Anish participated in the MATS Summer 2024 Cohort under the mentorship of Christian Schroeder de Witt (University of Oxford). He published "Efficient Dictionary Learning with Switch Sparse Autoencoders" (https://arxiv.org/abs/2410.08201), a novel SAE architecture aimed at reducing the compute cost of training SAEs. He is currently pursuing an undergraduate degree in Computer Science at MIT, where he is grateful to be advised by Max Tegmark.
The Summer 2024 cohort marked a significant expansion, supporting approximately 90 scholars with 40 mentors—the broadest mentor selection in MATS history. This cohort incorporated MATS as a 501(c)(3) nonprofit organization, formalizing its institutional structure. The program expanded its research portfolio to include at least four governance mentors alongside technical research streams, reflecting growing interest in AI policy and technical governance work. The 10-week research phase continued in Berkeley, with scholars conducting work across mechanistic interpretability, evaluations, scalable oversight, and governance research.Notable outputs from this cohort include research on targeted manipulation and deception in LLMs trained on user feedback, which was accepted to NeurIPS workshops, and contributions to an AI safety via debate paper that won best paper at ICML 2024. One scholar co-founded Decode Research, a new AI safety organization focused on building interpretability tools.
Anish Mudide
