Aashiq Muhamed
CMU (PhD candidate)
CMU (PhD candidate)
Aashiq participated in the MATS Summer 2024 Cohort under the mentorship of Lucius Bushnaq and Jake Mendel (Apollo Research). His research on "Specialized Sparse Autoencoders for Interpreting Rare Concepts in LLMs" was published at Attrib NeurIPS 2024. The work explores effective strategies for learning subdomain tail features in modern sparse autoencoders (SAEs).
Aashiq holds a Master's degree from Stanford University and a Bachelor's degree from IIT Roorkee, where he was awarded the President's Gold Medal. Before transitioning to AI safety, he spent five years as an Applied Scientist at Amazon.
He is currently pursuing a Ph.D. in machine learning and alignment at Carnegie Mellon University. Learn more about his work at https://aashiqmuhamed.github.io/.
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.
Aashiq Muhamed
