About Me
I am a fourth year PhD student at MIT EECS, where I am grateful to be under the guidance of Professor Nir Shavit as a member of the Shavit Lab. Prior to my graduate studies, I received my BA in computer science and in neuroscience from Columbia University, where I conducted research at the Peter Sims Lab.
I am curious about the internal mechanisms of large language models, from individual neurons to distributed feature representations, and how they can inform more efficient computation. I use mechanistic interpretability to uncover these principles, motivated by a broader interest in computation across both artificial and biological neural systems.
My research is supported by an IBM AI Research Grant.
News
- August 2026: I led the successful renewal of our IBM AI Research Grant, building on results from the research plan proposed in the original grant.
- May 2026: A project I advised, Characterizing plastic regions in neural networks, was accepted into ICML 2026 Workshop CATS
- April 2026: My work Expand neurons, not parameters was accepted into ICML 2026; I gave a talk on my work Negative pre-activations differentiate syntax at MIT in Cambridge
- March 2026: My work The feature-space alignment hypothesis for neural network sparsity was accepted into ICLR 2026 Workshop Sci4DL
- January 2026: My work Negative pre-activations differentiate syntax was accepted into ICLR 2026
- October 2025: I served as the primary author of the proposal Feature Aware Sparsity for Effective Parameter Reduction, which was selected for an IBM AI Research Grant to support my PhD research in the Shavit Lab
- June 2025: My work Input differentiation via negative computation was accepted into ICML 2025 Workshop HiLD
- May 2025: My work A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex was accepted for publication in the journal Neural Networks
- March 2025: I gave a talk on my work Wasserstein distances, neuronal entanglement, and sparsity at Red Hat in Boston
- January 2025: My work Wasserstein distances, neuronal entanglement, and sparsity was accepted into ICLR 2025 as a Spotlight Presentation
- October 2024: I was selected as a Cerebras Research Fellow
- May 2024: I received my SM from MIT EECS on Sparse Expansion and neuronal disentanglement
- April 2024: I received an Honorable Mention from the NSF GFRP
Selected Works
The sparsity whisperer
Preprint on arXiv, 2026
Linghao Kong, Inimai Subramanian, Micah Adler, Dan Alistarh, Dan Gutfreund, & Nir N. Shavit
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Expand neurons, not parameters
Published in ICML 2026
Linghao Kong*, Inimai Subramanian*, Yonadav Shavit, Micah Adler, Dan Alistarh, & Nir N. Shavit
Download Paper | arXiv | Code
An interpretable latency model for speculative decoding in LLM serving
Preprint on arXiv, 2026
Linghao Kong, Megan Flynn, Michael Peng, Nir N. Shavit, Mark Kurtz, & Alexandre Marques
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Negative pre-activations differentiate syntax
Published in ICLR 2026
Linghao Kong, Angelina Ning, Micah Adler, & Nir N. Shavit
Download Paper | arXiv | Code
The feature-space alignment hypothesis for neural network sparsity
Presented at ICLR 2026 Workshop Sci4DL
Linghao Kong, Micah Adler, & Nir N. Shavit
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Wasserstein distances, neuronal entanglement, and sparsity
Published in ICLR 2025 as a Spotlight Presentation
Shashata Sawmya*, Linghao Kong*, Ilia Markov, Dan Alistarh, & Nir N. Shavit
Download Paper | arXiv | Code
