Amar Venugopal

PhD candidate in Economics at Stanford University with a PhD minor in Computer Science, researching applied econometrics at the intersection of causal inference and machine learning, especially experiments and causal inference with text data.

AI-for-Science Advances Depend on Evaluation, Not Just Generation

In a Stanford AI+Science lightning-talk session introduced by Surya Ganguli, four young researchers made a common case: AI-for-science is useful only when paired with rigorous evaluation. Aishwarya Mandyam, Amar Venugopal, Steven Dillmann and Alda Elfarsdóttir each treated AI systems or outputs as claims to be tested — through uncertainty estimates for clinical policies, causal checks on generated text, executable benchmarks for scientific agents, and empirical links between corporate climate language and later emissions.

Stanford HAIMay 15, 20267 min read