Lauren Gillespie

Hello! I'm a 2nd year Computer Science PhD student at Stanford University also affiliated with the Carnegie Institution for Science. You can usually find me either doing research, working, coding, and occasionally gardening🌱.

A headshot of me, Lauren.

I'm co-advised by Prof. Moisés Expósito-Alonso, a Principal Investigator in the Department of Plant Biology 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. Start to understand the specific causal mechanisms behind loss of biodiversity across the tree of life

  3. Ultimately develop informed, precision strategies for mitigating this loss and adapting our ecosystems to a warmer and drier climate future.

Recent Updates

[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!
[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.

Recent Work

2021 ESA Annual Meeting An image is worth a thousand species: Combining deep neural networks and high-resolution satellite imagery to predict plant biodiversity
Lauren Gillespie, Megan Ruffley, Moisés Expósito-Alonso
2020 AGU Fall Meeting Using Taxonomically-Informed Convolutional Neural Networks To Predict Plant Biodiversity Across California From High-Resolution Satellite Imagery Data
Lauren Gillespie, Moisés Expósito-Alonso

Previous Projects

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