stanford master's thesis computer science

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.

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

There’s a forever home for your capstone, honors thesis, or master’s thesis—archived in the Stanford Digital Repository and accessible online via SearchWorks, the library catalog. It’s free and the process takes just a few minutes.

Start your deposit today  

Who is eligible

• Stanford undergraduate students who have produced a senior capstone project, honors thesis, or similar culminating work are welcome.
• Stanford master’s students outside of the School of Engineering who have written a thesis may deposit their work.
• The Stanford Digital Repository (SDR) is a service available to all Stanford students, faculty, and staff who produce research, scholarly works, or institutional records of long-term value. 

What to expect

• Once you log in, look for the name of the capstone or thesis collection on your dashboard. (Don’t see it on the dashboard? Check with your program contact to request depositor access to the collection.)
• After you submit, your deposit may be queued for review and approval. If so, you will receive a notification when the review is completed. On approval, your deposit will be available online at a persistent URL (PURL) and will be findable in SearchWorks, too.
• Go ahead and share your PURL with your friends and family, and add it to your resume, too!

Watch this brief overview video demonstrating how to deposit your work into the SDR.

More helpful resources

• Dissertation and thesis submission (PhD, JSD, DMA, engineering master's)  
• Guide to student publishing
• Directory of student works collections in the SDR
• SDR services website

Questions? 

Reach out to the SDR team by email .

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

PhD | Thesis Proposal

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The student must present an oral thesis proposal and submit the form to their full reading committee by Spring quarter of their fourth year. The thesis proposal form  must be filled out, signed, and approved by all committee members. Then, submitted to the CS PhD Student Services ( [email protected] ). 

The thesis proposal allows students to obtain formative feedback from their reading committee that'll guide them into a successful and high-quality dissertation. The thesis proposal (a private session only with the student's advisor/co-advisor and reading committee members) should allow time for discussion with the reading committee about the direction of the thesis research. The suggested format should include:

• A description of the research problem and its significance;
• A description of previous work in the area and the "state of the art" prior to the student's work; 
• A description of preliminary work the student has done on the problem, and any research results of that work; 
• An outline of remaining work to be done and a timeline for accomplishing it.

Master's Thesis

An M.S. thesis should demonstrate, through a substantial original project, the student’s proficient use of methods associated with academic area(s) in which the Primary Advisor is able to provide supervision. The thesis must be of sufficient quality that it merits the degree of Master of Science in the judgment of the student’s Primary Advisor and Second Reader. The final thesis is subject to the approval of the Symbolic Systems Program Office. The thesis must be written in English, to ensure that the faculty and staff of the Program’s Directorate can read and understand it.

• Master's theses are  due at Noon on the day of the  University Dissertation/Thesis Deadline  for the quarter in which you are graduating. You must be a registered Stanford student during the quarter in which you graduate.
• Your thesis must be signed by two readers: your primary advisor and a second reader. Review eligibility requirements
• Your thesis must contain signatures for each reader in the following format (with parenthetical and bracketed text filled in as appropriate)

"To the Directors of the Program on Symbolic Systems: I certify that I have read the thesis of (Printed Name of Student) in its final form for submission and have found it to be satisfactory for the degree of  [Master of Science/Bachelor of Science with Honors]. Signature  Date (Printed Name of Reader) (Printed Name of Reader's Department )"

• If signatures cannot be obtained hand-written, then your reader(s) may sign by email sent to symsys-directors [at] lists.stanford.edu (symsys-directors[at]lists[dot]stanford[dot]edu) prior to the deadline for thesis submission, using the wording above. Any electronic signature must be sent from the reader's officially listed university email account. The signature page should read "Signed electronically" on the signature line of your turned-in thesis, with all other information present as above.
• In hard copy, bound, two-sided, on 8 1/2 x 11" paper, delivered to: Associate Director, Symbolic Systems Program, Mail Code 2150, Margaret Jacks Hall, Stanford, CA 94305-2150
• In a PDF version through Stanford Digital Repository .

Master's Theses Examples

• Recent master's theses in our program are available in the Stanford Digital Repository .
• M.S. theses are also kept in hard copy in the Symbolic Systems Program office (460-040) and may be viewed by arrangement with the Associate Director or an Advising Fellow. They may not be checked out.

Statistics & Data Science MS Overview

Program overview.

The M.S. in Statistics and Data Science are terminal degree programs that are designed to prepare individuals for career placement following degree completion. The M.S. does not directly lead to admission to the Statistics Ph.D. program however, those with a strong academic record in statistics and probability theory, and demonstrate promising ability to conduct in-depth research should consider applying to the doctoral program in Statistics.

• Advanced graduate study pathways

Students are expected to live within commuting distance of Stanford campus to ensure significant engagement with the department and faculty. Students are not required to live on-campus (graduate housing), but many find it more conducive due to competitive rental market in neighboring cities and transportation logistics.

• Residency Policy for Graduate Students
• Campus housing (section on this page)

Department orientation for new Stats and DS students

Our mandatory New Student Orientation typically takes place on the Thursday before Autumn Quarter classes begin. I will offer an online meeting in August to explain enrollment and best practices.

University orientation events will be announced in September. These are hosted by the Graduate Life Office (GLO) and known by their acronym, NGSO. Students should plan to arrive on campus one to two weeks before the start of classes for the quarter.

Familiarize yourself with the Academic Calendar to anticipate pending deadlines throughout your time in the program.

2024-25 First Days of Classes and End of Terms

( These dates are subject to change at the discretion of the University.)

• Winter break: December 16 – January 3
• Spring break: March 24 – March 28
• Spring 2024-25: March 31 and June 11
• Summer 2024-25: June 23 and August 16

(updated Feb. 2024)

Length of the program

Students typically finish the degree program in 5 or 6 quarters (excluding summer). With a vast schedule of awesome courses offered during the year, the idea of staying longer is quite appealing to many, but one must weigh the cost of tuition and living expenses of enrolling beyond the degree's required 45 units. 

For those who can manage more than three courses each quarter, enrolling in 11+ units of required courses would allow a student to complete the degree in a shorter period of time (less cost of living/housing expenses).

We advise students to take 1-2 required courses each quarter and an elective course of interest in order to make satisfactory degree progress.

Suggested first quarter enrollment

First quarter enrollment example for the statistics ms:.

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem.

Prerequisites: Integral Calculus of Several Variables (Math 52) and familiarity with infinite series, or equivalent (4 units)

After taking Stats 118, the students should be able to:

• Understand the principles of probability in discrete and continuous cases without measure theoretic detail.116Apply counting techniques to solve probability problems in spaces with regularity or symmetry.
• Recognize important distributions in the exponential families and their connections.
• Apply probability models to real-world situations, and recognize famous problems in disguise, like the Birthday problem, the Ballot problem, and the Matching problem.
• Derive expectations and variances of random variables in structured probability spaces.
• Exploit probabilistic symmetries to solve simple problems.
• Understand results such as the Central Limit Theorem and Poisson approximation, and recognize their importance in statistical applications.
• Gain familiarity with more advance topics in probability.

February 2024

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case-based methods, and data visualization.

Prerequisites: Introductory courses in statistics or probability (e.g., STATS 60 ), linear algebra (e.g.,  MATH 51 ), and computer programming (e.g.,  CS 105 ) (3 units)

After taking STATS 202 the students should be able to:

• Understand the distinction between supervised and unsupervised learning and be able to identify appropriate tools to answer different research questions.
• Become familiar with basic unsupervised procedures including clustering and principal components analysis.
• Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminant analysis, K-nearest neighbors, splines, generalized additive models, tree-based methods, and support vector machines.
• Gain a practical appreciation of the bias-variance tradeoff and apply model selection methods based on cross-validation and bootstrapping to a prediction challenge.
• Analyse a real dataset of moderate size using either R or Python.
• Develop the computational skills for data wrangling, collaboration, and reproducible research.
• Be exposed to other topics in machine learning, such as missing data, prediction using time series and relational data, non-linear dimensionality reduction techniques, web-based data visualizations, anomaly detection, and representation learning.

Linear algebra for applications in science and engineering: orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, least-squares, the condition number of a matrix, algorithms for solving linear systems. MATH 113 offers a more theoretical treatment of linear algebra. MATH 104 and ENGR 108 cover complementary topics in applied linear algebra. The focus of MATH 104 is on algorithms and concepts; the focus of ENGR 108 is on a few linear algebra concepts, and many applications.

Prerequisites: Intro linear algebra, multivariate calculus ( MATH 51 ) and programming experience on par with CS 106 . (3 units)

Learning objectives: Learn concepts and theorems well enough to formulate real world problems in the language of linear algebra and apply linear algebraic techniques to solve the problems.

First-quarter enrollment example for Stats-Data Science:

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: STATS 116 . Please note that students must enroll in one section in addition to the main lecture.

Terms: Aut, Win | Units: 4

This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: basic probability and statistics, familiarity with R.

Terms: Aut | Units: 3

M.S. Program advisor assignments

M.S. program advisors assignments will be announced in September. MS advisor assignments are determined over the summer and will be announced in September. To ensure equity and easy distribution rules, students are assigned by their last name (alpha order).

If needed, you'll be able to discuss with your program advisor at the start of the quarter to help you determine the appropriate enrollment before the final study list deadline . Please see the information concerning course placement in the FAQ section below.

• Guidelines and expectations to help establish a professional and respectful academic advising culture

Independent Study (for Elective credit)

While research is not a required component of the degree, the desire to participate in research has been an increasing trend through recent years.

A common request n that has come up in the past few years is regarding the ability to conduct research (for credit), with faculty as independent study/directed reading/independent research.

[More on networking opportunities: Please also browse the information on relevant seminars, student groups and organizations near the bottom of this page.]

While there exists a way to earn credit for independent study/research ( STATS299 ) under the supervision of their program adviser or other Statistics faculty. One must obtain approval from the advisor and provide clearly defined objectives and expected outcome(s) before enrolling in their section.

• Develop a goal statement for what the student hopes to accomplish and the purpose of the independent study. (List your goals by explaining what you hope to gain in terms of knowledge, skills, etc.)
• Select and/or develop learning objectives related to the goal statement. (Using broad statements, list each objective and/or learning activity in the plan.)
• Develop a timetable for implementation of activities and completion of course requirements. (Include what it is that you expect to do and produce and dates for completion and submission. List the types of activities/assignments that the you will be completing by the end of the quarter.)

Other (teaching/research) opportunities

Assistantships.

Campus assistantships are not a guarantee and should not be relied upon to fund your tuition.

TA/RA opportunities within the Statistics dept are designated for the doctoral students as it is a predominant training component of their 5-year program . There is very little chance that either of these opportunities would be available to students outside of the Statistics doctoral program. If an opportunity becomes available, it will be announced to the Statistics graduate student population.

Statistics faculty do not manage the hiring of RA/TA, nor do they have funding to support Masters students.

An assistantship may sometimes be obtained from related departments and schools. It is the student's responsibility to find these opportunities and there are no guarantees. Begin an online search for Course Assistant applications at least three months before the start of the next quarter as departments need to start the hiring process well ahead of time.

!Do not commit to a TA/CA position if you do not have sufficient time to devote to the job.

Some departments or schools hire our M.S. students for hourly research assistant positions. This type of work is not to be confused with full or partial tuition allowance (GAP 7.3) . Before accepting any work, confirm with the hiring department or school whether it is an hourly position, or if it is a type of tuition allowance.

Career prospects

At this time, the department does not publish job placement data of its graduates. Instead, we can provide a general trend of job placements in recent years:

Many students find employment in data science, research analytics, software engineering, program management within the technology sector (operations research), or the finance industry (asset management, acquisitions/mergers, business analytics) as well as various governmental services. The majority of our graduates have found employment in the Bay Area and other major cities around the world.

Stanford Career Education hosts career fairs throughout the year, and there is a tremendous benefit to our campus being situated in Silicon Valley . To participate, students upload their resumes in advance via Handshake, indicate which field/industry and companies they are interested in and industry partners reach out to schedule interviews.

Stanford Career Education also explains Where to Find Jobs & Internships  !

We don't collect data on salaries. This information can be gathered in an online search of job recruitment and financial education sites.

• Data about mathematicians and statisticians from the U.S. Bureau of Labor Statistics

Advanced Graduate Study

The number of students who pursue graduate programs is steadily increasing.

Statistics MS students that feel strongly about entering a 5-year program of research in statistical theory and applications should meet with their program advisor to discuss which programs and schools are an appropriate place and time to apply. With careful planning, students will be able to build a strong program that will make them highly competitive applicants wherever they apply.

Previous years' graduates had been accepted to doctoral programs in Statistics at Columbia, University of Washington, Wharton School, UC Berkeley and UCLA.

Common questions from incoming Statistics Masters students

Stanford does not require a deposit to confirm your acceptance or initiate matriculation.

The student bill for autumn quarter is due in October.

• Student Services: Understand Your Student Bill and Payment System

Student Visa Application in Axess: " Initiate I-20 or DS-2019; Request. " You may do so immediately following accepting in Axess. The I-20 process will begin after submission of required documentation. Bechtel International Center will contact you if they require any further information.

• Review the steps to request/transfer the I-20
• Statistics M.S. course requirements

Courses that you've taken at your previous institution (or applicable work experience) should be taken into account for the following scenarios:

Statistics students: Autumn Quarter

Probability Theory

• Students returning to school may wish to brush-up on their skills in statistics and probability and should also enroll in STATS116 ; Summary notes courtesy of Professor Dembo.
• Students should be comfortable with probability at the level of STATS116/MATH151 (summary of material) and with real analysis at the level of Math115. Past exposure to stochastic processes is highly recommended.
• A new course STATS221 focuses on topics in discrete probability that are well beyond undergraduate probability, with particular emphasis on random graphs and networks. While at a level and style similar to STATS217, the material of STATS221 is more modern, and do not overlap any of STATS 217/218/219 (nor with the STATS310 sequence or with MATH236).

Theoretical Statistics

• For those familiar with the material in this problem set then STATS200 is recommended (autumn). If the problem set poses a struggle, then we suggest starting with STATS116
• Using the STATS200 course description to determine if the course content would be redundant material for you, STATS305A (autumn) is recommended instead.

Linear Algebra

• MATH104 Applied Matrix Theory
• Choosing between MATH104 & 113 (outline courtesy of the Math Department)
• CME364A Convex Optimization I
• MATH115 Functions of a Real Variable
• MATH171 Fundamental Concepts of Analysis
• CME302 Numerical Linear Algebra

Programming

• CS106A Programming Methodologies (A, W, S, Su)
• CS106B Programming Abstractions (A, W, S, Su)
• CS106AX Programming Methodologies in JavaScript and Python (Accelerated)
• or other higher-level course in the same area for those who have programming experience beyond the courses described above.

Data Science students: autumn quarter

• For those with little or basic programming experience, it is common for students to start with a course in CS106  (A, B or AX) or CS107  (no longer offered: CME211 notes ).
• Using the STATS200 course description  to determine if the course content would be redundant material for you, STATS305A (autumn) is recommended instead.
• Consider taking a course under the suggested electives section .

ExploreCourses , the university's academic database, can be searched using the program code (e.g., STATS116, CS106, MATH104, etc.) or by subject. Please pay special attention to the quarter(s) that courses will be offered, as not all courses are offered at all times, and some are not offered more than once per year. The course schedule is updated in August each year; ExploreCourses will redirect to the new database when it goes live.

For Autumn 2024-25, students whose matriculation status is CLEAR will be able to enroll in courses early September(9:00 PM Pacific time ).

• U pdate your address .

Axess enrollment allows students to plan their quarter starting:

• August 28 ( Mon ) Planning opens for undergraduate, graduate, and Graduate School of Business (GSB) students.

Stanford's course registration system allows students to enroll in courses with conflicting meeting patterns. While this is allowed at the start of the quarter (first three weeks), it is generally discouraged due to time constraints and expectations; the course should be dropped by the end of Week 3 ( Final Study List deadline ).

Instructors will not accommodate a student whose classes have conflicting end-quarter exams.

Resources from Bechtel International Center

New International Students:

• Release of Enrollment Holds: All F and J students are required to bring their passport, I-20 or DS-2019 , and a recent print out/screen shot on digital device of your admissions I-94 electronic record to one of the Maintaining Your Legal Status workshops in order to have your enrollment hold removed. The hold will be removed within 24 hours.
• Prior to attending this workshop, you must update your SEVIS (U.S.) address and U.S. phone number on Axess. Instructions on how to update your address can be found on the Bechtel website: How to update your address

F-1 Students Who Attended Other U.S. Schools:

• All F-1 transfer students must complete the check-in process within 15 days of the program start date. This can only be done after you have updated your SEVIS (U.S.) address field and U.S. phone number in Axess and have attended one of the Maintaining Your Legal Status workshops at Bechtel .
• After these two requirements have been met you will receive an e-mail instructing you to come to Bechtel to pick up your Transfer Completed I-20.

Most students report that they were almost always able to enroll in the courses they needed each quarter. It is recommended that students make themselves available at the time that enrollment opens (9 pm Pacific).

If enrollment is closed and the course does not have a waiting list, students should contact the instructor to communicate their desire or need to take the course. Explain that the course is needed for your degree and confirm that you will not be enrolled in a course with a conflicting meeting pattern or final exam. Where possible the instructor will try to accommodate your request.

In some instances, be sure to carefully read the course description for enrollment steps. Some courses require the student to submit an application.

Minimum units allowed during the regular academic year each quarter is 8 units which is considered full-time enrollment. Most students enroll in 8 units each quarter and many are able to enroll 10 units.

A few students are able to manage 11-15 units each quarter to finish their degree in less time.

If you need to enroll part-time (minimum 3 units), check your eligibility for Part-Time Enrollment in the Graduate Academic Policy and Procedures guide.

Most students take 5-6 quarters to finish their degrees, not including summer quarter. Some students can finish it in as few as 4 quarters, many choose to stay for 6 quarters (A,W,S) over two academic years.

Some students choose to take fewer required courses each quarter due to a more taxing course-load or due to outside commitments. They may also want to take other courses outside of the degree's requirements.

A thesis is not required for the Master's degree. Those who are interested in pursuing a thesis project, finding the right faculty is vital to starting any level of research. It takes considerable time and planning before permission is granted. Those who are successful then enroll in the Statistics STATS299 Independent Study course (up to 3 units) under the section number of their M.S. program advisor (or other faculty advisor).

As is stated in the admission offer letter, completing the M.S. degree in Statistics at Stanford is not a bridge to the Statistics Ph.D. at Stanford.

In addition to their faculty advisor, many students feel comfortable approaching and speaking with faculty and instructors. Bear in mind, Stanford faculty are often committed to various ongoing research projects; it can be difficult to connect or network with Stanford faculty and researchers without learning about what they do. We suggest attending any of the myriad seminars across campus that are of interest to you; which will open up an unparalleled domain of networking possibilities where you can learn about the diverse world of Stanford research.

Most first-year students choose to live on campus in graduate housing . However, there are also many students who prefer to live off-campus in the surrounding Bay Area .

Graduate students are guaranteed campus housing their first year.

Graduate Housing Lottery The Graduate Housing Lottery is the process by which new and continuing graduate students, as well as non-matriculated students such as post-docs, apply for 2022-23 and summer 2022 housing. Students will have the opportunity to rank their desired housing options and form groups. Housing is available for single students, couples, and families.

• Graduate Housing Lottery Website includes information about housing options and the Lottery
• Housing Lottery explained
• Lottery FAQ
• 2023-24 Graduate Housing Brochure
• Other campus housing options via RDE Community Housing

The campus housing application is available via Axess in April:

• Go to the Student drop-down menu and select Housing and Dining
• Select Apply for Housing
• Follow the instructions to submit your application.

R&DE Student Housing Assignments will be hosting a series of webinars covering the Graduate Housing Lottery:

• April 28 from 4-5 pm
• April 5: Application portal opens.
• May 3: Applications due for summer 2022 and 2022-23
• May 27: Assignments announced for summer 2022 and 2022-23

On-campus housing:

• Schedule your move-in date (campus residents)
• What to bring (and what not to bring)
• Useful resource links for international students

New to California?

• Emergency readiness in campus housing
• Earthquake information from Stanford CardinalReady
• Be Quake Safe at Stanford

If these items aren't already in your suitcase, be sure to purchase them before the end of autumn quarter!

• Reusable water bottles (at least 2)
• Reusable thermos (for Statistics coffee and espresso to-go!)
• An umbrella (or a big rain poncho to drape over yourself and your backpack)
• A waterproof jacket
• Comfortable walking shoes

If you plan to bring/purchase a bike (scooter/skateboard)

• 2 sturdy locks
• Bicycle repair kit
• A rechargeable light for your handlebar
• Wear reflective clothing at dusk and night
• Sign up for a bike safety class

Bike Information and Resources for New Students

Bike Safety repair stations throughout Stanford's campus

As on most college campuses, Stanford students predominantly rely on a bike to get around. For those without access to a car, Caltrain, VTA or SamTrans provide more than adequately fulfill transportation needs up and down the peninsula (including airport shuttles). In addition, Stanford's free Marguerite shuttle service provides access to the campus to/from surrounding cities (Menlo Park, Palo Alto, parts of Redwood City) and to and from the Caltrain stations in Menlo Park and Palo Alto. Bay Area commute-traffic congestion rivals that of other major cities, which means driving on the peninsula to Stanford is impacted during peak hours.

• Marguerite was the name of the horse-drawn bus run by Jasper Paulsen in the earliest days of the university.
• Free transit options and incentive programs
• Parking permits
• Bay Area traffic information via 511.org

Finding things to do after you relocate to Stanford

• Campus events calendar
• Science and Engineering events calendar
• Graduate Students campus community center
• Math & Science Library
• Tresidder Memorial Union
• Stanford Arts Map
• Cantor Center for the Arts
• Virtual Tours   
• Stanford Magazine
• The Six Fifty.com
• Visit Stanford's Office of Student Engagement .
• Learn about student organizations in the School of Engineering .
• Civic opportunities are listed with the Haas Center for Public Service .
• Interested in art, design, music or the performing arts? Find your niche within Stanford Arts Groups .

Academic Resources and Support

There are many resources available across Stanford. Masters students most often take advantage of the workshops and career fairs sponsored by BEAM and similar events offered by the School of Engineering's Xtend Career Forum for the Data Science program.

• The Graduate Life Office (GLO) hosts New Graduate Student Orientation (NGSO) Week.
• Professional Development
• Interdisciplinary Learning
• Academic resources abound at the Office of Accessible Education .
• The Stanford Daily offers a curated list of various campus resources.

The statistics courses taught by the Department typically require some knowledge of the programming language R . Many courses rely on Python coding.

• List of Software available on Farmshare (a shared computing environment)

Recommended resources:

• Software for Data Analysis by John Chambers
• Hands-On Programming with R by G. Grolemund
• R Packages by H. Wickham

Yes: the Statistics Seminar is offered by the department, and the Probability Seminar is offered jointly with Stanford Math . Additionally, many other departments hold seminars that are open to students of all disciplines:

• Biomedical Data Science
• CS Computer Forum
• GSB Organizational Behavior

Stanford student groups that may be of interest are:

• Stats for Social Good
• CS for Social Good

International students who are employed off-campus are subject to the policies outlined by Bechtel International Center concerning Curricular Practical Training .

In order to be eligible to be hired, international students (F-1) MUST file for CPT via BechtelConnect and enroll in the course STATS298 Industrial Research for Statisticians .

Please follow the Statistics department protocol for CPT before starting the application .

Getting to know Stanford

Stanford Celebrates 125 Years

• Stanford Stories No. 25: Early Stanford Women
• Historical Timelines
• Images of Main Quad Then and Now

Notification/Obligation to Read Email For many University communications, email to a student's Stanford email account is the official form of notification to the student, and emails sent by University officials to such email addresses will be presumed to have been received and read by the student. Emails and forms delivered through a SUNet account by a student to the University may likewise constitute formal communication, with the use of this password-protected account constituting the student's electronic signature. Read the entire policy pertaining to University Communication with Students.

Summer quarter distance-learning enrollment option (NDO student)

Master’s degree students who will matriculate autumn quarter have the option to take statistics courses online via the Stanford Center for Professional Development (SCPD) before arriving on campus. Registration and enrollment is administered through SCPD (NDO student status).

Matriculation will proceed as usual with autumn quarter start.

If you have any questions about course placement for summer quarter, please email Caroline Gates ( cgates [at] stanford.edu (cgates[at]stanford[dot]edu) ), your Student Services Officer in Statistics.

• International student visas will be processed over summer with a start date in September.
• CS dept policy: Students are obligated to enroll in the maximum unit for the CS course as a NDO student.

Summer tuition: 1/10th the full-time tuition cost + SCPD fees

Prior to Graduate Admissions matriculating your student record for autumn, Statistics and Data Science students may enroll in one or two courses online:

• STATS 117 and STATS 118 Theory of Probability I & II (3 & 4 units respectively)
• Essentials of Stochastic Processes by Rick Durrett
• An Introduction to Statistical Learning with Applications in R by G. James, D, Witten, T. Hastie and R. Tibshirani
• The Art and Science of Java by Eric Roberts
• Programming Abstractions in C by Eric Roberts

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Spring 2024 Recognition Ceremony Program

Congratulations to the electrical and computer engineering Class of 2024!

And a special thank you to all of the friends and family who have supported our graduates during their time at CU Boulder. 

Table of Contents 

  Today's Ceremony

  Awards and Honors  

  PhD Candidates

  Master's Candidates  

Master of Science with Thesis

Master of science.

• Master of Engineering

  Bachelor's Graduates  

• Electrical and Computer Engineering
• ​ Electrical Engineering

Today's Ceremony

Processional

Welcoming Remarks Professor Chris Myers, Department Chair and Palmer Leadership Chair in Electrical, Computer & Energy Engineering

Acknowledgement of Award Winners

Holland Teaching Award Faculty Keynote Assistant Professor Joshua Combes​

Undergraduate Student Address Jasleen Batra

Master's Student Address Gabriel Altman

PhD Student Address Neeraj Prakash

Presentation of the PhD Candidates  Professor Sean Shaheen, Associate Chair for Research and Graduate Education

Presentation of Master’s Candidates Professor Sean Shaheen, Associate Chair for Research and Graduate Education

Presentation of BS Candidates Associate Teaching Professor Mona ElHelbawy, Associate Chair for Undergraduate Education

Closing Remarks Professor Chris Myers

Reception Please join us to continue celebrating our graduates. Light refreshments will be provided. 

Awards and Honors 

• Best Thesis: Jieqiu Shao
• Excellence in Graduate Research: Lukas Buecherel
• Excellence in Graduate Teaching: Aravind Venkitasubramony
• ​Undergraduate Academic Engagement: Kahlid Shahba and Zane McMorris
• Undergraduate Community Impact Award: Jasleen Batra
• Undergraduate Perseverance Award: Bruno Armas and Insar Magadeev
• Outstanding ECEE Undergraduate: Olivia Egbert and Suhana Zeitzius

Doctor of Philosophy Candidates

Conrad Corbella Bagot Advised by Professor Won Park  Dissertation to be defended in Summer 2024

Lukas Buecherl Advised by Professor Chris Myers Dissertation: "Decoding Genetic Circuit Failures: Analyzing Static and Dynamic Failures in Genetic Circuitry"

Paige Danielson  Advised by Professor Zoya Popovic Dissertation to be defended in Summer 2024

James William Hurtt Advised by Professor Kyri Baker Dissertation: "On the Techno-Economic Merits and Challenges of Clean Hybrid Energy Systems in Contemporary Power Systems" 

Connor Nogales Advised by Professor Gregor Lasser  Dissertation: "Broadband Supply Modulated PAs for Efficient and Linear Transmit Arrays"

Neeraj Prakash Advised by Professor Shu-Wei Huang  Dissertation: "High-Energy Single-Cavity Fiber Dual-Comb Source"  

Anthony Romano Advised by Professor Zoya Popovic Dissertation: "Monolithic Integration of Millimeter Wave Circuits in Advanced GaN Processes"

Jieqiu Shao Advised by Professor Marco Nicotra Dissertation: "Quantum Optimal Control and its Applications to Shaken Lattice Interferometry and Superconducting Qubits"

Terrence Skibik Advised by Professor Marco Nicotra Dissertation: "Advancements in Model Predictive Control for Real-Time Applications" 

Dong-Chan Son Advised by Professor Dejan Filipovic  Dissertation to be defended in Summer 2024 

Timothy Sonnenberg Advised by Professor Zoya Popovic Dissertation: "GaN MMICs for Millimeter-Wave Front Ends" 

Jack Wampler Advised by Professor Eric Wustrow Dissertation: "Opt Out at Your Own Expense - Designing Systems for Adversarial Contexts" 

Songyi Yen Advised by Professor Dejan Filipovic Dissertation: "Unconventional Arrays for HF and Other Applications" 

Master's Candidates 

Gabriel Altman Advised by Professor Dejan Filipovic Dissertation to be defended in Summer 2024 

Sai Abhishek Aravind Advised by Professor Marco Nicotra Dissertation: "Influence of Discretization on Hypersampled Model Predictive Control"

• Lauren Teresa Baker 
• Suraj Ajjampur 
• Chris Thomas Alexander 
• Tasneem Alnajdi 
• Gabriel Altman 
• Akshith Aluguri 
• Nileshkartik Ashokkumar 
• Timothy Bailey 
• Donggeun Bak 
• Rylee Beach 
• Harsh Beriwal 
• Khalid Mohamed Abdelgalil Bakhit 
• Vishwanath Bhavikatti 
• Devang Boradhara 
• Alexander Bork 
• Naman Buch 
• Isha Burange 
• Aamir Suhail Burhan 
• Chandana Challa 
• Ruthvik Rangaiah Chanda 
• Chandinee Chandrasekaran 
• Shashank Chandrasekaran
• Rajesh Chittiappa 
• Hyoun J. Cho 
• Padmakshi Dahal 
• Tyler Davidson 
• Sauranil Debarshi 
• Aneesh Sadanand Deshpande 
• Varsha Dewangan 
• Paras Dhameliya 
• Kshitija Ramesh Dhondage 
• Jichao Fang 
• Harinarayanan Gajapathy 
• Joshua Galeno 
• Avirup Gupta 
• Avirup Kumar Gupta 
• Angel Manuel Hernandez Ortega 
• Ranjith Janardhana 
• ​Ajaykumar Kandagal
• Ayswariya Kannan 
• Sricharan Kidambi 
• Rakshit Kulkarni 
• Lalit Kumar 
• Abhinav Kumar 
• Ankit Kumar 
• Anuhya Kuraparthy 
• Sylvia Llosa 
• Spandana Mahendra 
• Erick Mancera 
• Kanin James McGuire 
• Colin Bruce McRae 
• Daniel Mendez 
• Nicole Danisha Milligan 
• Rylan Moore 
• Amey Chandrakant More 
• Sayali Sanjay Mule 
• Aditi Vijay Nanaware 
• Vidhya Palaniappan 
• Vaishnavi Sudhakar Patekar 
• Divyesh Shashikant Patel 
• Viraj Gopal Patel 
• Akash Patil 
• Mihir Jivan Patil 
• Aakash Pednekar 
• May An Ying van de Poll 
• Karthik Baggaon Rajendra 
• Chirayu Rajpurohit 
• Ritika Ramchandani 
• Thomas Ramirez 
• Lexie Roberts 
• Jessica Roosz 
• Satish Kumar Sankella 
• Cija Sathishkumar 
• Arun Kumar Sesha 
• Saquib Yasir Shaikh 
• Chinmay Venkatesh Shalawadi 
• Daanish Mohammed Shariff 
• Isha Sharma 
• Gregory James Southards 
• Malola Simman Srinivasan Kannan 
• Mangala Sneha Srinivasan 
• Rajesh Srirangam 
• Vaibhavi Vivek Thakur
• Swapnil Alkesh Trivedi 
• Vignesh Vadivel 
• Robert Enright Van Trees 
• Swathi Venkatachalam
• Shrinithi Venkatesan
• Mrunal Ankush Yadav
• Omkar Abhay Yeole

Master of Engineering 

• Francis Xavier Bergh 
• Ashwin Ravindra 
• Abhishek Limaye 
• Viveka Salinamakki 

Bachelor of Science Graduates

Bachelor of science, electrical and computer engineering.

• Ahmed Adam 
• Yusef Jamal Al-Balushi 
• Saud Almuzaiel 
• Bruno Armas 
• Abhinav Avula 
• Jasleen Batra 
• William Boenning – Cum Laude 
• John Cates 
• Richard Chuang 
• Nicholas Alexander Cisne 
• Kailer Hawk Driscoll 
• Sullivan Fleming 
• Aidan Francis Hanlon Fitton
• William Stockdale Foerster
• Timothy Houck 
• Daniel Juhwan Lee
• Peter William Magro 
• Louis Marfone 
• Frank McDermott 
• Weston Carroll McEvoy – Magna Cum Laude 
• Zane McMorris 
• Caden McVey 
• Dominic Fawzi Menassa 
• Sarah Mesgina 
• Daniel Orthel 
• Madelyn Polly – Summa Cum Laude 
• Guillermo Alexander Rivas Calles 
• Samuel Robertson 
• Ginn Sato – Summa Cum Laude 
• Connor Smith 
• Aidan St. Cyr 
• Taylor Stevenson 
• Taylore Todd 
• Anton Manuel Vandenberge 
• Alexander Joseph Walker – Summa Cum Laude 
• William White 
• Suhana Zeutzius – Summa Cum Laude 

​Bachelor of Science, Electrical Engineering

• Ali Karam Ali 
• Nasser Taleb Allanqawi 
• Meshal Alosaimi 
• Michelle Amankwah 
• Erika Antúnez  
• Andrew Aramians 
• Joshua Thomas Bay – Cum Laude 
• Katherine Christiansen 
• Michael Takuya Driscoll – Magna Cum Laude 
• Olivia Egbert – Cum Laude  
• Travis Fahrney 
• Luke Hanley – Cum Laude  
• Nicholas Haratsaris  
• Luke Jeseritz – Summa Cum Laude 
• Ryan McCallan 
• Oscar Omar Medina-Salazar 
• Tucker Mothersell
• Vincent Nemeth
• Matthew Joel Pollard – Cum Laude 
• Stewart Patrick Rojec – Magna Cum Laude 
• Khalid Shahba – Summa Cum Laude 
• Nathan Sharp 
• Danny Ming Sit 
• Timothy Henry Tomerlin 
• Robert B Traxler 

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Master’s Thesis Defense

Thesis Committee Members: Dr. Guanglan Zhang (Advisor), Dr. Lou Chitkushev (first reader) and Dr. Derin Keskin (second reader)

Monday, May 13th, 2024 10:00am EST 1010 Commonwealth Ave, Room 122

For those unable to attend in person, the thesis defense will be available through Zoom – http://www.bu.edu/metit/edu-technology/zoom-web-conferencing/

Select “Computer Science” from the drop down menu.

As MHC binding is considered the most selective step in T cell recognition, many existing bioinformatics systems focus on modeling this step to predict MHC binders. However, modeling MHC binding alone is insufficient for accurate immunogenicity predictions, often resulting in false positives. With the recent technological advancements, large amounts of mass spectrometry (MS)-identified MHC class I ligands became available to the public, making it possible to incorporate information from antigen processing steps before MHC binding. We collected >5,000 binding peptides and >4,000 eluted ligands for H2-Dᵇ, and>5,000 binding peptides and >5,000 eluted ligands for H2-Kᵇ. The thermostability assessment of MHC peptide binding evaluates the strength and duration of the interaction between the peptide and the MHC molecule under varying temperatures. Our collaborators at the Dana-Farber Cancer Institute performed temperature gradient experiments to investigate the stability of peptide-MHC complexes for H2-Dᵇ and H2-Kᵇ alleles under three temperature conditions, 37°C, 50°C, and 70°C, using the MS technique.  We ended up with over 3,000 H2-Dᵇ binding peptides and over 5,000 H2-Kᵇ binding peptides. The data enable us to perform a comprehensive thermostability analysis of MHC binding.

In this thesis project, we developed a computational system for identifying T-cell epitopes in C57BL/6 mice by integrating relevant contributing factors, such as the antigen processing steps before MHC binding and thermostability, with the MHC binding predictions. Utilizing deep learning methods, we first trained and rigorously validated the binding prediction models using naturally eluted H2-Kᵇ and H2-Dᵇ ligands collected from public resources. Then, we built Thermostability models using proprietary data generated by our collaborators. We compared the performance of our models with that of NetMHCPan-4.1, an online prediction tool validated by many benchmark studies to be one of the most accurate predictors. Our integrated model, combining the binding and the Thermostability models, exhibited superior predictive capabilities using an external validation dataset, surpassing the overall performance of the NetMHCPan-4.1 model.  We consolidated the models into a user-friendly web-based application named PREDBL6 to facilitate accurate predictions of immunogenic peptides that stably bind H2b molecules and stimulate immune responses in C57BL/6 mice. To our knowledge, this is the first online T cell epitope prediction system that simulates MHC binding and considers other antigen processing steps and thermostability in a model organism.

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Alum Alexander Levine Honored with Charles A. Caramello Distinguished Dissertation Award

University of Maryland Department of Computer Science alum Alexander Levine (Ph.D. '23, computer science) has been awarded the Charles A. Caramello Distinguished Dissertation Award for his dissertation titled "Scalable Methods for Robust Machine Learning." Levine, now a postdoctoral fellow at the University of Texas at Austin , focused on developing machine learning models that maintain accuracy amid distortions. The award ceremony is scheduled for Tuesday, May 14, at the Stamp Student Union. The award is for the dissertation he completed in 2023.

The Charles A. Caramello Distinguished Dissertation Award is given annually by the Graduate School to recognize dissertations that provide highly original contributions that make an unusually significant contribution to the discipline. Levine is among four recipients of the award this year.

Awardees receive an honorarium of $1,000. Additionally, they may be nominated for further recognition at the national level through the CGS/ProQuest Distinguished Dissertation Award competition, which selects outstanding dissertations from across the country to honor achievements in graduate research.

“I feel honored that my work has been recognized by this award,” Levine said. “I am deeply thankful for all of the support I received during my time at UMD from my advisor, my collaborators, my dissertation committee and the rest of the UMD computer science community. I am fortunate to have worked with such talented people on such interesting problems.”

Advised by Associate Professor Soheil Feizi , Levine's dissertation introduces innovative methods for ensuring the robustness of machine learning models, specifically in scenarios where input data may be subtly altered or distorted, including malicious tampering. This research is particularly relevant as machine learning applications become increasingly prevalent in areas requiring high reliability and security.

Levine explained that practitioners can implement these systems more confidently in safety-critical applications by developing machine learning techniques with well-understood robustness guarantees. He noted that the capabilities of machine-learning-based systems have expanded dramatically in just the last couple of years, increasing their use in various sectors. Levine emphasized the growing importance of ensuring these systems' robustness as their applications broaden.

Levine is currently expanding his research focus.

“At UT Austin, my research focus has shifted to representation learning for sequential decision-making tasks,” Levine shared. “In particular, I have been working on frameworks that allow deep learning to be used in combination with search-based planning techniques, so that we can benefit from both the powerful capabilities of modern deep learning and the interpretability, flexibility and efficiency of classical planning methods. ”

Levine received the Larry S. Davis Doctoral Dissertation Award in the Fall of 2023 . Named in honor of Computer Science Professor Emeritus Larry Davis , the award, given by UMD’s Department of Computer Science, highlighted dissertations that were exceptional in their technical depth and potential for significant impact.

—Story by Samuel Malede Zewdu, CS Communications 

The Department welcomes comments, suggestions and corrections.  Send email to editor [-at-] cs [dot] umd [dot] edu .

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