About
I’m a Master’s student in Machine Learning at MBZUAI, where I am fortunate to be advised by Dr. Junpei Komiyama. My research sits at the intersection of foundation model training and mechanistic interpretability, focusing on pretraining and post-training dynamics, machine reasoning, and multi-agent systems. I am particularly interested in how reasoning and internal mechanisms emerge during training, and how understanding them can help us build models that are safer, more robust, and better aligned.
Previously, I worked on latent reasoning in vision-language models with Dr. Biwei Huang at UC San Diego and on inference-time alignment of language models with Dr. Berkay Celik at Purdue University. I also had the opportunity to collaborate with Dr. Ferdinando Fioretto at the University of Virginia on understanding misalignment in self-evolving coding agents during iterative self-improvement.
Open to research internships for summer 2027.
the background is a loss landscape ·
- sgd
- momentum
- adam
- muon
tap any empty spot to race them downhill
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Publications * indicates equal contribution.
- NeurIPS-W 2025Logit–Entropy Adaptive Stopping Heuristic for Efficient Chain-of-Thought ReasoningNeurIPS 2025 - Efficient Reasoning Workshop
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