student projects

The Hybrid Augmented Inverse Probability Weighting (H-AIPW) estimator has demonstrated efficiency gains in randomized experiments by incorporating predictions from foundation models. However, its current form requires a fixed sample size to be determined before the experiment begins. Since the efficiency gains depend on the accuracy of the model, which is unknown in advance, it is not clear how to determine a “sufficient” sample size a priori.

This project aims to extend the H-AIPW framework to the sequential setting. Leveraging recent theoretical advances in sequential inference, we propose to develop a robust methodology that guarantees valid statistical inference at any stopping time, regardless of the bias in the predictions from the foundation models.

Key objectives include:

• Develop a sequential version of the H-AIPW estimator and establish theoretical guarantees that allow the construction of anytime-valid confidence intervals
• Validate the proposed methodology across several experimental settings, assessing both the efficiency improvements and the robustness of inferential guarantees

Prerequisites: Strong background in mathematics and statistics

The Hybrid Augmented Inverse Probability Weighting (H-AIPW) estimator has demonstrated efficiency gains in randomized experiments by incorporating predictions from foundation models. However, these gains have only been demonstrated in the context of political and social science experiments, limiting the generalizability of the findings to other domains.

This project proposes applying the H-AIPW framework within the medical domain. We aim to demonstrate substantial efficiency improvements by utilizing predictive machine learning models trained on extensive observational healthcare data (e.g. Medicare data).

Key objectives include:

• Developing predictive machine learning models using large-scale observational healthcare data
• Evaluating efficiency gains on an extensive collection of clinical trials using the H-AIPW estimator and related frameworks

Key Skills & Qualifications:

• Proficiency in Python and deep learning frameworks (e.g. PyTorch)
• Strong foundation in machine learning and statistics
• Interest in medical AI and clinical data applications
• Ability to collaborate in a multidisciplinary team

Frequent droughts threaten the livelihoods of pastoral communities in the Horn of Africa. Early warning systems (EWS) can provide crucial information to enable anticipatory action and improve food security. However, most EWS rely on weather variables, yet for grazing cattle the vegetation condition on the ground is more important.

This project proposes to train machine learning methods on satellite imagery in East Africa to provide forecasts of vegetation health. We aim to improve on previous works, by studying the benefits from increasing spatial resolution of the satellite data and through adopting a probabilistic view.

Key objectives include:

• Developing probabilistic machine learning models using large-scale Earth observation data
• Evaluating accuracy gains from predicting vegetation health status at higher spatial resolution

Key Skills & Qualifications:

• Proficiency in Python and deep learning frameworks (e.g. PyTorch)
• Strong foundation in machine learning and statistics
• Interest in AI for Earth and climate science
• Ability to collaborate in a multidisciplinary team