Amit Sharma

I’m a machine learning researcher focused on improving the reasoning abilities of AI systems.

My work combines two seemingly incompatible ideas: the messy but generalizable capabilities of language models with the principled but rigid capabilities of causal reasoning. Early in 2023, I saw the potential of large language models (LLMs) for inferring causal relationships, a key part of scientific discovery. This has led to LLM-based algorithms that achieve up to 96% accuracy on inferring cause and effect across scientific fields, including medicine (Covid-19), climate science (Arctic sea ice coverage), and engineering. This work has now expanded into the PyWhy-LLM project, an open-source tool for building a causal AI assistant for science.

On the other end, I work on how causality can help improve AI model reliability. My research has led to open-source software such as DoWhy for causal reasoning and DiCE for counterfactual explanation that are used by millions globally. These days, I’m most excited by Axiomatic Training, a framework for building reasoning verifiers that can correct a language model’s output in real-time. This approach has shown that even a small 8B-parameter model can achieve nearly double the accuracy of frontier LLMs on causal reasoning tasks.

I’m also passionate about designing technology interventions that can have a positive societal impact (e.g., see MindNotes app). If you are interested in working with me at MSR India, drop me an email. We hire interns throughout the year. There are also postdoctoral positions available. Additionally, if you are an undergraduate or a masters student, our lab runs an excellent pre-doctoral Research Fellows program.

• Education
• Interests

• [2015] Ph.D. in Computer Science, Cornell University

  [2010] B.Tech. in Computer Science, IIT Kharagpur

• Causal inference | Causality and machine learning

  AI reasoning | Accelerating scientific discovery