Lauren Gillespie

Hello! I'm a 4th year Computer Science PhD student at Stanford University also affiliated with the Carnegie Institution for Science. I'm a machine learning researcher and aspirational ecologist interested in understanding how plant biodiversity is distributed and how it's adapting to a rapidly changing world. You can find me either doing research, working, coding, and occasionally gardening 🌱

A headshot of me, Lauren.

My background

I'm co-advised by Prof. Moisés Expósito-Alonso, a Principal Investigator in the Departments of Plant Biology and Global Ecology at the Carnegie Institution for Science and co-affiliated with Stanford's Department of Biology, and Prof. Noah Goodman from Stanford CS and Psychology. I enjoy splitting my time between MoiLab and CoCoLab, and I can also be occasionally found hanging around Stanford's NLP Group and CRFM as well.

All my life, I've been fascinated by the natural world and the plethora of amazing species and ecosystems that have evolved here. However, even in my relatively short life, I've born personal witness to the devastating effects of anthropogenic climate change. From massive in scale, like Hurricane Harvey which decimated my home state of Texas in 2017, to the minuscule, such as watching tree after tree in my neighborhood succumb to the invasive fungus oak wilt, the dizzyingly fast rate of anthropogenic climate change is a clear and present danger.

The aim of my work is to increase understanding of global change ecology using state-of-the-art machine learning. Broadly speaking, my research touches on research topics spanning machine learning, global ecology, genomics, population genetics, and remote sensing.

Specifically, I aim to:

  1. Better quantify biodiversity loss and ecosystem change at a variety of spatial and temporal scales
  2. Understand what genetic and phenotypic mechanisms plants are using to adapt to this rapid change
  3. Find where in space these genetic reservoirs of adaptive diversity lie
  4. Ultimately to develop informed, precision strategies for helping restore and adapt our ecosystems to a warmer and drier climate future.

Recent Work

EcoEvoRxiv 2022 Power and limitations of the mutations-area relationship to assess within-species genetic diversity targets for post-2020 Sustainable Development Goals
Moisés Expósito-Alonso, Jeff Spence, Megan Ruffley, Lucas Czech, Meixi Lin, Oliver Selmoni, Lauren Gillespie, Krisy Mualim, Shannon Hateley
Science 2022 Genetic diversity loss in the Anthropocene
Moisés Expósito-Alonso, Tom Booker, Lucas Czech, Lauren Gillespie Shannon Hateley, Chris Kyriazis, Patty Lang, Laura Leventhal, David Nogues-Bravo, Veronica Pagowski, Megan Ruffley, Jeff Spence, Seba Tora Arana, Clemens WeiƟ, Erin Zess
bioRxiv An image is worth a thousand species: combining neural networks, citizen science, and remote sensing to map biodiversity
Lauren Gillespie, Megan Ruffley, Moisés Expósito-Alonso
2021 AGU Detecting Ecosystem Turnover and Realized Niches Using Remote Sensing Data, Citizen Science Observations and Deep Convolutional Neural Networks
Lauren Gillespie, Megan Ruffley, Moisés Expósito-Alonso
2021 TDWG An Image is Worth a Thousand Species: Scaling high-resolution plant biodiversity prediction to biome-level using citizen science data and remote sensing imagery
Lauren Gillespie, Megan Ruffley, Moisés Expósito-Alonso
[In Submission] On the Opportunities and Risks of Foundation Models: Environment and Ethics of Scale sections
Environment section: Peter Henderson, Lauren Gillespie, Dan Jurafsky. Ethics of Scale section: Kathleen Creel, Dallas Card, Rose Wang, Isabelle Levent, Alex Tamkin, Armin Thomas, Lauren Gillespie, Rishi Bommasani, Rob Reich.
2020 AGU Using Taxonomically-Informed Convolutional Neural Networks To Predict Plant Biodiversity Across California From High-Resolution Satellite Imagery Data
Lauren Gillespie, Moisés Expósito-Alonso

Updates and News

[September 2022] I'm thrilled that our work on the mutations-area relationship was accepted to Science! For a quick overview of the work, check out this perspective and for a discussion of the implications, check out this interview
[August 2022] Enjoyed presenting an update on the first chapter of my thesis, "Detecting Ecosystem Change From The Skies With Deep Learning" at ESA 2022!
[January 2022] Our grant ForestBench was selected for funding by the Climate Change AI Innovation Grant fund!
[January 2022] Our seed grant Scenes from the Anthropocene was selected for funding by the Ethics, Society and Technology Hub! Stay tuned for more updates as we explore ethical questions in conservation through the medium of film.
[December 2021] Enjoyed presenting about ecological monitoring at scale with citizen science observations, remote sensing, and deep learning as an oral presentation at AGU 2021!
[November 2021] Thrilled to have been selected to attend the Environmental Data Science Summit 2022.
[October 2021] I was honored to give a guest lecture on remote sensing and deep learning for answering climate change-related environmental questions at Muskingum University. Thanks especially to Dr. Alisa Neeman for the invite!
[October 2021] I had a a great time at TDWG 2021 presenting on predicting plant biodiversity in California using deep neural networks.
[October 2021] Very excited that our report on quantifying the scale of genetic extinction in the Anthropocene is now live on bioRxiv!
[August 2021] Our report on foundation models is live on arXiv. Specifically, check out the Environment and Ethics sections, which I helped co-author.
[August 2021] Enjoyed presenting my work on modeling plant biodiversity from satellite imagery as a contributed talk at Ecological Society of America 2021
[July 2021] Our paper on limiting dynamics of SGD is now live on arXiv.
[July 2021] I'm thrilled to have been selected for the 2021 TomKat Graduate Fellowship for Translational Research!
[June 2021] We just finished kicking off our first gathering of the BlackAIR Summer Reserach Grant program recipients! Thanks again to Black in AI for supporting this all-important program.
[May 2021] Had a great time giving a guest lecture at UC Davis' Environmental Data Science course! Thanks to Troy Magney for the kind invite.
[April 2021] Enjoyed getting to present my work on modeling plant biodiversity across California with remote sensing and deep learning to the Jetz Lab.
[January 2021] I had an excellent time speaking to NASA JPL's Carbon Cycle and Ecosystems group about my work modeling plant biodiversity across California from satellite imagery using deep learning.
[December 2020] Excited to have joined the 2020 Women in ML workshop discussing my upcoming work on modeling plant biodiversity from satellite imagery.
[June 2020] I'm thrilled to be joining the MoiLab and begin my journey in tackling big open issues in ecology, genetics, and conservation biology with machine learning!
[April 2019] I'm excited to be joining Stanford CS in the Fall of 2019 pursuing a PhD in Computer Science!
[April 2019] I'm beyond honored to have been selected as an 2019 NSF Graduate Research Fellow!
[January 2019] Excited to have spoken to the Southwestern community about my academic and research journey throughout undergrad.
[December 2018] I'm honored to have been selected as a CRA 2019 Outstanding Undergraduate Researcher.
[October 2018] I'm thankful to have won an undergraduate student poster presentation award at SACNAS 2018.

Previous Projects

[in submission] Limiting Dynamics of SGD: Modified Loss, Phase Space Oscillations, and Anomalous Diffusion
Dan Kunin, Javier Sagastuy-Brena, Lauren Gillespie, Eshed Margalit, Hidenori Tanaka, Surya Ganguli, Dan Yamins
2018 ALIFE Changing Environments Drive the Separation of Genes and Increased Evolvability in NK-Inspired Landscapes
Lauren Gillespie, Emily Dolson, Alex Lalejini, Charles Ofria
2018 GECCO Querying across time to interactively evolve animations
Isabel Tweraser, Lauren Gillespie, Jacob Schrum
[PDF] [Link] [Code] [Slides, etc.] *GECCO Travel Scholarship Recipient
2017 SIGHPC Understanding Congestion on Omni-Path Fabrics
Lauren Gillespie, Christopher Leap, Dan Cassidy
[PDF] [Link] *HPC Travel Scholarship Recipient
2017 GECCO Comparing direct and indirect encodings using both raw and hand-designed features in tetris
Lauren Gillespie, Gabriela Gonzalez, Jacob Schrum
[PDF] [Link] [Code] [Slides, etc.] *SACNAS 2018 Student Presentation Award