Potential Projects for 2024
Please review the available summer research projects below or on smartsheet. To view projects available for SURGE applicants in smartsheet, please filter projects by clicking on the top menu currently set to 'Filter Off' and switch it to 'SURGE'. SURGE is the only undergraduate research internship available at the Doerr School of Sustainability for non-Stanford students. You will be asked to rank your top three projects of interest in your application.
Please note SURGE 2024 projects will be posted starting on December 1st and updated through January 25th. You can also browse the list of faculty and their research interests and indicate your top two departments/programs or faculty of interest on your application. For examples of last year’s research projects, please check out the SURGE 2023 projects.
SURGE 2024 Projects
Computer vision geodesy of a volcanic caldera collapse
Category(s): Dynamic Earth; Natural Hazards
Department: Geophysics
Faculty PI: Paul Segall
In 2018, Kīlauea Volcano in Hawaii had a major eruption that covered 35 square kilometers of land with a cubic kilometer of lava. The eruption drained a magma reservoir beneath the volcano’s summit, which caused the overlying rock to collapse downward in a series of large earthquakes and form a new 500-meter-deep caldera. The months-long caldera collapse sequence was captured with video recorded continuously from multiple camera angles. This provides an unprecedented opportunity to create a ‘video geodesy’ dataset, which will yield insights into caldera collapse eruptions and earthquake cycles while advancing the use of computer vision in solid earth sciences.
We seek a student interested in using computer vision methods to resolve time-varying ground deformation and fracturing from the caldera collapse video. The student will work with geophysics postdoc Josh Crozier, geophysics professor Paul Segall, and Hawaiian Volcano Observatory geologist Matt Patrick. This will be a self-contained project with opportunities for producing a first-author publication and contributing to multiple ongoing research projects. The student should be proficient in at least one programing language, and familiarity with geodesy or computer vision would be beneficial but not required.
Skills/Interest/Background: Geology; Scientific/computer programming
Energy-Water Flexibility: Computational tools for resilient water infrastructure
Category(s): Climate mitigation and adaptation; Energy; Freshwater
Department: Civil & Environmental Engineering
Faculty PI: Meagan Mauter
Globally, water utilities consume anywhere from 4-6% of a region’s electricity demand. The energy footprint of the water sector is expected to grow as we build new resilient infrastructure to support a growing population under uncertain climate conditions. The Water and Energy Efficiency for the Environment Laboratory (WE3 Lab) aims to reduce the cost and carbon intensity of climate-resilient water technology, like desalination and water reuse. We develop physics-informed models and optimization tools for decision making in water-energy systems.
This project uses state-of-the-art techniques in computational science with real data from case study water facilities to study energy flexibility in drinking water infrastructure. The student will be tasked with constructing digital representations of water systems, developing data analysis pipelines, and applying optimization algorithms. Questions that can be investigated include: do interacting water systems across a network have synergistic effects in terms of energy flexibility; what are the limiting factors and operational constraints for flexible operations; and what are the trade-offs in optimizing for cost and emissions. The ideal candidate will have experience manipulating large data sets, contributing to collaborative software projects in Python, and have a passion for decarbonization. Previous experience in thermodynamic modeling or technoeconomic analysis is not required. Please find more information here.
Skills/Interest/Background: Data science/statistics; Engineering; Machine learning; Scientific/computer programming
Emergency mobile monitoring for California wildfire smoke
Category(s): Climate change; Climate mitigation and adaptation; Environmental justice; Sustainable society
Department: Earth System Science
Faculty PI: Noah Diffenbaugh
Scientists and conservationists are increasingly using environmental DNA (eDNA; the bits of DNA that organisms leave behind in the environment) as a way to study biodiversity, including in ocean environments. eDNA has the potential to make monitoring marine ecosystems easier and cheaper, but there are still many big scientific questions about what eDNA samples represent and how best to interpret the data they produce. But advancing the use of eDNA doesn’t just require scientific work; we also need a better understanding of how scientists, policymakers, and the public think about the promises and pitfalls of the technology.
We have several ongoing projects—ranging in approach from natural science to social science—that aim to advance the use of marine eDNA as a biomonitoring tool, especially in intertidal ecosystems. Research collaborators will have a primary focus within on these ongoing projects, with opportunities to contribute to other ongoing work. That specific focus might include: 1) continuing a comparison of previously generated eDNA data from an intertidal ecosystem with other types of biodiversity data from the site; 2) analyzing how articles in the media describe the potential uses of marine eDNA; 3) collecting and processing side-by-side eDNA samples and visual surveys in an intertidal ecosystem. None of these potential projects have any required qualifications, and each would help you build different combinations of skills. If you are interested in marine eDNA, but not any of the particular projects outlined, there may be other opportunities; please don’t hesitate to reach out.
Skills/Interest/Background: Data science/statistics; Scientific/computer programming
Advancing the use of environmental DNA as a tool for marine biodiversity monitoring
Category(s): Biodiversity; Marine Biology; Ocean
Department: Civil & Environmental Engineering
Faculty PI: Alexandria Boehm
Scientists and conservationists are increasingly using environmental DNA (eDNA; the bits of DNA that organisms leave behind in the environment) as a way to study biodiversity, including in ocean environments. eDNA has the potential to make monitoring marine ecosystems easier and cheaper, but there are still many big scientific questions about what eDNA samples represent and how best to interpret the data they produce. But advancing the use of eDNA doesn’t just require scientific work; we also need a better understanding of how scientists, policymakers, and the public think about the promises and pitfalls of the technology.
We have several ongoing projects—ranging in approach from natural science to social science—that aim to advance the use of marine eDNA as a biomonitoring tool, especially in intertidal ecosystems. Research collaborators will have a primary focus within on these ongoing projects, with opportunities to contribute to other ongoing work. That specific focus might include: 1) continuing a comparison of previously generated eDNA data from an intertidal ecosystem with other types of biodiversity data from the site; 2) analyzing how articles in the media describe the potential uses of marine eDNA; 3) collecting and processing side-by-side eDNA samples and visual surveys in an intertidal ecosystem. None of these potential projects have any required qualifications, and each would help you build different combinations of skills. If you are interested in marine eDNA, but not any of the particular projects outlined, there may be other opportunities; please don’t hesitate to reach out.
Skills/Interest/Background: Biology; Data science/statistics; Engineering; Field work; Lab work; Scientific/computer programming; Social sciences
Using molecular biology tools to support community-based marine management: creating an epigenetic clock to age the Hawaiian Moi fish (Polydactylus sexfilis)
Category(s): Climate mitigation and adaptation; Environmental justice; Food and agriculture; Ocean; Sustainability, education, and communication; Sustainable society
Department: Oceans
Faculty PI: Larry Crowder
Moi (Pacific Threadfin, Polydactylus sexfilis) are of significant cultural and commercial importance in Hawaiʻi. Their populations are protected through statewide fishing regulations that restrict harvesting from June through August annually which is intended to overlap with the peak spawning period for moi. However, lawaiʻa (Native Hawaiian fishers) have observed that moi spawning times are shifting and extending beyond the time frame of the seasonal closure period. This potentially means that protected months no longer fully overlap with moi reproductive cycles, putting adult moi at risk of being harvested prior to having the opportunity to spawn. In support of lawaiʻa partners, this project triangulates Hawaiian traditional methods with western scientific ones to determine the age, and therefore spawning time of juvenile moi. Our results will inform Hawaiian community management efforts to better protect moi populations during their spawning periods, which is crucial for maintaining healthy native fish populations and abundant marine resources in the context of our rapidly changing ocean.
For this project we are carrying out a new method to age fish using epigenetic markers. Working closely with a PhD student, you will learn to design and validate primers, and create a PCR protocol to amplify age-correlated regions of the Moi epigenome. We aim to create a procedure for marine stewards to determine the age of their Moi samples epigenetically from just a tail-clip (without killing any fish), and better monitor Moi populations in the future. An interest in molecular lab-work and allyship with native Hawaiian community efforts is important for joining our team. No previous lab experience is required and Native and first-gen/low-income students are especially encouraged to apply.
Skills/Interest/Background: Community-engaged work; Lab work
Ice penetrating radar: Science and engineering to explore ice sheets and icy moons
Category(s): Climate change; Planetary science
Department: Geophysics
Faculty PI: Dustin Schroeder
The Stanford Radio Glaciology research group focuses on the subglacial and englacial conditions of rapidly changing ice sheets and the use of ice penetrating radar to study them and their potential contribution to the rate of sea level rise. In general, we work on the fundamental problem of observing, understanding, and predicting the interaction of ice and water in Earth and planetary systems Radio echo sounding is a uniquely powerful geophysical technique for studying the interior of ice sheets, glaciers, and icy planetary bodies. It can provide broad coverage and deep penetration as well as interpretable ice thickness, basal topography, and englacial radio stratigraphy. Our group develops techniques that model and exploit information in the along-track radar echo character to detect and characterize subglacial water, englacial layers, bedforms, and grounding zones. In addition to their utility as tools for observing the natural world, our group is interested in radio geophysical instruments as objects of study themselves. We actively collaborate on the development of flexible airborne and ground-based ice penetrating radar for geophysical glaciology, which allow radar parameters, surveys, and platforms to be finely tuned for specific targets, areas, or processes. We also collaborate on the development of satellite-borne radars, for which power, mass, and data are so limited that they require truly optimized designs. Student projects are available in support of both ice penetrating radar instrument development and data analysis.
Skills/Interest/Background: Data science/ statistics; Engineering; Mathematics; Physics
Bofedales at risk: Investigating the impact of climate change and human activities on Andean high-altitude peatlands using satellite imagery
Category(s): Climate change; Ecology; Freshwater; Remote Sensing
Department: Earth System Science
Faculty PI: Elliott White
This project highlights the critical issues facing bofedales, unique high-altitude peatland ecosystems across the Andes, in the face of climate change. These ecosystems, characterized by cushion plants and intricate mat-like structures, play a vital role in purifying water, regulating regional water flow, supporting biodiversity, and mitigating climate change through carbon sequestration. The symbiotic relationship between bofedales and traditional ecological knowledge (TEK) of indigenous communities underscores their cultural and social significance. However, these ecosystems confront challenges such as altered hydrological cycles through glacial retreat, overgrazing, and mineral extraction, jeopardizing their survival and ecosystem services. This research project involves utilizing Google Earth Engine (GEE) and aims to leverage remote sensing technology, focusing on Terra and Aqua MODIS data. The student will assess spatial and temporal patterns in bofedales from 2001 to 2022, shedding light on climate change and human-induced stressors. This summer research opportunity welcomes students to delve into wetland and peatland stress, climate change, ecology, hydrology, remote sensing, spatial analysis, and computer programming, offering a chance to contribute to the conservation and sustainable management of these vital ecosystems. No prior experience is required, making it an ideal opportunity for those passionate about terrestrial landscapes, freshwater ecosystems, and advancing their coding and remote sensing skills.
Skills/Interest/Background: Ecosystem vulnerability; climate change; wetland ecology; hydrology; remote sensing; spatial analysis; computer programming
Fingerprinting and provenancing cement for supply chain transparency
Category(s): Planetary science; Social sustainability; Supply Chain Transparency; Sustainable design and construction; Sustainable society
Department: Civil & Environmental Engineering
Faculty PI: Sarah Billington
An estimated twenty-eight million people are trapped in modern slavery worldwide. The construction industry has been identified as a particularly large and high-risk sector for forced labor, with twelve raw and composite building materials listed by the U.S. Department of Labor as goods made with forced and child labor. Our team with expertise in civil engineering materials, sustainability, and geosciences is developing an approach to “fingerprint” cement, where fingerprinting refers to distinguishing between samples of various origins. These fingerprints will then be used to predict the geographic origins of the cement’s constituent materials (i.e., “provenance” the cement) for increased supply chain transparency and eventual identification of the risk of forced labor. Our work involves laboratory measurements, multivariate statistical analyses, working with large datasets, and machine learning image classification. Student researchers on this project will gain experience with (1) conducting literature reviews in a range of fields, including geochemical fingerprinting, supply chain management, and forced labor, (2) collecting, organizing, and visualizing large publicly available datasets using software such as R or python, (3) assisting with material sample preparation and testing in a laboratory context, and (4) learning to give technical presentations and write brief technical reports of their work. We prefer applicants who (1) have a strong interest in building materials and/or social sustainability in the built environment, (2) have some basic coding experience and coursework in statistics, and (3) are self-motivated and proactive communicators. These are not hard requirements, as the Billington lab is a supportive environment for undergrads to develop new skills, though they may help determine compatibility.
Skills/Interest/Background: Building materials; Social sustainability, Statistics
Flow in coastal environments
Category(s): Ocean
Department: Civil & Environmental Engineering
Faculty PI: Stephen Monismith
There are two possible areas of research: (1) Laboratory study of flow over corals including surface waves, work connected to current lab and field work on this topic; (2) Field work on flow in and around kelp forests - this would entail participation in field work that would likely be done at Hopkins Marine Station. The former activity would involve helping design and having primary responsibility for carrying out lab experiments in our large wave channel. The latter would engage the student in helping prepare for field deployment of flow instrumentation, participating in the deployment and recovery of those instruments, and some analysis of the data. Retrospective analysis of field data we have already collected might be involved as well.
Skills/Interest/Background: Physics; Civil, mechanical or aerospace engineering; Oceanography; Knowledge of a programming language like Python or Matlab; Exposure to some fluid mechanics
Hunting for small earthquakes in the Bay Area
Category(s): Dynamic earth; Evolution of Earth
Department: Geophysics
Faculty PI: Greg Beroza
The rise of high performance computing and artificial intelligence has enabled seismologists to find an order of magnitude more earthquakes than previously known, especially small ones that were hidden in noisy seismograms. This massive number of newly found earthquakes are providing insights into fault geometries, earthquake statistics, and the physics of faulting. The vicinity of Stanford, the San Francisco Bay area is crossed by several major geological faults that constantly produce earthquakes. The iconic San Andreas fault is the boundary between the North American plate and the Pacific plate and has hosted major earthquakes in the past, the 1906 earthquake among them.
We propose to use Machine Learning techniques and empirical signal detectors to search for unknown earthquakes in the vicinity of Stanford. We are seeking for self motivated and independent learners with some experience in seismology and earthquakes. Coding skills in Python and shell scripting are preferred but not required. Students will be exposed and become familiar with forefront seismological research. The computations will be performed in a shared computing cluster. After assembling the earthquake catalog, this will be used to draw interpretations on the tectonic setting, patterns of seismicity and faults and interactions between populations of earthquakes.
Skills/Interest/Background: Seismology; Earthquakes; CodingHunting for small earthquakes in the Bay Area
Deciphering the toxicity of wildfire smoke
Category(s): Air quality; Climate change; Natural hazards
Department: Earth System Science
Faculty PI: Scott Fendorf
Wildfires are increasingly common and severe in North America and smoke from these blazes has left no portion of the continental United States unaffected. Smoke carries inherent toxicity with public health implications, but these effects are not entirely understood. Further, it is unclear what the chemical variation within smoke is—and how such trends play out across the scale of the United States. This summer project will harness publicly available air quality data and use methods in machine / statistical learning to define possible couplings between landscape characteristics (properties of ecoregions) and wildfire smoke composition.
This will be a great project for someone who is primarily interested in developing skills in data science with application to environmental science / geochemistry. Some prior experience with R or Python is required, though we certainly don’t expect mastery. The summer student will work with large amounts of data in R and GIS with an overarching objective of telling a story about landscapes, wildfire, and smoke through creative construction of scientific figures and graphics. There may be an opportunity to enrich the construction of a data science story with custom laboratory analysis of wildfire smoke samples, though it will not be a primary task within the summer objectives. This is a thematically interdisciplinary project opportunity! We look forward to working with someone who is excited to work within and between the fields of data science and environmental science.
Skills/Interest/Background: R or Python, data science, geochemistry, Geographic information systems (GIS)
Modeling of asteroid impacts on terrestrial planets
Category(s): Evolution of life; Planetary science
Department: Earth and Planetary Sciences
Faculty PI: Laura Schaefer
Impacts play a key role in shaping the surfaces and atmospheres of terrestrial planets, and their signature is ubiquitous in our Solar System. Since it is very rare to witness them, planetary scientists have used modeling techniques to understand the effect of hypervelocity projectiles hitting a solid target. Those models can predict the crater formation process, its final size, its temperature change over time, the fate of materials present in the projectile and the target, the amount of rock vaporized and injected in the atmosphere of the planet, the formation of melt ponds. For giant impacts, it is possible to simulate how the shape and composition of the planet changes. The project is centered on impact simulations to investigate the cratering mechanics and thermal evolution of the target, assessing whether impacts can form temperature and pressure conditions to host liquid water and/or other necessary ingredients for life as we know it.
The student will learn how to run impact simulations with the iSALE2D shock-physics code and use it to model different impact scenarios on various planets (Mars, Venus, exoplanets). The project is suitable for students interested in planetary sciences, material science and physics. Previous general coding experience (e.g., Python) is desirable, but no previous experience with iSALE2D is required as it will be taught during the internship.
Skills/Interest/Background: Physics, Engineering, Geology, Scientific/computer programming
Understanding the role of atmospheric aerosols in weather and climate systems
Category(s): Climate change
Department: Earth System Science
Faculty PI: Yuan Wang
There is a significant challenge in comprehending and attributing climate changes due to the complexity of distinguishing the radiative impacts of greenhouse gases (GHGs) and aerosols. For example, the shifting trends in aerosol emissions - decreasing in Europe and the U.S. but rising in Asia - have led to periods of increased and decreased solar brightness, significantly affecting global atmospheric patterns. Moreover, the loading of biomass burning (BB) aerosols in the United States and globally, particularly wildfires, has escalated dramatically in recent years, due to the increases in the frequency and severity of fire activity under the warmer and drier climate. Aerosols enter the atmosphere either directly as primary particles or are formed as secondary particles through gas-to-particle conversion processes. These aerosols further undergo various chemical and physical changes and possess distinctive optical properties. Furthermore, the microphysical properties of man-made aerosols have a notable effect on cloud formation, influencing occurrences of heavy precipitation, floods, and extreme weather such as hurricanes. Current assessments of the nature and magnitude of aerosol impacts are severely hindered by an inadequate understanding of the regionally dependent atmospheric transformations of aerosol properties during transports and resulting impacts on cloud properties and radiative balance, remote sensing challenges in retrieving spatial information on specific aerosol types, and model representations of key aerosol processes. We have on-going projects to 1) utilize comprehensive remote sensing datasets to provide observational constraints for the key parameters of light absorbing aerosols in climate and air-quality models; 2) conduct multiscale modeling framework to assess impacts of regional aerosol trends; 3) using aerosols and pre-cursor gases as tracers to study overshooting deep convections.
Skills/Interest/Background: Scientific/computer programming, atmospheric physics and chemistry
Exploring how hydrothermal alteration within Earth’s seafloor modifies rock magnetism
Category(s): Magnetism; Ocean; Planetary science
Department: Geophysics
Faculty PI: Sonia Tikoo
Recent advancements in drilling technology have granted access to previously inaccessible rocks at depths reaching over 1 km below the seafloor, marking a significant breakthrough in the field. A recent international oceanic drilling expedition has successfully recovered rocks from depth along the mid-Atlantic ridge that exhibit intriguing magnetic and physical characteristics. These rocks present a unique opportunity to delve into the impact that fluid-rock interactions have on enhancing bulk magnetic properties. Our project aims to explore the correlation between the degree of fluid-rock interaction and magnetic mineral content. The investigative process will involve conducting rock magnetism experiments and utilizing the widely applicable skill of scanning electron imaging. Additionally, the student will get the chance to analyze the data collected via creating visualizations in the programming language, Python. By unraveling the complexities of fluid-rock interactions, we aspire to shed light on how these processes give rise to magnetic anomalies both on Earth’s seafloor and potentially on Mars. We are on the lookout for an enthusiastic student ready to dive into this hands-on experience. While prior exposure may influence the project's direction, no prior laboratory or coding expertise is necessary; the student will gain all essential skills throughout the program.
Skills/Interest/Background: Physics, Geology, Lab Work
Paleomagnetism: Understanding the magnetic history of small planetary bodies and the moon
Category(s): Planetary science
Department: Geophysics
Faculty PI: Sonia Tikoo
The Stanford Paleomagnetism and Planetary Magnetism Laboratory invites an undergraduate researcher to explore the magnetic histories of either a pallasite meteorite or a lunar breccia meteorite. Pallasites are rare stony-iron meteorites that are believed to originate from the interiors of planetesimals formed in the early solar system. The student will investigate the magnetic history of this pallasite meteorite to gain a unique perspective into the magnetic histories of small planetary bodies. Alternatively, the student may work with a lunar breccia, a type of meteorite formed on the lunar surface and transported to Earth through meteorite impacts. This sample provides insights into magnetic properties and the effect of shock events on lunar samples.
The student will play a leading role in conducting paleomagnetic and rock magnetic investigations, analyzing magnetic records preserved in their selected sample. This will include laboratory work for preparing extraterrestrial samples, conducting paleomagnetic experiments, measuring magnetic hysteresis, performing low-temperature magnetic measurements, and analyzing the data obtained. The student will also engage in electron microscopy, using a scanning electron microscope (SEM) and electron dispersive spectroscopy (EDS), to identify the potential magnetic carriers. We are seeking a student who is interested in conducting research in planetary geoscience. No prior experience in laboratory work or coding is required. The student will gain lab experience, develop data analysis, interpretation, and experimental design skills.
Skills/Interest/Background: Lab work, Scientific Programming, Physics, Geology
The soil remembers: Using isotopes to trace soil erosion and landscape evolution in Puerto Rico before and after Hurricane Maria
Category(s): Climate change; Climate mitigation and adaptation; Natural hazards
Department: Earth and Planetary Sciences
Faculty PI: Jane K. Willenbring
Hurricane Maria devastated Puerto Rico in 2017, taking thousands of lives and destroying much of the infrastructure and natural ecosystems that lay in its path. The societal impacts of Hurricane Maria are still being experienced by Puerto Ricans today, but what about Maria’s ecological impacts? We will use a series of isotopic tracers called fallout radionuclides to trace rates of soil erosion and suspended sediment fluxes in rivers that drain Puerto Rico’s El Yunque tropical rainforest. Our project builds on a unique set of soil cores and suspended sediment samples collected during 2011-2012: a handful of years before Maria made landfall. By returning to El Yunque and resampling these locations, we can compare fallout radionuclide inventories before and after this historic storm to better understand the volume of soil and sediment that was mobilized during this catastrophic storm event, as well as how El Yunque has recovered in the years since.
The Life & Landscape Lab seeks a motivated and enthusiastic student interested in exploring how El Yunque’s soils remember Maria. The project involves a considerable laboratory component, although previous laboratory experience is not required. The selected student will gain expertise in measuring various soil physical and biogeochemical properties, including fallout isotopes using gamma-ray spectroscopy. The student will also gain experience in analyzing and visualizing data in R and/or python, interpreting experimental results, and scientific writing. There may be an opportunity to conduct fieldwork in El Yunque during the project period if this is of interest to the selected applicant.
Skills/Interest/Background: Geology, Lab work, geochemistry, data analysis/statistics
“Snow pressure”: Deep-sea microbial activity governing the carbon cycle
Category(s): Ocean
Department: Earth System Sciences
Faculty PI: Anne Dekas
Photosynthesis occurs in the surface ocean, converting atmospheric CO2 (inorganic carbon) into various forms of particulate carbon. Part of this carbon is exported to the deep ocean, where it can be stored for millennia. Without this mechanism, global warming would be considerably higher, making this "biological carbon pump" one of the most important ecosystem services. Particles carrying carbon to the ocean floor, also called “marine snow” must sink at least 1,000 m before storing it long-term. While sinking, micro-organisms remineralize some of this organic carbon (turn it back into CO2). In parallel, other micro-organisms adapted to the high pressure of the deep-sea fix (consume) CO2, using chemical energy instead of sunlight available in the surface ocean. Who are these microbes? How and how much carbon do they remineralize or fix? To integrate all this information, a multidisciplinary approach combining microbiology, biogeochemistry and mathematics is required. Our team went last year in the North Atlantic Ocean to collect various valuable samples related to these questions. In this research project, the student will help analyze samples from this expedition to better understand deep-sea microorganisms and their role in the global carbon cycle. The results of this microbial-scale study will be extended to a larger scale, increasing understanding of the general carbon cycle and refining predictions of the efficiency of the biological carbon pump.
Skills/Interest/Background: Biology, Lab work, oceanography, microbiology
Advancing the use of artificial intelligence and machine learning for high pressure science
Category(s): Energy; Sustainable design and construction
Department: Earth & Planetary Sciences
Faculty PI: Wendy Mao
Pressure, a fundamental thermodynamic parameter, induces dramatic changes in materials and opens an enormous energy landscape to search for improved materials and new phases. Adding temperature and composition, the vastly expanded configurational space allows us to explore many phases with improved materials functionality, discover new pathways, and study transition mechanisms for preserving novel stable and metastable phases towards ambient conditions. In materials discovery, the central question is how to efficiently explore this wide phase and chemical space. Artificial intelligence (AI) and machine learning (ML) have the potential to hasten the energy-related materials development timeline by many times. Although pioneering efforts such as the Materials Project use modern data-mining approaches for analyzing materials properties and predicting new materials, the utility for materials design is mostly limited to conditions of zero pressure and temperature.
The student will engage in efforts for developing an AI framework for accelerating the materials discovery and understanding in the field of high-pressure materials science. We will use halide perovskites as a model materials platform. Halide perovskites are promising materials for many energy applications and possess soft lattices susceptible to compression. The use of AI for studying halide perovskites at high pressure is in the very early stages. The developments will illustrate the transformative potential of advancing AI to many complex materials systems.
Skills/Interest/Background: Machine learning, Materials Sciences/Chemistry/Physics
Plants, topography, and microclimates at Jasper Ridge Biological Preserve
Category(s): Climate change; Ecology; Evolution of life
Department: Earth System Science; Biology
Faculty PI: Chris Field
While the app on your phone may say it was sunny with a high of 70 in Palo Alto today, air temperatures likely got much warmer just above a paved street, and stayed cooler in a secluded and shaded grove of trees. In natural ecosystems, conditions are influenced by cooling effects of plants or nearby water bodies, shading, exposure to winds, and many other factors. Those conditions subsequently affect where species can live, and the rates of important processes such as photosynthesis and respiration. This project will examine how topography, plant communities, and vegetation management techniques affect microclimates in the varied habitats of Jasper Ridge Biological Preserve (JRBP). You will work with a senior scientist and members of his research team to measure environmental variables and ecosystem processes at small scales around JRBP. You will collect data from different locations within JRBP and use it to help disentangle the influences of geology and plant communities on microclimate, and to describe the range of microclimates occurring in the preserve. You will gain experience with fieldwork and species identification, environmental sensors and dataloggers, and data processing, analysis, and presentation. Your work will help to put local climatic variations and microclimatic buffering in the context of human-caused climate change.
Skills/Interest/Background: Biology, Field work, familiarity with Geographical Information Systems (not required)
Success of invasive plant species in a changing climate
Category(s): Climate change; Ecology; Evolution of life
Department: Earth System Science; Biology
Faculty PI: Chris Field
Climate change and the arrival of invasive species are changing how plants function and compete in many ecosystems. This project will evaluate how specific traits of native and invasive plant species, and the traits found in their communities, jointly contribute to competitive outcomes under manipulated precipitation and temperature environments, using California serpentine grassland annuals as a model system. You will be paired with a graduate student mentor to design an experiment related to these concepts and will gain experience with a combination of field, greenhouse, and laboratory work, including maintaining potted plant communities, measuring morphological plant traits, resource consumption, and reproductive output, processing, visualizing, and analyzing datasets in R or Python, and presenting your findings.
Skills/Interest/Background: Biology, Field work, lab work, Ecology
The Marketplace Activity Index: A new tool to track rural economies in low-income countries
Category(s): Climate mitigation and adaptation; Food and agriculture; Sustainable society
Department: Earth System Science
Faculty PI: David Lobell
Following an extraordinary global reduction in extreme poverty over the last century, progress on eliminating poverty has slowed. Remaining pockets of poverty worldwide are increasingly concentrated in remote rural areas where traditional data sources are sparse. This project will work with either applications or extensions of a novel type of data – remotely-sensed activity in rural marketplaces – that can shed light onto short-term trends in economic conditions in these areas. Potential applications include (i) how rural economies are affected by and recover from conflict, (ii) how road construction may relocate where economic activity takes place and (iii) how extreme weather events and climate change affect rural economies. More technical topics regarding extensions and refinement of the underlying methods may include (i) predicting goods traded at marketplaces using super-resolution methods or SAR imagery and (ii) designing and prototyping of a real-time monitoring dashboard to monitor humanitarian crises. The specific project will be decided closer to the summer based on student interests and data availability. This is a good project for students with a strong coding background (especially Google Earth Engine, Stata/R, Python), an interest in social science, and a desire to gain familiarity with remote sensing data and methods.
Skills/Interest/Background: Spatial data; statistical analysis; coding; development economics
Informing California agricultural sustainability through remote sensing
Category(s): Climate mitigation and adaptation; Food and agriculture
Department: Earth System Science
Faculty PI: David Lobell
Agricultural sustainability in California is faced by both nutrient and water management challenges, which are highly interlinked. This project will work towards combining satellite data and field-level datasets in California to investigate and monitor the effects of state-wide agricultural programs that aim to improve water efficiency and soil health. This would be a good project for students with a strong coding background (especially python) and an interest in learning about remote sensing data and its applications towards agricultural intervention policies.
Skills/Interest/Background: Python, data analysis, remote sensing and GIS experience.
Fish are food: Mapping aquaculture in West Africa using machine learning & remote sensing
Category(s): Climate mitigation and adaptation; Food and agriculture
Department: Earth System Science
Faculty PI: David Lobell
Aquaculture is critical to food security in West Africa, where half of the region rely on daily consumption of fish products. Data on production and regional suitability are necessary to track value in these supply chains and to support decision-making for planners and investors. However, mapping of ponds and their characteristics is very limited– the last national aquaculture statistical survey for Cote d’Ivoire was completed in 2014. This project aims to (i) Map current production region/zones and (ii) identify high-potential areas for sustainable intensification of Cote d’Ivoire’s aquaculture sector, using satellite imagery and various machine learning & deep learning methods to make these predictions. We have on-the-ground nonprofit partners who are ensuring that this research will be tangibly applied to further sustainable development in the region. This is a good project for students with a strong data science or programming background (especially Python) and a desire to gain familiarity with remote sensing data and methods. The exact details for the project will be built around the student’s background and interests.
Skills/Interest/Background: Python, Data Science, Machine Learning, Deep Learning
Fine-scale crop yield prediction based on remote sensing observations and machine learning methods
Category(s): Food and agriculture
Department: Earth System Science
Faculty PI: David Lobell
Under the pressure of a changing climate and the need to feed more people, it is essential to know the productivity of crops in advance of harvesting. Existing crop yield prediction research focuses on the regional level but few of them investigate the yield prediction at a finer scale due to the lack of ground truth yield data for evaluation. With the increasing availability of yield monitor data at the subfield level, there is a good chance that we can fill the gap. This project will take advantage of the recent yield monitoring data for corn, soybean, and wheat in the U.S., with an emphasis on using remote sensing data for subfield-level yield prediction. The target of this project is to provide insights into subfield-level crop yield prediction. Among the potential specific topics are: (i) comparing the performance of optical remote sensing data and hyperspectral remote sensing data on subfield-level yield prediction; (ii) investigating the feature importance of raw bands and vegetation indices on yield prediction and studying whether the optimal feature set would change for different crops at different locations; (iii) investigating whether adding phenology information and geolocation information can help improve the yield prediction accuracy.
Skills/Interest/Background: python, machine learning, remote sensing, image processing, time-series data processing
Informal agriculture/food trade & markets: Its role on food security in Sub-Saharan Africa
Category(s): Food and agriculture
Department: Earth System Science
Faculty PI: David Lobell
In low- and middle-income countries, a large share of agriculture and food trade is conducted by small-scale informal traders who avoid official checkpoints. These informal trade flows are completely missing from official trade statistics used in policy; despite playing a crucial role in food security, food prices and livelihoods in those areas. This project will work towards shedding light on the size and role played by informal agriculture and food trade (especially in Sub-Saharan Africa), exploring how informal trade interacts with official trade and assessing the role of agriculture markets in ensuring food security and their vulnerability to economic and climate shocks.
This project will use (existing) primary data collected on the ground, existing secondary data and will improve those datasets with data stemming from satellite sensors. A few potential topics include (but not limited to): (i) Creation of a dataset that includes informal agriculture and food trade flows across sub-saharan Africa; (ii) Measuring trade border congestion using satellite sensors; (iii) Spatial analysis of the composition of and food availability in agriculture markets and the role of economic and climate shocks. The specific project will be decided closer to the summer based on student interests and data availability. This is a good project for students who want to gain familiarity with development economic issues, regression analysis and causal inference. Students need a strong coding background (especially Stata/R, python) and familiarity with remote sensing data and methods.
Skills/Interest/Background: Statistics, Computer Programming, Machine Learning
Seismic ambient noise: Deciphering Earth’s secret humming
Category(s): Dynamic earth; Planetary science
Department: Geophysics
Faculty PI: Eric Dunham
Earth is constantly humming or vibrating in response to the crashing of ocean waves and turbulence in the atmosphere. These vibrations can be measured with seismometers and traditionally have been regarded as noise that complicates analysis of seismic signals from earthquakes and other sources. However, seismic ambient noise provides valuable constraints on the wide range of interactions between the solid Earth, oceans, and atmosphere. Understanding of the generation mechanisms of these noise signals has been advanced by both observational signal processing analysis and seismic modeling based on realistic noise source representations (e.g., turbulent vortices and wind in hurricanes). However, due to the complex nature of natural processes and the lack of specifically designed recording systems, many questions still remain and further detailed investigation is warranted.
In this project, students will perform analysis of various datasets, e.g., seismograms, Distributed Acoustic Sensing (DAS) recordings using fiber optic cables, atmospheric pressure and wind measurements collected at the SAFOD borehole adjacent to the San Andreas fault, and other sites, to study the seismic noise from wind. Interpretation of results requires the integration of diverse datasets and knowledge from physics-based modeling of the atmosphere-ground coupling, which allows students to compare or contrast observations with theories, an essential step in scientific research. Experience with scientific computing, e.g., using Python or MATLAB, is strongly recommended. Students interested in signal processing or physics modeling can focus on different parts of the topic. Previous background in Earth Science is not required.
Skills/Interest/Background: Scientific/computer programming
Physics-informed machine learning for turbulence
Category(s): Scientific machine learning
Department: Geophysics
Faculty PI: Ching-Yao Lai
Machine learning, particularly in the realms of mathematics and physical sciences, has seen a significant increase in application. Physics-Informed Neural Networks have become a prominent new method for solving differential equations in scientific machine learning. One challenge in this domain, especially in studies like turbulence, is the need for higher precision than what is typically required in areas like computer vision. Existing neural networks often encounter limitations in training loss, which plateaus over time, leading to less accurate predictions. Our project aims to develop enhanced PINN-based algorithms capable of solving 2-D turbulence governing equations with greater efficiency and accuracy than existing methods. The project will involve reviewing recent PINN advancements, adapting these techniques for turbulence equations, and evaluating the performance of our new algorithm against classical numerical methods.
We are seeking an enthusiastic undergraduate researcher to join us in this venture. The role offers an opportunity to develop coding skills and apply machine learning techniques to complex fluid dynamics problems. Students with background in math, computer science, statistics, applied math, and physics are all welcomed to apply.
Skills/Interest/Background: Applied math, Scientific machine learning
Irrigation or forestation: Exploring the nature-based solutions to mitigates conflicts in China’s food-energy-ecosystem sectors under climate change
Category(s): Climate change; Climate mitigation and adaptation; Energy; Food and agriculture
Department: Biology
Faculty PI: Gretchen Daily
As the world’s largest grain producer and leading country in greening, China has increasing food demand and ambitious net-zero goal by 2060. However, sectoral conflicts including land and water use have already been witnessed in food, energy production and ecological restoration. In future, climate adaptation by food sector can intensify these conflicts with energy and ecosystem sectors through irrigation and expanding croplands. The land management/practice, recognized as nature-based solution can be a critical lever for navigating the trade-offs in the water-energy-food-ecosystem (WEFE) nexus. The Nature Capital group aims to explore the optimal land management/strategies to mitigates future sectoral conflicts in China’s WEFE nexus. Our hydrology team integrates biophysical and economic models to optimization tools for decision-making analysis in WEFE nexus. We are seeking students interested in hydrological modeling and massive data analysis and offering the chance to produce peer-reviewed publications and contribute ecological restoration suggestions for policy makers. Experience in hydrological modeling and R, Python or MATLAB programing is preferred not required. Main supervisors of this project will be Dr. Schmitt and Dr. Ding (both from Natural Capital Project.
Skills/Interest/Background: Hydrological modeling, R, Python or MATLAB programing
Computer modeling of carbon dioxide removal via enhanced rock weathering
Category(s): Climate change; Climate mitigation and adaptation; Energy; Food and agriculture
Department: Earth System Science
Faculty PI: Kate Maher
Enhanced rock weathering (ERW) in agricultural fields is an important new strategy for carbon dioxide removal (CDR). However, verifying the amount of carbon dioxide removed is complex and requires computational modeling to complement field data and design deployment strategies. This project will involve simulating ERW using a reactive transport model, a numerical model that combines the physics of water and gas transport with geochemical processes, to answer questions regarding the efficacy and scalability of ERW. Depending on interest, the project can include uncertainty quantification and sensitivity analysis, and engagement with practitioners to explore data assimilation. Although the models are already developed, the ability to manipulate and evaluate data in python is desirable; coding experience will be necessary if there is interest in developing specialized processing scripts.
Skills/Interest/Background: Geochemistry, computer modeling, data science, climate technology
Mapping beaver habitat using remote sensing and artificial intelligence
Category(s): Climate mitigation and adaptation; Freshwater
Department: Earth System Science
Faculty PI: Kate Maher
After extensive hunting to near distinction, the North American beaver (Castor canadensis) is currently experiencing a substantial resurgence, emerging as a solution to numerous sustainability challenges. This resurgence can be attributed to the diverse ecosystem services provided by beavers and the transformative impact they exert on the environment. The alteration of river corridor hydrology by beavers induces intricate changes that have the potential to reshape water balance, increase carbon sequestration, influence water quality, and enhance biodiversity. Our collaborators from the University of Minnesota have identified beaver dams across several states across the American West using deep learning algorithms; our research group brings expertise in remote sensing, hydrology/ecology, and data science to identify beaver ponds, lodges, and vegetation structures, as well as quantify how beaver-induced landscape alter water, carbon and sediment storage, and impact water quality. By leveraging high-resolution remote sensing imagery from the National Agriculture Imagery Program and Planet Labs, this project focuses on accurately mapping beaver ponds. Key questions we seek to answer include the geographical distribution of these ponds and the most effective classification techniques (Decision Tree, Gradient Boosting, Random Forest, Neural Network) for accurately mapping beaver ponds. This project offers an opportunity for students to develop strong geospatial and coding skills.
This project provides a unique opportunity for undergraduate students to develop strong geospatial and coding skills. Students from diverse scientific backgrounds, including those beyond earth or environmental sciences, are encouraged to participate. Individuals with prior coding experience (in R, Python, or JavaScript) and familiarity with Google Earth and/or Google Earth Engine are particularly welcome.
Skills/Interest/Background: Beaver Landscape; Remote Sensing; Computer Programming; Artificial Intelligence
24/7 Carbon-free electrified fleets
Category(s): Climate change; Climate mitigation and adaptation; Energy; Sustainable society
Department: Civil and Environmental Engineering
Faculty PI: Ram Rajagopal
Stanford has completed the transition to 100 percent renewable electricity in March 2022, with on- and off-campus renewable electricity generation exceeding campus consumption on an annual basis. However, the campus is still plugged into the grid which carries carbon-based electricity. To completely eliminate emissions, Stanford’s next challenge is to match its electricity consumption with carbon-free resources at all hours of the year. To begin, this project will demonstrate a 24/7 Carbon-Free Electrified Stanford Campus Fleet by developing a scalable platform that intelligently coordinates solar, storage, electric bus route assignments, and bus charging for the Stanford Marguerite Shuttle. Additionally, the platform also optimizes for minimizing energy costs, providing system resiliency, and uniformly distributing vehicle utilization. This platform also serves as the first transferable model for bus fleets in school districts, corporate campuses, and municipalities to become 24/7 carbon-free. As an undergraduate researcher intern, you will have the unique opportunity to help refine and run software that optimizes the costs and emissions of the Marguerite Shuttle, then work in the field with bus operations staff to adopt these schedules and help achieve a 24/7 carbon-free electric bus fleet.
Skills/Interest/Background: Python, Optimization, Data Science, Communication
Simulating artificial upwelling strategies for marine carbon dioxide removal
Category(s): Climate mitigation and adaptation; Ocean
Department: Earth System Science
Faculty PI: Leif Thomas
To meet climate goals of limiting global warming to manageable levels, it is estimated that by mid-century around 10 Gt of CO2 per year will need to be removed from the atmosphere. The ocean naturally sequesters carbon from the atmosphere and various strategies, known collectively as marine carbon dioxide removal, have been proposed to enhance the oceanic sequestration of CO2. Artificial upwelling is one such strategy in which waters from depth, which are rich in nutrients, are pumped to the surface, enhancing primary production and possibly the draw-down of CO2 from the atmosphere. For this approach to be feasible, however, the energy costs from pumping and the outgassing of CO2 to the atmosphere caused by upwelling carbon dioxide-rich deep waters to the surface must be minimized. This project will simulate artificial upwelling strategies designed to satisfy these constraints. The designs are centered around a salt fountain, a pipe that can drive upwelling by tapping the energy stored in regions of the ocean where warmer-saltier waters overlie fresher-cooler waters. The project will involve developing and running numerical simulations and/or laboratory experiments to model the flow rates, nutrient fluxes, and the carbon chemistry of artificial upwelling driven by salt fountains to assess their efficacy and scalability.
Skills/Interest/Background: Programming
How do soils respond to prescribed fire?
Category(s): Climate change; Dynamic earth
Department: Earth System Science
Faculty PI: Scott Fendorf
Controlled fires, including prescribed fires and cultural burning, contribute to ecosystem health and have been shown to significantly reduce risk of catastrophic wildfires. Fire represents an important factor driving soil formation. For example, the chemical composition of soils are altered depending on fire severity. Prescribed fires are beneficial for fire-adapted ecosystems and can increase plant nutrient availability in soils, contributing to seed germination. Alternatively, we’ve observed that severe wildfires can generate metal toxins in soils that pose a threat to our air quality. Fire type (e.g., broadcast, piles), vegetation and amount, and weather conditions (e.g., humidity, winds) are some factors that influence how prescribed fire transforms underlying soil. Within soils, the geology, organic matter content, and moisture content can affect the nature and composition of fire-generated products.
This project will broadly explore how prescribed fires alter soil biogeochemistry depending on multiple environmental factors, including ecosystem type and soil properties. We are partnering with ecological preserves across the Bay Area with planned prescribed fires during the winter through summer months to track post-fire changes in soil properties. We are excited to welcome a motivated undergraduate student that is interested in gaining field and lab experience working with soils and other environmental samples, such as air particulate matter and water. The student will have the opportunity to collect samples at ecosystems recently burned in prescribed fires, prepare and execute multiple chemical analyses, including measuring soil pH, extractable metals, mineralogy, nutrients, and organic matter. No previous field or lab experience is required.
Skills/Interest/Background: Field work, Lab work, Chemistry
Developing field-appropriate methods to detect toxic lead in household paints
Category(s): Environmental justice
Department: Earth System Science
Faculty PI: Scott Fendorf
Lead is a potent neurotoxin and poses a serious threat to public health and human intellectual capital worldwide. While no levels of lead exposure are considered safe for humans, lead is particularly detrimental to children during the developmental period of their central nervous systems. Our past research in various countries worldwide has identified a need for field-appropriate rapid lead detection methods for environmental samples. A primary objective of this project is to improve lead detection for paint, a known source of lead exposure in many parts of the world. Lead is currently unregulated in paints in 55% of countries globally. The current gold standard approach relies on complex laboratory analyses which are not practical for low-resource settings. In collaboration with an international non-profit organization, we have acquired 211 household paint samples manufactured from 20 countries containing a range of Pb concentrations and seek to develop low-cost rapid detection approaches. During this project, the student will have the opportunity to learn how to prepare samples in the lab for quantitative chemical analysis using inductively coupled plasma mass spectrometry, x-ray fluorescence analysis, x-ray diffraction analysis, and colorimetric assays. The student will also gain skills in data management and analysis. A chemistry background and previous wet lab experience are helpful, but not necessary.
Skills/Interest/Background: Chemistry, Lab Work
Probing the metal chemistry of fire-generated nanoparticles
Category(s): Climate change; Natural hazards
Department: Earth System Science
Faculty PI: Scott Fendorf
Fire activity, including the extent, frequency, and intensity, along with the length of the fire season, has increased globally in the past decade. Fire impacts metals within the combustible or associated material and yet receives little attention despite having profound implications on human and ecosystem health. For example, wildfires can control the formation, transformation, and dispersion of toxic metal nanoparticles in soil, ash, and smoke. Because of their extremely small size, high surface area, and high reactive surface sites, nanoparticles control the (im)mobilization of contaminants and nutrients in soil and water; they also pose a threat to human health, primarily through inhalation. We are interested in deciphering how wildfires control the generation and chemical-physical properties of metal nanoparticles. Gaining a fundamental understanding of the reaction parameters controlling nanoparticle chemistry will provide predictive capacity on the risk of combustion products derived from wildfires.
We will study the composition, surface chemistry, morphology, and health risks of particles across the size distribution of > 100 nm to 10 µm from natural and prescribed fires, as well as lab-generated particulates using a range of advanced characterization techniques, including synchrotron radiation methods, transmission and scanning electron microscopy (TEM and SEM), and aqueous chemical analyses. We are looking for one or two motivated undergraduate students to help with various aspects of the project, including soil sample collection, nanoparticle synthesis, sample preparation, and characterization. A background in mineralogy, soil science, and/or chemistry is helpful but not required. Previous wet lab experience are helpful, but not necessary.
Skills/Interest/Background: Field Work, Lab Work, Soil Chemistry, Mineralogy
Deciphering the toxicity of wildfire smoke
Category(s): Air quality; Climate change; Natural hazards
Department: Earth System Science
Faculty PI: Scott Fendorf
Wildfires are increasingly common and severe in North America and smoke from these blazes has left no portion of the continental United States unaffected. Smoke carries inherent toxicity with public health implications, but these effects are not entirely understood. Further, it is unclear what the chemical variation within smoke is—and how such trends play out across the scale of the United States. This summer project will harness publicly available air quality data and use methods in machine / statistical learning to define possible couplings between landscape characteristics (properties of ecoregions) and wildfire smoke composition.
This will be a great project for someone who is primarily interested in developing skills in data science with application to environmental science / geochemistry. Some prior experience with R or Python is required, though we certainly don’t expect mastery. The summer student will work with large amounts of data in R and GIS with an overarching objective of telling a story about landscapes, wildfire, and smoke through creative construction of scientific figures and graphics. There may be an opportunity to enrich the construction of a data science story with custom laboratory analysis of wildfire smoke samples, though it will not be a primary task within the summer objectives. This is a thematically interdisciplinary project opportunity! We look forward to working with someone who is excited to work within and between the fields of data science and environmental science.
Skills/Interest/Background: R or Python, data science, geochemistry, Geographic information systems (GIS)
Mitigating wildfire risk in the West: Machine learning to enhance live fuel moisture content mapping from remote sensing
Category(s): Climate mitigation and adaptation; Freshwater; Natural hazards; Sustainable society
Department: Earth System Science
Faculty PI: Alexandra Konings
Extreme wildfires can ravage forest ecosystem functions and threaten human health by emitting particulate matter to the air. They can burn people’s homes, causing vast economic damage and fatalities. The size and frequency of extreme wildfires in the western US have also dramatically increased in the past years. Therefore, it’s vital to accurately predict and mitigate wildfire risks. Live fuel moisture content (LFMC, the amount of water in vegetation per unit dry biomass) is a key factor influencing the wildfire ignition and spread. However, measurements of LFMC are difficult and therefore rare, limiting the accuracy of wildfire predictions. Remotely sensed (i.e. satellite) observations provide a great opportunity to map LFMC estimation across large areas. Microwave remote sensing, unlike other remote sensing methods, can penetrate through clouds and into the canopy and thus is especially promising. Synthetic Aperture Radar (SAR) can map LFMC with high spatial resolution, but is available only every week or two. Radiometry satellites by contrast provide observations every few days, but their spatial resolution is more than 100 times coarser than SAR. Combining both data sources may lead to more accurate maps at both high spatial and temporal resolution.
Machine learning shows great power to automatically learn complex relations between variables from big data and achieves superior performance than traditional approaches. In this project, we will build machine learning models to integrate microwave remote sensing data from different sources, using field measurements for training and validation. We seek applicants with strong interests in working with big remote sensing data and applying machine learning methods to environmental issues. Previous experience in remote sensing is not required, but students should have some experience with computer programming. Experience with machine learning is preferred, but not necessary. The students will gain skills in applying machine learning models to analyzing remote sensing data.
Skills/Interest/Background: Computer Programming; Geoscience; Machine Learning
Understanding satellite measurements of plant water content
Category(s): Climate change; Dynamic earth
Department: Earth System Science
Faculty PI: Alexandra Konings
Climate change has and will continue to stress plants, reducing their growth and leading to mortality. An important way to understand how plants will respond in the future is through their interactions with water. Therefore, one of the best tools to understand how changing temperature and precipitation affect plants is to measure their water content. At the scale of individual trees, plant physiologists have developed many tools to estimate the water content of plants. These metrics can require large field campaigns to describe even a small stand of trees. Microwave remote sensing tools allow us to determine quantities indicative of water content across large spatial extents. Nonetheless, these measurements represent electrical quantities related to vegetation water content, and uncertainties remain about how measurements from different types of microwave sensors can best be used to map vegetation water content.
We are looking for motivated students to undertake a project aimed at quantitatively comparing different satellite measurements of vegetation water content using various remote sensing methods or considering the sensitivity of these measurements to temperature, in order to build better maps of water content in tree stands. This project will introduce the student to microwave remote sensing, plant physiology, and scientific data analysis. The ideal candidate will have experience manipulating data and be excited about delving into the world of satellite measurements. Knowledge of programming languages such as R or Python will be useful.
Skills/Interest/Background: R or Python skills will be useful, Suggested interests include climate change, remote sensing, plant physiology
Mapping Earth's thickest crust beneath Tibet and Himalaya
Category(s): Dynamic earth; Evolution of Earth; Natural hazards
Department: Geophysics
Faculty PI: Simon Klemperer
How have Earth's crust and tectonic plates responded to the collision between India and Asia that built Earth's highest mountains (Himalaya) and Earth's largest plateau (Tibet)? A basic parameter is crustal thickness: where has the crust been thickened by earthquake faulting? Where has it been thinned by lateral flow? Unless we know crustal thickness, i.e. the depth to the boundary (the Moho') between the crust and underlying mantle, we don't know whether specific earthquakes at specific depths are occurring in the crust or the mantle, hence whether it is the crust or the mantle that is deforming during collision. The Moho is deflected down under the weight of the Himalaya, but does it behave the same way everywhere along the Himalaya from Pakistan to Burma? or is it different in different places?
Lots of experiments have been done in individual locations, but there is a need to compile them systematically, across national borders and to remove systematic differences between datasets. That starts with a literature search and database construction, but needs seismological insight to understand assumptions and uncertainties, and data science to find the best mapping of data that are sometimes sparse, sometimes conflicting. Output could be a static map or an active web-site, and is likely to be of wide interest beyond Stanford. Additional information here
Skills/Interest/Background: Coding (could be R or python); Geology, seismology, data analysis
Should Pillar Point be closed to harvesting? Integrating social and ecological perspectives
Category(s): Environmental justice; Ocean; Sustainability, education, and communication
Department: Oceans
Faculty PI: Larry Crowder
Pillar Point SMCA is a haven for marine life, supporting rocky reef, kelp forest, and surfgrass habitat along with an abundance of wildlife. It is also an important area for recreational and subsistence harvesting, with fishers allowed to catch fish, crabs, and other species within the marine protected area (MPA) boundaries. However, there have been increasing reports of Pillar Point being in decline, with some groups calling for a complete closure of the area in order to protect the habitat. Before management action is taken, it is important to gain a clear understanding of the social-ecological importance and health of Pillar Point, ensure local users’ perspectives are heard, and provide decision-makers with the best available ecological data. This project will therefore collect and synthesize perspectives from different communities and users of Pillar Point with summaries of the available ecological data to best inform future management of the area.
For this project two students will help with study design and at-the-shoreline interviews for a range of local users including harvesters, biologists and recreational tide poolers. Students with foreign language skills, and from FLI and Native communities are especially encouraged to apply.
Skills/Interest/Background: Environmental justice, Foreign language fluency, Community-based coastal management, Conservation
Critical Mineral Exploration using Artificial Intelligence
Category(s): AI; Data Science; Machine Learning
Department: Earth & Planetary Sciences
Faculty PI: Jef Caers
Work with Stanford Mineral-X to help discover the critical minerals needed to transition to a greener energy future. Mineral-X works with several companies and real datasets, applies machine learning and artificial intelligence to accelerate discovery. We are looking for students that share our excitement in this project and have a solid background in programming (python) as well as data science and machine learning, visit mineralx.stanford.edu
Skills/Interest/Background: Python Programming
Ocean Biogeochemistry
Category(s): Climate change; Marine Biology; Ocean
Department: Earth System Science
Faculty PI: Kevin Arrigo
The Ocean Biogeochemistry Lab, run by Dr. Kevin Arrigo, studies the cycling of carbon and other materials within marine ecosystems and its exchange with the atmosphere (http://ocean.stanford.edu/). We combine laboratory studies and field research with data collected using satellite remote sensing techniques. These research results are synthesized within numerical models which gives us a better understanding of the underlying biogeochemical processes. Current projects are centered on under ice phytoplankton blooms and harmful algal blooms in the Arctic Ocean and nitrogen fixing bacteria from the North Pacific Subtropical Gyre. Summer students who work in the lab will develop a specific project related to these ongoing projects. They will process samples and data under the guidance of graduate students or research associates and will participate in regular lab meetings where they will present their research progress.
Skills/Interest/Background: Oceans, Carbon
Data-driven assessment of biomass-based terrestrial carbon dioxide removal techniques
Category(s): Climate change; Climate mitigation and adaptation
Department: Earth System Science
Faculty PI: Kate Maher
Carbon dioxide removal (CDR) is essential to limit global warming. Terrestrial nature-based climate solutions, such as biochar, biomass burial and enhanced rock weathering, are promising CDR methodologies with the potential for significant socioeconomic benefits. To maximize global CDR, implementation of CDRs needs to be optimized and monitoring, reporting and verification (MRV) needs to be standardized. Planning and MRV of CDR techniques requires demonstration of additionality, or that the carbon dioxide would not have otherwise been sequestered. Currently there is no consensus on the selection of counterfactual scenarios and long-term consequences are not accounted for. In addition, MRV is hindered by cost and uncertainty of current monitoring methods and lack of available activity-level data. One of our ongoing projects focuses on assessing the data needs and accounting principles for MRV to ensure reliable and durable removal. Undergraduate projects could involve assessing the data that is needed for MRV of biochar CDR in tandem with current verification protocols. Research questions could be:
1) What national or state-level data is needed to guide biomass and biochar CDR deployment and verify additionality? What future data could guide long-term deployment?
2) How robust is the data used to demonstrate carbon emissions from implementation?
3) How can co-benefits, such as reduced water use or improved water quality, be tracked?
4) What data and processes could enable assessment of current verification and resulting certificates?
Tasks could involve assessing and assembling current data products across multiple federal agencies and developing an interoperable data set for biomass and biochar deployment in different regions. We are looking for a student who has a basic science background, interest in learning basic GIS skills, and experience in analyzing complex data sets and/or programming skills. You will gain experience in the scientific method and insight into the voluntary carbon market.
Skills/Interest/Background: Data science, programming, soil chemistry
Extinction threat and its potential evolutionary consequences in rays
Category(s): Evolution of life; Marine Biology; Ocean
Department: Earth and Planetary Sciences
Faculty PI: Jonathan Payne
Shovelnose rays and their allies (collectively referred to as the shark-like rays) are a long-lived lineage of cartilaginous fishes that first evolved during the Mesozoic Era (~150 million years ago). The group comprises 65 extant species in five families: Rhinobatidae, Pristidae, Glaucostegidae, Rhinidae, and Trygonorrhinidae. Recent assessments of the conservation status for this group indicate that shark-like rays are by far the most threatened order amongst modern ray fishes and include entire families classified as either Critically Endangered or Endangered. Life history characteristics including slow growth, late maturity, and low fecundity coupled with targeted fisheries are commonly invoked to explain the high extinction vulnerability in this group. The eventual extinction of many shark-like ray species could reduce the species richness of cartilaginous fishes and possibly vacate ecological and ecosystem functions currently filled by members of this group. How such an extinction would impact the overall morphological variability (i.e., disparity) of the group remains unknown.
This project will focus on complementing existing work on biodiversity studies of shark-like rays by examining the relationship between threat status and body shape along with associated ecological traits. Given the nature of extinctions, which often impact each facet of biodiversity (e.g., species, morphology, function, ecology) differently, this project will shed light on how body shape morphology predicts extinction risk. More specifically, are elevated extinction levels in rays coupled with different gradations of anatomical variability (i.e. high/low shape disparity)? If so, what are the evolutionary and ecological consequences of this non-randomness in extinction threat across rays?
Skills/Interest/Background: Marine Biology, R programming (beginner-level)