topics that are often limited to smaller scale experimentation, the trend has definitely been to scale up models, datasets and experiments.
Over the past months, I have given several talks about Monte Carlo conformal prediction and the problem of calibrating with uncertain ground truth, for example, stemming from annotator disagreement. Each time, the audience had great questions and ideas for extensions and interesting applications.
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