Humanitarian AI


We've been trying to create more opportunities for people to contribute to humanitarian AI projects. The term can be quite broad, which makes it important that work is coordinated across teams, to maintain coherence.
  • Workshops: I've been lucky to contribute to the organization of the AI for Social Good workshops at ICLR and ICML, the Computer Vision for Global Challenges workshop at CVPR, and the Machine Learning for Development workshop at NeurIPS.
  • Internships: Mila hosts internships specifically around humanitarian AI projects, and I've been fortunate to work with some really wonderful students through the program.
  • Reading group: We organized a reading group on this topic, to help students at Mila share the types of projects they were interested in.
  • Review materials: I created this summary and this spreadsheet as a reference for people who want to learn more about social applications of data science and machine learning.

Remote Sensing

Effective processing of aerial imagery is important for a variety of socially relevant applications, from conversation monitoring to crisis preparedness. We've worked directly on applications as well as pursued improved methodology.
  • Multiframe super-resolution: With a team from Element AI, we have experimented with approaches to align and fuse low-resolutions imagery, inspired by the observation that, while high-resolution data are often expensive, low-resolution views are often plentiful.
  • Foundational mapping: With a team from Intel AI for Social Good, we are working towards providing locations of bridges, to provide better foundational data for disaster preparedness planning.
  • Interactivity: Mapping data are often very heterogeneous, so even the best models tend to require human validation before being used in the field. This study considers some approaches to interactively refining preliminary outputs, using weak supervision on prediction masks.
  • Interpretability: Remote sensing models hold the potential to transform a few types of socially relevant monitoring efforts, from poverty prediction to deforestation tracking. These studies explore the use of Concept Activation to make these models more accessible to the domain experts who use them. See these demos (1, 2) and reports (1, 2).

Climate Change

From detecting leaks in methane pipes to accounting for uncertainty in future reservoir loads, machine learning has the potential to amplify a wide variety of (big and small) climate change mitigation and adaptation efforts.
  • Climate Change AI: This initiative includes researchers across continents and research domains, brought together by the vision of a low-carbon future supported by thoughtful machine learning research and applications. You should join the mailing list!
  • Review paper: This paper provides a structure for thinking about climate change and machine learning. The hope is that this taxonomy helps guide closer connections between researchers in mitigation and adaptation domains and methodological practitioners.
  • Applications: Aside from the CCAI initiative, our team at Mila has been investigating a few of the problems highlighted in the review, including predictive maintenance for wind farms, modeling extremes in time series, and communication of long-term climate change impacts .


Remembrances of States Past

This piece explains recurrent neural networks using interactive views of a one-dimensional example. It also highlights connections between sequential processing and statistical sufficiency, and probably has the silliest title of anything I've written (yet!).

Microbiome-inspired Methodology


We made an effort to strengthen links between visualization and statistical modeling, with the high-level view that both fields offer approaches to data compression for human consumption. It was awarded the Jerome H. Friedman Applied Statistics Dissertation Award and was chosen as Stanford's nomination to the Council for Graduate Studies Dissertation Award (Math, Physical Sciences, and Engineering).

Regime Detection

We survey a variety of algorithmic and probabilistic approaches to the problem of applying dynamic regimes in the microbiome, along with illustrative examples and code.

Latent Variable Modeling

We applied probabilistic text modeling ideas to the microbiome, comparing methods and providing example workflows.


We developed an R package for simultaneous visualization of trees and heatmaps, based on linked brushing.


An R package for interactive visualization of tree-structured time series, based on focus / context and linked brushing ideas.

Multitable Methods

We experimented with several perspectives for learning from multitable datasets, and offer some guidelines for their application to the microbiome context.

Perturbation Study

We studied the effects of colon cleanouts on the microbiome, applying novel methods to incorporate phylogenetic tree structure.

Microbiome Workflow

We described and provided code for a full workflow for microbiome data processing and analysis.


Using htmlwidgets, we developed an R package that generates interactive plots of standard multivariate analysis methods, via the FactoMineR, ade4, and vegan packages.


We created an R package implementing multiple testing procedures applicable to group and hierarchically structured data, and demonstrated their relevance in the microbiome setting.

Statistics and Social Good


Data Science for Social Good and SEDESOL worked together to design and implement a pilot machine learning system for enhancing the distribution of social services in Mexico.


As a Data Ambassador for DataKind San Francisco I helped a volunteer team scope and develop a data exploration tool for SupplyBank.Org to select partner sites to distribute baby hygiene kits effectively and equitably.

Opioid Atlas

Stanford Statistics for Social Good and the Global Oncology Initiative worked together to develop interactive visualizations to educate the public about inequities in access to palliative care.

The Indigo Education Company

Through Statistics for Social Good we implemented a Shiny app to facilitate exploratory views of student survey data, all code is on github.

Parking and Transportation Services

Using results from the Stanford Commute Survey, we segmented commuters who drive to work, in order to target incentives for more environmentally friendly commutes.


We evaluated the effectiveness of financial stability and job search programs at SparkPoint drop-in centers located throughout the SF Bay Area.

Africa Soil Property Prediction

Our team from Statistics for Social Good placed in the top 11% in this kaggle competition.

Great Nonprofits

We investigated the extent of "courtesy bias" in online nonprofit reviews, and shared our results with the Stanford Social Innovation Review.

Other Academic Collaborations

Stanford NEMS

We designed algorithms to detect cytokine mixtures using data from novel nanoscale sensors.

Stanford HIV Database

We adapted local FDR methods to perform inference of APOBEC mutations.

Industry Projects

In the past, I've worked on industry consulting projects and internships.

Climate Corporation

As an intern, I implemented a bayesian approach rainfall disaggregation, in order to incorporate a large amount of lower-resolution data into the company's forecasting pipeline.