David Dao

GainForest.EarthProtocol Labs R&D

david@gainforest.net

(Dated: October 7, 2026)

Abstract

David Dao

Dr. David Dao is a machine learning researcher working on AI for science and the commons, with a focus on nature and climate. He is co-founder and Chief Scientist of GainForest.Earth and co-leads Economies & Governance at Protocol Labs R&D. He holds a PhD in computer science from ETH Zurich and has done research at Berkeley AI Research and Stanford University.

Figure 1: Three volumes, , and . Bring one into focus, then select it to open it and turn its pages.

Essays

  1. [E1]David Dao. Governing the Commons in the Intelligent Age. January 2025.GainForest's vision of an equitable biodiversity data marketplace to finance conservation and govern the natural commons.

Publications

2025

  1. [P28]Christian Geckeler, Steffen Kirchgeorg, Georg Strunck, Frederik Bendix Thostrup, Florencia Sangermano, Andrea Desiderato, Martina Lüthi, Meret Jucker, Mailyn Adriana Gonzalez Herrera, Nicolás D. Franco-Sierra, Paola Pulido-Santacruz, Jia Jin Marc Chang, Yin Cheong Aden Ip, Elvira Mächler, Asger Svenning, Guillaume Mougeot, Toke Thomas Høye, Fabian Fopp, Loic Pellissier, David Dao, Kristy Deiner, Claus Melvad, Salua Hamaza and Stefano Mintchev. Field Deployment of BiodivX Drones in the Amazon Rainforest for Biodiversity Monitoring. IEEE Transactions on Field Robotics 2, 2025.

2024

  1. [P27]Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer, Eben N. Broadbent, David Dao, Ce Zhang, Angelica Almeyda Zambrano and Simeon Max. OAM-TCD: A globally diverse dataset of high-resolution tree cover maps. NeurIPS, 2024.
  2. [P26]Levin M. Moser, Nodar Gogoberidze, Andréa Papaleo, Alice Lucas, David Dao, Christoph A. Friedrich, Lassi Paavolainen, Csaba Molnar, David R. Stirling, Jane Hung, Rex Wang, Callum Tromans-Coia, Bin Li, Edward L. Evans III, Kevin W. Eliceiri, Peter Horvath, Anne E. Carpenter and Beth A. Cimini. Piximi - An Images to Discovery web tool for bioimages and beyond. bioRxiv, 2024.
  3. [P25]Bojan Karlaš, David Dao, Matteo Interlandi, Sebastian Schelter, Wentao Wu and Ce Zhang. Data Debugging with Shapley Importance over Machine Learning Pipelines. ICLR, 2024.

2023

  1. [P24]Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon and Xiaoxiang Zhu. GEO-Bench: Toward Foundation Models for Earth Monitoring. NeurIPS, 2023.
  2. [P23]David Dao. Advancing Algorithms and Applications for Data Valuation in Machine Learning. PhD thesis, ETH Zurich, 2023.

2022

  1. [P22]Lucas Czech, Björn Lütjens, Dava Newman and David Dao. ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning. NeurIPS Climate Change AI workshop, 2022.
  2. [P21]Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang and Xiaoxiang Zhu. ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. AAAI, 2022.

2021

  1. [P20]Ghislain Fourny, David Dao, Can Berker Cikis, Ce Zhang and Gustavo Alonso. RumbleML: program the lakehouse with JSONiq. arXiv, 2021.
  2. [P19]Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez and David Vazquez. Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark. NeurIPS Climate Change AI workshop, 2021.
  3. [P18]Laure Berti-Equille, David Dao, Stefano Ermon and Bedharta Goswami. Challenges in KDD and ML for Sustainable Development. KDD, 2021.
  4. [P17]Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Xiaoxiang Zhu and Ce Zhang. Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery. ICML Climate Change AI workshop, 2021. Spotlight (top 5%).
  5. [P16]Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li and Dawn Song. Scalability vs. Utility: Do We Have To Sacrifice One for the Other in Data Importance Quantification? CVPR, 2021.
  6. [P15]Leonel Aguilar, David Dao, Shaoduo Gan, Nezihe Merve Gürel, Nora Hollenstein, Jiawei Jiang, Bojan Karlaš, Thomas Lemmin, Tian Li, Yang Li, Susie Rao, Johannes Rausch, Cedric Renggli, Luka Rimanic, Maurice Weber, Shuai Zhang, Zhikuan Zhao, Kevin Schawinski, Wentao Wu and Ce Zhang. Ease.ML: A Lifecycle Management System for MLDev and MLOps. CIDR, 2021.

2020

  1. [P14]Simona Santamaria*, David Dao*, Björn Lütjens* and Ce Zhang. TrueBranch: Metric Learning-based Verification of Forest Conservation Projects. ICLR Climate Change AI workshop, 2020. Best proposal award (top 2%).
  2. [P13]David Dao*, Johannes Rausch*, Iveta Rott and Ce Zhang. Xingu: Explaining Critical Geospatial Predictions in Weak Supervision for Climate Finance. ICLR Climate Change AI workshop, 2020.

2019

  1. [P12]David Dao, Johannes Rausch and Ce Zhang. GeoLabels: Towards Efficient Ecosystem Monitoring using Data Programming on Geospatial Information. NeurIPS Climate Change AI workshop, 2019.
  2. [P11]Lun Wang, Joseph P. Near, Neel Somani, Peng Gao, Andrew Low, David Dao and Dawn Song. Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations. VLDB Poly workshop, 2019.
  3. [P10]Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gürel, Bo Li, Ce Zhang, Costas J. Spanos and Dawn Song. Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms. VLDB, 2019.
  4. [P9]David Dao, Catherine Cang, Clement Fung, Ming Zhang, Reuven Gonzales, Nick Beglinger and Ce Zhang. GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery. ICML Climate Change AI workshop, 2019.
  5. [P8]David Dao*, Ruoxi Jia*, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song and Costas J. Spanos. Towards Efficient Data Valuation Based on the Shapley Value. AISTATS, 2019.

2018

  1. [P7]David Dao, Dan Alistarh, Claudiu Musat and Ce Zhang. DataBright: A Data Curation Platform for Machine Learning based on Markets and Trusted Computation. ICML GaMeDATA workshop, 2018.
  2. [P6]Nick Hynes, David Dao, David Yan, Raymond Cheng and Dawn Song. A Demonstration of Sterling: A Privacy-Preserving Data Marketplace. VLDB demo, 2018.

2017

  1. [P5]Holger Hennig, Paul Rees, Thomas Blasi, Lee Kamentsky, Jane Hung, David Dao, Anne E. Carpenter and Andrew Filby. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods, 2017.

2016

  1. [P4]David Dao, Adam N. Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh and Anne E. Carpenter. CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinformatics, 2016. [website] [documentation] [code]
  2. [P3]David Dao. Image-based chemical-genetic profiling using Deep Neural Networks. Master's thesis, TU Munich, 2016.

2015

  1. [P2]Guy Yachdav, Tatyana Goldberg, Sebastian Wilzbach, David Dao, Iris Shih, Saket Choudhary, Steve Crouch, Max Franz, Alexander Garcia, Leyla J. Garcia, Bjorn A. Gruening, Devasana Inupakutika, Ian Sillitoe, Anil S. Thanki, Bruno Vieira, Jose M. Villaveces, Maria V. Schneider, Suzanna Lewis, Steve Pettifer, Burkhard Rost and Manuel Corpas. Anatomy of BioJS, an open source community for the life sciences. eLife, 2015. [talk] [slides] [code]

2014

  1. [P1]David Dao, Tomáš Flouri and Alexandros Stamatakis. Automated Plausibility Analysis of Large Phylogenies. Pattern Recognition in Computational Molecular Biology, 2014. [code]

* Equal contribution. Citation counts are on Google Scholar.

Former students

  • Lasse Wolff Anthony, ETH Zurich
  • Thomas Huber, Uppsala University
  • Nils Lehmann, UvA, now TUM
  • Ghjulia Sialelli, ETH Zurich
  • Marc Watine, ETH Zurich, then Harvard
  • Gyri Reiersen, TUM, now CTO of Tanso
  • Kenza Amara, ETH Zurich, then Facebook AI
  • Simona Santamaria, ETH Zurich, now CTO of RYVER.AI
  • Iveta Rott, ETH Zurich, then McKinsey
  • Mina Huh, KAIST
  • Levin Moser, ETH Zurich, then MIT
  • Catherine Cang, UC Berkeley, then Airbnb
  • Ming Zhang, ETH Zurich, then Roche
  • Luca Lanzendorfer, ETH Zurich, then Mercedes-Benz Research
  • Florian Chlan, ETH Zurich, then Amazon
  • Nino Weingart, ETH Zurich, then BSI
  • Christopher Friedrich, Reutlingen, then MIT

Scientific service

  • Lead, AI4PG grants, USD 150,000 in total, 2026.
  • Program committee, NeurIPS Climate Change AI workshop, 2026.
  • Co-lead, ETH BiodivX, a consortium of more than 50 researchers for XPRIZE Rainforest, 2023–2024.
  • Program committee, ICLR Climate Change AI workshop, 2024.
  • Co-convener, Scalable Biodiversity Assessment with Geospatial Foundation Models and Ecosystem Modeling, session at AGU24, 2024.
  • Co-initiator, AI + Environment Summit Zurich, 2023.
  • Program committee, ICLR Climate Change AI workshop, 2023.
  • Program committee, NeurIPS Climate Change AI workshop, 2022.
  • Reviewer, Climate Change AI Innovation Grants, USD 1.8 million in total.
  • Organizer, Climate Change AI side event at COP26, with Climate TRACE and CAIC.
  • Program committee, NeurIPS Climate Change AI workshop, 2021.
  • Tutorial, KDD Challenges in ML for Sustainable Development, 2021.
  • Program committee, ICML Climate Change AI workshop, 2021.
  • Co-lead organizer, NeurIPS Climate Change AI workshop, 2020.
  • Organizer, ICML Economics of Privacy and Data Labor workshop, 2020.
  • Co-organizer, ICLR Climate Change AI workshop, 2020.
  • Program committee, NeurIPS Climate Change AI workshop, 2019.
  • Reviewer, ISC-HPC, 2015.

Selected media

  1. [M7]The Value of Data. Protocol Labs, 2025.Long-format interview on universal data income and data valuation for machine learning.
  2. [M6]CNA Documentary: Tech To Save The World - Forest Futures. Channel News Asia, 2025.Documentary on our work leveraging technology to restore mangroves in the Philippines.
  3. [M5]Atmos: Indigenous Groups Are Safeguarding Culture with Their Own ChatGPT. Atmos, 2025.On winning the XPRIZE Rainforest through our work with Indigenous-led AI.
  4. [M4]How Can AI and the Community Drive Climate Action?. ETH Zurich Podcast, 2025.Long-format interview on how AI can help tackle climate change.
  5. [M3]Conservation Data Income. Ma Earth Podcast, 2024.Long-format interview on our work at GainForest.
  6. [M2]WEF: A Wake-Up Call from Nature. World Economic Forum, 2022.Panel discussion on the urgent need for nature-based solutions.
  7. [M1]TEDx: Learning from nature's stewards. TEDxGeneva, 2021.On the origin story of GainForest.

Contact

Write to me at david@gainforest.net.

© 2026 David Dao