https://forms.gle/2T8Tcf1VLUwyQNZd9

Graduate student is seeing a research assistant!

I am seeking undergraduate research assistants to support an ongoing research project at the intersection of machine learning, applied econometrics, and public policy. The project examines how large language models differ in quality, cost, and environmental impact, and how decision makers can optimally route tasks across models to balance performance with social costs such as energy use and emissions.

The research involves working with large-scale text data, training and evaluating neural network models, and conducting statistical analyses of the environmental costs associated with alternative AI deployment and routing policies.

This position is well suited for students with strong quantitative interests, particularly those curious about how modern machine learning tools intersect with economics, statistics, and public decision making. The role is intended for motivated undergraduates who are eager to gain hands-on experience with real research problems in AI and statistical analysis.

This is an unpaid research assistant position. Participation does not guarantee co-authorship on the resulting research.

Prior experience with large language models or deep learning is not required, though it is appreciated. Students with an interest in machine learning will receive hands-on coding guidance and training in implementing, training, and tuning deep neural network models, as well as exposure to the inner workings of large language models through applied research tasks.