Ashesh Rambachan
@asheshrambachan
econometrics | machine learning
Princeton | Harvard Econ | Microsoft Research | MIT (now).
ID: 534776489
https://economics.mit.edu/people/faculty/ashesh-rambachan 23-03-2012 22:35:51
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๐ข My wonderful colleagues Nina Roussille and Jacob Moscona (and I) are jointly hiring predoctoral research fellows to work in the exciting research environment MIT Economics! Please spread the word! ๐ Econ RA Listings
With the Spring semester starting at Massachusetts Institute of Technology (MIT), I'm excited to be teaching two new courses on machine learning and economics with ๐๐๐ง๐๐ก๐ข๐ฅ ๐๐ฎ๐ฅ๐ฅ๐๐ข๐ง๐๐ญ๐ก๐๐ง (one for PhD students and one for undergraduates). We'll be posting lecture slides here for anyone to follow along: economics.mit.edu/people/facultyโฆ
Calculating the social return on algorithmic interventions, specifically their Marginal Value of Public Funds, across multiple domains โ regulation, criminal justice, medicine, and education, from Jens Ludwig, ๐๐๐ง๐๐ก๐ข๐ฅ ๐๐ฎ๐ฅ๐ฅ๐๐ข๐ง๐๐ญ๐ก๐๐ง, and Ashesh Rambachan nber.org/papers/w32125
Are algorithms overhyped? New research from Harris Policy's Jens Ludwig, Chicago Booth's ๐๐๐ง๐๐ก๐ข๐ฅ ๐๐ฎ๐ฅ๐ฅ๐๐ข๐ง๐๐ญ๐ก๐๐ง, & Ashesh Rambachan on the effectiveness of algorithms for addressing public policy problems suggests the opposite: ow.ly/HhFZ50QGI9o
Thank you Schmidt Sciences for the support! Such an honor to be in this incredible cohort of researchers. Excited to work towards ensuring AI functions as intended in real-world settings. Berkeley Statistics Berkeley Computing, Data Science, and Society schmidtsciences.org/ai2050-early-cโฆ
Recently accepted by #QJE, โIdentifying Prediction Mistakes in Observational Data,โ by Ashesh Rambachan (Ashesh Rambachan): doi.org/10.1093/qje/qjโฆ
#QJE May 2024, #6, โIdentifying Prediction Mistakes in Observational Data,โ by Ashesh Rambachan (Ashesh Rambachan): doi.org/10.1093/qje/qjโฆ
LLMs donโt behave like people, even though we may expect them to. A new study from LIDS PIs Ashesh Rambachan and ๐๐๐ง๐๐ก๐ข๐ฅ ๐๐ฎ๐ฅ๐ฅ๐๐ข๐ง๐๐ญ๐ก๐๐ง shows someoneโs beliefs about an LLM play a significant role in the modelโs performance and are important for how it is deployed. bit.ly/3y2zmRm