Research Tools: code, datasets, & models

This repository was archived by the owner on Jun 11, 2026. It is now read-only. This repository contains code and data for “Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs”…

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED—a general, model-agnostic framework for designing contextual experiments using information-theoretic principles.

We present a dataset of distributed energy resources (DERs) for the contiguous U.S. using only publicly available data. The primary focus of the dataset is on distribution-level utility-scale and distributed solar and storage, given their…

We view Large Language Models as stochastic language layers in a network, where the learnable parameters are the natural language prompts at each layer. We stack two such layers, feeding the output of one layer…

In the Harnessing AutoMobiles for Safety, or HAMS, project, we use low-cost sensing devices to construct a virtual harness for vehicles. The goal is to monitor the state of the driver and how the vehicle…

Code and Data artifact for NeurIPS 2023 paper – “Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context”. `multispy` is a lsp client library in Python intended to be used to build applications…

Official PyTorch implementation for the paper: “Renee: End-to-end training of extreme classification models” accepted at MLSys 2023.

INTREPID (stands for INTeractive learning via REPresentatIon Discovery) is a library that contains various interactive learning algorithms that learn a representation (or a latent state) from observational data in order to complete their tasks.

Chart Reader is a web-based accessibility engine, which enables rendering of accessible visualizations for screen reader uses to read and better understand the visualizations and underlying data.

This repository contains the official code for our IEEE S&P 2023 paper using GPT-2 language models and Flair Named Entity Recognition (NER) models. It allows fine-tuning (i) undefended, (ii) differentially-private and (iii) scrubbed language models…