Young woman in graduation gown

The Math & Comp Sci honors program encourages intensive study in an area of mathematical science in addition to meeting the requirements for the major. 

The Honors program allows for a capstone experience, building upon the student’s current academic knowledge and strengthening their understanding in a specific field of study/concentration.

Honors work may be concentrated in fields such as biological sciences and medicine, environment, physics, sports analytics, investment science, AI/Machine learning, etc. A list of examples are further down.

Graduating with Honors in Math & Comp Sci does not require a thesis/research project, however, for those who are interested in pursuing research for that purpose, we encourage students to meet with their faculty advisor to discuss initiating their objective and see the Research or MCS & Thesis Process webpage for additional support.


In addition to meeting all requirements for the B.S., the student must:

  1. Maintain a GPA of at least 3.5 in all major coursework. 
  2. Students should complete 15 units of graduate level coursework. Included in these 15 units can be any of the following:

    • Related research from a 199 course

    • Participation for credit in a small group seminar

    • Directed reading

  3. Complete a final report which should:

    • Include their name, degree and the title of their work.

    • Be typed with 12pt font, single-spaced, minimum 1 page (no longer than 2 pages) with a one-inch margin at the top and bottom of each page.

    • Explain a theme between the student’s coursework, their interests, and how they relate to MCS.

    • Describe how each course selected added to the student's knowledge and understanding in the area chosen for concentration.

The student's work must demonstrate in-depth learning of a topic or shared idea in the breadth of the MCS major (examples down below), and all students are held to Stanford’s Honor Code.


Suggested electives for students pursuing Honors:

  • CME 206 (3 units): Introduction to Numerical Methods for Engineering
  • CS/STATS 229 (3-4 units): Machine Learning
  • CS 248 (3-4 units): Interactive Computer Graphics
  • EE 364A (3 units): Convex Optimization I
  • MATH 171 (3 units): Fundamental Concepts of Analysis
  • MATH 172 (3 units): Lebesque Integration and Fourier Analysis
  • MATH 205A (3 units): Real Analysis
  • STATS 202 (3 units): Data Mining and Analysis
  • STATS 216 (3 units): Introduction to Statistical Learning
  • STATS 217 (3 units): Introduction to Stochastic Processes I
  1. Student must have a major grade point average (GPA) of 3.5.

  2. Submit an MCS program Honors Proposal form to their faculty advisor by the final study list deadline at least two quarters prior to the quarter the student expects to graduate.

  3. Once the form is signed, declare honors in Axess and submit to the aekuhn [at] stanford.edu (MCS student services officer) to ensure their honors declaration is approved.

  4. Final report is due by the last day of classes of the quarter the student expects to graduate (Minimum 1 page).

  5. Once coursework and final report are completed, have the student’s faculty advisor sign the second page of the MCS program Honors Proposal form.

  6. Students should verify that honors appears on their unofficial transcript.

Students should discuss their plans for an honors curriculum in MCS with their faculty advisor as early as possible. Doing so at the time of MCS major declaration allows for students to develop ideas about the concentration they are interested in and plan for the coursework needed to fulfill the honors work.

Note: Some students in the past have been able to apply a class or two of related coursework/research for credit from abroad towards their honors requirements. An example: Oxford tutorial. If a student has further questions please reach out to their faculty advisor to discuss possible options for their honors work.

The purpose of MCS program honors proposal form is to help students develop a proposal in an area/concentration of interest and draft a statement demonstrating a correlation with the chosen coursework.

  • Learning and Artificial Intelligence

  • Quantitative Finance - including bond trading and portfolio hedging with derivatives; portfolio optimization, construction, and evaluation; bottoms-up financial analysis and factor-based investing; and derivatives pricing and modeling.

  • Machine Learning with Implications to Healthcare (Computational Biology / Ethics).

  • Algorithmic Trading and Statistical Methods in Modern Finance

  • Developing algorithms to improve diagnostic and therapeutic methods for human diseases

  • Artificial Intelligence and Physical Science

  • Sports Analytics  

  • CS theory, especially different types of algorithms and algorithmic paradigms

  • Detecting the difference between human and non-human responses in game-theory type games

  • Demonstrating Bayesian models and networks potential in a legal analysis context to evaluate real-world applicability of Bayesian networks to criminal law and civil litigation

  • Analyzing phylogenetic trees of bacterial data

Note: If a student would like to see MCS Final Reports for reference please email cgates [at] stanford.edu (cgates[at]stanford[dot]edu), the student must be an MCS major interested in honors. 

Examples & Awards

Example 1: Statistical Methods in Social Science

In my honors program I explored the use of statistical methodology in problems in social science. In doing so I sought to apply the quantitative and computational tools I developed in the core classes of the MCS curriculum to problems of social significance. As part of my program, I crossed departmental borders to gain subject-area knowledge of issues in education and sociology while deepening my skills in statistical analysis in particular areas relevant to these fields. I approached the program as a preparation for doctoral study in statistics with an emphasis on application in social science and public policy. (Educ 316; Educ 351A; Stats 209; Stats 305)

Example 2: Virtual Worlds: A New Frontier in Law & Economics

For the past three years, I have conducted several multidisciplinary studies that bring together elements of law and quantitative finance, which thus far have been utilized in my works on virtual worlds, a new frontier in both law and economics. Below are the summaries:

  • Yang, R. 2012. The Personal and Economic Utility of Virtual World Bots: A Defense for Fair Use. Arizona State Sports and Entertainment Law Journal. Vol. 2, Issue 2

  • Yang, R. 2013.  Could the Virtual Be Similar to the Real? A First Look from an Efficient Markets Perspective.Quarterly Journal of Finance, Vol. 3, Issue 4

  • Yang, R. 2013. When is BitCoin a Security Under U.S. Securities Law? Journal of Technology Law and Policy. Vol. 18, Issue 2

University Awards, Distinction, Society, and More


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