Biomedical Informatics Master's Program

Must complete 32 credit hours (23 core/9 electives) from the following courses:

• Elements in Biomedical Informatics: BIMI 6100 Fall, 4 credit hours

  The objective of this course is to teach graduate students the necessary backgrounds of biomedical informatics with computer applications in biomedicine and health care. The biomedical data, their acquisition, storage, ethics, and use in biomedical decision-making for probabilistic clinical reasoning will be introduced. The basic underlying cognitive science issues will be studied in which information is processed by the human mind and biomedical informatics. Students will acquire essential concepts for biomedical computing, system design, and engineering in health care. The application of natural language, text processing, and imaging structures in biomedicine will be discussed. Finally, the challenges associated with technology assessment and the evaluation of clinical information systems, imaging systems in radiology, computers in medical education, and health care financing are introduced. Student presentation/discussion sessions following each block will allow students to present (4 times) and discuss the principles and the applications of the latest biomedical informatics technologies.
   

• Introduction to Data Science for Biomedical Informatics: BIMI 6200, Fall, 3 credit hours

  Data science has become a central element of both scientific research and industry. The objective of this course is to teach graduate students the necessary backgrounds of data science biomedical informatics with MySQL, R, Python, and MATLAB programs. The overview of biomedical data science- introduction to database, biological database, data storage, and biomedical data showcase will be introduced. The basic underlying data structures, algorithms, and inference from a biomedical informatics point of view will be studied. Students are expected to become comfortable with SQL, R, Python, and MATLAB programming along with the structure and syntax of functions. Advanced coding skills, techniques, ideas, and new packages (library) management will be accomplished practically. Exploratory analysis, data visualization, report prosecution techniques and data science and analytics in health care will be discussed. Finally, necessary concepts of imaging with multi-omics data integration are introduced. Student presentation/discussion sessions following each block will allow students to present (4 times) and discuss the problem of programming.
   

• Fundamentals of Data Analytics: BIMI 6300, Spring, 3 credit hours

  The objective of this course is to teach graduate students to understand the current state of multivariate and multi-view statistical analysis in an age of complex biomedical data. Overview of multivariate and multi-view data will be addressed. The basic underlying linear regression (e.g., multivariable, multivariate, logistic, and ridge recession) will be studied and practiced with biomedical data. Sparse linear models along with the elastic net, linear dimensionality reduction methods (supervised and unsupervised), linear discriminate analysis, and linear cluster analysis will be discussed with practical applications. As advanced tools, adaptive basis function models, graphical models, artificial neural networks, penalized multivariate analysis, and fusion-based approaches will be introduced to evaluate biomedical data. Finally, students will learn how to use R /Python as a tool and apply the multivariate and multi-view data processing techniques to solve some biomedical problems such as gene-gene interaction, prediction, classification, and multi-omics integration analysis. Student presentation/discussion sessions following each block will allow students to present (4 times) and discuss the principles and the applications of the latest multivariate technologies.
   

• Health Informatics: BIMI 6400, Spring, 3 credit hours

  The goal of this course is to teach graduate students for the increasing necessity for computation in modern health informatics research. Foundations of Health informatics, Health informatics database, and theoretical foundations of health informatics will be discussed. Models, Theories and Research for program evaluation, Technical Infrastructure to support healthcare, administrative application in Healthcare, clinical decision support systems in healthcare and public health informatics will be studied. The engaged ePatient, social media tools for practice and education, personal health records, and mHeal will be introduced. Finally, Managing the life cycle of a health information system, usability, standards, safety, analytics, governance structures, legal and regulatory issues in health informatics will be studied.

• Statistical Machine and Deep Learning in Biomedical Practice: BIMI 7100, Fall, 3 credit hours

  The objective of this course is to teach graduate students for a comprehensive understanding of automatically detectable patterns in data, and then to use the identified patterns to predict future data. A background of probability theory, functional analysis, and overview of statistical machine learning with data representation and features engineering will be launched. A popular machine learning tool for classification and regression, support vector machine and its variants will be studied and practiced with biomedical data. The notion of positive definite kernel and kernel-based methods will be discussed with real-world applications. Nonlinear dimensionality reduction techniques, including manifold learning (t- SNE and UMAP) will be introduced and evaluated in Biomedical data. Principles of Bayesian statistics, Gaussian processes, Markova and hidden Markova models, and Markov random fields will be discussed with applications. Deep neural networks-based learning approaches and adversarial learning approaches will be considered for biomedical data. Finally, students will learn how to use Python as a tool and apply machine learning, and deep learning techniques to solve some real-world biomedical problems. Student presentation/discussion sessions following each block will allow students to present (4 times) and discuss the principles and the applications of the latest multivariate technologies.
   

• Biomedical Informatics Workshop: BIMI 7210, Fall & Spring, 1 credit hours

  The Biomedical Informatics Workshop is designed to promote reading, writing, oral presentation skills, and critical analysis of biomedical data, and research related to are a key tool for critically appraising articles and keeping up to date with the current literature. BIMI 7210 Workshop I - IV (1 credit hour) allows credit for participation in these journal clubs.

• Biomedical Informatics Research Methods: BIMI 7220, Fall, 4 credit hours

  The Biomedical Informatics Workshop is designed to promote reading, writing, oral presentation skills, and critical analysis of biomedical data, and research related to are a key tool for critically appraising articles and keeping up to date with the current literature. BIMI 7210 Workshop I - IV (1 credit hour) allows credit for participation in these journal clubs.

• Biomedical Informatics Research Methods: BIMI 7230, Spring, 2 credit hours

  The Biomedical Informatics Workshop is designed to promote reading, writing, oral presentation skills, and critical analysis of biomedical data, and research related to are a key tool for critically appraising articles and keeping up to date with the current literature. BIMI 7210 Workshop I - IV (1 credit hour) allows credit for participation in these journal clubs.

• Biomedical Data Science with Cloud Computing: BIMI 7300 Fall, 3 credit hours

  The objective of this course is to teach graduate students the necessary backgrounds of programming and high-performance computing techniques in data science with cloud computing. Background of the computer inside and overview of optimization tools will be reviewed and discussed. Advanced technologies, high-performance computing, cloud computing, and MapReduce will be learned. Open- source software Hadoop and Spark and cloud service (Amazon, Google, and Microsoft) will be discussed in a step-by-step process to solve problems in Big Data and computation. The relational data management, the next generation digitally enhanced science, and interactive visualization tools will be addressed and implemented with biomedical data. Finally, students will learn the latest research topics on cloud platforms and can understand some commercial cloud systems through projects.

• Genomic Sequence and Omics Data Analysis: BIMI 7500, Spring, 3 credit hours

  The objective of this course is to teach graduate students to understand why big data are assuming a crucial role in Biomedical Informatics. Big data overview, big data in data science, and big data in biomedical informatics will be addressed. Details of big data visualization tools and compressive results interpretation will be introduced and performed. Fundamental and advanced (machine and deep learning) big data analytics approaches will be studied for functional applications. The typical use such as disease privation, multi-omics, medical imaging, and brain imaging, will be focused on understanding the efficiency of Big data. Finally, the audience will discover Big data management challenges, technologies, and leadership skills. Student presentation/discussion sessions following each block will allow students to present (4 times) and discuss the principles and the applications of the latest big data technologies.

• Special Topics: BIMI 7980, Fall & Spring, 1-6 credit hours

  The Special topics course as designed by visiting or permanent faculty. For description, consult the department. Course may be repeated up to unlimited credit hours under separate titles.

• Independent Study: BIMI 7990, Fall & Spring, 1-6 credit hours

  The first two years are generally devoted to coursework and research. Subsequent years focus on independent research that culminates in a dissertation. Students accepted into the BMS BMI track are required to join faculty in the Division of Biomedical Informatics and Genomics but may consider one of other faculty outside of the Division for committee members or co-mentors (with approval of the Division Chief). BIMI 7990 Directed Independent Study allows credit for independent research under the direction of a mentor or co-mentor in the Division of Biomedical Informatics and Genomics.

• Research Methodology of Biomedical Informatics: BIMI 8500, Fall & Spring, 2 credit hours

  This course consists of formal oral presentation and critical discussion to familiarize students, postdoctoral fellows, and outside speakers with the innovative research topics and methodology development of biomedical informatics. This course will use the combined format of a journal club as well as a research meeting. Students are required to present a seminar on a currently published peer- reviewed research paper approved by the course director, on a research idea, or on their own research work. The seminar will cover the advanced biomedical informatics topics, including bioinformatics, genome informatics, transcriptomics, metagenomics, metabolomics, proteomics, biomedical imaging, drug repurposing, health informatics and telehealth, public health informatics, etc. Students are required to participate in discussions following the presentation. In the discussion, students may also talk about their concerns regarding research, scientific paper writing, and grant writing with their peers and faculty members.

• Master’s Thesis Research: BIMI 9980, Fall & Spring, 0 credit hours

  The goal is to develop a deeper understanding of a research field in biomedical informatics and gain capability to design a conceptual framework, conduct data analysis, and write a dissertation proposal. Designation: This course is for graduate students.

Applicants must:

• Earn at least a baccalaureate degree (or its equivalent) from an accredited institution of higher education prior to the start of classes
• Show a strong science coursework foundation
• Demonstrate English language proficiency through standardized testing, if English is not your native language

A Bachelor’s degree (B.S. or B.A., preference majors are computer sciences, biological sciences, statistics, biostatistics, bioinformatics, public health, engineering, biomedical engineering, and mathematics) with GPA ≥ 3.1 on a 4.0 scale.

In addition, TOEFL ≥ 85 for applicants whose first language is not English. Language testing will be waived if the student has received a degree from an accredited U.S. institution.

The following documents must be submitted for candidates to receive full consideration:

• Transcripts: Official transcripts from all previous undergraduate and graduate studies are required. Applicants whose highest degree is from a foreign university must have their credentials evaluated course by course; even if the primary language of instruction is English and/or the school uses a 4-point grading system. International applications will not be reviewed without this evaluation. Applicants are encouraged to have their institution send credentials electronically to bimi@tulane.edu.
   
• Personal Statement: Applicants must prepare a personal statement that demonstrates how their career goals and interests align with their selected degree program. Students will submit their statements through the application portal.
   
• Required Letters of Recommendation: Applicants must request letters of recommendation through the application portal. Recommenders can include professors, employers, or other academic mentors. Letters of recommendation should indicate the applicant’s academic history and/or potential for success in the Biomedical Informatics and Genomics Graduate Program.
   
• Test Scores: The GRE test score submission is not a requirement at this time; however, we encourage you to submit your test scores if available.
   
• English Proficiency: Applicants who are not native speakers of English must demonstrate an adequate command of the English language. Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) scores, or any other evidence of English proficiency is required. A minimum TOEFL score of ≥ 85 or 7.0 overall band score for the IELTS is normally required. English language testing requirements are waived for applicants who have received a degree from an institution where instruction is conducted in English. All application materials must be in English.

We are currently accepting applications for the academic year 2025-2026 and will remain open until July 15, 2025.  Applications are only reviewed once all required documentation is received. Applicants will be admitted on a rolling basis, therefore, early submission of applications is highly encouraged. There is no application fee.

For questions regarding the program, please contact us at bimi@tulane.edu.

The core curriculum emphasizes biomedical applications of data science and big data knowledge. Students must complete a minimum of 30 credit hours, with 21 being core credit hours and at least 9 elective credit hours.

All students are required to take at least one biologically relevant elective course within their elective credit hours. Other elective courses may be substituted with permission of the Program Director.

Visit the Tulane Catalog for more information on the required coursework and all elective options.

There is no application fee.

Your personal statement should describe your purpose in pursuing a MS in Biomedical Sciences. Include an explanation of any past research and future research interests, indicating how they are related to your professional goals. Please limit your statement to approximately two pages. You will also be asked to include the names of any Tulane School of Medicine faculty members whose research is of interest.

Applicants must request one letter of recommendation in the application system to be able to submit an application. Letters from professors who know your academic history and capabilities or researchers with whom you have worked are weighted much more heavily than friends, acquaintances or people you know in other capacities. The letters should address your potential to succeed in Tulane’s Graduate Program in Biomedical Sciences.

Recommenders will receive an email with instructions for uploading their letter into the application system along with a rating scale for other attributes.

Official transcripts of all undergraduate and graduate study programs are required. Electronic official transcripts from the school or a transcript service are preferred, sent to bimi@tulane.edu.  We also accept mailed sealed official transcripts, our institution code is 6178. Photocopied transcripts, faxed transcripts, and student copies of transcripts are not acceptable for admission. Final transcripts indicating receipt of degree must be submitted before the beginning of the incoming semester. Email and postal mail addresses are provided in the application system. International applicants whose highest degree is from an international university must have those transcripts evaluated by a service (www.naces.org) and the evaluation must be received before the admissions committee will review your application.

In addition to admissions requirements, applicants who are not native speakers of English must demonstrate an adequate command of the English language. Test of English as a Foreign Language (TOEFL) scores, International English Language Testing System (IELTS) scores, or other evidence of English proficiency are required. For more information about the tests, visit the TOEFL or IELTS website or contact ETS. When requesting ETS to send your score to the Graduate Program in Biomedical Sciences, please use code 6178. English language testing requirements are waived for applicants who have received a degree from an institution where instruction was conducted in English.

For the MS program, minimum scores of 90 for the TOEFL or 7.0 overall band score for the IELTS are normally required. Applicants with lower scores will be evaluated based on their complete application package. An applicant whose competence in English is unproven or insufficient may be admitted with probationary status on the condition that competence will be proven or improved. The student may be required to prove competence by earning an acceptable score on a test of English. A student who scores below the acceptable level of competence may be required to register for less than a full graduate program and to take English as a Second Language instruction without credit until the ESL program director certifies the student's competence.

Applicants whose highest degree is from a foreign university must have their credentials evaluated. This evaluation is required even if the primary language of instruction is English and/or the school uses a 4 point grading scale. Applications will not be reviewed without this evaluation.  There are no exceptions. The university will accept evaluations done by any credentialing agency listed on the National Association of Credential Evaluation Services web site (http://www.naces.org). Tulane does not have a preference for which credentialing agency you use.