| F | G | H | I | J | K | L | ||
|---|---|---|---|---|---|---|---|---|
1 | Project Title | Department/Program | Project Description | Research areas | Suggested skills/ interest/background of scholar | Faculty (Hyperlinked to research group website) | Project mentor | |
2 | Environmental impact of alternative mass timber floor systems | Civil & Environmental Engineering | Based on sustainability and aesthetic benefits, mass timber has seen increased adoption in the United States in the last decade. Structural systems using mass timber beams, columns, or wall and floor panels generally have lower embodied carbon than equivalent concrete and steel alternatives, where embodied carbon refers to the greenhouse gas emissions associated with a building’s construction. However, most mass timber floor systems still require a concrete topping to achieve sufficient acoustic insulation, fire protection, and lateral loading capacity. The objective of this research is to identify alternative floor systems for mass timber construction and characterize each system’s potential to reduce embodied carbon while also satisfying acoustic, fire, and structural design criteria. The primary task for this project will be using software tools to conduct comparative life-cycle assessment (LCA) of mass timber floor systems, implementing an established approach for characterizing the environmental impact of a product over its lifespan. Through consultation with research mentors and industry specialists, the student will identify alternative designs for floor systems in mass timber buildings that may be considered. The project takes place in the context of a larger study evaluating the resiliency and sustainability of mass timber buildings in high seismic regions, so the focus will be on products available for use in the western United States. Depending on student interests, additional tasks may include [i] performing uncertainty quantification for the life-cycle assessments, or [ii] numerical modeling of floor systems to assess floor vibrations and/or performance under seismic loads. Prior experience may be considered to shape the project trajectory. However, no prior experience is required. The student will acquire all necessary skills during the program. Through completion of the project, the student will at minimum build skills in literature review, environmental life-cycle assessment, data analysis, and data visualization. | Engineering, Climate Change | Engineering, Physics, Statistics | Barbara Simpson | Morgan McBain (Grad student) | |
3 | p-NeighborDrive: Privacy-Friendly Neighborhood Well-Being Sensing through Vehicle Mounted Sensors | Civil & Environmental Engineering | Homelessness has risen sharply across U.S. cities since the COVID-19 pandemic. This escalation has intensified demand for essential services for the unhoused population while straining the limited resources available to city authorities and community organizations. Effective allocation of food, sanitation, shelter, outreach, water, and medical services depends on knowing where and when needs are most acute. p-NeighborDrive, a neighborhood-sensing platform that equips everyday utility vehicles such as garbage trucks, food trucks, and buses with lightweight, privacy-preserving sensor modules to capture the unhoused population's well-being through the environmental stressors such as litter, heat exposure, air quality hotspots, sanitation, and road safety risks. The goal of the project is to enable city agencies and Non-Government Organizations (NGOs) to deploy outreach resources more efficiently by identifying emerging stress zones in real time. The student will analyze multi-modal sensor data collected across the City of San Jose to a) quantify environmental stressors at the neighborhood scale from sensors, b)correlate these stressors with spatial patterns of the unhoused population, and c)identify how the built environment influences well-being and mobility patterns among vulnerable populations. The student will be using skills regarding signal processing, machine learning, and data science/statistics. | Engineering | Engineering,Machine learning | Hae Young Noh | Jatin Aggarwal (Grad student) | |
4 | Planning Resilient Urban Water Systems Under Climate and Socioeconomic Uncertainty | Civil & Environmental Engineering | Cities worldwide face increasing strain on their water systems as climate change, population growth, and shifting urban development patterns drive rising water scarcity. Long-term water resources planning must account for uncertain drought conditions, changing water needs, and evolving infrastructure constraints—challenges that traditional prediction-based planning approaches struggle to address. Our research group develops modeling frameworks that help planners evaluate, compare, and design robust and adaptive water management strategies capable of performing well across many plausible future conditions. This project will introduce the student to modern techniques for planning water systems under deep uncertainty. The student will help build simplified models of urban water supply and demand, analyze climate and hydrologic datasets, and explore how uncertainty affects long-term system performance. They will have opportunities to run scenario analyses, experiment with decision-making under deep uncertainty (DMDU) tools, and investigate how robust or adaptive planning strategies can reduce vulnerability to droughts and other stresses. Potential research questions include: How do different climate futures alter reliability of urban water systems? Which management strategies perform well across many uncertain futures? What trade-offs arise between cost, resilience, and environmental impacts when planning for the long term? | Climate Change,Engineering,Freshwater | Computer programming,Engineering,Statistics; Climate science; Systems modeling; Sustainability | Sarah M. Fletcher | Aniket Verma (Grad student) | |
5 | Natural Language Processing (NLP) for Constructing Datasets on Forced Labor in Mining and Building Material Supply Chains | Civil & Environmental Engineering | 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. One of the largest obstacles to understanding and modeling forced labor risks associated with imported materials, is a lack of data globally. Beyond a few concentrated efforts, such as the global slavery index, most information on the scope and prevalence of modern slavery is limited to news stories, in depth qualitative investigations, and NGO reports. This project aims to leverage existing qualitative text data sources, together with natural language processing (NLP) to generate new datasets on forced labor and enrich existing government records with additional features. We are looking for a student who would be interested in 1) compiling existing databases of news articles and investigative reports across selected geographies using manual and automated approaches; 2) hand-coding gathered documents for information of interest; 3) developing Python scripts to evaluate and compare different NLP algorithms for information extraction; 4) conducting literature review; and 5) learning to give technical presentations and write concise technical reports summarizing findings. | Engineering,Social Science | Computer programming,Engineering,Machine learning | Sarah Billington | Antonio Torres Skillicorn (Grad student) | |
6 | Microbial Life on Plastics | Civil & Environmental Engineering | Plastics are now recognized as critical sustainability challenges. Beyond their physical persistence, plastics release a wide range of chemical additives as they age in the environment. These additives leach into rivers, soils, wastewater systems, and oceans, where they interact directly with surrounding microbial communities that may respond, adapt, or even transform them. As a result, plastic additives may play a direct role in shaping microbial ecology, influencing environmental health, and determining the long-term fate of plastic pollution. Understanding how these chemicals interact with microbes is essential for developing strategies that support pollution reduction, bioremediation, and planetary health. This project offers several possible directions that all contribute to the overarching goal of understanding how plastic additives influence microbial community structure and ecological function. Students may (1) conduct a broad literature review and data analysis of plastic additives to evaluate their environmental relevance and identify which chemicals should be prioritized for future experimental research; (2) use activated sludge as a model system to investigate how exposure to representative chemicals reshapes microbial composition or activity; or (3) compare biofilm formation on plastic carriers with those on more inert materials such as glass to assess whether material properties drive ecological selection. Depending on their interests, students may choose or combine these directions while gaining experiences in environmental microbiology, microbial ecology, chemistry, and environmental sustainability. | Engineering,Evolution of Life,Other | Biology,Chemistry,Engineering,Field work,Laboratory work | Yaochun Yu | Yaochun Yu (Faculty) | |
7 | Depassivation-driven Reinforcement Corrosion in a Low-Carbon Geomimetic Cement Alternative vs. Ordinary Portland Cement (OPC) | Civil & Environmental Engineering | Ordinary Portland cement (OPC) production contributes over 8 percent of annual greenhouse gas emissions due to limestone calcination and high-temperature kiln operations, highlighting the need for sustainable alternatives. One key barrier to scaling such alternatives for structural applications is their lack of compatibility with steel reinforcement. This project investigates how Phlego, a novel low-carbon geomimetic binder interacts with embedded steel compared to OPC with a focus on key degradation processes —such as carbonation and chloride ingression— that can lead to corrosion of steel reinforcement. A student will conduct controlled laboratory experiments to assess corrosion resistance in both OPC and the Phlego. In addition to experimental work, the student will assist in a comparative supply-chain analysis of the binders, identifying differences in raw-material sources and processing that influence scalability and sustainability. This hands-on project introduces students to sustainable materials research, corrosion testing, and quantitative data analysis. By the end of the internship, the student will understand how binder chemistry and microstructure can affect durability, and how Phlego may extend the lifespan of infrastructure while reducing emissions. Desired Skills: Students with interest in civil or environmental engineering, materials science, chemistry, or earth science are encouraged to apply. No prior lab experience is required. Familiarity with data processing or plotting in Python and MATLAB is helpful but not essential. Curiosity about sustainable construction materials and scalable technology is most important. | Climate Change,Engineering,Social Science | Chemistry,Engineering,Geology,Laboratory work | Sarah Billington & Tiziana Vanorio | Daria Fontani Herreros (Grad student) | |
8 | Tracking Eddies in Coastal Environments | Civil & Environmental Engineering | Wave breaking in coastal environments generates complex circulation patterns that influence the mixing and transport of particles, including contaminants, bacteria, larvae, and plastics. Horizontal eddies within the surf zone (the breaking wave region along the coast) are generated by breaking waves. Despite their ecological importance and prevalence, the characteristics of surf-zone eddies, such as their size, persistence, and impact on particle transport, remain poorly understood. For this summer research opportunity, the student will apply an eddy-tracking algorithm to analyze surface currents previously estimated with particle image velocimetry from laboratory experiments. They will examine eddy trajectories, length scales, and circulation under different wave conditions and compare the findings with those from previous numerical model simulations. Through this research experience, the student will gain insight into coastal engineering and fluid mechanics, learn about topics related to coastal resiliency, and actively engage with a laboratory group focused on coastal processes. Additionally, the student will have the opportunity to assist in ongoing experimental studies on wave-driven eddies in the Bob and Norma Street Environmental Fluid Mechanics Lab. This opportunity is well-suited for students with programming skills in Python or Matlab and backgrounds in physics, mathematics, engineering, or environmental science. | Engineering,Natural Hazards,Ocean | Computer programming,Engineering,Mathematics,Physics | Christine Baker | Christine Baker (Faculty) | |
9 | Understanding Youth Energy Literacy: AI-Assisted Analysis of a Multi-Year High School Energy Education Program | Civil & Environmental Engineering | Energy literacy, which encompasses understanding how the electric grid works, how personal choices affect energy consumption, and how individual actions connect to broader climate systems, is increasingly vital yet remains underrepresented in formal education. Since 2017, Stanford's Sustainable Systems Lab (S3L) has operated the Designing Your Energy Lifestyle (DYEL) program, an innovative summer program that teaches middle and high school students to analyze their household electricity data using data visualization tools and explore energy topics through authentic research experiences. Over eight years, DYEL has engaged hundreds of participants from across the United States and internationally, generating a rich archive of educational materials, participant work, and program documentation. This project offers an undergraduate researcher the opportunity to systematically analyze this multi-year dataset to understand how youth engage with energy and sustainability topics. The research will examine questions such as: What energy and climate topics most interest young people, and how have these interests evolved over time? How do participants' data analysis skills and energy literacy develop through the program? What factors predict sustained engagement with energy and sustainability after program completion? The dataset spans a particularly interesting period, covering multiple presidential administrations, the COVID-19 pandemic and associated shift to virtual learning, and rapidly evolving public discourse around climate change and artificial intelligence. The undergraduate researcher will work with diverse data types including participant surveys, homework assignments, final research presentations, course materials, and speaker session recordings. A key methodological component involves using large language models (LLMs) and other AI tools to assist in analyzing this heterogeneous dataset, providing an opportunity to develop practical skills in responsible AI use for research. The student will learn to design prompts, validate AI-generated insights, and integrate computational methods with traditional qualitative analysis. Beyond data analysis, the researcher will participate directly in the 2026 DYEL summer program as a volunteer assistant, gaining firsthand experience in energy education delivery and mentorship of high school students. This hands-on component provides valuable context for interpreting the historical data while developing science communication and teaching skills. This interdisciplinary project sits at the intersection of energy systems, sustainability education, and social science research. It is well-suited for students interested in how education shapes attitudes and behaviors around climate and energy, or those curious about applying AI tools to social science questions. No prior experience with energy systems or education research is required; enthusiasm for learning across disciplines and comfort working with varied data formats are most important. The student will receive mentorship from researchers who have founded and been running this high school program for years, and will have opportunities to contribute to academic publications and conference presentations based on this work. | Climate Change,Energy,Engineering,Social Science | Computer programming,Engineering,Machine learning,Statistics | Ram Rajagopal | Chad Zanocco (Research staff), Tao Sun (Postdoc) & June Flora (Research staff) | |
10 | Investigating Ocean Anoxia During Earth's Largest Mass Extinction | Earth & Planetary Sciences | 252 million years ago, the Earth faced its largest mass extinction event–the Great Dying, also known as the End-Permian Mass Extinction. This catastrophic event resulted in the extinction of over 90% of marine species, caused by a deadly mix of increasing temperatures, lack of oxygen, ocean acidification, and a build-up of toxic hydrogen sulfide. The greenhouse gases released during this extinction mirror those of modern climate change, making it even more important to figure out the causes behind this catastrophic ecosystem collapse. Carbonate rocks (like limestone) form on the seafloor during these events and capture records of ancient ocean chemistry. In this project, you will investigate ocean anoxia during the end-Permian Mass Extinction by looking at a geochemical signal called the cerium anomaly in rock samples from China. Cerium anomaly is the ratio of the element cerium to other rare-earth elements in the sample, and when it is elevated, it tells us that the seawater the rock formed in had less oxygen. In this project, you will gain experience with the whole process of geochemical analysis, from preparing your samples, to making geochemical measurements, and analyzing and interpreting your data. First, you will do sample preparation (cutting and crushing) to get the rocks ready for analysis. Then, you will analyze the samples using inductively coupled plasma mass spectrometry (ICP-MS) to measure the concentrations of cerium and other elements in the samples. You will interpret this data and learn how to visualize it using R. Finally, you will complete a literature review of published cerium anomaly papers and create a data compilation that includes your newly generated data. We are looking for a student who is broadly interested in geology, chemistry, and/or Earth science and is excited to learn about Earth history. Prior experience with lab work is desirable but not required. | Climate Change,Evolution of Earth,Evolution of Life | Chemistry,Geology,Laboratory work | Jonathan Payne | Susannah Herz (Grad student) | |
11 | Explaining the Earth's carbon budget using planetesimal devolatilization & population synthesis | Earth & Planetary Sciences | Carbon is a vital element to a rocky planet’s habitability and climate evolution, yet how the Earth and other terrestrial planets obtained their modern C reservoir is still not well understood. Compared to the Sun, the Earth lost ~99.99% of its C during its formation. One significant stage of C loss during the Earth’s accretion is the outgassing of planetesimals, 100 km – sized planet building blocks. These small rocky bodies are heated by the decay of short-lived radioactive isotopes, which can drive thermal metamorphism & metal-silicate differentiation, as well as volatile loss. Previous thermochemical models have shown that the extent of C depletion in planetesimals are controlled by their composition, size, and timing of accretion. These trends afford us a unique opportunity to constrain the initial feedstock of the Earth by matching the current Earth’s C budget with the C inventory in the population(s) of planetesimals after their thermochemical evolution. In this project, using a planetesimal evolution model, you will extensively probe the parameter space of C depletion as a function of a planetesimal’s size, composition and accretion time. You will then construct a simple population synthesis model that tracks the C inventory of arbitrary planetesimal populations as they evolve. Finally, you will construct simple test cases rooted in the planetesimal formation literature and test their viability for explaining the Earth’s C abundance. By participating in this project, you will gain foundational knowledge in cosmochemistry and planet formation. You will also gain hands-on experience running numerical simulations using high performance computers, as well as applying statistical tools to synthesized datasets. You will also practice soft skills such as scientific communication, data visualization and literature research. You would be a good fit to the project if you are interested in planetary sciences, have experience coding in python (or happy to learn), and are generally comfortable with math and thermodynamics. Some prior knowledge of statistics is preferred but not required. | Evolution of Earth,Other | Chemistry,Computer programming,Mathematics,Physics,Statistics | Laura Schaefer | Bo Peng (Postdoc) | |
12 | Worms at Work: Catalyzing soil regeneration with charismatic macrofauna | Earth & Planetary Sciences | Farmers and ranchers are experimenting with regenerative agricultural practices to save on fertilizer and fuel, take advantage of new incentives (e.g., voluntary carbon markets, certification premiums), and improve system resilience to extreme climate events, such as drought and flooding. Changes within the soil play a pivotal role in determining when and where regenerative practices deliver on these promises. Farmers often point to greater abundance of soil fauna–especially earthworms–as evidence that practices are working, but with typical worm dispersal rates <10 m/year, this recovery can be slow and dominated by a small number of exotic species. What is the potential to jumpstart this process for landscape-scale regeneration? How can we “reworm” degraded lands faster and, where possible, with climate-adapted native species? The Life and Landscape Lab is developing in-soil earthworm management as a strategy for (agro)ecosystem restoration and a natural pathway for verifiable carbon dioxide removal. The student’s individual project will contribute to ongoing lab mesocosm experiments, with potential tasks including: measurement of greenhouse gas fluxes, processing and analysis of soil samples to quantify organic carbon dynamics, and quantification of earthworm functional traits. Opportunities to participate in relevant fieldwork can also be arranged. In addition to lab skills, the student will gain experience with experimental design, statistics, and data visualization in an open science framework. Prior experience with soil science or biogeochemistry would be a plus, but we welcome applications from any discipline. | Climate Change,Dynamic Earth,Food and Agriculture | Biology,Chemistry,Field work,Geology,Laboratory work,Statistics | Jane Willenbring | Nate Looker (Postdoc) | |
13 | Mapping Air Health Inequity with AI and Community-Based Sensing | Earth System Science | Urban air pollution is a major driver of non-communicable diseases, but its health burden is not distributed equally. Vulnerable communities are often systematically exposed to higher levels of pollution due to their proximity to emission sources (e.g., highways, industrial zones). Conventional, city-level monitoring masks these "hyperlocal" inequities, preventing targeted, effective interventions and perpetuating environmental injustice. This project aims to develop a novel, high-resolution framework to quantify these disparities using a combination of field monitoring and data science. We will create a new, spatially-explicit Air Health Inequity (AHI) Index by fusing data from both low-cost sensors and high-end instruments with public health and socio-demographic data. The ultimate goal is to move beyond simply diagnosing the problem and instead provide policymakers and community organizations with an actionable, data-driven tool to model interventions and build healthier, more just cities. Potential projects will involve: 1. Deploying a network of air quality sensors in vulnerable communities to generate granular, street-level data on key pollutants (e.g., PM2.5, NO2, Black Carbon); 2. Fusing and analyzing diverse datasets, including the new sensor data, spatially-resolved public health records (e.g., asthma-related emergency visits), land-use maps, and socio-demographic data; 3. Assisting in the development and validation of machine learning models to create the novel "Air Health Inequity (AHI) Index"; 4. Using the AHI index as a diagnostic tool to model the potential health equity benefits of specific interventions, such as rerouting truck traffic or creating new urban green spaces. | Engineering,Field work,Machine learning,Statistics | Data science/statistics; Machine learning; Scientific/computer programming; Field work; Environmental justice; Public health | Rob Jackson | Tie Zheng (Postdoc) | |
14 | Understanding the relationship between wildfire severity and fuel moisture in the Western United States | Earth System Science | Wildfires are increasingly affecting the Western United States and its people. Wildfire’s ecological effects can be summarized in an index called burn severity. Although fire has historically been beneficial for forests in the West, increasingly high-severity fires threaten the health and carbon storage of America’s forests. However, even though wildfire is known to be affected by weather and forest properties, how fuels, weather, and wildfire interact is still not well understood. A better grasp of these interactions would improve wildfire models. This study will use remote sensing products and meteorological data to explain the relationship between burn severity and live fuel moisture content (LFMC). LFMC is a variable that quantifies the water content in live vegetation. Low LFMC values – i.e. dry conditions – promote wildfire risk, but how does LFMC affect severity – i.e. wildfire outcomes? This project will explore when, where, and why LFMC interacts with burn severity, with a specific focus on the vast heterogeneity of wildfire regimes in the American West. If results allow and there is interest from the student, this work offers an opportunity for a first-author publication. This project requires proficiency in scientific programming in Python. An ideal candidate will have experience with remote sensing data, gridded climate data, and/or statistical analysis. No previous hydrometeorological or wildfire experience is required. | Climate Change,Dynamic Earth,Natural Hazards | Remote sensing, water-fire-vegetation dynamics | Alexandra Konings | Mitchell Hung (Grad student) | |
15 | Characterizing methane production across diverse tropical wetlands | Earth System Science | Tropical wetlands are among the largest and most uncertain sources of methane emissions to the atmosphere. Large uncertainties remain about how the microbial production of methane - or methanogenesis - varies across diverse tropical wetland ecosystems. Environmental factors like vegetation, geochemistry, and hydrology significantly influence methane production, and therefore emissions, in wetlands across the globe. In boreal and arctic wetlands, there are well-established patterns of how methane production pathways vary across environmental gradients. This mechanistic understanding of methane production is lacking for tropical wetlands. Better understanding of how methane production varies across tropical wetlands may help reduce uncertainty in predictions of methane emissions from these ecosystems, as different methanogenic pathways vary in their rate of methane production and have different environmental controls. This project aims to characterize pathways of methane production and their controls across diverse tropical wetlands. Measuring the stable isotope composition of methane from wetlands can indicate the pathway responsible for methane production in different sites. The student involved in this project will learn how to make these measurements, as well as conduct laboratory analyses assessing how differences in wetland chemistry impact methane production. We have been collecting samples from wetlands across the Americas, Africa, and Asia for this project. Some of these samples have been analyzed, but many still need to be measured in the laboratory. We are keen to welcome an undergraduate with an interest in biogeochemistry and/or ecosystem science seeking to learn skills in laboratory analysis as well as data analysis. The student would 1) analyze samples at the Stanford Stable Isotope Biogeochemistry Lab and Environmental Measurements Facility and 2) conduct statistical analysis and data visualization in R to assess how characteristics of tropical wetlands influence methane production. Pending ongoing work in our research group, there may be opportunities to learn field sampling methods at local wetland sites. Previous lab experience is highly preferred, and exposure to R or another programming language is helpful but not required. | Climate Change,Dynamic Earth,Freshwater | Chemistry,Laboratory work,Statistics | Alison Hoyt | Clarice Perryman (Postdoc) | |
16 | Comparing community assemblage of entomopathogens at different sites | Earth System Science | This project aims to understand how different microorganisms, ranging from nematodes to bacteria and fungi, all targeting the same nutrient source (insects) coexist in the soil ecosystem. We previously isolated numerous nematode and fungal species from multiple sites at Jasper Ridge Biological Preserve ('Ootchamin 'Ooyakma). The community assemblages of these entomopathogens vary widely, even in close proximity. We have found that some sites contain extremely pathogenic nematodes, while they are oddly not present elsewhere. This summer, we hope to test the dynamics of these different communities using in vivo and in vitro lab experiments. The student will conduct co-infection of insect larva with different fungi, nematodes, and bacteria that have been isolated from nematodes, as well as competition experiments on petri dishes. This work will allow a better understanding on how all those organisms interact with each other and ultimately inform development of biocontrol agents in agriculture. The student must be comfortable handling insect larvae. | Evolution of Life,Food and Agriculture,Other | Biology,Field work,Laboratory work,Statistics | Tadashi Fukami | Chloe Golde (Grad student) | |
17 | Temperature effects on nectar microbes of sticky monkeyflower | Earth System Science | This project aims to understand the effects of temperature and extreme events on microbial communities that live in nectar. We work with the nectar microbes of the sticky monkeyflower (Diplacus aurantiacus), a native California plant. These microbes (bacteria and yeast) arrive to flowers via pollinators and other flower visitors. The outcome of the interactions between bacteria and yeast is influenced by their order of arrival to newly opened flowers. At the same time, we know that microbes are sensitive to temperature, affecting their population growth rates. As a result, rising temperatures and extreme environmental perturbations can affect the outcome of competitive interactions through their effects on populations. Using nectar microbes, the student and mentor will jointly develop an independent project that aims to understand the outcome of species interactions and community dynamics under climate change. You will gain experience on basic microbiology techniques and develop data analysis skills using R. | Climate Change,Evolution of Life,Other | Biology,Field work,Laboratory work,Statistics | Tadashi Fukami | Rosa McGuire (Postdoc) | |
18 | Using remote sensing to understand the future of marine primary productivity in a stormier Arctic | Earth System Science | Among the many documented changes to the Arctic tied to anthropogenic forcing and the increased atmospheric concentration of greenhouse gases since the Industrial Revolution, including higher air and sea surface temperatures and dramatic sea ice loss, the intensity and frequency of storms have increased. Recent research has emerged suggesting that these storms drive vertical mixing and upwelling, important processes which can bring nutrients to the surface for phytoplankton. As the base of the food web and main contributors to primary productivity, phytoplankton play a critical role in carbon cycling and the broader Arctic ecosystem. The student researcher will develop and carry out a specific project utilizing satellite remote sensing data to analyze the biological impact of increased “storminess” (i.e., wind speed, frequency of storm events) in the region. With the guidance from research mentors, the student will learn and apply basic principles of remote sensing and oceanography, aggregate and interpret large datasets, and work in a highly collaborative research environment with frequent opportunities to present their research progress. Extensive programming experience is not necessary, but some familiarity with Python, R or similar language is helpful. Students with academic backgrounds and interest in biology, earth systems, oceanography, or climate science are encouraged to apply. | Climate Change,Ocean | Biology | Kevin Arrigo | Claudette Proctor (Grad student) | |
19 | Impacts of weather extremes on urban air pollution through human and natural pathways | Earth System Science | Weather extremes such as heatwaves and stagnation events are becoming more frequent in many cities, with profound consequences for urban air quality and human health. These events not only alter atmospheric chemistry and pollutant dispersion but also trigger changes in human behavior (e.g., energy use, transportation) and natural emissions such as biogenic organics, creating a complex web of interactions that shape pollution episodes. This summer project invites students to explore these coupled processes using real observational datasets and state-of-the-art modeling outputs. Students will analyze long-term trends and spatial patterns of weather extremes, examine how they modify emissions and chemistry, and assess how these changes translate into air-quality responses. Work will include processing meteorological and air-quality data in R or Python, visualizing patterns across major urban regions, and identifying key drivers of pollution during extreme events; students with relevant interests and skills may also incorporate machine-learning or other data-driven methods to uncover predictive relationships. This project is ideal for students interested in climate–air-quality interactions, environmental data science, or urban sustainability, and no prior experience is required beyond curiosity and a willingness to learn. | Climate Change,Energy,Natural Hazards | Biology,Chemistry,Computer programming,Engineering,Physics,Statistics | Yuan Wang | Yuhan Wang (Postdoc) | |
20 | Monitoring Soil Carbon Storage Following Tropical Mangrove Restoration | Earth System Science | Mangrove forests are coastal ecosystems that provide many critical ecosystem services including carbon storage, helping to mitigate climate change. However, these ecosystems have also experienced widespread disturbance resulting in losses of stored carbon and emissions of CO2 to the atmosphere. With increasing interest for science-based mangrove restoration across the tropics, more research is needed to assess the efficacy of restoration strategies and to support adaptive management. In this project we will analyze soil samples from restored tropical mangrove forests to assess mechanisms and timescales of soil carbon recovery following restoration. To track inputs and sources of carbon we plan to analyze soil samples for carbon content (elemental analysis) and isotopes (stable isotope composition and carbon dating). We will also analyze samples for a variety of other physiochemical parameters such as grain size (laser particle diffraction) to identify drivers of new carbon storage. Overall, this research will help to inform science-based restoration efforts and management of mangrove ecosystems to encourage coastal carbon sequestration. We are excited to work with an undergraduate student interested in biogeochemistry and/or ecosystem sciences looking to gain experience in laboratory analysis of environmental samples. The student would get the opportunity to prepare and analyze samples for multiple analyses in the lab, and gain experience using R for data analysis and data visualization. Prior experience with analyzing environmental soil or water samples in a laboratory setting is preferred, and exposure to R or another programming language is helpful but not required. | Climate Change | Chemistry,Laboratory work,Statistics | Alison Hoyt | Julie Shahan (Grad student) | |
21 | Designing a Floating Eddy Covariance System to Monitor Wetland Methane Emissions | Earth System Science | Aquatic landscapes contribute to roughly half of all global methane (CH4) emissions, contributing to climate change. Tropical ecosystems in particular are a major source of CH4 emissions, estimated to account for 64% of natural CH4 emissions. However, tropical CH4 emissions are poorly constrained and not well understood due to an extreme lack of measurements. The eddy covariance technique is the state-of-the-art approach for constraining wetland CH4 emissions. However, formidable measurement challenges in tropical wetlands have led to the current scarcity of data, including remote site locations, lack of infrastructure and funding, and harsh environmental conditions. To address this data gap, we aim to develop a proof-of-concept innovative boat-based eddy covariance measurement system for deployment in tropical wetlands. This system will be used to address the overarching research question: What is the role of tropical wetlands in the global CH4 budget? This work will build upon previous mobile eddy flux measurement setups that were vehicle-based and floating systems designed for CO2 exchange. We are excited to work with an undergraduate student interested in engineering and ecosystem sciences to address key technical and logistical considerations for deployment of the setup in various wetland environments. This will include research of various platform designs for stability and feasibility of future deployment in remote field sites with factors such as tides, winds, rain, and wildlife. Work over the summer may also include lab or field testing, and processing and analysis of any preliminary data collected during testing. Prior experience with a programming language such as R, Python, or Matlab is helpful. | Climate Change,Engineering | Computer programming,Engineering,Field work,Physics | Alison Hoyt | Julie Shahan (Grad student) | |
22 | Dirt detectives: Tracing plant inputs to soil organic carbon under altered fire and grazing regimes | Earth System Science | Soils are a critical component of climate-change mitigation because they store roughly twice as much carbon as the atmosphere and terrestrial vegetation combined. Yet long-term carbon storage and stabilization depends on what is going into the soil. One of the major inputs of carbon in soils is through the plants and their roots. The chemical composition of plant inputs determines how easily microbes can break them down and whether carbon is quickly respired back to the atmosphere or stabilized in the soil. This means that shifts in plant functional types and their traits directly impact soil carbon. Disturbance regimes like fire and grazing can dramatically reshape plant communities, changing both the source and quality of their inputs. For example, suppressing fire or herbivore activity in savannas, ecosystems with long histories of both disturbance types, can shift vegetation towards woody-dominated communities with more lignin, a carbon-rich compound that is more difficult for microbes to decompose. In this example, lignin may stay in the soil longer before returning to the atmosphere. By measuring stable isotopes of plant material (leaves, litter, and roots) collected from experimental savannas, the student will become a “forensic ecologist” and help trace which plants contributed more to soil C pools. They will investigate the journey of carbon and nitrogen from plants into their roots under altered fire and grazing regimes. We are seeking a student who is excited to use laboratory techniques to quantify carbon and nitrogen in plant and root samples. The student will learn the full scientific pipeline, from preparing samples for analysis on an Elemental Analyzer–Isotope Ratio Mass Spectrometer (EA-IRMS) and working with the research team to analyze the data, to drawing conclusions about how carbon and nitrogen may influence microbial activity and creating visualizations for a final presentation. Beyond lab skills, the student will also build scientific knowledge in savanna and fire ecology and participate as part of an active research group. Applicants should be prepared for this experience to be laboratory-intensive. The applicant should have foundational lab skills and be comfortable in a laboratory setting. | Climate Change | Biology,Laboratory work | Adam Pellegrini | Kimber Moreland (Research staff) & Courtney Currier (Postdoc) | |
23 | Identifying microbial sources of lipid fossils through gene expression in the lab | Earth System Science | In this project, students will use molecular microbiology techniques to determine if bacteria can make certain lipid molecules that are precursors to chemical fossils found in the rock record. Lipids, or fats, are molecules produced by all living organisms today that are chemically robust and readily preserved in ancient rocks. Geochemists are able to detect these chemical fossils, and, if properly interpreted, lipid fossils can provide insight into major evolutionary transitions, past environmental conditions, and the impacts of past global climate events on biodiversity. But proper interpretation of these fossils is dependent on many factors including understanding which modern organisms produce these lipids today. This project will focus on one interesting set of lipids know as glycerol dialkyl glycerol tetraethers or GDGTs. GDGTs are thought to be produced exclusively by a subset of microbes known as archaea and GDGTs have proven to be robust fossils for past sea surface temperatures in records dating as far back as 100 million years. In the Welander group we have discovered the genes in archaea necessary to produce GDGTs. However, surveying the large database of microbial genomes for these GDGT synthesis genes revealed that there are a few bacterial species not related to the archaea microbes that also have these genes. Thus, it is possible that an alternative source of GDGTs, and their preserved remnants in the rock record, are bacteria that have not been studied before. To determine if bacteria that have GDGT biosynthesis genes in their genomes are capable of producing these lipids, students will express these genes in the lab and determine if they are functional. To do so, students will learn how to construct a vector that will allow them to express the GDGT genes in E. coli. This project will teach students how to grow microbes in the lab, how to clone genes into plasmids, and how to introduce those plasmids into E. coli. Students will also be taught how to use mass spectrometry to detect and measure lipid production in bacteria. This is an excellent opportunity for STEM students from diverse fields who are interested in learning how the fields of Earth and environmental sciences intersect with microbiology and students who are interested in experiencing the interdisciplinary nature of geobiology research. Prior experience in a microbiology or biochemistry lab would be helpful but not necessary. | Evolution of Life,Other | Biology,Chemistry,Laboratory work | Paula Welander | Esther Munoz (Postdoc) | |
24 | Testing the function of archaeal diterpenoid cyclases | Earth System Science | Lipids with cyclopentane and hexane motifs are commonly synthesized from linear terpenoid precursors – that is, linear lipids composed of repeating linked isoprenes. A common example is cholesterol; its biosynthetic pathway involves the cyclization of the linear six-isoprene molecule oxidosqualene into a four-ringed structure. This reaction can only happen by way of enzymes, which are commonly known as cyclases. Throughout the eukaryotes, cyclic molecules synthesized from terpenoids are known to serve as the precursors to various hormones (steroids) and to modify the fluidity of lipid membranes. Bacterial cyclic terpenoids may serve a similar function, while also protecting cells against oxidative stress and playing a role in host-symbiont interactions. Archaea were historically thought of as exceptions; although they contain some terpenoids with cyclic moieties, those lipids are synthesized with other enzymes, and are largely linear with small cyclic sections. The absence of conventional cyclic terpenoids in archaea, despite their close evolutionary relationship to eukaryotes, which in most cases cannot survive without them, is a mystery. A newly discovered class of archaeal cyclases evolutionarily related to the eukaryotic and bacterial cyclases may instead indicate that some archaea do incorporate cyclic terpenoids into their membranes. These enzymes are found in the phyla Thermoplasmatota, Methanobacteriota, Nitrososphaerota, and in the phyla which are ancestral to eukaryotes, the Promethearchaeota and Heimdallarchaeota. Archaeal cyclases are related to bacterial and eukaryotic cyclases which synthesize two-ringed lipids from a diterpenoid substrate. Among them, only one cyclase from the Heimdallarchaeota has been shown to possess enzymatic activity. We are interested in testing archaeal cyclases for enzymatic activity. Due to advances in metagenomics and nucleotide synthesis, cyclase genes can be obtained from repositories like NCBI for expression without culturing of their host organisms. An undergraduate researcher would be responsible for preparing plasmids for introduction to expressing organisms, growing plasmid-carrying organisms, extracting lipids from cultures, and analyzing lipid extracts with mass spectrometry and chromatography techniques. The undergraduate researcher will have some level of freedom in choosing which cyclases to express; although we intend to express cyclases from the Thermoplasmatota, an interested undergraduate could choose other cyclases from metagenomic data for expression. What are we looking for in an undergraduate researcher? We intend this project to be approachable for any student who has taken coursework dealing with cell biology, genetics, and microbiology. Prior experience working with plasmids and cultured microbes is not necessary. An undergraduate researcher will be trained in techniques such as plasmid transformation, culturing of bacteria, lipid extraction, and GCMS. While this project is intended as an exploration of the evolutionary history of lipid synthesis, and is thus targeted towards students who are interested in evolutionary biology, we welcome applicants who would approach this project through a different lens. | Dynamic Earth,Evolution of Life | Biology,Chemistry | Paula Welander | Charles Hu (Grad student) | |
25 | Contribution of plant mediated rapid carbon cycling to the rice methane budget | Earth System Science | Rice fields are cultivated under flooded conditions, leading to methane production in the soil. Methane is a potent greenhouse gas, having 34 times more heat-trapping potential compared to carbon dioxide. Emissions from rice account for approximately 15% of the global methane budget. Generally, methane emissions are thought to be derived from anaerobic decomposition of soil organic matter. However, emissions can also potentially occur through rapid C transformation from CO2 to CH4 through plant photosynthesis and root exudation. Through 13-C isotope labelling, we aim to examine the contribution of plant-mediated rapid carbon cycling. We are searching for a student with interests broadly in agriculture, soil science, or plant sciences. A successful student would work closely with Stanford faculty members (Scott Fendorf, Anne Dekas, and Alison Hoyt) and researchers (Zhenglin Zhang, Sophia Forstmann), as well as researchers at UC Berkeley, where the greenhouse trial will be conducted. Depending on the student’s specific interest, we expect the student to be responsible for a subset of the analyses from the gas, plant or soil phase. For example, a student interested in soil and plant sciences could be tasked to help with 1-2 harvest events and be responsible for the processing and analysis of soil porewater and plant material. A successful student should have a keen attitude to learn new skills, be able to independently execute laboratory protocols after training, and proactively organise collected data. We do not expect prior experience. The student will gain hands-on experience in analyses related to isotope enrichment and expand their professional network in a highly interdisciplinary and multi-institutional project. | Climate Change,Food and Agriculture | Biology,Chemistry,Field work,Laboratory work | Scott Fendorf | Zhenglin Zhang (Postdoc) & Sophia Forstmann (Grad student) | |
26 | Finding the culprit - where is the increasing methane in the atmosphere coming from? | Earth System Science | Methane (CH4) is a potent greenhouse gas, and determining sources of methane emissions is critical to advancing our understanding of the global climate system. Atmospheric concentrations of methane have increased sharply since 2007, and there is growing evidence that wetland methane emissions may be partially responsible for this surge. In this project, you will investigate a poorly understood climate change feedback – warmer and wetter conditions may be increasing methane emissions from wetlands, especially in the tropics – compounding anthropogenic emissions. In this internship, you will contribute to the Terrestrial Carbon Cycle Group goal to measure tropical methane fluxes across spatial and temporal scales. We aim to characterize wetland greenhouse gas emissions and to better understand the role of these ecosystems in the global carbon budget and climate predictions. The Lab’s research focuses on the tropical wetlands given their large contribution to global greenhouse gas emissions, their vulnerability, limited data and overall high uncertainty respecting the global methane projections. We will work with the selected student to develop a project tailored to their skills and interests. The research is expected to involve a combination of local fieldwork and laboratory work, as well as data analysis and literature review. Possible project directions could include developing and testing an improved field sampling device, learning new laboratory measurements, working with isotope data, and/or analysis of soil, water, and air samples from tropical field sites. Students with backgrounds in Earth and environmental science, engineering fields, chemistry, ecology or related disciplines are encouraged to apply. | Climate Change,Dynamic Earth | Biology,Chemistry,Computer programming,Laboratory work | Alison Hoyt | Sandra Lorena Santamaria Rojas (Grad student) & Newton Huy Nguyen (Research staff) | |
27 | Exploring Tribal Citizen Perspectives in Future Energy Planning | Earth System Science | Energy sovereignty is a critical component among tribal nations seeking future pathways rooted in Indigenous values, perspectives, and knowledge. The Indigenous Energy Pathways project aims to combine tribal community perspectives with energy transition modeling practices of energy systems. This project is supported by two years of engagement with the Navajo Nation to identify goals, barriers, and opportunities to create and foster an energy landscape that not only addresses the needs of the community and nation but also integrates important cultural knowledge and wisdom. This SURGE summer project will consist of analyzing field work data collected from focus groups and one-on-one interviews with Navajo Nation citizens. Evaluation and thematic analysis of qualitative data will be focused in part on topics determined in advance and in part on one or more topics based on the student’s interest. For example, if a student is interested in the role of youth, gender, and/or spatial distribution of perspectives, the student can assess how these characteristics may or may not influence energy futures thinking. From this analysis, the undergraduate researcher will contribute to the translation of qualitative themes to an energy scenario that will be modeled using the Low Emissions Analysis Platform. The student will have an opportunity to engage with energy transition models. No prior experience in qualitative research, modeling, or geospatial analysis is required to be part of the project. We encourage students interested in social science and energy research, Indigenous energy sovereignty, and decarbonization to reach out. This project will involve collaboration with Kimberly Yazzie, Professor at University of British Columbia, Vancouver, CA. | Energy,Social Science,Other | Computer programming | Chris Field | Raven Alcott (Grad student) | |
28 | Tracking Plastic Contamination in California's Compost: From Waste Bin to Soil | Earth System Science | California diverts millions of tons of organic waste to composting facilities each year, transforming food scraps and yard trimmings into valuable soil amendments for farms and gardens. However, a hidden problem threatens this circular economy: plastic contamination. From produce stickers and takeout containers to misleadingly labeled "compostable" plastics, these materials slip through sorting systems and end up in finished compost products. When applied to soils, these plastics persist in the environment, potentially impacting soil health, water dynamics, and plant growth. Understanding what types of plastics contaminate our compost and how much is the critical first step toward developing solutions that protect both our soils and ecosystems. This summer research project focuses on quantifying and characterizing plastic contamination in commercial compost from facilities across California's Salinas Valley, a major agricultural region and testing ground for state composting policies. You will be part of a larger effort to document baseline contamination levels, identify the most problematic plastic types, and understand how contamination varies across different compost suppliers and feedstock sources (such as curbside collection versus green waste-only streams). What will you be doing? Plastic identification and categorization: Examining plastic particles under microscopes, photographing specimens, and categorizing contaminants by type (films, fragments, fibers), color, and approximate size. Data collection and documentation: Weighing and measuring plastic particles, recording observations in spreadsheets, maintaining detailed sample logs, and organizing digital image libraries Laboratory analysis support: Assisting with spectroscopic techniques (such as FTIR) to confirm polymer types of collected plastics Data synthesis: Helping compile findings to identify patterns in contamination across facilities and feedstock types What will you gain from this experience? You'll develop practical skills in environmental sampling, laboratory techniques, and data management while contributing to research that addresses a real-world sustainability challenge. You'll gain experience with scientific protocols, attention to detail in data collection, and an understanding of how waste management systems intersect with soil health and agricultural sustainability. Who are we looking for? We welcome students from environmental science, biology, ecology, agricultural science, or related STEM fields who are curious about sustainability and eager to tackle hands-on laboratory and field work. No prior research experience is required, just enthusiasm, careful attention to detail, organizational skills, and a willingness to work both independently and collaboratively. Basic familiarity with Excel or Google Sheets is helpful but not required. An interest in waste management, circular economy, soil science, or environmental policy is encouraged. | Climate Change,Food and Agriculture | Biology,Chemistry,Laboratory work,Statistics | Scott Fendorf | Sophia Forstmann (Grad student) | |
29 | Impact of Prescribed Fire on Soil Carbon Cycling | Earth System Science | Wildfires across California are increasing in both frequency and intensity. Prescribed fires have been shown to be effective at preventing catastrophic wildfires, however their effects on soil biogeochemistry, particularly soil C stability, are not well understood. As soils serve as the largest terrestrial carbon sink, understanding fire’s impacts on C dynamics is critical for assessing whether prescribed burning ultimately leads to net C loss or can contribute to long-term soil C stabilization. This project aims to explore the effects of prescribed fire on soil carbon pools and mineral content, as well as investigate the potential for ash-derived C to be incorporated into more stable, mineral-associated carbon pools in the months following a burn. Using soils collected from a prescribed fire in the Blodgett Research Forest (Georgetown, CA), we will analyze basic soil geochemistry (pH, total C/N), gas efflux (CO2/N2O), bulk mineralogy (X-ray diffraction), and particulate versus minerally-associated carbon. We are seeking an enthusiastic student interested in gaining hands-on experience in both field and laboratory methods relating to soil biogeochemistry. Students will work primarily in the laboratory, preparing and analyzing soil samples, with opportunities to go to the field to conduct additional soil sampling and gas measurements. Students with any level of experience are encouraged to apply! | Climate Change,Natural Hazards | Biology,Chemistry,Field work,Laboratory work | Scott Fendorf | Katie Huy (Grad student) | |
30 | Carbon capture material synthesis and characterization for negative emissions and water production | Energy Science & Engineering | Reverting the negative effects of climate change requires removing green house gases from the atmosphere at massive scales. We are aiming to synthesize polymer-based materials with high capacity to remove CO2 from the air and then release it at mild temperatures, so that the CO2 can be sequestered or converted to useful products. Specifically, our vision is to power this process with heat coming from data centers, an extremely abundant energy source that is currently wasted. By developing materials that can use this low temperature heat for carbon capture, we can convert energy intensive computation and artificial intelligence into carbon-negative and water-positive (as our target materials can simultaneously produce water from the air). The student(s) in this project will: -Synthesize carbon capture materials; -Explore the effect of synthesis parameters in physical morphology; -Characterize carbon capture performance at different temperatures and humidity; -Model the physical processes occurring during carbon capture into the sorbents | Energy,Engineering,Freshwater | Chemistry,Engineering,Laboratory work | Carlos Diaz Marin | Carlos Diaz Marin (Faculty) | |
31 | Experimental Visualization of Geological Carbon Storage Processes | Energy Science & Engineering | Geological carbon storage (GCS) is a key technology to address hard-to-abate greenhouse gas emissions such as from cement or steel manufacture. Injecting carbon dioxide (CO2) into appropriate subsurface formations provides long-term, secure storage. Understanding CO2 flow behavior and immobilization mechanisms is essential to ensuring safe and effective storage as well as gaining stakeholder support. Laboratory models that visually demonstrate CO2 transport, trapping, and the influence of geological heterogeneity, provide valuable insights into mechanisms and convincing data for discussion. We have developed such a model at Stanford called VSUL—VISUaLizer for GCS dynamics. It is packed with different sands that simulate porous formations within the earth. CO2 transport is recorded via a high resolution cameral. VSUL highlights the importance of geological features, such as sealing formations, and CO2-trapping mechanisms in maintaining secure storage. We seek a student to conduct experiments using VSUL to understand convective mixing. An important way in which CO2 is immobilized in the subsurface is by dissolving into the saline brine phase and then migrating downward under the influence of gravity. This is referred to as convective mixing because the CO2-laden brine mixes under the action of convection with the CO2-free brine deep in the earth. During summer 2026, the student selected will mount an experimental campaign to document convective mixing using VSUL. Experimental variables that we will explore include the salinity and density of the saline brine phase and the presence of impurities in sand that react with the dissolved CO2. We also seek to introduce quantitative measurements using image acquisition software applied to the experimental setup. | Energy,Engineering | Image analysis experience | Tony Kovscek | Tony Kovscek (Faculty) & Bolivia Vega (Research staff) | |
32 | Modeling Fault Activation from Clean Energy Subsurface Operations | Energy Science & Engineering | Global commitments to a carbon-free future have driven rapid expansions of subsurface operations for clean energy such as geothermal energy, carbon sequestration, and hydrogen storage. All such operations are now widely recognized to induce unrest in pre-existing faults in the form of earthquakes and aseismic (slow) slip. Our research group develops computational codes to model a wide range of physical processes associated with such hazards. Research results may have implications for the safety and efficiency of subsurface renewable energy operations. Summer interns will have the opportunity to apply the theory of geophysics and fluid dynamics and to apply programming skills to understand such hazards through numerical models. Students who enjoy mathematical analysis can help to derive equations and solve analytical problems; experience with differential equations, continuum mechanics, and Fourier transform would be useful. Students who enjoy programming can assist with code development; prior programming experience in MATLAB, Python, C++, or another language is required. Based on the student's interests, the project may be designed such that running simulations of existing code is more important than development of new code, and vice versa. A strong background in mechanics and/or programming is a must for all applicants since all projects involve computer simulations of solid and fluid mechanics problems. Previous experience with earth science is not required. | Climate Change,Dynamic Earth,Energy,Engineering,Natural Hazards | Computer programming,Engineering,Mathematics,Physics | Eric Dunham | Taeho Kim (Postdoc) | |
33 | Rainfall, Soil Erosion, and Agricultural Productivity: Linking Climate and Land Degradation Using Satellite Data | Environmental Social Sciences | Rainfall, Soil Erosion, and Agricultural Productivity: Linking Climate and Land Degradation Using Satellite Data With Steve Berggreen (postdoc) and Solomon Hsiang (faculty mentor) Land degradation is one of the greatest threats to sustainable food production systems. A major driver of this process is soil erosion, often caused by the washing out of soil nutrients through non-linear rainfall-runoff dynamics. As climate change intensifies, rainfall events are expected to become more extreme, yet we still know surprisingly little about how these changes affect agricultural productivity through soil erosion. This project will combine established rainfall-soil erosion models with recent high-resolution, satellite-derived global datasets on rainfall intensity, soil properties, terrain slope, and vegetation indices to quantify the effects of extreme rainfall on soil erosion and downstream agricultural productivity. The ultimate goal is to better understand how climate variability translates into land degradation and yield losses, especially in resource-constrained agricultural systems. Undergraduate Learning Opportunities The undergraduate researcher will assist in compiling and harmonizing global geospatial datasets (rainfall, soil, slope, and agricultural outcomes). Depending on their interests, they may also contribute to data visualization and statistical analysis. Through this project, the student will gain hands-on experience working with large-scale satellite datasets, environmental modeling, and modern data-science tools. They will develop practical skills in Python (including libraries such as NumPy and GeoPandas), Google Earth Engine, and other open-source tools for spatial analysis, which will position them well for a future research career within the intersection of climate science, environmental change, and agricultural economics. Desired Background and Pre-requisites: A background in environmental earth sciences, agricultural economics, computer science, or related fields is an advantage. Familiarity with Python programming and experience handling large-scale spatial datasets (e.g., via Google Earth Engine or APIs) would be helpful but are not required. Most importantly, the student should be curious, motivated, and eager to learn about the environmental and agricultural impacts of climate change, as well as how to manage and analyze large-scale geospatial data. | Climate Change,Food and Agriculture,Social Science | Statistics | Solomon Hsiang | Jeanette Tseng (Research staff) & Steve Berggreen (Postdoc) | |
34 | The Social and Economic Impacts of Frac Sand Mining | Environmental Social Sciences | The Social and Economic Impacts of Frac Sand Mining With Suraj R. Nair (postdoc) and Solomon Hsiang (faculty mentor) The rapid expansion of frac sand mining in the United States – particularly across Wisconsin, Minnesota, and Texas – has transformed rural landscapes and local economies. Extracting high-purity silica sand for hydraulic fracturing (or “fracking”) has generated substantial economic activity, but also deep concern over its environmental footprint and social consequences. Communities near mining operations face declining water quality, air pollution, road degradation, and shifts in property values and public health outcomes. Yet, despite the industry’s scale, there is no comprehensive assessment of the impacts of these mines. This project aims to assess the socio-economic impacts and consequences of these mines. In order to do so, we combine data on the locations of these frac-sand mines, survey and satellite based measures of economic activity, health, and environment quality, with state-of-the art econometric tools. Undergraduate Learning Opportunities The undergraduate researcher will assist in compiling and harmonizing global geospatial datasets. Depending on their interests, they may also contribute to data visualization and statistical analysis. Through this project, the student will gain hands-on experience working with large-scale satellite datasets, environmental modeling, and modern data-science tools. They will develop practical skills in Python (including libraries such as NumPy and GeoPandas), Google Earth Engine, and other open-source tools for spatial analysis, which will position them well for a future research career within the intersection of climate science and environmental economics. Desired Background and Pre-requisites: A background in environmental earth sciences, economics, statistics, computer science, or related fields is an advantage. Familiarity with Python programming and experience handling large-scale spatial datasets (e.g., via Google Earth Engine or APIs) would be helpful but are not required. Most importantly, the student should be curious, motivated, and eager to learn about the environmental and agricultural impacts of climate change, as well as how to manage and analyze large-scale geospatial data. | Climate Change,Energy,Social Science | Statistics | Solomon Hsiang | Jeanette Tseng (Research staff) & Suraj Nair (Postdoc) | |
35 | Developing generalizable tools to measure infrastructure quality and assess urban inequality | Environmental Social Sciences | With Suraj R. Nair (postdoc) and Solomon Hsiang (faculty mentor) Cities are a key driver of economic growth across the world, and the global urban population is expected to double by 2050. As a result of this rapid growth, urban areas harbor some of the most pressing challenges facing our societies, including rising inequality, increased vulnerability to climate change and natural disasters, and rapid environmental degradation. Understanding and addressing these challenges remains central to building sustainable and resilient economies. This project aims to develop a set of generalizable and scalable tools to evaluate urban infrastructure conditions, such as road and building quality and access to sanitation. In order to do this, we aim to combine Google Streetview imagery – a highly granular, but relatively underutilized source of data – with modern machine learning models that are optimized for object detection and semantic segmentation. This approach would allow rapid assessment and evaluation of thousands of kilometers of streets that would often be prohibitively expensive or time-intensive to survey in person, revealing features that are not visible in other sources of freely available data like satellite imagery. The project could then link these image-derived infrastructure measures with administrative boundaries, census data, and socio-economic indicators to study patterns of inequality, service delivery gaps, and investment disparities. Such analysis would help identify neighborhoods where infrastructure deterioration is most acute, where upgrades may deliver the greatest benefits, and how conditions vary across urban cores, peri-urban zones, and informal settlements. Ultimately, this research would demonstrate how widely available street-level imagery can support evidence-based urban policy, guiding more equitable and efficient infrastructure planning. Undergraduate Learning Opportunities The undergraduate researcher will assist in compiling and harmonizing global geospatial datasets. Depending on their interests, they may also contribute to data visualization and statistical analysis. Through this project, the student will gain hands-on experience working with large-scale satellite datasets, environmental modeling, and modern data-science tools. They will develop practical skills in Python (including libraries such as NumPy and GeoPandas), Google Earth Engine, and other open-source tools for spatial analysis, which will position them well for a future research career within the intersection of climate science and environmental economics. Desired Background and Pre-requisites: A background in environmental earth sciences, economics, statistics, computer science, or related fields is an advantage. Familiarity with Python programming and experience handling large-scale spatial datasets (e.g., via Google Earth Engine or APIs) would be helpful but are not required. Most importantly, the student should be curious, motivated, and eager to learn about the environmental and agricultural impacts of climate change, as well as how to manage and analyze large-scale geospatial data. | Social Science,Other | Machine learning,Statistics | Solomon Hsiang | Jeanette Tseng (Research staff) & Suraj Nair (Postdoc) | |
36 | Ice Penetrating Radar: Science and Engineering to Explore Ice Sheets and Icy Moons | Geophysics | 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. Summer only. | Climate Change,Dynamic Earth,Engineering | Engineering,Physics | Dustin Schroeder | Dustin Schroeder (Faculty) | |
37 | Inference of Ice Sheet Viscosity and Basal Friction Using Physics-Informed Neural Networks | Geophysics | Large uncertainties around the basal friction of the Greenland and Antarctic Ice Sheets remain a challenging problem for precise predictions of future sea level rise, which is an immediate and prominent consequence of climate change. This project will leverage the tool of physics-informed neural networks to simultaneously infer viscosity and friction in order to address this problem. Student interns will apply and hone skills in computer science, mathematics, and physics to develop and apply our algorithm, as well as analyzing its outputs. Depending on individual interests, students may assist with processing ice sheet observation data, running the inference codes on high-performance GPU compute, and/or algorithm development, validation and implementation in Python JAX. Students will need strong quantitative skills to succeed in this project. Prior programming experience in Python and some familiarity with UNIX-like operating systems (for example, Linux and macOS) is required. Experience with calculus-based mechanics and machine learning algorithms is strongly preferred but not required. Previous experience in Earth Science is not required. | Climate Change,Dynamic Earth,Other | Computer programming,Machine learning,Mathematics,Physics,Statistics | Ching-Yao Lai | Jasper Chen (Grad student) | |
38 | Developing and Deploying Laser-Based sensors for methane and trace gases | Geophysics | Methane (CH4) is the third most significant greenhouse gas (GHG), behind H2O and CO2. It is 80 times more potent on a per-molecule basis than CO2, but is at lower concentrations in the atmosphere, 2 ppm vs. 424 ppm (parts per million). On short time scales, CH4 is the easiest GHG to address to mitigate adverse effects on climate. Therefore, understanding the details of the underlying science and quantifying emissions from sources of methane is crucial for addressing climate change. However, the global methane budget shows large uncertainties in the emissions coming from diffuse sources, such as natural wetlands, and agriculture (e.g. rice paddies). The uncertainties are due primarily to the heterogeneity of methane emissions from diffuse sources that vary in space and time. Reducing these uncertainties requires measurement methods that better capture the contribution from large area, dispersed sources with relevant spatial and temporal resolution. This project involves helping with the development, testing, and deployment of low-power laser-based optical sensors for continuous measurements of methane fluxes on large spatial scales. We are looking for research assistants to work on projects associated with the development of our CH4 flux measurement capabilities. Projects can range from hands-on experimental work with lasers, optics, and electronics in the lab, to instrument control software, to data analysis. Therefore, interests and skills could range from basic lab research to modeling and coding. This is an excellent opportunity for students interested in applied engineering/optics/physics that is focused on problems of environmental science. There is also potential for students to assist with field deployment of the optical sensors. This project is hosted by Leo Hollberg in Geophysics and working in collaboration with Profs. Alison Hoyt and Scot Fendorf in Earth System Science, and Prof. Catherine Gorle in Civil and Environmental Engineering. | Climate Change,Food and Agriculture,Freshwater | Biology,Chemistry,Computer programming,Engineering,Field work,Laboratory work,Mathematics,Physics,Statistics | Leo Hollberg | Newton Nguyen (Research staff) | |
39 | Reconstructing Climate Change and Ocean Warming from Corals Skeletons | Oceans | Tropical corals are natural archives of past ocean and climate variability. As corals grow, they incorporate oxygen and carbon isotopes and trace elements from seawater into their calcium carbonate skeletons, creating a continuous chemical record of sea surface temperature, rainfall, and runoff. These coral “climate logs” provide monthly to seasonal records that extend centuries beyond the reach of instrumental data, helping scientists better understand long-term ocean warming and climate variability. The island of Mauritius, located in the southwestern Indian Ocean, lies in a region that strongly influences regional climate dynamics, making its corals valuable indicators of changing ocean-atmosphere dynamics. The primary objective of this project is to generate new high-resolution climate reconstructions from coral cores collected from Mauritian coral reefs. The project has two key components: (1) using computed tomography (CT) scanning to quantify annual density bands and coral growth rates, and (2) subsampling coral slabs for isotopic analysis to infer changes in sea surface temperature and hydrological balance through time. Together, these datasets will allow the student to identify periods of stress and bleaching and reconstruct historical warming trends. This project is heavily laboratory-based, and the student will receive hands-on training in sample preparation, CT scanning, geochemical analysis, and data processing. The student will take a lead role in generating and interpreting the CT-based growth data, comparing it with isotopic measurements. No prior experience in geochemistry or laboratory work is required–only enthusiasm, curiosity, and a willingness to learn. Students will gain research lab experience and will directly contribute to an ongoing effort to build long-term climate reconstructions from Indian Ocean corals. | Climate Change,Ocean | Biology,Chemistry,Laboratory work,Statistics | Rob Dunbar | Matt Illing (Grad student) & David Mucciarone (Research staff) | |
40 | Improving Coral Climate Reconstructions with Bayesian Statistical Tools | Oceans | Coral skeletons record past variations in ocean temperature, salinity, and chemistry, offering some of the most continuous and precisely dated archives of tropical climate over the past several centuries. However, translating these chemical signals into accurate reconstructions of past ocean temperature or rainfall is complicated by uncertainties in coral growth rates, chemical alteration or weathering, and measurement error. Bayesian inferencing techniques offer a powerful framework for addressing these uncertainties. The primary objective of this project is to develop and test a Python-based framework that applies Bayesian methods to coral paleoclimate data. Specifically, the framework will (1) generate probabilistic age models for coral records, (2) identify and flag potential outlier geochemical measurements using Bayesian mixture modeling, and (3) regress coral geochemical measurements against ocean temperature datasets. Together, these methods will advance the accuracy and reproducibility of coral-based reconstructions of past ocean conditions. The student will lead the development of an open-source Python package (or collection of scripts) implementing these techniques, designed for use by coral paleoclimatologists and climate data scientists. Tasks will include writing and testing code, validating the methods on existing coral datasets, producing clear documentation, and publishing the final product on GitHub. Depending on interest, the student may assist with ongoing laboratory work in the Stanford Stable Isotope Biogeochemistry Lab focused on coral reefs. No prior experience in geochemistry or laboratory work is required. Students with programming experience in Python and familiarity with GitHub are encouraged to apply. Experience with Bayesian inferencing is an advantage, but not a necessity. | Climate Change,Ocean | Chemistry,Computer programming,Mathematics,Statistics | Rob Dunbar | Matt Illing (Grad student) & David Mucciarone (Research staff) | |
41 | Habitat and health of valley garter snakes (Thamnophis sirtalis fitchi) on Staten Island Preserve in the Sacramento-San Joaquin River Delta | Oceans | Snakes have strong ecological linkages to apex predators and primary consumers. Semi-aquatic snakes, such as widely-distributed garter snakes bridge terrestrial and aquatic ecosystems. This project focuses on surveying valley garter snakes (Thamnophis sirtalis fitchi) of Staten Island Preserve, a wildlife-friendly farm in the Sacramento-San Joaquin River Delta. These snakes are dependent on dynamic habitats such as irrigation canals that supply aquatic food resources and provide refugia from avian predators. For this project, our intern(s) will assist a postdoctoral researcher and other members of “Team Delta” with fieldwork on Staten Island Preserve. Three times a week, we will survey snakes using modified minnow traps in the irrigation canals of potato, rice, and corn fields. We ask whether habitat complexity correlates with snake health and whether there is a tradeoff with body condition and injury. More aquatic sites may offer high risk, high reward with better prey availability but at the expense of greater risk to avian attacks. Confounding this, better ephemeral vegetation may offer protection from avian predators. To determine habitat complexity, we will document the capture site and quantify vegetation cover. We will quantify snake health via body condition and visual injuries to the head, body, and tail. The results of this study will provide much-needed mechanistic information on the distribution and resources (habitat and prey) necessary for semi-aquatic snakes to inhabit these agricultural habitats and can inform their protection and management in analogous areas, especially since the giant garter snake (Thamnophis gigas), a federally endangered and newly recognized California state snake, relies on similar and neighboring habitats. Intern duties include being willing to read the primary literature, participate in fieldwork (including taking morphological measurements and PIT tagging snakes) and data collection on Staten Island, analyze video and photographic data, learn how to conduct statistical data visualizations and analyses in the open-source statistical software R, and interpret and present their findings. As the summer progresses, it is expected that the intern will be timely with their data entry onto a shared Google Doc on days where fieldwork is not conducted. On these days, the intern may meet with their postdoctoral mentor either in-person on Stanford main campus or on Zoom. Qualifications include being an enthusiastic and motivated intern who is excited to get out in the field for potentially early and long field days in the hot, dry climate of Sacramento. Our intern will be respectful towards farmworkers and other personnel of Staten Island Preserve and willing to work in a team environment, have good communication skills, and handle snakes with care. | Climate Change,Evolution of Life,Food and Agriculture,Freshwater | Biology,Computer programming,Field work,Geology,Statistics | Jeremy Goldbogen | Elsie Carrillo (Postdoc) | |
42 | Extending eDNA to capture genetic variation for estimates of white shark abundance in the Monterey Bay | Oceans | Environmental DNA (eDNA) is a non-invasive ecological monitoring technique that accurately detects the presence of rare, elusive predators like white sharks (Carcharodon carcharias). Compared to other methods like acoustic tagging and visual observation, eDNA can be a cheaper and easier way to reliably monitor white sharks. Simply collect water from the ocean, extract the DNA, and assay for white shark-specific DNA. So far, eDNA has primarily been used to detect whether a shark is present in the sampling location or not. It has not been expanded to answer questions such as which white shark was detected, what type of white shark was detected, or how many white sharks were detected. This project will implement a new model to estimate the local abundance of white sharks at Ano Nuevo, a white shark aggregation site in the Monterey Bay. Particularly, for each sampling day the student will compare the eDNA model’s estimate of white shark abundance to two other methods: the number of tagged sharks detected by an acoustic receiver and the number of sharks photographed at the surface. The student will learn how to filter water from white shark aggregation sites in the Monterey Bay, extract the eDNA, design and test primers, amplify white shark DNA, and bioinformatically analyze DNA sequences. The ideal student will have some basic molecular biology lab experience (i.e. pipetting) and preferably some programming ability in R. The student will gain hands-on experience with lab and field work, as well as learn about technologies to monitor and conserve threatened marine species. If desired, the student can also participate in science communication activities such as creating content for the Monterey Bay White Sharks Instagram page. | Evolution of Life,Ocean | Biology,Computer programming,Field work,Laboratory work,Statistics | Barbara Block | Raksha Doddabele (Grad student) | |
43 | Predicting the impacts of large infrastructure development and global change on the distribution of schistosomiasis, a neglected tropical disease. | Oceans | Predicting future distributions of infectious diseases is critical to the health and wellbeing of vulnerable populations, particularly in sub-Saharan Africa. Schistosomiasis - a debilitating disease of poverty affecting ~250 million people globally - is influenced by large infrastructure developments (e.g., dams), rapid unplanned urbanization, and inadequate water and sanitation infrastructure. Environmental and hydrological factors determine the distribution of freshwater snails that serve as intermediate hosts in the parasite's life cycle. Our lab is developing cost-effective, user-friendly, and scientifically robust quantitative tools to rapidly assess schistosomiasis transmission risk in response to global change and infrastructure development. These tools will enable the inclusion of disease risk considerations in Environmental Impact Assessments and facilitate testing of transmission reduction strategies. The summer intern will contribute by building scripts to summarize and compare models of predicted disease impact, identifying and integrating new data streams to improve model responsiveness, optimizing interfaces between models and user needs, and testing the tool with simulated scenarios of infrastructure development in endemic regions. The project provides hands-on experience in disease ecology modeling, data integration, and the development of decision-support tools for public health and development planning. The ideal candidate will have strong interest in developing data-driven tools for infectious disease control or natural resource management. Proficiency in R or Python is required, along with background in statistics (at least one introductory course). Interest in global health, ecology, or environmental science is highly desirable. Experience with GIS or spatial analysis tools is preferred but not required. Background in disease ecology or epidemiology would be helpful but is not necessary. We welcome students from diverse academic backgrounds who are eager to apply quantitative approaches to complex global health challenges. This position will be based on Stanford's main campus, with co-mentors based at the Hopkins Marine Station. There may be occasional travel to Hopkins Marine Station. | Food and Agriculture,Freshwater,Natural Hazards,Social Science | Biology,Computer programming,Engineering,Machine learning,Mathematics,Statistics | Giulio De Leo | Talya Shragai (Research staff), Andrew Chamberlin (Research staff) & Kayla Kauffman (Postdoc) | |
44 | Behind the Levees: How Local Capacity Shapes Flood Protection in California | Emmett Interdisciplinary Program in Environment and Resources | Across California’s Central Valley, long earthen embankments called levees are built along rivers and channels to keep floodwater from spilling into nearby neighborhoods and farmland. Most levees are decades old and managed by local agencies with limited staff, budgets, and technical support. Building on ongoing Stanford research, this project asks: how does the capacity of local levee owners shape their ability to maintain and upgrade levee infrastructure in California’s Central Valley? Working primarily on campus, the student will work with the National Levee Database and related datasets to identify and address gaps or inconsistencies, and create clear visualizations that compare different types of levee owners in terms of miles of levee managed, budgets, and other basic indicators of capacity. The student will also help prepare interview protocols and background materials for conversations with levee owners and operators, and may have the opportunity to join in on a small number of interviews. The student may additionally help draft communication materials for households at risk from levee failure in the Central Valley, as part of a collaboration with the California Department of Water Resources. By the end of the summer, the student will have produced a short memo and simple visuals that contribute to a broader research effort on levee governance, climate adaptation, and environmental justice in California. Skills/Interest/Background: This project is well suited for students interested in interdisciplinary sustainability research who are curious about how physical infrastructure, policy, and communities interact in California, and who would like to build experience with interviews as well as basic data organization and visualization; no specific programming background is required. | Engineering,Freshwater,Natural Hazards,Social Science | Computer programming,Engineering,Field work,Statistics | Jenny Suckale | Hannah Melville-Rea (Grad student) | |
45 | Unraveling how ammonia-oxidizing archaea power Earth’s nitrogen and carbon cycles—one enzyme at a time | Earth System Science | Microbes are essential in shaping Earth’s climate. One of the most abundant and widespread microbial groups on Earth are the ammonia-oxidizing archaea (AOA). AOA play a critical role in the global nitrogen cycle by performing the first step of nitrification and also impact the global carbon cycle by performing (dark) CO2 fixation. In addition, AOA also influence global climate via their production of N2O, a potent greenhouse gas with ~300X the warming potential of CO2. Studying the enzymes that AOA use to perform their nitrogen and carbon cycling activities is crucial to better understand how AOA influence Earth’s climate. Our research group is interested in characterizing the phylogenetics, biochemistry, and physiology of the enzymes responsible for the nitrogen and carbon cycling activities of AOA. As a student working on this project, we will help you design an exciting and manageable summer project based around an AOA enzyme of your choice. Based on your interests and experience, your project will be tailored towards computational research, laboratory research, or both. You will gain fundamental skills in computational methods (e.g., phylogenetics and protein structural prediction) and/or laboratory techniques (e.g., protein expression/purification, structural/functional characterization, and general molecular biology skills). Prior laboratory experience is helpful but not required. If you have a general interest and enthusiasm for environmental microbiology, protein biochemistry, and/or climate-related research, please consider applying. | Climate Change,Evolution of Life,Other | Biology,Chemistry,Laboratory work | Christopher Francis | Benjamin Shapero (Grad student) | |
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