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.

( 🐦 Twitter / 💼 LinkedIn / 🎓 Scholar / 💻 Github / 📄 CV )

Email: m[khalifa]@nvidia.com (remove brackets)



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

[Paper]

Gaming the Judge visualization

Process Reward Models That Think

Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran, Jaekyeom Kim, Hao Peng, Moontae Lee, Honglak Lee, Lu Wang

TMLR 2026

[Paper] [Code]

ThinkPRM visualization

A Distributional Approach to Controlled Text Generation

Muhammad Khalifa*, Hady Elsahar*, Marc Dymetman*

ICLR 2021

[Paper] [Code] [Blog]

Distributional Control visualization

GRACE: Discriminator-Guided Chain-of-Thought Reasoning

Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang

EMNLP Findings 2023

[Paper] [Code]

GRACE visualization

Learning to Reason via Program Generation, Emulation, and Search

Nathaniel Weir*, Muhammad Khalifa*, Linlu Qiu, Orion Weller, Peter Clark

NeurIPS 2024

[Paper]

COGEX visualization

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

[Paper]

Source-Aware Training visualization

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

[Paper]

Model Merging visualization