It is crucial that students, Advisory Committees, and/or Program Directors monitor the students’ progress throughout the duration of their academic training. Continued financial support is contingent upon maintaining satisfactory progress at all times. Additionally, failure to achieve and maintain satisfactory progress, after counseling is sought from the Program, Advisory Committee and/or Dean of the Graduate School, can result in academic probation and ultimately, dismissal from the Program.

MDSAI students maintain satisfactory progress by:

• Maintaining matriculation on a full-time basis unless permission granted by the program co-directors

• Maintaining a minimum semester GPA of 3.0

• Maintaining a cumulative GPA of 3.0 for the core courses

• Meeting with program leadership at least once per term

• Actively working with a capstone advisor to develop and complete a capstone project

This section covers the following program requirements:

• Curricular Requirements

• Requirements to Graduate

For students who entered the MDSAI (MSBDS) program prior to 2025, please refer to your curriculum checklists on the Blackboard site.

Core Curriculum (6 credits)

All students must complete three of the four fundamental core curriculum courses in their first semester.

• BSR1030 Core I: Biochemistry & Molecular Biology (2 credits) – Fall

• BSR1031 Core II: Pharmacology & Drug Discovery (2 credits) – Fall

• BSR1032 Biomedical Sciences Core III: Cell & Developmental Biology (2 credits) – Fall

• BSR1033 Core IV: Neuroscience (2 credits) – Fall

Other Required Coursework (9.5 credits)

• Computer Systems: The computer systems course is split into 3 separate 1 credit courses. The courses are offered in a specific sequence during the fall term. Each course will be graded separately.

  • BDS1005 Computer Systems: UNIX/LINUX Fundamentals (1 credit)

  • BDS1006 Computer Systems: Architectures & Applications in Scientific Computing (1 credit)

Mandatory Training Sessions (1 credit)

Students must take the following two additional mandatory training requisites:

• BSR1021 Responsible Conduct of Research (0.5 credit) – Fall

• BSR1022 Rigor and Reproducibility (0 .5 credit) – Spring

Elective Credits (4.5-11 credits)

Students take elective credits to pursue complementary coursework in areas of most significant interest to their research topic.

Capstone Research Project (3-9 credits)

Students in the MSDSAI program complete a culminating capstone project. Capstone research projects are performed using a wide range of approaches with various of biomedical applications. Below is an illustrative list or research areas:

• computational genomics

• computational biophysics

• systems pharmacology

All students will need to meet the following degree requirements in order to successfully earn the MDSAI degree:

• Complete a minimum of 30 graduate credits

• Complete the Core Curriculum with an average grade of B (3.0) or higher

For the capstone project the student will join a lab and train directly with the research mentor. The project should apply Biomedical Data Science techniques learned while in the MDSAI program. Students will work directly with their Capstone Advisor to develop the subject of the capstone project. Students must submit a written capstone document and give an oral presentation of the project to the program co-directors and the capstone advisor. It is recommended the student have the capstone advisor review the written document before submission.

The Written Document

The document should be reviewed by the capstone advisor prior to orally presenting the project to the program co-directors. The following structure and guidelines are suggested:

Title and Capstone Advisor

Acknowledgements

Abstract: Provide a summary of your capstone project. Present the major elements of the work in a condensed form.

Introduction: Provide a critical review of the literature that is most pertinent to the work performed. It is important in this section to develop the rationale for the work performed. It should make obvious the basis of the questions addressed by the work. It should describe the basis for the approach taken to answer these questions. It should also provide insight into the relation of the thesis to the current state of knowledge in the field. Critical evaluation of the literature is a necessity. Finally, the introduction should clearly state a hypothesis that will be tested by the studies.

Methods: Describe the primary techniques used. Do not repeat details of published methods. This is not intended to be a recipe book of the methods used. Instead it is a general overview of the procedures used and details of elements that are specific to the work.

Results: Describe what has been accomplished, accompanied by appropriate figures and tables.

Discussion: Examine the results, explain their significance, and answer the question posed in the Introduction. Place the findings in the context of what is currently known in the field, demonstrating how the understanding of the field is extended by the work.

Conclusion/Summary: Summarize and state the significance of the results.

References: In the text, cite all references in the name-and-year system (e.g. Strong and Jones, 1991). The reference list should be arranged alphabetically by the last name of the first author in a standard format with titles. The student should consult standard reference publications for appropriate citation styles.

Oral Presentation

• When the student is ready to orally present the capstone project, the student will work with the program manager to set the day and time.

• Students must send the program directors a copy of the written document 1 week prior to presentation date.

• The presentation will last approximately 60 minutes with the first half will have the student presenting, then a short Q & A with the audience and about 20-25 minute discussion with the student, the capstone advisor, and the program directors.

Capstone Deadlines

Students should submit their written capstone document and orally present their project during the last term before graduation. Please see deadline dates for graduation below.

Required Forms

• – To be submitted when students join a lab during 1st year

During the first term students will have the program course directors as their advisors. Students will search for a lab to join throughout the term. The program manager will monitor to make sure the student is taking the required coursework. Once matched in the lab, the research mentor will become the student’s Capstone Advisor and work directly with the student to develop the capstone project and provide guidance throughout.

The 30-credit Master of Science in Biomedical Data Science and AI (MDSAI) program is a unique opportunity for students with a strong quantitative background to apply computational biology and data science techniques to biomedical problems.

During the first two semesters, students take courses immersed in concepts such as cellular and molecular biology, experimental design, statistical analysis, responsible conduct in research, and critical analysis and presentation of primary biomedical literature. Students also learn fundamental principles of data science applied to biomedical problems, including programming logic, computer architecture, algorithms, ML, and various AI tools. The third and fourth semesters focus on advanced electives and mentored laboratory research in the student’s area of interest, culminating in a Master’s capstone project.

For students who entered the MDSAI (MSBDS) program prior to 2025, please refer to your curriculum checklists on the Blackboard site.

First Year

• Students must select three of four fundamental core courses (6 credits total):

  • BSR1030 Core I: Biochemistry & Molecular Biology (2 credits)

  • BSR1031 Core II: Pharmacology & Drug Discovery (2 credits)

Year 2

• BDS9001 Biomedical Data Science Capstone Project (3-9 credits)

• Electives - Students complete their credit requirements with electives

Milestones

• Selecting a lab – During the first term, research mentors will be invited to present their research to the first year students. As a result students are encouraged to contact the research mentor to discuss joining the lab and work on a capstone project. Students are also encouraged to contact research mentors who did not present. Students are expected to join a lab by the end of the first term.

• Research Agreement Form – Once a student and a research mentor has agreed on joining a lab, students will need to submit the .

This chapter covers the MS in Biomedical Data Science and AI Program. Students can find the following information in this section.

• Program Information

• Program Requirements

For students who entered the program prior to 2025, please refer to your checklists on the Blackboard site.

Program Description

The Master of Science in Biomedical Data Science and AI (MDSAI) program at the Icahn School of Medicine at Mount Sinai integrates training and education in various aspects of biomedical sciences with machine learning, computer systems, and big data analysis, as well as access to large electronic medical record- linked biomedical repositories. Housed within the Graduate School of Biomedical Sciences, our program offers a unique opportunity for students with a strong quantitative background to hone the skills necessary to enrich—and ultimately to lead—the biomedical workforce of tomorrow.

Our curriculum provides rigorous training in both biomedical sciences, through an intensive semester- long core course, and quantitative data analysis, through innovative required and elective courses. Electives can include choices in our exciting biomedical innovation and entrepreneurship curriculum. Our goal is to motivate students to devise innovative approaches to challenging biomedical problems in order to revolutionize personalized medicine and healthcare.

Learning Objectives for the MDSAI Program Graduates of the MDSAI program will be able to:

• Apply computational, mathematical, and statistical reasoning to analyze and interpret complex biomedical data.

• Integrate machine learning and computer systems concepts with biomedical science to address challenges in health and disease.

• Conduct advanced analyses using big data tools and visualization techniques to derive meaningful insights from large-scale biomedical datasets.

Program Website:

Program Email: