
I am a Research Scientist at NVIDIA Research in New York City, where I work on scientific reasoning and autonomous agents. I received my PhD in Computer Science and Engineering from the University of Michigan, where I was advised by Honglak Lee and Lu Wang. My research broadly focuses on LLM reasoning, test-time learning and inference, and post-training, with a growing interest in building agents capable of autonomous scientific discovery.
Previously, I had the opportunity to work with wonderful researchers across academia and industry. In 2024, I joined the CodeGen team at Cohere, led by Matthias Gallé, where I worked on large-scale model merging. In 2023, I interned at the Allen Institute for AI (AI2), working with Iz Beltagy and Hao Peng on training language models to attribute their generated content to pretraining data. In 2021, I worked at Amazon AWS with Kathy McKeown. Earlier, I interned at Naver Labs Europe, where I explored controllable text generation and energy-based models with Hady Elsahar and Marc Dymetman.
Fun fact: I play the piano, write, and produce my own music.
Email: m[khalifa]@nvidia.com
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Selected Works
(For a complete list, visit my google scholar)
Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Sungryull Sohn, Yunxiang Zhang, Moontae Lee, Hao Peng, Lu Wang, Honglak Lee
EMNLP 2026 Findings
Learning to Reason via Program Generation, Emulation, and Search
Nathaniel Weir*, Muhammad Khalifa*, Linlu Qiu, Orion Weller, Peter Clark
NeurIPS 2024
Source-Aware Training Enables Knowledge Attribution in Language Models
Muhammad Khalifa, David Wadden, Emma Strubell, Honglak Lee, Lu Wang, Iz Beltagy, Hao Peng
COLM 2024
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian, Tom Hosking, Honglak Lee, Lu Wang, Ahmet Üstün, Tom Sherborne, Matthias Gallé
arXiv 2024