biomedical data science phd programs

Program Description

Biomedical data science and informatics is an interdisciplinary field that combines ideas from computer science and quantitative disciplines-such as statistics, data science, and decision science-to solve challenging problems in biology, medicine and public health.

Clemson University and the Medical University of South Carolina offer a joint PhD degree in Biomedical Data Science and Informatics. This unique collaboration combines Clemson’s strengths in computing, engineering, and public health with MUSC’s expertise in biomedical sciences. The goal of the program is to produce the next generation of data scientists, prepared to manage and analyze big data sources in order to improve public health. This program is unique to South Carolina and very few programs nationally focus on data science applied to the health and biomedical fields.

The nation’s transition to new healthcare delivery models and the exponential growth in biomedical data translate to a need for professionals with both expertise in data science and experience in biomedical research. Graduates possess marketable skills for informatics careers in biology, medicine, and public health, the ability to develop prescriptive analytics from large data sources, and are prepared to lead research programs in the academic and healthcare industry fields. Specialized tracks include precision medicine, population health, and clinical and translational informatics.

Students have a designated “home institution” at which they are physically located, but take graduate classes from both institutions. Students are not required to travel between campuses to take courses, as courses are available to students both on-campus and via synchronous remote connection. Courses are offered on the Clemson main campus, MUSC main campus, the University Center of Greenville, and the Zucker Family Graduate Education Center on CURI campus in North Charleston.

For more information, please visit https://www.cs.clemson.edu/bdsi/ .

Summary of Degree Requirements

PhD students are required to take a minimum of 65 credit hours to complete the program. These hours are divided into five areas:

• Area I: Biomedical Informatics Foundations and Applications (15-16 hours)
• Area II: Computing/Math/Stats/Engineering (18 hours)
• Area III: Population Health, Health Systems, and Policy (5-6 hours)
• Area IV: Domain Biology/Medicine (3-4 hours)
• Area V: Lab rotations, seminars, doctoral research (24 hours)

Graduate Program in Quantitative Biomedical Sciences

Dartmouth graduate students earn top spots at national big data case competition.

Competing for the first time, four teams from the QBS MS program, including a team of two graduate students and one undergraduate, created a diagnostic tool to solve the problem posed to 37 different teams.

QBS Associate Director: Rob Frost, PhD

Dr. Frost brings a unique depth of experience to QBS leadership in his role as Associate Director.

QBS Director: Scott Gerber, PhD

Dr. Gerber Highlights the Importance of Interdisciplinary Research and Training

4+1 Programs

Epidemiology 4+1 Program

Health Data Science 4+1 Program

Medical Informatics 4+1 Program

QBS Faculty Member Diane Gilbert-Diamond Recognized for Outstanding Mentoring

Dr. Gilbert-Diamond's approach to mentoring acknowledges the emotional and academic development of her mentees, and she has created an environment where everyone feels supported.

Embark on an Elite Academic Experience at Dartmouth College

Dartmouth’s Quantitative Biomedical Sciences (QBS) program has ushered in an unparalleled academic experience that challenges students to think critically with an interdisciplinary lens to solve complex biomedical problems facing local and global populations.

Bridging the intersection of health care, epidemiology biomedicine, biostatistics, population health, and big data, QBS faculty deliver cutting-edge theory while also serving as dedicated mentors who are passionate about student outcomes and success. They are accessible and attentive to student needs and foster an environment of collaboration, engagement, and lifelong learning.

Learn about our MS and PhD Programs

QBS and the Dartmouth Geisel School of Medicine offer 3 unique, interdisciplinary Masters degree programs .

Masters in Health Data Science

Masters in Epidemiology

Masters in Medical Informatics

QBS and the Guarini School of Graduate and Advanced Studies offers an unparalleled PhD in Quantitative Biomedical Sciences .

Learn More about QBS

The dartmouth qbs difference.

Our QBS programs give students the opportunity to be part of a lively, passionate community of students that thrives on collaboration, the sharing of ideas, and supporting and challenging one another to be their best. This sets the stage for students to collaborate across disciplines and areas of expertise to find unique insights in data that lead to truly innovative solutions for the world. Meet our current PhD students and current master’s students to learn more about our community.

Six Reasons To Join QBS at Dartmouth:

Why Dartmouth QBS?

QBS Community

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• Biomedical Data Science, MS

The current explosion of biomedical data provides an awesome opportunity to improve understanding of the mechanisms of disease and ultimately to improve human health care. However, fully harnessing the power of high-dimensional, heterogeneous data requires a new blend of skills including programming, data management, data analysis, and machine learning. 

The MS degree program in biomedical data science covers core concepts and allows for concentrated coursework, in both methodology and application. 

Please consult the table below for key information about this degree program’s admissions requirements. The program may have more detailed admissions requirements, which can be found below the table or on the program’s website.

Graduate admissions is a two-step process between academic programs and the Graduate School. Applicants must meet the minimum requirements of the Graduate School as well as the program(s). Once you have researched the graduate program(s) you are interested in, apply online .

Applicants include both those with bachelor’s degrees in an area of data-science (e.g., computer science, statistics), as well as health professionals and clinicians (e.g., MD's, PharmD's, RN's). It is expected that admitted applicants will have demonstrated an aptitude for computer science and math, fundamental programming skills, knowledge of data structures and algorithms, and at least two semesters of college calculus. The program will consider applicants who have a wide range of undergraduate backgrounds; providing opportunities to develop necessary skills immediately upon entering the program.

Applying to the Program

• A formal online application with required fee through the UW–Madison Graduate School
• Three letters of recommendation
• Unofficial transcripts from each higher-education institution attended
• A statement of purpose
• International degree-seeking applicants must prove English proficiency
• Evidence of quantitative preparation, including at least two semesters of college calculus (similar to  MATH 221 -  MATH 222 ) and either a course in linear algebra (similar to  COMP SCI 200  - COMP SCI 300 ) or courses in programming and data structures

For additional information about admission to the program, see  MS Program in Biomedical Data Science  on the department website.

Graduate School Resources

Resources to help you afford graduate study might include assistantships, fellowships, traineeships, and financial aid.  Further funding information is available from the Graduate School. Be sure to check with your program for individual policies and restrictions related to funding.

Program Resources

Funding guarantees are not provided for students in this program. Students are encouraged to explore funding options available across campus.

Minimum Graduate School Requirements

Major requirements.

Review the Graduate School minimum academic progress and degree requirements , in addition to the program requirements listed below.

Mode of Instruction

Mode of Instruction Definitions

Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.

Evening/Weekend: ​Courses meet on the UW–Madison campus only in evenings and/or on weekends to accommodate typical business schedules.  Students have the advantages of face-to-face courses with the flexibility to keep work and other life commitments.

Face-to-Face: Courses typically meet during weekdays on the UW-Madison Campus.

Hybrid: These programs combine face-to-face and online learning formats.  Contact the program for more specific information.

Online: These programs are offered 100% online.  Some programs may require an on-campus orientation or residency experience, but the courses will be facilitated in an online format.

Curricular Requirements

Required Courses

Between the Concentration Electives and Data Science Electives, students must complete at least 6 credits of computer sciences-oriented courses and 6 credits of statistics-oriented courses. Computer sciences-oriented courses include those in the Department of Computer Sciences course listing ( COMP SCI ).  Statistics-oriented courses include those in the Department of Statistics course listing ( STAT ), in addition to B M I/​POP HLTH  552 Regression Methods for Population Health and B M I/​POP HLTH  651 Advanced Regression Methods for Population Health .  A specific section of B M I 826 Special Topics in Biostatistics and Biomedical Informatics can satisfy as either a computer sciences-oriented course or a statistics-oriented course at the discretion of the MS Program Steering Committee.

Graduate School Policies

The  Graduate School’s Academic Policies and Procedures  provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Major-Specific Policies

Prior coursework, graduate credits earned at other institutions.

With program approval, students are allowed to transfer no more than 9 credits of graduate coursework from other institutions. Coursework earned ten or more years prior to admission to a master's degree is not allowed to satisfy requirements.

Undergraduate Credits Earned at Other Institutions or UW-Madison

Refer to the Graduate School: Transfer Credits for Prior Coursework policy.

Credits Earned as a Professional Student at UW-Madison (Law, Medicine, Pharmacy, and Veterinary careers)

Credits earned as a university special student at uw–madison.

With program approval, students are allowed to transfer no more than 9 credits of course work numbered 300 or above taken as a UW–Madison University Special student. Coursework earned ten or more years prior to admission to a master's degree is not allowed to satisfy requirements.

Refer to the Graduate School: Probation policy.

Advisor / Committee

All students are required to conduct a yearly progress report meeting with their advisor, scheduled by December 17 and completed by April 30.

Credits Per Term Allowed

Time Limits

Refer to the Graduate School: Time Limits policy.

Grievances and Appeals

These resources may be helpful in addressing your concerns:

• Bias or Hate Reporting  
• Graduate Assistantship Policies and Procedures
• Office of the Provost for Faculty and Staff Affairs
• Employee Assistance (for personal counseling and workplace consultation around communication and conflict involving graduate assistants and other employees, post-doctoral students, faculty and staff)
• Employee Disability Resource Office (for qualified employees or applicants with disabilities to have equal employment opportunities)
• Graduate School (for informal advice at any level of review and for official appeals of program/departmental or school/college grievance decisions)
• Office of Compliance (for class harassment and discrimination, including sexual harassment and sexual violence)
• Office Student Assistance and Support (OSAS)  (for all students to seek grievance assistance and support)
• Office of Student Conduct and Community Standards (for conflicts involving students)
• Ombuds Office for Faculty and Staff (for employed graduate students and post-docs, as well as faculty and staff)
• Title IX (for concerns about discrimination)

Grievance Policy for Graduate Programs in the School of Medicine and Public Health

Any student in a School of Medicine and Public Health graduate program who feels that they have been treated unfairly in regards to educational decisions and/or outcomes or issues specific to the graduate program, including academic standing, progress to degree, professional activities, appropriate advising, and a program’s community standards by a faculty member, staff member, postdoc, or student has the right to complain about the treatment and to receive a prompt hearing of the grievance following these grievance procedures. Any student who discusses, inquiries about, or participates in the grievance procedure may do so openly and shall not be subject to intimidation, discipline, or retaliation because of such activity. Each program’s grievance advisor is listed on the “Research” tab of the SMPH intranet .

This policy does not apply to employment-related issues for Graduate Assistants in TA, PA and/or RA appointments.  Graduate Assistants will utilize the Graduate Assistantship Policies and Procedures (GAPP) grievance process to resolve employment-related issues.

This policy does not apply to instances when a graduate student wishes to report research misconduct.  For such reports refer to the UW-Madison Policy for Reporting Research Misconduct for Graduate Students and Postdoctoral Research Associates .

Requirements for Programs

The School of Medicine and Public Health Office of Basic Research, Biotechnology and Graduate Studies requires that each graduate program designate a grievance advisor, who should be a tenured faculty member, and will request the name of the grievance advisor annually.  The program director will serve as the alternate grievance advisor in the event that the grievance advisor is named in the grievance.  The program must notify students of the grievance advisor, including posting the grievance advisor’s name on the program’s Guide page and handbook.

The grievance advisor or program director may be approached for possible grievances of all types.  They will spearhead the grievance response process described below for issues specific to the graduate program, including but not limited to academic standing, progress to degree, professional activities, appropriate advising, and a program’s community standards.  They will ensure students are advised on reporting procedures for other types of possible grievances and are supported throughout the reporting process.  Resources on identifying and reporting other issues have been compiled by the Graduate School.

• The student is advised to initiate a written record containing dates, times, persons, and description of activities, and to update this record while completing the procedures described below.
• If the student is comfortable doing so, efforts should be made to resolve complaints informally between individuals before pursuing a formal grievance.
• Should a satisfactory resolution not be achieved, the student should contact the program’s grievance advisor or program director to discuss the complaint. The student may approach the grievance advisor or program director alone or with a UW-Madison faculty or staff member. The grievance advisor or program director should keep a record of contacts with regards to possible grievances.  The first attempt is to help the student informally address the complaint prior to pursuing a formal grievance. The student is also encouraged to talk with their faculty advisor regarding concerns or difficulties.
• If the issue is not resolved to the student’s satisfaction, the student may submit a formal grievance to the grievance advisor or program director in writing, within 60 calendar days from the date the grievant first became aware of, or should have become aware of with the exercise of reasonable diligence, the cause of the grievance.  To the fullest extent possible, a grievance shall contain a clear and concise statement of the grievance and indicate the issue(s) involved, the relief sought, the date(s) the incident or violation took place, and any specific policy involved.
• The grievance advisor or program director will convene a faculty committee composed of at least three members to manage the grievance.  Any faculty member involved in the grievance or who feels that they cannot be impartial may not participate in the committee.  Committee composition should reflect diverse viewpoints within the program.
• The faculty committee, through the grievance advisor or program director, will obtain a written response from the person or persons toward whom the grievance is directed. The grievance advisor or program director will inform this person that their response will be shared with the student filing the grievance.
• The grievance advisor or program director will share the response with the student filing the grievance.
• The faculty committee will make a decision regarding the grievance. The committee’s review shall be fair, impartial, and timely.  The grievance advisor or program director will report on the action taken by the committee in writing to both the student and the person toward whom the grievance was directed.
• The grievant will be notified in writing, within 5 business days of the written appeal, acknowledging receipt of the formal appeal and establishing a timeline for the review to be completed.
• The senior associate dean or their designee may request additional materials and/or arrange meetings with the grievant and/or others.  If meetings occur, the senior associate dean or their designee will meet with both the grievant and the person or persons toward whom the grievance is directed.
• The senior associate dean or their designee will assemble an ad hoc committee of faculty from outside of the student’s graduate program and ask them to prepare a written recommendation on whether to uphold or reverse the decision of the program on the student’s initial grievance.  The committee may request additional materials and/or arrange meetings with the grievant and/or others.  If meetings occur, the committee will meet with both the grievant and the person or persons toward whom the grievance is directed.
• The senior associate dean or their designee will make a final decision within 20 business days of receipt of the committee’s recommendation.
• The SMPH Office of Basic Research, Biotechnology, and Graduate Studies must store documentation of the grievance for seven years. Grievances that set a precedent may be stored indefinitely.
• The student may file an appeal of the School of Medicine and Public Health decision with the Graduate School.  See the Grievances and Appeals section of the Graduate School’s Academic Policies and Procedures .

Steps in the grievance procedures must be initiated and completed within the designated time periods except when modified by mutual consent. If the student fails to initiate the next step in the grievance procedure within the designated time period, the grievance will be considered resolved by the decision at the last completed step.

• Professional Development

Take advantage of the Graduate School's  professional development resources to build skills, thrive academically, and launch your career. 

• Learning Outcomes
• Understand, apply, and evaluate common informatics theories, methods, and tools related to biological and biomedical problems, health care and public health.
• Apply, adapt, and validate an existing approach to a specific biomedical and health problem.
• Produce solutions that address academic or industrial needs using informatics tools and knowledge.
• Evaluate the impact of biomedical informatics applications and interventions.
• Understand the challenges and limitations of technological solutions.
• Demonstrate scholarly oral and written presentations.
• Adhere to the professional and legal standards of conduct in Biomedical Data Science.

Faculty:  Broman, Buchanan, Burnside, Chappell, Chen, Chung, Craven, Dewey, Doan, Dyer, Elwert, Gangnon, Gianola, Gitter, Keles, Kendziorski, Kim, Lu, Mao, Mumford, Newton (chair), Ong,  Palta, Patel, Peissig, Rosa, Rosenberg, Roy, Singh, Sorkness, Tang, Yandell, Velten, Wang, Yu, Zhang, Zhu

• Requirements

Contact Information

Biostatistics and Medical Informatics School of Medicine and Public Health Biomedical Data Science, MS [email protected] https://www.biostat.wisc.edu/

Beth Bierman, Graduate Coordinator [email protected] 608-265-8649 4745 Medical Sciences Center 1300 University Ave., Madison WI 53706

Shelley Maxted, Graduate Coordinator [email protected] 608-263-4825 4745 Medical Sciences Center 1300 University Ave., Madison, WI 53706

Mark Craven, Director of MS Program [email protected] 608-265-6181 4775a Medical Sciences Center 1300 University Ave., Madison, WI 53706

Michael Newton, Biostatistics & Medical Informatics Chair [email protected] 608-262-0086 1245a, K6/434 Medical Sciences Center 1300 University Ave, Madison, WI 53706

Grievance Advisor, Colin Dewey, Professor [email protected] 608-263-7610 2128 Genetics-Biotechnology Center Building 425 Henry Mall, Madison, WI 53706

Graduate Program Handbook View Here

Graduate School grad.wisc.edu

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UCLA Graduate Programs

Program Requirements for Bioinformatics (Medical Informatics)

Applicable only to students admitted during the 2024-2025 academic year.

Bioinformatics

Interdepartmental Program College of Letters and Science

Graduate Degrees

The Medical Informatics Program offers the Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) degrees in Medical Informatics.

Admissions Requirements

Master’s Degree

All academic affairs for graduate students in the program are directed by the program’s faculty graduate adviser, who is assisted by staff in the Graduate Student Affairs Office. Upon matriculation, students are assigned a three-faculty guidance committee by the faculty graduate adviser.

The chair of the guidance committee acts as the provisional adviser until a permanent adviser is selected. Provisional advisers are not committed to supervise examination or thesis work and students are not committed to the provisional adviser. Students select a permanent adviser before establishing a comprehensive examination or thesis committee.

Areas of Study

This area of study exposes students to foundational concepts in medical informatics, providing a background in clinical data, big data management, and analyses of new and emergent data utilized to guide biomedical research and healthcare. Study comprises of an introduction to computational methods, clinical and biomedical knowledge representation, and exposure to core informatics topics.

Foreign Language Requirement

Course Requirements

Students must be enrolled full time and complete 40 units (11 courses) of graduate (200 or 500 series) course work for the master’s degree. All courses must be taken for a letter grade, unless offered on S/U grading basis only.

Students must complete all of the following: (1) eight core courses (30 units): Bioengineering 220, 223A, 223B, one course from BE 224A or Bioinformatics M222 through M226, BE 224B, BE M226, BE M227, and BE M228; (2) eight units of Bioinformatics 596; and (3) two units of 200-level seminar or journal club courses approved by the program.

Teaching Experience

Not required.

Field Experience

Capstone Plan

The master’s capstone is an individual project in the format of a written report resulting from a research project. The report should describe the results of the student’s investigation of a problem in the area of medical informatics under the supervision of a faculty member in the program, who approves the subject and plan of the project, as well as reading and approving the completed report. While the problem may be one of only limited scope, the report must exhibit a satisfactory style, organization, and depth of understanding of the subject. A student should normally start to plan the project at least one quarter before the award of the M.S. degree is expected. The advisory committee evaluates and grades the written report as not pass or M.S. pass and forwards the results to the faculty graduate adviser. Students who do not pass the evaluation are permitted one additional opportunity to pass, which must be submitted to and graded by the advisory  committee by the end of the 6th quarter.

The capstone plan is available for students in the Medical Informatics field. However, students in Computational & Systems Biology major are required to follow the Thesis Plan only.

Thesis Plan

Every master’s degree thesis plan requires the completion of an approved thesis that demonstrates the student’s ability to perform original, independent research.

Students must choose a permanent faculty adviser and submit a thesis proposal by the end of the third quarter of study. The proposal must be approved by the permanent adviser who served as the thesis adviser. The thesis is evaluated by a three-person committee that is nominated by the program and appointed by the Division of Graduate Education. Students must present the thesis in a public seminar.

Time-to-Degree

Normative time-to-degree for all fields is five quarters.

Doctoral Degree

The Medical Informatics Advising Committee, chaired by the Faculty Graduate Advisor, advises students during the first year and is available to students throughout their tenure of their study.

Upon entering their second year in the program, students will select a mentor who will serve as their dissertation chair, research advisor, and primary graduate advisor. Together the student and the mentor will convene a doctoral committee who will guide the student throughout their research, the University Oral Qualifying Exam, Doctoral Dissertation Defense, and will approve the final dissertation.

Individual Development Plan: Beginning with a mandatory training workshop in the first quarter of graduate study, students are required to generate an Individual Development Plan via myIDP Website: http://myidp.sciencecareers.org/ in order to map out their academic and professional development goals throughout graduate school. The myIDP must be updated annually, and the resulting printed summary discussed with and signed by (Year 1) the student’s advising committee member, or (Years 2-5) thesis adviser, and then turned in to the Graduate Student Affairs Office to be placed in the student’s academic file each year by June 1.

Annual Committee Meetings: Beginning one year after advancement to doctoral candidacy, and in each year thereafter until completion of the degree program, students are required to meet annually with their doctoral committee. At each meeting, students give a brief, 30-minute oral presentation of their dissertation research progress to their committee. The purpose of the meeting is to monitor the student’s progress, identify difficulties that may occur as the student progresses toward successful completion of the dissertation and, if necessary, approve changes in the  dissertation project. The presentation is not an examination.

Annual Progress Report: All students are required to submit a brief report (a one-page form is provided) of their time-to-degree progress and research activities indicating the principal research undertaken and any important results, research plans for the next year, conferences attended, seminars given, and publications appearing or manuscripts in preparation. Annual Progress report must be submitted to the Bioinformatics IDP Student Affairs Office for review by the Program Director.

Major Fields or Subdisciplines

These fields include computer science, translational bioinformatics, imaging informatics, public health informatics, and social medicine.

Students are required to enroll full-time in a minimum of 12 units each quarter. In addition to basic course requirements, all students are required to enroll in Bioinformatics 596 or 599 each quarter.

Students who have gaps in their previous training may take, with their thesis adviser’s approval, appropriate undergraduate courses. For example, students without statistical background are recommended to take STATS 100B (Introduction to Mathematics Statistics) in their 1st year. Students without a Computer Science background are recommended to take COM SCI 180  Introduction to Algorithms and Complexity), COM SCI 145 (Introduction to Data Mining), COM SCI 146 (Introduction to Machine Learning), or COM SCI 148 (Introduction to Data Science). However, these courses may not be applied toward the required course work for the doctoral degree.

Students must complete all of the following: (1) eight core courses (30 units) Bioengineering 220, 223A, 223B, one course from BE 224A or Bioinformatics M223 or M226, BE 224B, BE M226, BE M227, and BE M228; (2) MIMG C234; (3) eight units of Bioinformatics 596; (4) four units of 200-level seminar or journal club courses approved by the program; and (5) six electives, chosen from the following list: Bioinformatics M223, M226; Biomathematics 210, M230, M281, M282; Biostatistics 213, M232, M234, M235, 241, 276; Computer Science 240A, 240B, 241B, 245, 246, 247, 262A, M262C, 262Z, 263A, 265A, M268, M276A; Electrical and Computer Engineering 206, 210A, 210B, 211A, M217, 219; Information Studies 228, 246, 272, 277; Linguistics 218, 232; Neuroscience CM272; Physics in Biology and Medicine 210, 214. M248; Statistics 221, M231A, 231B, M232A, M232B, 238, M241, M243, M250, 256. Please note: other elective courses can be taken with the agreement of the Home Area Director and the student’s PI/faculty mentor. Courses must be taken for a letter grade, unless offered on S/U grading basis only.

Written and Oral Qualifying Examinations

Academic Senate regulations require all doctoral students to complete and pass university written and oral qualifying examinations prior to doctoral advancement to candidacy. Also, under Senate regulations, the University Oral Qualifying Examination is open only to the student and appointed members of the doctoral committee. In addition to university requirements, some graduate programs have other pre-candidacy examination requirements. What follows in this section is how students are required to fulfill all of these requirements for this doctoral program.

All committee nominations and reconstitutions adhere to the  Minimum Standards for Doctoral Committee Constitution .

Doctoral students must complete the core courses described above before they are permitted to take the written and oral qualifying examinations. Students are required to pass a written qualifying examination that consists of a research proposal outside of their dissertation topic and the University Oral Qualifying Examination in which they defend their dissertation research proposal before their doctoral committee. Students are expected to complete the written examination in the summer following the first year and the oral qualifying examination by the end of fall quarter of the third year. The written qualifying examination must be passed before the University Oral Qualifying Examination can be taken.

During their first year, doctoral students perform laboratory rotations with program faculty whose research is of interest to them and select a dissertation adviser from the program faculty inside list by the end of their third quarter of enrollment. By the end of their second spring quarter, students must select a doctoral committee that is approved by the program chair and the Division of Graduate Education.

Written Qualifying Examination

The Written Qualifying Examination (WQE) must take place in the summer following the first year of doctoral study. In order to be eligible to take the WQE, students must have achieved at least two passing lab rotation evaluations, as well as at least a B average in all course work. Students are expected to formulate a testable research question and answer it, by carrying out a small, well-defined and focused project over a fixed one-month period. It must include the development of novel bioinformatic methodology. The topic and methodologies are to be selected by the student. The topic requires advance approval by the faculty committee, and may not be a project from a previous course, a rotation project, a project related to the student’s prior research experience, an anticipated dissertation research topic, or an active or anticipated research project in the laboratory of the student’s mentor. The WQE must be the student’s own ideas and work exclusively. Students are expected to complete a WQE paper of publication quality (except for originality), with a maximum length of 10 pages, single-spaced, excluding figures and references. This paper is submitted to the Student Affairs Office and graded by a faculty committee on a pass or no-pass basis. Students who do not pass the examination are permitted one additional opportunity to pass, which must be submitted to and graded by the faculty committee no later than the end of the summer of the first year.

Oral Qualifying Examination

The University Oral Qualifying Examination must be completed and passed by the end of the fall quarter of the third year. Students prepare a written description of the scientific background of their proposed dissertation research project, the specific aims of the project, preliminary findings, and proposed bioinformatic approaches for addressing the specific aims. This dissertation proposal must be written following an NIH research grant application format and be at least six pages, single spaced and excluding references, and is submitted to the students’ doctoral committee at least 10 days in advance of the examination. Exclusive of their doctoral committee members, students are free to consult with their dissertation adviser, or other individuals in  formulating the proposed research. The examination consists of an oral presentation of the proposal by the student to the committee. The student’s oral presentation and examination are expected to demonstrate: (1) a scholarly understanding of the background of the research proposal; (2) well-designed and testable aims; (3) a critical understanding of the bioinformatic, mathematical or statistical methodologies to be employed in the proposed research; and (4) an understanding of potential bioinformatic outcomes and their interpretation. This examination is graded Pass, Conditional Pass, or Fail. If the doctoral committee decides that the examination reflects performance below the expected mastery of graduate-level content, the committee may vote to give the student a Conditional Pass. A student who receives a Conditional Pass will be required to modify or re-write their research proposal, so as to bring it up to required standard. In the case of a Conditional Pass, the student will be permitted to seek the advice of their committee in modifying or re-writing the proposal. Any required re-write or modification will be submitted to, and reviewed by the doctoral committee. A second oral presentation is not necessary unless the doctoral committee requires so. The signed Report on the Oral Qualifying Examination & Request for Advancement to Candidacy will be retained in the Graduate Student Affairs Office until the student has satisfied the doctoral committee’s request for revision or re-write. Students are allowed only one chance to revise or re-write their proposal.

Advancement to Candidacy

Students are advanced to candidacy upon successful completion of the written and oral qualifying examinations.

Doctoral Dissertation

Every doctoral degree program requires the completion of an approved dissertation that demonstrates the student’s ability to perform original, independent research and constitutes a distinct contribution to knowledge in the principal field of study.

Final Oral Examination (Defense of the Dissertation)

Required for all students in the program.

Students are expected to complete the written qualifying examination in the summer following the first year of study and the University Oral Qualifying Examination by the end of fall quarter of the third year. Normative time-to-degree is five years (15 quarters).

Academic Disqualification and Appeal of Disqualification

University Policy

A student who fails to meet the above requirements may be recommended for academic disqualification from graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing academic disqualification of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA .

Special Departmental or Program Policy

Students must receive at least a grade of B- in core courses or repeat the course. Students who received three grades of B- or lower in core courses, who fail all or part of the written or oral qualifying examinations twice, or who fail to maintain minimum progress may be recommended for academic disqualification by vote of the entire interdepartmental program committee. Failure to identify and maintain a thesis adviser is a basis for recommendation for academic disqualification. Students may appeal a recommendation for academic disqualification in writing to the interdepartmental program committee, and may personally present additional or mitigating information to the committee, in person or in writing.

Applying to Pathway to the PhD

The Pathway to the PhD (P2P) program is a 3-week mentored, hands-on, research-intensive experience for juniors and sophomores that runs during the January break for UMass Boston undergraduates interested in careers in biomedical research.

For 2024, the program will run from January 2 to January 19.

Admission is by application using our online application.

The application deadline for the current year is October 18, 2023 .

Online Application

Apply using our  online application .

Preference given to juniors and sophomores; freshmen are  not   eligible .

Required Application Documents

• one letter of recommendation from a faculty member who knows you, although submitting two letters of recommendation will strengthen your application
• a personal statement covering your interest in biomedical research and the P2P program, career goals, relevant experience
• a diversity statement detailing your commitment and/or contribution to diversity to the biomedical sciences
• a GPA of 3.2 and above is recommended  but not required

P2P applicants will be notified by November 9, 2023.

Immunizations & Health Records Documentation

Immunization and health records for accepted students should be ready for review by November 16, 2023 and all students must be cleared by 12/30/2023.

Students will be informed to schedule an appointment with Tufts Medical Center’s Employee Health Services. Flu vaccination (within 3 months of the January program) and COVID documentation along with Immunization records for all accepted students must be ready to be presented to Tufts Medical Center Employee Health Services, ASAP after this date. A list of customary immunizations can be found on the program application.  Employee Health confirm all that will be needed.

For information on the P2P program,  contact Aimee Shen , PhD, Program Director.

For information on the application process,  contact Kimberly Burke , Program Administrator.

For Current Students

Biomedical data science program information.

Nearly all of the DBDS program policies and procedures are described in the Student Handbook, updated 12/13/23.

Current DBDS Courses

Student handbook, cs/stats/math/eng electives, coterminal ms advice, hcp ms advice, presentations, dbds student wiki.

Find our current DBDS courses here. 

Biomedical Data Science Student Handbook

The DBDS Student Handbook is a reference for DBDS program policies.

DBDS Student Handbook, updated 12/13/2023 ( pdf ).

Note : the DBDS Curriculum has changed. Students starting after July 1, 2018 should use the new curriculum, which is described on the ExploreDegrees pages . Others should follow what is on this page.

Take a look at our current courses. 

Curriculum (admitted before July 1, 2018)

Computer science, statistics, mathematics, and engineering electives.

• STATS 315A is recommended, not required.

Curriculum (admitted before Aug 1, 2016)

Dbds core courses (12 units).

• Required: BIOMEDIN 212 and the three courses from the list below.
• BIOMEDIN 202 Biomedical Data Science
• BIOMEDIN 212 Introduction to Biomedical Data Science Research Methodology
• BIOMEDIN 214 Representations and Algorithms for Computational Molecular Biology
• BIOMEDIN 215  Data Driven Medicine
• For PhD students who have received waivers (see below) for any of the core courses, then you should replace them with an equivalent number of units from another BIOMEDIN course, or CS/Stats/Math/Eng  elective . You may, with permission, also replace waivered courses with DBDS 299 units.
• HCP students: Note that BIOMEDIN 212 is a project class that has to-date been only available on campus, so you are currently exempt (replace with other course from DBDS, or from the CS/Stats/Math/Eng electives, as below). We have contemplated offering a remote version of 212, so please contact us if you are interested.
• Core courses should be taken for grade, not pass/fail (all students).

Computer Science, Statistics, Mathematics, and Engineering Electives (24 units)

• See our  electives page  for the full list.
• If the course does appear on the list, then it is in principle acceptable, but realize that your entire elective list must be approved. This is to ensure some degree of coherence and to avoid excessive overlap of course material. It is therefore possible to submit an elective list, all of whose courses are listed, but which considered together would not be approved.
• Note that CS 107 and CS 108 can count towards the CS/Stats/Math/Eng category. CS 106A, B cannot count for this category, but can be counted as Unrestricted Electives.
• BIOMEDIN 224 and BIOMEDIN 258 are basically courses in biology and medicine, so don’t count as this category. All other BIOMEDIN courses can count in this category, if needed.
• Up to 9 units can be 100-199 level; the rest must be 200 and above.
• Up to 8 units can be taken pass/fail.

Social and Ethical Issues Electives (4 units)

• To find all the approved courses in this category, type “dbds::ethics” into the search box in explorecourses, or click here .
• Note that MED 255 is required for all MS and PhD students engaged in NIH-funded research at Stanford. It is not required but strongly recommended for coterm MS students not doing research. There is no distance education version of this class for HCP students.
• HCP students: currently, only two classes satisfy this requirement and are offered through SCPD. These are: MS&E 256: Technology Assessment and Regulation of Medical Devices (Spring) and ME 208: Patent Law and Strategy for Innovators and Entrepreneurs, (Autumn). For HCP students who hold an MD degree or equivalent: this set of electives is waived on the basis of your medical school training, so replace with 4 more unrestricted units.

Unrestricted Electives (6 units)

• Any graduate level class (200 and above) can be used for this category. Classes 100 and above can also count, subject to the restrictions listed below.

Biology/Medicine Electives (for PhD students only) (9 units)

• In order to reach a total of 52 units of core curriculum, PhD students should take an additional 9 units; this should include 6 units of biology or medicine classes relevant to their research interests, 2 units of BIOMEDIN 290 Biomedical Data Science Teaching Methods and one additional unit of unrestricted elective.
• All 6 units can be taken as Credit/No Credit.
• All courses taken towards a graduate degree must be 100 level or above. At least 50% must be 200 level or above.
• BIOMEDIN 201 can be taken up to three times for credit.
• MS: total of 45 units required at Stanford.
• PhD: total of 135 units, of which at least 27 units should come from DBDS, CS/Stats/Math/Engr electives, Biology/Medicine electives, or courses satisfying the Ethics requirement (including MED 255). They may not include research, teaching, journal club, or other classes can that only be taken pass/fail such as some seminars.
• Coterms: see our  Coterm page .
• HCP MS students: see the  SCPD Student Handbook .
• Many more details on  ExploreDegrees .

The core curriculum generally entails a minimum of 45 units of course work for MS students and 54 units of course work for PhD students, but can require substantially more or less depending upon the courses chosen and the previous training of the student. BIOMEDIN 299, 801, and 802 may be taken for satisfactory/no credit (S/NC).

Waivers: The varying backgrounds of students are well recognized and no one is required to take courses in an area in which he or she has already been adequately trained; under such circumstances, students are permitted to skip courses or substitute more advanced work using a formal annual process administered by the DBDS executive committee, in which students demonstrate satisfaction of core curriculum prerequisites, and request permission to receive core curriculum credit for classes taken previously in areas of the core curriculum. Students design appropriate programs for their interests with the assistance and approval of their Biomedical Data Science academic adviser.

Also, see the curriculum requirements for the MS and PhD degrees listed on the DBDS  page in exploredegrees .

Advice on choosing courses and useful links

• Fill in a  course plan , paying attention to listed prerequisites and when course will be offered.
• Ask other students.
• See the  student wiki .
• Discuss with your academic advisor.
• Read the  Student Handbook .
• Stanford Explorecourses (enter “BIOMEDIN” to find DBDS courses)
• Stanford Center for Professional Development (SCPD)  (for HCP students)

There are two categories of electives in our curriculum:

• Computer science, mathematics, statistics, and engineering electives. This page is a list of courses which can used for this category.
• Unrestricted electives. These can be any graduate-level courses at Stanford at or above the 100 level (subject to degree-specific limits).

Note that a course not on this list is not an allowable elective. If you want to add a course to this list, send an email to DBDS Student Services ( [email protected] ) for consideration. Your particular course plan still needs to be approved by your academic advisor. Not all subsets of the following list are acceptable, for example, in the case of significant overlap between courses. Also, to find a course on this list, you may need to look under its cross-listed course ID.

APPPHYS 215: Numerical Methods for Physicists and Engineers APPPHYS 217: Estimation and Control Methods for Applied Physics APPPHYS 223: Stochastic and Nonlinear Dynamics (BIO 223) APPPHYS 223B: Nonlinear Dynamics: This Side of Chaos APPPHYS 293: Theoretical Neuroscience APPPHYS 315: Methods in Computational Biology APPPHYS 345: Advanced Numerical Methods for Data Analysis and Simulation

BIO 223: Stochastic and Nonlinear Dynamics (APPPHYS 223)

BIOC 223: Open Problems in Biology

BIODS 205: Bioinformatics for Stem Cell and Cancer Biology BIODS 215: Topics in Biomedical Data Science: Large-scale inference BIODS220: Artificial Intelligence in Healthcare (CS271, BIOMEDIN220) BIODS 237: Deep Learning in Genomics and Biomedicine (BIOMEDIN 273B, CS 273B, GENE 236) BIODS 239: Introduction to Analysis of RNA Sequence Data (BIOC 239) BIODS 253: Software Engineering for Scientists BIODS 260A,B,C: Workshop in Biostatistics (STATS 260A,B,C) BIODS 271 (CS 277): Foundation Models for Healthcare

BIOE 115: Computational Modeling of Microbial Communities (MI 245) BIOE 210: Systems Biology (BIOE 101) BIOE 285: Computational Modeling in the Cardiovascular System (CME 285, ME 285) BIOE 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, CME 279, CS 279) BIOE 291: Principles and Practices of Optogenetics BIOE 300B: Engineering Concepts Applied to Physiology BIOE301E: Computational Protein Modeling Laboratory BIOE331: Protein Engineering BIOE 332: Large-Scale Neural Modeling BIOE 334: Engineering Principles in Molecular Biology

BIOMEDIN 219: Mathematical Models and Medical Decisions BIOMEDIN 220: Artificial Intelligence in Healthcare (BIODS 220, CS 271) BIOMEDIN 221: Machine Learning Approaches for Data Fusion in Biomedicine BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222) BIOMEDIN 224 does NOT count towards this category BIOMEDIN 226: Digital Health Practicum in a Health Care Delivery System BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (EPI 261, STATS 261) BIOMEDIN 245: Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345) BIOMEDIN 248: Clinical Trial Design in the Age of Precision Medicine and Health (BIODS 248, BIODS 248P, STATS 248) BIOMEDIN 248B: Causal Inference in Clinical Trials and Observational Study (II) BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252) BIOMEDIN 262: Computational Genomics (CS 262) BIOMEDIN 273A: The Human Genome Source Code (CS 273A, DBIO 273A) BIOMEDIN 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, CS 273B, GENE 236) BIOMEDIN 279: Computational Biology: Structure and Organization of Biomolecules and Cells (CS 279, BIOPHYS 279, BIOE 279, CME 279) BIOMEDIN 371: Computational Biology in Four Dimensions (CS 371, BIOPHYS 371, CME 371) BIOMEDIN 374: Algorithms in Biology (CS 374) BIOMEDIN 472: Data Sciencee and AI for COVID-19 (BIODS 472, CS 472)

BIOPHYS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, CME 279, CS 279) BIOPHYS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, CME 371, CS 371)

BIOS 221: Modern Statistics for Modern Biology (STATS 366) BIOS 234: Personalized Genomic Medicine

CBIO 243: Principles of Cancer Systems Biology

CHEM 263: Machine Learning for Chemical and Dynamical Data

CME 100: Vector Calculus for Engineers (ENGR 154) CME 102: Ordinary Differential Equations for Engineers (ENGR 155A) CME 103: Introduction to Matrix Methods (EE 103) CME 104: Linear Algebra and Partial Differential Equations for Engineers (ENGR 155B) CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C) CME 108: Introdution to Scientific Computing (MATH 114) CME 151A: Introduction to Data Visualization in D3 CME 200: Linear Algebra with Application to Engineering Computations (ME 300A) CME 204: Partial Differential Equations in Engineering (ME 300B) CME 207: Numerical Methods in Engineering and Applied Sciences CME 211: Software Development for Scientists and Engineers CME 212: Advanced Software Development for Scientists and Engineers CME 213: Introduction to Parallel Computing using MPI, openMP, and CUDA CME 214: Software Design in Modern Fortran for Scientists and Engineers (EARTHSCI 214) CME 250: Introduction to Machine Learning CME 250A: Machine Learning on Big Data CME 251: Geometric and Topological Data Analysis (CS 233) CME 285: Computational Modeling in the Cardiovascular System (BIOE 285, ME 285) CME 263: Introduction to Linear Dynamical Systems (EE 263) CME 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOE 279, BIOPHYS 279, CS 279) CME 292: Advanced MATLAB for Scientific Computing CME 302: Numerical Linear Algebra CME 303: Partial Differential Equations of Applied Mathematics (MATH 220) CME 309: Randomized Algorithms and Probabilistic Analysis (CS 265) CME 323: Distributed Algorithms and Optimization CME 330: Applied Mathematics in the Chemical and Biological Sciences (CHEMENG 300) CME 334: Advanced Methods in Numerical Optimization (MS&E 312) CME 362: An Introduction to Compressed Sensing (STATS 330) CME 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CS 371) CME 500: Numerical Analysis and Computational and Mathematical Engineering Seminar CME 510: Linear Algebra and Optimization Seminar

CS 103: Mathematical Foundations of Computing CS 106A and 106B do NOT count towards this category CS 107: Computer Organization and Systems CS 108: Object-Oriented Systems Design CS 109: Introduction to Probability for Computer Scientists CS 109L: Statistical Computing with R Laboratory CS 110: Principles of Computer Systems CS 111: Operating Systems Principles CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280) CS 129: Applied Machine Learning CS 131: Computer Vision: Foundations and Applications CS 140: Operating Systems and Systems Programming CS 142: Web Applications CS 143: Compilers CS 144: Introduction to Computer Networking CS 145: Introduction to Databases CS 147: Introduction to Human-Computer Interaction Design CS 148: Introduction to Computer Graphics and Imaging CS 149: Parallel Computing CS 154: Introduction to Automata and Complexity Theory CS 155: Computer and Network Security CS 157: Logic and Automated Reasoning CS 161: Design and Analysis of Algorithms CS 164: Computing with Physical Objects: Algorithms for Shape and Motion CS 166: Data Structures CS 167: Readings in Algorithms CS 193C: Client-Side Internet Technologies CS 193P: iPhone and iPad Application Programming CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning CS 221: Artificial Intelligence: Principles and Techniques CS 223A: Introduction to Robotics (ME 320) CS 224D: Deep Learning for Natural Language Processing CS 224M: Multi-Agent Systems CS 224N: Natural Language Processing (LINGUIST 284) CS 224S: Spoken Language Processing (LINGUIST 285) CS 224U: Natural Language Understanding (LINGUIST 188, LINGUIST 288) CS 224W: Social and Information Network Analysis CS 225A: Experimental Robotics CS 225B: Robot Programming Laboratory CS 226: Statistical Techniques in Robotics CS 227B: General Game Playing CS 228: Probabilistic Graphical Models: Principles and Techniques CS 229: Machine Learning CS 229A: Applied Machine Learning CS 229T: Statistical Learning Theory (STATS 231) CS 230: Deep Learning CS 231A: Introduction to Computer Vision CS 231B: The Cutting Edge of Computer Vision CS 231N: Convolutional Neural Networks for Visual Recognition CS 232: Digital Image Processing (EE 368) CS 236: Deep Generative Models CS 236G: Generative Adversarial Networks CS 238: Decision Making under Uncertainty (AA 228) CS 240: Advanced Topics in Operating Systems CS 240E: Embedded Wireless Systems CS 240H: Functional Systems in Haskell CS 242: Programming Languages CS 243: Program Analysis and Optimizations CS 244: Advanced Topics in Networking CS 244B: Distributed Systems CS 244C: Readings and Projects in Distributed Systems CS 244E: Networked Wireless Systems (EE 384E) CS 245: Database Systems Principles CS 246: Mining Massive Data Sets CS 246H: Mining Massive Data Sets Hadoop Lab CS 248: Interactive Computer Graphics CS 248A: Computer Graphics: Rendering, Geometry, and Image ManipulationCS 249A: Object-Oriented Programming from a Modeling and Simulation Perspective CS 249B: Large-scale Software Development CS 254: Computational Complexity CS 255: Introduction to Cryptography CS 259: Security Analysis of Network Protocols CS 261: Optimization and Algorithmic Paradigms CS 262: Computational Genomics (BIOMEDIN 262) CS 263: Algorithms for Modern Data Models (MS&E 317) CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309) CS 266: Parameterized Algorithms and Complexity CS 267: Graph Algorithms CS 268: Geometric Algorithms CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210) CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212) CS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A) CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236) CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214) CS 275: Translational Bioinformatics (BIOMEDIN 217) CS 276: Information Retrieval and Web Search (LINGUIST 286) CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOMEDIN 279, BIOPHYS 279, BIOE 279, CME 279) CS 295: Software Engineering CS 309A: Cloud Computing CS 315A: Parallel Computer Architecture and Programming CS 315B: Parallel Computing Research Project CS 316: Advanced Multi-Core Systems (EE 382E) CS 319: Topics in Digital Systems CS 328: Topics in Computer Vision CS 329: Topics in Artificial Intelligence CS 331A: Advanced Reading in Computer Vision CS 331B: 3D Representation and Recognition CS 334A: Convex Optimization I (CME 364A, EE 364A) CS 340: Topics in Computer Systems CS 341: Project in Mining Massive Data Sets CS 343: Advanced Topics in Compilers CS 344: Topics in Computer Networks CS 344E: Advanced Wireless Networks CS 345: Advanced Topics in Database Systems CS 347: Parallel and Distributed Data Management CS 348A: Computer Graphics: Geometric Modeling CS 348B: Computer Graphics: Image Synthesis Techniques CS 349: Topics in Programming Systems CS 349C: Topics in Programming Systems: Readings in Distributed Systems CS 354: Topics in Circuit Complexity CS 355: Advanced Topics in Cryptography CS 357: Advanced Topics in Formal Methods CS 358: Topics in Programming Language Theory CS 359: Topics in the Theory of Computation CS 361A: Advanced Algorithms CS 361B: Advanced Algorithms CS 362: Algorithmic Frontiers: Effective Algorithms for Large Data CS 364A: Algorithmic Game Theory CS 364B: Topics in Algorithmic Game Theory CS 366: Graph Partitioning and Expanders CS 367: Algebraic Graph Algorithms CS 369: Topics in Analysis of Algorithms CS 369N: Beyond Worst-Case Analysis CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371) CS 373: Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, GENE 245, STATS 345) CS 374: Algorithms in Biology (BIOMEDIN 374) CS 375: Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249) CS 379C: Computational Models of the Neocortex CS 427: Hero’s Journey: AI and Game Theory in 3D Real-time Storytelling CS 431: High-Level Vision: Object Representation (PSYCH 250) CS 442: High Productivity and Performance with Domain-specific Languages in Scala CS 447: Software Design Experiences CS 448: Topics in Computer Graphics CS 448B: Data Visualization CS 468: Topics in Geometric Algorithms: Differential Geometry for Computer Science CS 520: Knowledge Graphs CS 522: Seminar in Artificial Intelligence in Healthcare CS 545: Database and Information Management Seminar CS 547: Human-Computer Interaction Seminar CS 528: Machine Learning Systems Seminar

DBIO 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, CS 273A)

EE 101A: Circuits I EE 101B: Circuits II EE 102A: Signal Processing and Linear Systems I EE 102B: Signal Processing and Linear Systems II EE 103: Introduction to Matrix Methods (CME 103) EE 108A: Digital Systems I EE 108B: Digital Systems II EE 168: Introduction to Digital Image Processing EE 169: Introduction to Bioimaging EE 179: Analog and Digital Communication Systems EE 248: Fundamentals of Noise Processes EE 256: Numerical Electromagnetics EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258) EE 261: The Fourier Transform and Its Applications EE 262: Two-Dimensional Imaging EE 263: Introduction to Linear Dynamical Systems (CME 263) EE 264: Digital Signal Processing EE 276:  Information Theory EE 277: Reinforcement Learning: Behaviors and Applications (MS&E 237) EE 278A: Probabilistic Systems Analysis (EE 178) EE 278B: Introduction to Statistical Signal Processing EE 279: Introduction to Digital Communication EE 282: Computer Systems Architecture EE 284: Introduction to Computer Networks EE 292M: Parallel Processors Beyond Multi-Core Processing EE 361: Principles of Cooperation in Wireless Networks EE 364A: Convex Optimization I (CME 364A, CS 334A) EE 364B: Convex Optimization II (CME 364B) EE 365: Stochastic Control EE 368: Digital Image Processing (CS 232) EE 369A: Medical Imaging Systems I EE 369B: Medical Imaging Systems II EE 369C: Medical Image Reconstruction EE 373A: Adaptive Signal Processing EE 373B: Adaptive Neural Networks EE 376A: Information Theory (STATS 376A) EE 376B: Network Information Theory (STATS 376B) EE 376C: Universal Schemes in Information Theory EE 378A: Statistical Signal Processing EE 378B: Inference, Estimation, and Information Processing EE 379: Digital Communication EE 382C: Interconnection Networks EE 382E: Advanced Multi-Core Systems (CS 316) EE 384A: Internet Routing Protocols and Standards EE 384C: Wireless Local and Wide Area Networks EE 384E: Networked Wireless Systems (CS 244E) EE 384M: Network Science EE 384S: Performance Engineering of Computer Systems & Networks EE 386: Robust System Design EE 387: Algebraic Error Control Codes EE 388: Modern Coding Theory EE 398A: Image and Video Compression EE 464: Semidefinite Optimization and Algebraic Techniques

EPI 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis EPI 224: Genetic Epidemiology (GENE 230) EPI 225: Introduction to Epidemiologic and Clinical Research Methods EPI 226: Intermediate Epidemiologic and Clinical Research Methods EPI 239: Applications of Causal Inference Methods (EDUC 260 A, STATS 209B) EPI 251: Design and Conduct of Clinical Trials EPI 258: Introduction to Probability and Statistics for Clinical Research EPI 259 : Introduction to Probability and Statistics for Epidemiology (HUMBIO 89X) EPI  262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (STATS 262)

ENGR 150: Data Challenge Lab ENGR 154: Vector Calculus for Engineers (CME 100) ENGR 155A: Ordinary Differential Equations for Engineers (CME 102) ENGR 155B: Linear Algebra and Partial Differential Equations for Engineers (CME 104) ENGR 155C: Introduction to Probability and Statistics for Engineers (CME 106) ENGR 205: Introduction to Control Design Techniques ENGR 206: Control System Design ENGR 207A: Linear Control Systems I ENGR 207B: Linear Control Systems II ENGR 209A: Analysis and Control of Nonlinear Systems

GENE 236: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, CS 273B) GENE 244: Introduction to Statistical Genetics (STATS 345)

HRP 252: Outcomes Analysis (BIOMEDIN 251, MED 252) HRP 255: Decoding Academia: Power, Hierarchies, and Transforming Institutions

IMMUNOL 206A: Systems and Computational Immunology IMMUNOL 206B: Directed Projects in Systems and Computational Immunology IMMUNOL 207: Essential Methods in Computational and Systems Immunology IMMUNOL 208: Advanced Computational and Systems Immunology

MATH 104: Applied Matrix Theory MATH 106: Functions of a Complex Variable MATH 107: Graph Theory MATH 108: Introduction to Combinatorics and Its Applications MATH 109: Applied Group Theory MATH 110: Applied Number Theory and Field Theory MATH 111: Computational Commutative Algebra MATH 113: Linear Algebra and Matrix Theory MATH 114: Introdution to Scientific Computing (CME 108) MATH 115: Functions of a Real Variable MATH 116: Complex Analysis MATH 118: Mathematics of Computation MATH 120: Groups and Rings MATH 121: Galois Theory MATH 122: Modules and Group Representations MATH 131P: Partial Differential Equations I MATH 132: Partial Differential Equations II MATH 136: Stochastic Processes (STATS 219) MATH 137: Mathematical Methods of Classical Mechanics MATH 143: Differential Geometry MATH 144: Riemannian Geometry MATH 145: Algebraic Geometry MATH 146: Analysis on Manifolds MATH 147: Differential Topology MATH 148: Algebraic Topology MATH 149: Applied Algebraic Topology MATH 151: Introduction to Probability Theory MATH 152: Elementary Theory of Numbers MATH 154: Algebraic Number Theory MATH 155: Analytic Number Theory MATH 159: Discrete Probabilistic Methods MATH 161: Set Theory MATH 171: Fundamental Concepts of Analysis MATH 172: Lebesgue Integration and Fourier Analysis MATH 173: Theory of Partial Differential Equations MATH 174: Calculus of Variations MATH 175: Elementary Functional Analysis MATH 177:  Geometric Methods in the Theory of Ordinary Differential Equations MATH 193: Polya Problem Solving Seminar MATH 205A: Real Analysis MATH 205B: Real Analysis MATH 210A: Modern Algebra I MATH 210B: Modern Algebra II MATH 210C: Lie Theory MATH 215A: Complex Analysis, Geometry, and Topology MATH 215B: Complex Analysis, Geometry, and Topology MATH 215C: Complex Analysis, Geometry, and Topology MATH 216A: Introduction to Algebraic Geometry MATH 216B: Introduction to Algebraic Geometry MATH 216C: Introduction to Algebraic Geometry MATH 217A: Differential Geometry MATH 220: Partial Differential Equations of Applied Mathematics (CME 303) MATH 221A: Mathematical Methods of Imaging (CME 321A) MATH 221B: Mathematical Methods of Imaging (CME 321B) MATH 222: Computational Methods for Fronts, Interfaces, and Waves MATH 224: Topics in Mathematical Biology MATH 226: Numerical Solution of Partial Differential Equations (CME 306) MATH 227: Partial Differential Equations and Diffusion Processes MATH 228: Stochastic Methods in Engineering (CME 308) MATH 230A: Theory of Probability (STATS 310A) MATH 230B: Theory of Probability (STATS 310B) MATH 230C: Theory of Probability (STATS 310C) MATH 231A: An Introduction to Random Matrix Theory (STATS 351A) MATH 231C: Free Probability MATH 232: Topics in Probability: Percolation Theory MATH 233: Probabilistic Methods in Analysis MATH 234: Large Deviations Theory (STATS 374) MATH 236: Introduction to Stochastic Differential Equations MATH 239: Computation and Simulation in Finance MATH 243: Functions of Several Complex Variables MATH 244: Riemann Surfaces MATH 245A: Topics in Algebraic Geometry: Moduli Theory MATH 245B: Topics in Algebraic Geometry: Intersection Theory MATH 245C: Topics in Algebraic Geometry: Alterations MATH 247: Topics in Group Theory MATH 248: Ergodic Theory and Szemeredi’s Theorem MATH 248A: Algebraic Number Theory MATH 249A: Topics in number theory MATH 249B: Topics in Number Theory MATH 249C: Topics in Number Theory MATH 252: Algebraic Groups MATH 254: Geometric Methods in the Theory of Ordinary Differential Equations MATH 256A: Partial Differential Equations MATH 256B: Partial Differential Equations MATH 257A: Symplectic Geometry and Topology MATH 257B: Symplectic Geometry and Topology MATH 257C: Symplectic Geometry and Topology MATH 258: Topics in Geometric Analysis MATH 259: mirror symmetry MATH 261A: Functional Analysis MATH 263A: Lie Groups and Lie Algebras MATH 264: Infinite Dimensional Lie Algebra MATH 266: Computational Signal Processing and Wavelets MATH 269: Topics in symplectic geometry MATH 270: Geometry and Topology of Complex Manifolds MATH 271: The H-Principle MATH 272: Topics in Partial Differential Equations MATH 280: Evolution Equations in Differential Geometry MATH 282A: Low Dimensional Topology MATH 282B: Homotopy Theory MATH 282C: Fiber Bundles and Cobordism MATH 283: Topics in Algebraic and Geometric Topology MATH 283A: Topics in Topology MATH 284: Topics in Geometric Topology MATH 284A: Geometry and Topology in Dimension 3 MATH 284B: Geometry and Topology in Dimension 3 MATH 286: Topics in Differential Geometry MATH 287: Introduction to optimal transportation MATH 295: Computation and Algorithms in Mathematics MATH 301: Advanced Topics in Convex Optimization MATH 310: Algorithms MATH 384: Seminar in Geometry MATH 385: Seminar in Topology MATH 388: Seminar in Probability and Stochastic Processes MATH 389: Seminar in Mathematical Biology MATH 394: Classics in Analysis MATH 395: Classics in Geometry and Topology

MGTECON 634: Machine Learning and Causal Inference

ME 285: Computational Modeling in the Cardiovascular System (BIOE 285, CME 285) ME 261: Dynamic Systems, Vibrations and Control (ME 161) ME 300B: Partial Differential Equations in Engineering (CME 204)

MED 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, STATS 211) MED 263: Advanced Decision Science Methods and Modeling in Health (HRP 263) MED 252: Outcomes Analysis (BIOMEDIN 251, HRP 252)

MI 245: Computational Modeling of Microbial Communities (BIOE 115)

MS&E 120: Probabilistic Analysis MS&E 211: Linear and Nonlinear Optimization MS&E 220: Probabilistic Analysis MS&E 223: Simulation MS&E 226: “Small” Data MS&E 228: Applied Causal Inference with Machine Learning and AI MS&E 252: Decision Analysis I: Foundations of Decision Analysis MS&E 263: Healthcare Operations Management MS&E 310: Linear Programming MS&E 312: Advanced Methods in Numerical Optimization (CME 334) MS&E 328: Foundations of Causal Machine Learning MS&E 335: Queueing and Scheduling in Processing Networks MS&E 352: Decision Analysis II: Professional Decision Analysis MS&E 355: Influence Diagrams and Probabilistics Networks MS&E 454: Decision Analysis Seminar MS&E 463: Healthcare Systems Design

NBIO 228: Mathematical Tools for Neuroscience

NENS 230: Analysis Techniques for the Biosciences Using MATLAB

OIT 673: Data-driven Decision Making and Applications in Healthcare

PSYCH 248: Advanced fMRI modeling and analysis PSYCH 248A: fMRI Analysis Bootcamp PSYCH 253: Advanced Statistical Modeling

STATS 110: Statistical Methods in Engineering and the Physical Sciences STATS 116: Theory of Probability STATS 166: Computational Algorithms for Statistical Genetics (GENE 245, STATS 345) STATS 191: Introduction to Applied Statistics STATS 200: Introduction to Statistical Inference STATS 201: Design and Analysis of Experiments STATS 202: Data Mining and Analysis STATS 203: Introduction to Regression Models and Analysis of Variance STATS 205: Introduction to Nonparametric Statistics STATS 206: Applied Multivariate Analysis STATS 207: Introduction to Time Series Analysis STATS 208: Introduction to the Bootstrap STATS 209: STATS 209: Introduction to Causal Inference STATS 209B: Applications of Causal Inference Methods STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 206, MED 206) STATS 212: Applied Statistics with SAS STATS 213: Introduction to Graphical Models STATS 214: Machine Learning Theory STATS 215: Statistical Models in Biology STATS 216: Introduction to Statistical Learning STATS 216V: Introduction to Statistical Learning STATS 217: Introduction to Stochastic Processes STATS 218: Introduction to Stochastic Processes STATS 219: Stochastic Processes (MATH 136) STATS 222: Statistical Methods for Longitudinal Data (EDUC 351A) STATS 231: Statistical Learning Theory (CS 229T) STATS 245: Data, Models, and Decision Analytics STATS 253: Spatial Statistics (STATS 352) STATS 260A: Workshop in Biostatistics (BIODS 260A) STATS 260B: Workshop in Biostatistics (BIODS 260B) STATS 260C: Workshop in Biostatistics (BIODS 260C) STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261) STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262) STATS 270: A Course in Bayesian Statistics (STATS 370) STATS 285: Massive Computational Experiments, Painlessly STATS 290: Paradigms for Computing with Data STATS 300: Advanced Topics in Statistics STATS 300A: Theory of Statistics STATS 300B: Theory of Statistics STATS 300C: Theory of Statistics STATS 305A: Applied Statistics I STATS 305B: Applied Statistics II STATS 305C: Applied Statistics III STATS 306A: Methods for Applied Statistics STATS 306B: Methods for Applied Statistics: Unsupervised Learning STATS 310A: Theory of Probability (MATH 230A) STATS 310B: Theory of Probability (MATH 230B) STATS 310C: Theory of Probability (MATH 230C) STATS 314: Advanced Statistical Methods STATS 315A: Modern Applied Statistics: Learning STATS 315B: Modern Applied Statistics: Data Mining STATS 316: Stochastic Processes on Graphs STATS 317: Stochastic Processes STATS 318: Modern Markov Chains STATS 319: Literature of Statistics STATS 320: Heterogeneous Data with Kernels STATS 321: Modern Applied Statistics: Transposable Data STATS 322: Function Estimation in White Noise STATS 324: Multivariate Analysis STATS 325: Multivariate Analysis and Random Matrices in Statistics STATS 329: Large-Scale Simultaneous Inference STATS 330: An Introduction to Compressed Sensing (CME 362) STATS 338: Topics in Biostatistics STATS 341: Applied Multivariate Statistics STATS 345: Introduction to Statistical Genetics (GENE 244) STATS 345: Computational Algorithms for Statistical Genetics (GENE 245, STATS 166) STATS 351A: An Introduction to Random Matrix Theory (MATH 231A) STATS 352: Spatial Statistics (STATS 253) STATS 355: Observational Studies (HRP 255) STATS 362: Monte Carlo STATS 366: Modern Statistics for Modern Biology (BIOS 221) STATS 367: Statistical Models in Genetics STATS 370: A Course in Bayesian Statistics (STATS 270) STATS 374: Large Deviations Theory (MATH 234) STATS 375: Inference in Graphical Models STATS 376A: Information Theory (EE 376A) STATS 396: Research Workshop in Computational Biology

Funding Sources for DBDS PhD Students

All DBDS PhD students are admitted with a funding plan in place. However, we require that enrolled students apply to internal and external funding for which they are eligible . This is typically done in one of the first three years of graduate study. Below are listed the funding sources that would be available to most students. You are encouraged to seek out others as well. Note that for some of these sources having an MS will make you ineligible, or the MS time will count as years of graduate study.

• NSF Graduate Research Fellowship Program (GRFP) . Open to first  or  second year graduate students. In general, we recommend that you apply in your second year. Contact your research or academic advisor if you want to apply in your first year. Eligibility: No previous graduate training (e.g., masters degree), must be US citizen or permanent resident. Due date: late October.
• Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship (F31) . Eligibility: US citizen or permanent resident. You should apply in the fall of your 3rd year, immediately after passing your qualifying examination. There are more details in the DBDS Student Handbook. You should also coordinate with DBDS Admin. Apply for Cycle III, due date Dec 8 . Note that you need to take a  Stanford course  beforehand.
• DOE Computational Science Graduate Fellowship . You apply in your first year of graduate school. Eligibility: US citizen or permanent resident. Due date:  January .
• National Defense Science & Engineering Graduate Fellowship (NDSEG) . You apply in your first or second year of graduate study. Eligibility: US citizen (not permanent resident).
• Stanford Interdisciplinary Graduate Fellowship (SIGF) . You can apply in your first three years of graduate study. There are no citizenship requirements. Due date: March.
• Stanford BIO-X .
• A comprehensive list of predoctoral funding opportunities is available on the  Stanford Research Management Group website .

Checklist for coterminal students

The rules are a bit complicated. Here’s a summary:

• See the  Coterminal Masters Degree  page,  VPGE Coterminal Degrees  page,  Stanford University Procedures for Coterminal Students , and DBDS  curriculum description  in exploredegrees.
• When asking questions about your particular circumstances, please make sure that you have listed all the courses towards your MS on a  flowsheet , and make that available to us, as nearly all questions involve some aspect of that.
• You are required to have at least 180 units (undergraduate) and 45 units (graduate).
• No units may be counted towards both undergraduate and DBDS MS degree.
• 45 units are required for the DBDS MS.
• All courses counted towards the MS must be DBDS-related courses at or above the 100 level (as approved by advisor and DBDS program).
• At least 23 units must be courses at or above the 200 level.
• At least 27 units must be taken for a grade (unless, through exceptional circumstances such as the 2020 crisis, this is imposible, in that case, consult DBDS Student Services).
• DBDS core courses may not be taken pass/fail. Exceptions: BIOMEDIN 200 (no longer offered), 201, 205, 206, 207, 290, 299, 390, 801, 802, and MED 255, or DBDS courses that are offered only on a S/NC basis (including during the 2020 crisis).
• A complete list of elective courses that can count for CS/Stats/Math/Eng is listed  here . Up to 6 units of CS/Stats/Math/Eng can be taken pass/fail.
• You can apply to DBDS upon completion of 120 units, but no later than the quarter prior to the expected completion of the undergraduate degree.
• Coterminal students are permitted to count coursework taken in the three quarters immediately prior to their first graduate quarter toward their graduate degree (Summer quarter is not included in the count).
• The  Coterminal Course Transfer eForm  is used for transferring courses from the undergraduate career to the graduate career, and vice versa. This must be done before the undergraduate degree is conferred.
• You may take any course you want at Stanford while in the program, including electives not at all related to DBDS, as long as you also have an approved DBDS-related 45-unit program of study that satisfies the requirements for the DBDS degree.
• For a “Core Biomedical Data Science” course, you can take another course in the same category, any course listed under BIOMEDIN or BIODS, or any course in the CS/Stats/Math/Eng electives list.
• For a “CS/Math/Stats/Eng” course, you can take another course in the same category.
• For a “Social/Ethics” course, you can take another course in the same category, or any other course appropriate for graduate degree credit.
• Example: You took CS181W as an undergraduate. This class satisfies the DBDS MS Social and Ethical requirement; instead of taking graduate courses towards this distributional requirement, you now take an additional 4 units of DBDS-related electives. Enter CS181W on your flowsheet, and put “Y” in the “UG?” column. Note that it then contributes no units to the DBDS MS. Then list a course (or courses) for four units under DBDS-related electives.
• Example: You took BIOMEDIN 217 as an undergraduate. This class is part of the DBDS core. List the course in the DBDS core part of the flowsheet, and enter “Y” in the “UG?” column. Then list another approved course (or courses) for four units under DBDS-related electives.
• See the  example spreadsheet .
• In general, for choosing the courses that go under your (now expanded) set of units under DBDS-related electives, pick graduate courses with some thematic relationship to Biomedical Data Science. Feel free to discuss with the DBDS Program staff if you are unsure whether a course will be approved as DBDS-related. Courses such as BIOMEDIN 299 are fine.
• Students must finish the master’s degree within three years of their first co-terminal quarter. Note that taking a leave of absence does NOT extend your time to degree. If you want to take more than three years, you will need to petition the registrar.

Checklist for HCP MS students

This document is some advice about how to navigate our curriculum and how to choose courses. There are differences between the HCP MS requirements and those listed for the other MS and PhD degrees; those differences are highlighted here.

• Start by reviewing the curriculum at  DBDS curriculum description  in ExploreDegrees,  the SCPD website , and the  SCPD HCP Program Handbook . You should also review the  DBDS Student Handbook .
• Core courses: Although many classes necessary for the degree are available online, some requirements may be fulfilled through implementation of an alternative plan to be approved by the program. DBDS 212 requires special arrangements with the instructor, so we can include you in the class remotely if possible. Note that core courses should be taken for a grade, not pass/no credit (unless not offered for a grade).
• If Stats 200 is not offered remotely, then in that case you can consider taking: MS&E 226 or EPI 259 and EPI 261 (both).
• Computer science, statistics, mathematics, and engineering electives (18 units). Courses that can count for CS/Stats/Math/Eng is listed on the  Electives page . Up to 6 units of CS/Stats/Math/Eng can be taken pass/fail. If you are following the new curriculum (admitted after Aug 1, 2016), note that CS 161 and STATS 200 are required. These two courses, or some of their prerequisites, may not be available through SCPD. You should submit a course plan with next best alternatives: these may include similar courses at Stanford, or courses at other institutions (note that outside courses would not count towards the required 45 units).
• Social and Ethical issues (4 units): Type “dbds::ethics” into the search box in explorecourses . Note that although MED 255 is listed as required, it is required only for MS and PhD students engaged in NIH-funded research at Stanford. This typically does not apply to HCP students.
• Unrestricted electives (14 units): They need to be at or above the 100 level.
• How to put the courses in order. Use one of the  course flowsheets . First, check ExploreCourses and the SCPD website for which quarter the courses are offered. Also, check the listed prerequisites for each class, and also the course websites for additional information about what’s expected so you can estimate how difficult each class will be for you. Typically, SCPD students take one course per quarter. Note that SCPD imposes an upper limit of three courses per quarter. Also, use the DBDS  Student Wiki  for advice on courses.
• The following courses are only available P/F: BIOMEDIN 201, 205, 206, 207, 290, 299, 801, 802, and MED 255.
• All courses counted towards the MS must be at or above the 100 level.
• You can include up to 18 units that you took Non-Degree Option before entering the MS program.
• At least 27 units must taken for a grade.
• Requests to transfer from part-time (HCP) to full-time (Academic MS) are reviewed by the DBDS Exec on a case-by-case basis. Final decisions are at the DBDS Exec’s discretion. Please note the following limitations (for students enrolling in the HCP program starting Fall 2020) :
• Students must  complete a minimum of two (2) quarters in the part-time program excluding summer quarter or enrollment as a non-degree option student, before requesting to transfer to full-time. Therefore, the soonest the transfer can be discussed and approved is during the first DBDS Exec meeting of the third quarter of the student in the HCP program
• Students must complete a minimum of 10 units of letter-graded courses that meet requirements for the DBDS MS degree before commencing their first full-time (Academic MS) quarter
• GPA will be considered as part of the request.
• Students can make a maximum of two (2) transfers during the program (e.g. transfer from part-time to full-time and back to part-time).
• Students should consider the availability of courses online before requesting to switch from full-time to part-time, especially if this may interfere with their ability to satisfy the requirements of the degree.

Advice on Talks and Presentations

• 10 Suggestions for Improving your Scientific Talks  Lawrence Fagan
• Informatics Journal Club and Research Talk Template . PowerPoint presentation for Tuesday Talks (Journal Club and Student Research)
• Journal Club Template  (Altman/Bagley). Revised version of the above.
• Pre-Proposal Talk Guidelines Suggestions for DBDS Pre-Proposal Talks by Dr. Lawrence Fagan
• Russ Altman’s Translational Bioinformatics Year in Review slides:  2008 ,  2009 ,  2010 ,  2011 ,  2012 ,  2013 ,  2014 ,  2015 ,  2016 ,  2017 ,  2018
• Russ Altman, “ How to Write a Grant Proposal “
• Russ Altman, “ How to Review a Grant Proposal “
• Russ Altman, “ How to Give a Talk “

Bagley, DBDS Journal Club Template Video

Forms for dbds students, course flowsheets by degree program.

• Coterminal MS

Obtain signatures and submit all Milestone forms to the DBDS Student Services Officer, unless otherwise stated.

• MS Program Proposal Form  – All MS students must complete and submit by end of first quarter of enrollment.
• Application for Doctoral Candidacy  – To be completed and submitted after passing Qualifying Exam.
• Doctoral Dissertation Reading Committee form  – To be completed and submitted before scheduling Defense, also required to qualify for TGR.
• University Doctoral Orals/Defense Exam form  – Date, time, and location of the Exam should be confirmed with committee at least 2 months in advance. The Defense Exam form should be completed and submitted to the Student Services Officer at least 3 weeks before the Defense. Please make sure that you, the Chair, and the other committee members are familiar with the  University policy  in advance of the Exam.

TGR (Terminal Graduate Residency)

• TGR form  – TGR allows students to register at a reduced tuition rate while working on a dissertation, thesis, or department project. Must have completed 135 total units to qualify. Submit to the Office of the University Registrar.
• Graduate Quarter Petition  – To be submitted to the Office of the University Registrar prior to the quarter of intended graduation.
• Commencement Walk Through Petition – To be submitted to DBDS Student Services Officer, if student has not completed degree requirements, but would like to participate in June commencement. Walkthrough candidates who submit their forms by May 1 will be in listed in the June Commencement Program.
• For travel policy, please refer to the DBDS  Student Handbook
• Register with  Stanford Travel Egencia  before arranging any business travel.
• Student Request for Travel Required by DBDS before booking your travel or paying registration fees. The GSA per diem rates  can be used to estimate your lodging & meals expenses on the travel request form.
• Student Certification Form  Required for all travel beforehand. Required by Stanford University before booking your travel or paying registration fees.
• Travel Reimbursement Expense Summary  Required by DBDS after the trip in you are submitting receipts for personal reimbursement.
• International travel requirements can be found under  Fly America  rules.

Other Forms

• Non-travel ( Petty Cash )  Reimbursement Form   Petty Cash Form
• student_request_for_travel_hand-written (word doc)[23] Use to form to transfer into HCP MS or from HCP MS to Academic MS program. Must be submitted at least 3 weeks before the quarter in which the program change will start. See form for more details.
• Other Registrar Forms

Biomedical Data Science Student Wiki

The DBDS Student Wiki

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Master of Engineering in Data Science & Decision Analytics

• Master of Engineering in Data Science & Decision Analytics
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Program Length

There are complex algorithmic frameworks behind most online platforms deciding which vehicle to assign to a passenger in a ride sharing system depending on the geographical allocation of the vehicle supply, what price to charge for a product depending on the inventories or which fulfillment center to use to store an incoming shipment. Cornell Tech’s Master’s in Decision Science and Decision Analytics (DSDA) will provide you the tools to understand the data-to-algorithms-to-decisions pipeline by drawing from tools in machine learning, optimization and statistics.

Introduction to Cornell Tech

About the master of engineering in data science and decision analytics degree.

Cornell Tech’s DSDA Masters allows you to develop a theoretical and practical understanding of the data-to-models-to-decisions pipeline in entrepreneurial environments by drawing from tools in machine learning, optimization and statistics. After going through the program, you will be able to develop and deploy algorithms to drive the decisions of online business such as e-retailers, ride-sharing systems and ad exchanges. Working with the faculty that are world leaders in optimization, statistics, causal analysis and machine learning, as well as being immersed in the tech start-up community of New York City, you will get comfortable with analyzing data, building decision models that turn data into decisions and deploying these models at large scale. In parallel with your academic technical courses, you will also complete Studio courses – an essential component of every Cornell Tech program that allow you to solve real business problems faced by tech companies and launch your own startups. During the course of Studio courses, you will work in cross-disciplinary teams with students in computers science, electrical engineering, business and law programs to study all facets of the problem you are focusing on.

Who Should Apply?

Cornell Tech’s DSDA Masters is ideal for students with a passion for data analysis that create insights to drive business decisions. The technical requirements are that you have completed one course on linear algebra, one course on intermediate-level probability and statistics, and one course on programming before applying. Cornell Tech offers another Masters degree in Operations Research and Information Engineering (ORIE). As is the case with all Masters programs at Cornell Tech, both DSDA and ORIE programs have an entrepreneurial focus, but DSDA emphasizes the intersection of optimization, machine learning and probabilistic analysis with computing, whereas ORIE emphasizes the intersection of optimization, machine learning and probabilistic analysis with business.

Technical Topics Covered

• Optimization for AI
• Statistics for Data Science
• Probabilistic Analysis
• Machine Learning
• Modeling Under Uncertainty

Studio Topics Covered

• Entrepreneurship
• Law for Non-Lawyers
• Leadership for Digital Transformation
• Product Management
• Startup Funding & Pitching