PhD student at The Ohio State University working on understanding and controlling large language models. I also vibe code research prototypes, developer tools, and AI systems.

• I am a PhD student in Computer Science & Engineering at The Ohio State University.

• My research focuses on mechanistic interpretability, and sparse representations in LLMs.

• I study how internal representations encode concepts and how we can steer model behavior with interpretable directions.

• My goal is to make LLMs more transparent, controllable, and reliable.

• Understanding Linear Steering (ongoing)
  Investigating the geometry, linearity, and causal structure of steering directions in LLM representation space.

• AbsTopK: Rethinking Sparse Autoencoders For Bidirectional Features ArXiv, OpenReview
  Developed a principled proximal-gradient framework that unifies SAE variants (ReLU, JumpReLU, TopK) and reveals that non-negativity constraints prevent bidirectional feature representation. Proposed AbsTopK, a magnitude-based sparse operator that recovers complete semantic axes and improves interpretability and steering in LLMs.

• From Emergence to Control: Probing and Modulating Self-Reflection in Language Models Arxiv
  Showed that linear directions in representation space can enable and control self-reflection behavior in pretrained LLMs without finetuning.

For a full list of publications:

• Google Scholar
• OpenReview

If you are interested in collaboration, feel free to open an issue or connect with me.