Thanks for visiting the ML QuESt Lab! We are a team of scholars developing and applying machine learning methodology in service of ecology and conservation.

News

November 2023: Our work on model evaluation has also been accepted at the 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment. This 5-page paper is an extended abstract of the 25-page TMLR paper. We are pleased that our submission was selected as one of four spotlight talks, and Jing will present at the workshop in December!

September 2023: New paper out! Jing Wang has been working hard on this one, and it’s great to see it officially published. This is the beginning of our investigation into model evaluation in geospatial contexts. We learned a lot and will be continuing this line of research!

May 2022: Rebecca Hutchinson was promoted to Associate Professor with indefinite tenure. Rebecca will be on sabbatical for the 2022/2023 academic year.

February 2022: Our paper, led by Laurel Hopkins, has been published in Landscape Ecology.

December 2021: Mark Roth successfully defended his MS project, On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data. Mark has accepted a Data Scientist position with Climate.

July 2021: Eugene Seo passed her PhD defense on Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection. She will begin a postdoc at Brown University in the fall.

May 2021: Our paper, led by Mark Roth, won the Best Paper (proposals track) award at the ICML 2021 Workshop on Tackling Climate Change with Machine Learning.

April 2021: Rebecca Hutchinson received an NSF CAREER award: Machine Learning Methods for Spatial Data with Applications in Ecology.

June 2020: Laurel Hopkins was awarded a NASA FINESST Fellowship to support her PhD work: Developing Habitat Summaries with Deep Learning-Based Methods for Advancing Wildlife Conservation.