stanford phd thesis

Dissertations and Theses

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Congratulations on being close to the finish line with your dissertation or thesis.

After you’ve applied to graduate and enrolled, dissertations and theses may be submitted online through the Dissertation & Thesis Center in Axess.  

Once you finish submitting your dissertation or thesis in Axess, and it has been approved by the university, the submission is considered final and no further changes are permitted. 

The electronic submission process is free of charge and allows you the ability to check your pre-submission requirements and when ready, upload a digital copy of your dissertation or thesis. 

You can learn more about the center on the How to Use the Dissertation & Thesis Center webpage.

• FAQs: Dissertation & Theses
• How to Submit Your Signature Page
• How to Use the Dissertation & Thesis Center
• How to Request to Use Copyrighted Material

Note: The online submission process is not available for master's theses or undergraduate honors theses. Please consult with your department directly regarding submission procedures.

Follow these guides to ensure you meet all the requirements for submitting your dissertation or thesis. 

• Prepare Your Work for Submission
• Submit Your Dissertation or Thesis
• Steps After Submission

Submission Deadlines for Conferral

You must apply to graduate and enroll before you can access the Dissertation & Thesis Center in Axess.

The Dissertation & Thesis Center opens to submissions on the first day of instruction each quarter for which the student has applied to graduate.

The quarterly deadlines are set as late in the quarter as possible, providing the time necessary for review of the dissertation or thesis, including review of final degree requirements by the Registrar's Office and the departments. 

You are strongly encouraged to submit your work at least two weeks prior to the deadline to ensure that all requirements can be met in time for the conferral of your degree. 

Once you finish submitting your dissertation or thesis in Axess, and it has been approved by the university, the submission is considered final and no further changes are permitted. 

After the final reader approves the dissertation, it typically takes about seven (7) business days for the university to process the submission.  

Deadlines by Quarter

Dissertation deadlines are strictly enforced.  No exceptions are made. By noon on the final submission deadline date, all of the following steps must be completed:           

• The student enrolls and applies to graduate;
• The student confirms the names of reading committee members in Axess, and designates a Final Reader;
• The student submits reading committee signatures;
• The student completes the necessary University Milestones;
• The student’s candidacy is valid through degree conferral;
• The student submits the final dissertation or thesis in Axess;
• The designated Final Reader certifies the final draft of the dissertation or thesis submitted in Axess.

For help, contact the Student Services Center .                                                                        

For faculty and staff information on Dissertations, visit Inside Student Services.

Dissertation and thesis submission (PhD, JSD, DMA, engineering master's)

Learn more about dissertation and thesis submission

Graduated and enrolled Stanford students may submit their dissertations and theses through Axess. The electronic submission process is free of charge. The service provides the ability to check your pre-submission requirements, and, when ready, you can upload a digital copy of your dissertation or thesis.  

Learn how to use the Dissertation and Thesis Center

Who is eligible?

The online Dissertation and Thesis Center in Axess is currently available to Stanford PhD, JSD, DMA, and engineering-degree students only.

What to expect

• After you have applied to graduate and have enrolled, you will see the Dissertation and Thesis Center in Axess.
• You’ll want to  prepare your work for submission , following the guidelines for format and title page.
• It may be helpful for you to check out  how to request to use copyrighted material , if you have questions.
• Next, you’ll submit your dissertation or thesis by following this helpful  checklist for submitting your dissertation or thesis .
• After submission, a  certificate of final reading will be created by your Final Reader, using the online submission workflow.
• In addition, you’ll need to  obtain approval from each member of your reading committee .
• After you have fulfilled all requirements and your dissertation or thesis has been approved by the University Registrar, it will be cataloged, preserved in the Stanford Digital Repository, and made available online via  SearchWorks , the online library catalog. 
• Please note: if embargoed, your dissertation or thesis PDF will be available only to Stanford affiliates for the duration of the embargo.

For more detailed information about submitting your dissertation or thesis online, refer to this set of dissertations and theses web pages provided by the Student Services Center.

Capstone and thesis submission (undergraduate honors, master's)

Check out the capstone and thesis submission (undergraduate honors, master's) page for information about submitting these types of theses.

Questions about the dissertation and thesis submission service? 

If you have questions about submitting your dissertation or thesis, please contact the  Student Services Center .

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

PhD | Dissertation Requirement

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The most important requirement for the PhD degree is the dissertation. The dissertation must be accepted by the student's reading committee. The Graduate Degree Progress office in the Registrar's Office distributes a comprehensive list of directions concerning the preparation and submission of the final draft. Students have the option of submitting their dissertation online. 

See the Registrar's Graduate Degree Progress webpage for detailed information on dissertation submission.

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Below you will find every SEP Ph.D. thesis written since 1974. Theses written since 1992 are available online in HTML format or for download as compressed postscript (“ps.gz”) documents, while the older ones are downloadable as both PDF and ps.gz documents. The source code is also available for the recent theses, in “tar.gz” format.

• Bader, Milad. “Seismic Source and Elastic Full-Waveform Inversion Using Distributed Acoustic Sensing and Perforation Shots in Unconventional Reservoirs SEP-193 (2023)” .
• Yuan, Siyuan. “Large-Scale and Continuous Subsurface Monitoring Using Distributed Acoustic Sensing in Urban Environments SEP-191(2023)” .
• Farris, Stuart. “Seismic Velocity Model Building With Deep Convolutional Neural Networks SEP-190 (2023)” .
• Barnier, Guillaume. “Full Waveform Inversion by Model Extension SEP-188 (2022)” .
• Jennings, Joseph. “Automatic Interpretative Seismic Image-Focusing Analysis. SEP-186 (2022) ” .
• Huot, Fantine. “A Story In Three Parts: Earthquakes, Microseismic, And Tectonic Tremors SEP-185 (2021)” .
• Biondi, Ettore. “Target-Oriented Elastic Full-Waveform Inversion. SEP-183 (2021)” .
• Vargas, Alejandro. “Joint Inversion of Reflectivity and Background Subsurface Components. SEP-182 (2020)” .
• Ma, Yinbin. “Time-Lapse Inversion of Anisotropic Velocity Linked to Geomechanics. SEP-180 (2020)” .
• Le, Huy. “Anisotropic Full Waveform Inversion With Pore Pressure Constraints SEP-178 (2019)” .
• Dahlke, Taylor. “Velocity Model Building Using Shape Optimization Applied to Level Sets. SEP-175 (2019)” .
• Martin, Eileen. “Passive Imaging and Characterization of the Subsurface With Distributed Acoustic Sensing. SEP-173 (2018)” .
• Chang, Jason. “Imaging With Ambient Seismic Noise: Beyond Surface-Wave Microseisms. SEP-171 (2018)” .
• Alves, Gustavo. “Elastic Full Waveform Inversion of Multicomponent Data. SEP-169 (2017)” .
• Barak, Ohad. “ Seismic Rotational Data: Acquisition, Processing and Applications. SEP-167 (2017)” .
• Shen, Yi. “Wave-Equation Migration Q Analysis. SEP-166 (2016)” .
• Maharromov, Musa. “Time-Lapse Inverse Theory. SEP-162 (2016)” .
• Leader, Chris. “The Separation and Imaging of Continuously Recorded Seismic Data. SEP-161 (2016)” .
• Zhang, Yang. “Velocity Model Building Using Residual Moveout-Based Wave-Equation Migration Velocity Analysis. SEP-159 (2015)” .
• Wong, Mandy. “Imaging With Multiples by Least-Squares Reverse Time Migraiton. SEP-157 (2014)” .

PhD Dissertations

• Ryan Triolo (2023): Efficient electricity systems under high penetration of renewable and distributed resources
• Thomas Navidi (2023): Coordination of distributed energy resources for distribution grid reliability
• Gustavo Cezar (2023):  Powernet: a cloud-based environment for behind the meter resources coordination
• Zhecheng Wang (2022):  Energy atlas: machine-learning-based mapping and analysis for sustainable energy and urban systems
• Siobhan Powell (2022):  Electric vehicle charging: understanding driver behaviour and charging controls to improve impacts on the electricity grid
• Xiao Chen (2022):  Human-centric demand side management : lifestyles, privacy, and fairness
• Jose Bolorinos (2021):  Demand flexibility models for urban water utilities
• Sid Patel (2019):  Valuation of distributed energy resources based on consumption data
• Yizheng Liao (2018):  Integrated infrastructure health monitoring: detection, estimation, and learning
• Raffi Sevlian (2017):  Sensing and strategies for power distribution system situational awareness
• Junjie Qin (2017):  Distributed energy resource networks: planning, control and market design
• Jungsuk Kwac (2015):  Data mining for demand management: segmentation, targeting, and analytics visualization
• Adrian Albert (2014):  Problems, models, and algorithms in data-driven energy demand management
• Amir Kavousian (2014):  Data-driven ranking of building energy efficiency utilizing stochastic energy efficiency frontiers

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• Sa da Fonseca, J. (2024). Probabilistic assessment of pore pressure prediction with Bayesian Geophysical Basin Modeling [PhD, Stanford University]. https://searchworks.stanford.edu/view/in00000069359
• Wang, Y. (2023). A beautiful marriage between POMDPs and subsurface applications : decision making for subsurface systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019458
• Kanfar, R. (2023). Stochastic geomodelling and analysis of karst morphology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000033193
• Kashefi, K. (2023). Deep learning algorithms for computational mechanics on irregular geometries [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14732781
• Hall, T. (2023). Efficient greenfield mineral exploration [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/in00000019459
• Wang, L. (2023). Integrating data and models for sustainable decision-making in hydrology [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
• Miltenberger, A. (2022). A measure-theoretic approach to Bayesian hypothesis testing and inversion with geophysical data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14233991
• Yang, L. (2021). Quantifying and visualizing uncertainty of 3D geological structures with implicit methods [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/14641244
• Petrov, S. (2021). Seismic image segmentation with deep learning [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/qf836dh0076
• Athens, N. (2021). Stochastic inversion of gravity data in fault-controlled geothermal systems [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13826071
• Pollack, A. (2020). Quantifying Geological Uncertainty and Optimizing Technoeconomic Decisions for Geothermal Reservoirs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13680060
• Pradhan, A. (2020). Statistical learning and inference of subsurface properties under complex geological uncertainty with seismic data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13753880
• Nesvold, E. (2019). Building informative priors for the subsurface with generative adversarial networks and graphs [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13423377
• Muradov, R. (2019). Inference of Sub-Resolution Stacking Patterns from Seismic Data in Spatially Coupled Models [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/km001pf4033
• Al Ibrahim, M. (2019). Petroleum System Modeling of Heterogeneous Organic-Rich Mudrocks [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250212
• Park, J. (2019). Uncertainty quantification and sensitivity analysis of geoscientific predictions with data-driven approaches [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/13250154
• Mendes, J. (2018). Morphdynamic Analysis and Statistical Synthesis of Geomorphic Data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12746435
• Dutta, G. (2018). Value of Information Analysis for Time-Lapse Seismic Data in Reservoir Development [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12742067
• Li, L. (2017). A Bayesian Approach to Causal and Evidential Analysis for Uncertainty Quantification throughout the Reservoir Forecasting Process [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12137330
• Aydin, O. (2017). A Bayesian Framework for Quantifying Fault Network Uncertainty Using a Marked Point Process Model [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11950552
• Grujić, O. (2017). A Subsurface Modeling with Functional Data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12212152
• Yang, G. (2017). Holistic Strategies for Prediction Uncertainty Quantification of Contaminant Transport and Reservoir Production in Field Cases [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/12123097
• Tong, Y. (2016). Basin and Petroleum System Modeling with Uncertainty Quantification: a Case Study on the Piceance Basin, Colorado [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11685974
• Luebbert, L. (2016). Quantitative Analysis of Dissimilarities Between Different Methods of Seismic Inversion to Facies Realizations [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11956907
• Shin, Y. (2016). Reservoir Modeling with Multiple Geological Scenarios for Deformation of Reservoir Structure and Evolution of Reservoir Properties [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11616857
• Xue, C. (2016). The Application of OPTSPACE Algorithm and Comparison with LMAFIT Algorithm in Three dimensional Seismic Data Reconstruction via Lowrank Matrix Completion [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11700910
• Lee, J. (2015). Joint Integration of Time-Lapse Seismic, Electromagnetic, and Production Data for Reservoir Monitoring and Management [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11391864
• Satija, A. (2015). Reservoir Forecasting Based on Statistical Functional Analysis of Data and Prediction Variables [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/11061336
• Wang, Y. (2015). Rule-Based Reservoir Modeling by Integration of Multiple Information Sources: Learning Time-Varying Geologic Processes [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/11085541
• Xu, S. (2014). Integration of geomorphic experiment data in surface-based modeling: from characterization to simulation [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/10403736
• Jeong, C. (2014). Quantitative Reservoir Characterization Integrating Seismic Data and Geological Scenario Uncertainty [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/10530921
• Suman, A. (2013). Joint inversion of production and time-lapse seismic data: application to Norne field [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9959027
• Bertoncello, A. (2011). Conditioning surface-based models to well and thickness data [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9274420
• Park, K. (2011). Modeling Uncertainty in Metric Space [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9100156
• Honarkhah, M. (2011). Stochastic simulation of patterns using distance-based pattern modeling [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/9238345
• Wang, J. (2010). A Metropolis sampling method to assess uncertainty of seismic impedance inverted from seismic amplitude data [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/ch090ff5484
• Haugen, M. (2010). Exploring direct sampling and iterative spatial resampling in history matching [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/dn958js5816
• Jia, B. (2010). Linking geostatistics with basin and petroleum system modeling: Assessment of spatial uncertainties [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/mv127hj3223
• Trainor-Guitton, W. (2010). On the value of information for spatial problems in the Earth sciences [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/8652432
• Kuralkhanov, D. (2010). Study of pattern correlation between time lapse seismic data and saturation changes [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/xj886by3185
• Leiva, A. (2009). Construction of hybrid geostatistical models combining surface based methods with object-based simulation: use of flow direction and drainage area [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/8390172
• Suman, A. (2009). Uncertainties in rock pore compressibility and effects on seismic history matching [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/8390194
• Polyakova, E. (2008). A general theory for evaluating joint data interaction when combining diverse data sources [PhD Thesis, Stanford University]. https://searchworks.stanford.edu/view/7860490
• Fadaei, S. (2008). Streamline assisted history matching of naturally fractured reservoirs using the probability perturbation method [MS Thesis, Stanford University]. https://searchworks.stanford.edu/view/7814802