Since 2023, I've been gainfully employed as a Research Scientist at Google DeepMind. While my intellectual interests are broad, I choose to invest my time in alignment with what I care most about: addressing problems rooted in real-world impact for social and global good.

🤖 My work on factuality and semantic uncertainty in LLMs has supported building more reliable, trustworthy LLM-based systems (Gemini, Expected Reward Prediction, with Applications to Model Routing). 

🦜Since 2022, part of my core work in AI for Nature & Sustainability has focused on building robust, generalizable foundational models for bioacoustics to measure and monitor biodiversity. If you have no idea what this means, check out some of the impactful here:

• • BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics (Hamer et al. 2023)

  • Birds, bats and beyond: evaluating generalization in bioacoustics models (van Merriënboer et al. 2024)

  • Perch 2.0: The Bittern Lesson for Bioacoustics (Perch Team 2025) or read the blog 

  • Help scientists find key rainforest animals & support urgent conservation efforts with Forest Listeners (featured at COP 30) 

While at Google Research from 2019 to early 2023 (first as an AI Resident, then as a Research Engineer), my work centered on the intersection of theory and practice, particularly within algorithmic recourse, algorithmic fairness, and federated learning. 

Outside (and within!) AI/ML research, I'm a disability and neurodiversity educator, and I regularly consult and advise on large-scale projects that support universally accessibility and design. Allyship is an action, not an identity---and is as important in the research community as it is outside of work. You can find me on LinkedIn, but I'm pretty social media averse and may be slow to respond.