Climate, Life, Earth, and Planets (CLEPS)


2005+ Physics-statistics rapidly quantifies tropical cyclone-induced risk in a changing climate.
2017+ Nonparametric tail bounds enable robust risk-informed sustainable decision-making.

2020+ Neural Physics downscales wind, rain, and flood hazards to very high resolutions.
2023+ Machine Learning accelerates the search for storm extremes in a changing climate.

We seek advances Machine Learning-Physics and Statistical-Physical methods to rapidly project the hazard from weather extremes in a changing climate, coupling hazard with vulnerability of population and exposure of infrastructure to quantify the risk from extremes. We quantify time-dependent risk to solve decision problems for sustainable outcomes. Currently, we are studying the south-west Bangladesh, Broward County, Boston, Puerto Rico, Cape Town, and Bangladesh, but our approach is not limited to these. Visit to learn more!


1996+ Using Machine Learning and Computer Vision for Conservation.

In continuous development and research since 2002, our SLOOP system was the first system to use "generic image features" in an image retrieval paradigm coupled with humans and crowd-sourcing to identify individual animals from photographs.  Today, our methods incorporate deep learning and show amazing performace by learning to improve recall co-actively. Human relevance feedback is used to improve system performance, which in turn asks the humans to make fewer strategic judgments. Our approaches are useful for image recognition applications (e.g., retail) but our primary focus is in conservation. Presently, we are working with Koalas! Visit to learn more.


2021+ Machine Intelligence Improves Global Seismic Monitoring.
2022+ Neuro-Physical Inverters help discover Geothermal Energy.

We are developing new informative optimization and inference techniques to locate, associate and estimate earthquakes from observations at seismic stations across the world. A key benefit of our approch is to identify the number of events and their location uncertainties much faster than purely sampling-based approaches to inference. 


2019+ Using ML-Physics to detect and characterize exoplanets.

Using a counter-factual approach, we learn what the photometric environment without a transit would look like from the vast amounts of unlabeled data, then train detectors using physics and the modest number of labeled examples for what a transiting exoplanet light curve might look like. The results are super fun! We are also interested in analyzing spectral signatures for signs of life.


The themes in our group and beyond are explored through CLEPS workshops, see CLEPS22 for the most recent workshop.