The Biomedical Informatics curriculum is designed to provide a uniform foundation in the essentials of the field while meeting the needs of a wide range of students with different backgrounds and career goals. The educational objectives consist of core courses, which provide a foundation in general Biomedical Informatics methods, techniques, and theories. The qualitative, quantitative, and information technology objectives enable students to apply these methods to one or more domain/training areas, which includes clinical informatics and clinical research informatics, translational informatics, and consumer health and public health informatics. In addition, the department offers a specialization in data science.

Students must demonstrate competence in areas that serve as a building block for Biomedical Informatics by successfully completing relevant graduate level courses. A number of Biomedical Informatics courses are offered to meet the educational objectives. Students may take courses to meet an educational objective and domain requirements that are not currently listed on the approved list below by emailing the syllabi to the graduate program manager for advance approval from the Training Committee. Students may consult with their academic or research advisors for suggestions on courses that may qualify for approval. 

Examples of Domain/Training Area Trajectories
PhDs and MAs:

Data Science: 2 Quant Objectives + 1 IT Objective + any 2 Domain Courses
Clinical Informatics/Clinical Research Informatics: 3 educational objectives (1 qual, 1 quant, 1 IT) + any 2 Domain Courses (if an MD or Nursing PhD, you may choose substitute courses with your academic advisor instead of domain courses if desired)
Translational/Bioinformatics: 2 Quant Objectives + 1 IT Objective + any 2 Domain Courses
Consumer Health Informatics/Public Health Informatics: 2 Quant Objectives + 1 IT Objective + any 2 Domain Courses

Data Science: 2 Quant Objectives + 2 Domain Courses
Clinical Informatics/Clinical Research Informatics: 2 Qual, Quant, or IT Objectives (at least one should be Quant or IT) + any 2 Domain Courses
Translational/Bioinformatics: 2 Qual, Quant, or IT Objectives + any 2 Domain Courses
Consumer Health Informatics/Public Health Informatics: 2 Qual, Quant, or IT Objectives + any 2 Domain Courses

PhD students may enroll in up to 20 credits per term without incurring additional charges. Any points in excess of 20 are not covered by the department, but may be covered by the student’s research advisor with prior approval before enrolling.  Depending upon degree type, in addition to the credit load from the core, objectives, and domain courses, students may also need to register for Research Projects (BINF G6001, BINF G9001 and BINF G9999), the Ethics Course (CMBS G4010), the MPhil Course (BINF G8010), or Research Seminar (BINF G4099). More information on these courses can be found at the bottom of the page.

Course Directory
Go to the online Columbia University Directory of Classes for the official and most up-to-date information on course offerings. If the instructor has made their course information open to the public, you may follow the link to CourseWorks, the University online course management system, by clicking the section link for each course.

Core Courses – 5 Courses

By the end of the core, students should be familiar with problems, issues, and applications in Biomedical Informatics, and are expected to apply general theories and methods to solve problems.

BINF G4000 Acculturation to Programming and Statistics (Prof. Karthik Natarajan, fall) This course is targeted for biomedical scientists looking for working knowledge of programming and statistics. This is a fast-paced, hands-on course covering the following topics: programming basics in Python, probabilities, elements of linear algebra, elements of calculus, and elements of data analytics. Students are expected to learn lecture material outside of the classroom and focus on labs during class. All labs evolve around real-world biomedical and health datasets. Only open to DBMI enrolled students in our MA or PhD program. BINF G4000 must be taken fall term of entry. Instructor provides placement exam on first day of class. Students may test out of the course based on placement exam results.

BINF G4001 Introduction to Computer Applications in Health Care & Biomedicine (Prof. Gamze Gürsoy, fallTaught on main (Morningside) campus.  An overview of the field of biomedical informatics, combining perspectives from medicine, computer science and social science. Use of computers and information in health care and the biomedical sciences, covering specific applications and general methods, current issues, capabilities and limitations of biomedical informatics. Biomedical Informatics studies the organization of medical information, the effective management of information using computer technology, and the impact of such technology on medical research, education, and patient care. The field explores techniques for assessing current information practices, determining the information needs of health care providers and patients, developing interventions using computer technology, and evaluating the impact of those interventions. BINF G4001 must be taken fall term of entry.

BINF G4002: Methods II: Machine Learning For Healthcare (Prof. Amelia Averitt, spring) Survey of the computational methods underlying the field of medical informatics. Explores techniques in mathematics, logic, decision science, computer science, engineering, cognitive science, management science and epidemiology, and demonstrates the application to health care and biomedicine.

BINF G4003: Methods I: Symbolic AI for Health Care (Prof. Chunhua Weng, fall) Survey of foundational symbolic methods for modeling health information systems and for making those models explicit and sharable.  The topics cover clinical terminologies (e.g., ICD-9, SNOMED-CT, MeSH, UMLS), biomedical ontologies (e.g., GO, Disease Ontology, PharmGKB), knowledge representation, computerized practice guidelines, semantic interoperability, and text processing. Prerequisites: Acculturation to Programming and Statistics (BINF G4000) or permission of instructor.

BINF G6002 Methods III: Research Methods (Prof. Lena Mamykina, spring) for Clinical, Public Health or Translational students. Provides an overview of research methods relevant to biomedical informatics. The overall goal of the course is to prepare the student to participate in and perform scientific research. Competencies of the course include learning to design a study of a biomedical informatics resource; perform quantitative and qualitative analysis relating to a biomedical informatics resource; and write a biomedical informatics-related research proposal. By the end of the course, all trainees must be able to write a biomedical informatics-related research summary and complete certification in responsible conduct of research.

BINF G4013 Biological Sequence Analysis (Prof. Richard Friedman, spring) for Bioinformatics students. Taken in lieu of BINF G6002 Research Methods (Mamykina). Biological Sequence Analysis introduces the basics of sequential, structural, and functional genomics.  The course is both a lecture and lab course, in which students learn the basic bioinformatic principles and apply these principles through laboratory exercises. The course accommodates both students with a computational background with little previous biology, and students from a primarily biological background, with little previous computation. Topics include biological databases, sequence comparison, database searching, multiple sequence alignment, biological regular expressions, profile methods (including hidden Markov models), protein and RNA structure prediction,  mapping, primer design, genomic analysis, molecular phylogetics, microarray and RNASeq analysis, pathway analysis, and machine learning for biomedical classification and outcome prediction.


Information Technology (IT)
BIST P8105 Data Science
COMS W4111 Introduction to Databases
COMS W4181 Security I
COMS W4156 Advanced Software Engineering
COMS W4444 Programming and Problem Solving
COMS W4995 Networks and Crowds
COMS W4995 Introduction to Data Visualization
COMS E6111 Advanced Database Systems
COMS E6998 Cloud & Big Data
COMS E6998 High Perf Machine Learning
COMS 6998 Machine Learning Datasets
CSOR W4231 Analysis of Algorithms
CSOR W4246 Algorithms for Data Science
EAEE E4009 GIS-Research, Environment, Infrastructure Management
EECS E6893 Information Processing: Big Data Analytics
ELEN E6883 An Introduction to Blockchain Technology
GR5243/GU4243: Applied Data Science – Hands-on Machine Learning with Python
IEOR 4526 Analytics on the Cloud
IEOR E4575 Operations Research: Policy for Privacy Technologies
IEOR E6998 001 Special Topics in Computer Science: Privacy Preserving Systems
QMSS G4063 Data Visualization
STAT GR5702 Exploratory Data Analysis and Visualization

Quantitative (Quant)
APMA E4300 Computational Math: Introduction to Numerical Methods
BINF GU4008 003
Special Projects: Advanced Machine Learning for Health and Medicine
BINF G5001
 Data Science for Mobile Health

BIST P6104/P6114 Introduction to Biostatistical Methods
BIST P8110
Applied Regression II
BIST P8116 
Design of Medical Experiments

BIST P8122 Statistical Methods for Causal Inference
BIST P8124 Graphical Models for Complex Health Data
BIST P8157 Longitudinal Data Analysis
BIST P9120 
Topics in Statistical Learning and Data Mining

BMEB W4020 Comp Neuro: Circuits In Brain
BMEN E4460 Deep Learning in Biomedical Imaging
BMEN E4480 Statistical Machine Learning for Genomics
CHEN 4180 Machine Learning for Biomolecular and Cellular Applications
CMBS 5305 Topics in Mathematical Genomics and Biology
COMS E6998 005 Topics in Computer Science: Representation Learning
COMS W4705 Natural Language Processing
COMS W4721 Machine Learning for Data Science
COMS W4761 Computational Genomics
COMS W4762 Machine Learning for Functional Genomics
COMS W4771 Machine Learning
COMS W4772 Advanced Machine Learning or COMS E6898 Topics: Information Processing: From Data to Solutions
COMS W4775 Causal Inference I
COMS W4995 Applied Deep Learning
COMS W4995 
Applied Machine Learning
COMS W4995 
Causal Inference for Data Science
COMS W4995 
Machine Learning Functional Genomics
COMS 6998-7
Statistical Methods for NLP

ECBM E4040 Neural Networks and Deep Learning
EECS E6691 Advanced Deep Learning
EECS E6720 Bayesian Models for Machine Learning
EECS E6893 Big Data Analytics
ELEN E4903 Machine Learning
HBSS 4199 or HBSS 4160 Introduction to Biostatistics (Teachers College –
IEOR E4540 Data Mining
IEOR 4720 Deep Learning
IEOR 4742 Deep Learning for OR and FE
POLS GU4716 Quantitative Methods II: Applied Regression and Causal Inference
QMSS GR5063 or QMSS G4063 Data Visualization 
QMSS G5016 Regression Modeling of Temporal Processes
QMSS GR5058 Data Mining For Social Science
QMSS GR5067 Natural Language Processing for Social Sciences
STAT W4026 Applied Data Mining
STAT W4107 or STAT GU4204 
Statistical Inference
STAT W4240 
Data Mining

STAT G6104 Applied Statistics
STAT G6509/GR6701 Foundations of Graphical Models

Qualitative (Qual)
BINF G4008 001 Intelligent Decision Support: History, Paradigms, Applications
BINF G4008 002 Interrogating Ethics and Justice in Digital Health
BINF G5000 Quality in Health Care
BINF G6002 Research Methods (core for clinical and translational, but can count as Qualitative objective for data science emphasis and non-postdoc MAs when not taken for core requirement)
COMS W4170 User Interface Design
NURS N9352 Qualitative Research Design & Methods
ORLJ 4009 Understanding behavioral research
ORLJ 5018 
Using survey research in organizational consulting

ORL 6500 Qualitative research methods in organizations: Design and data collection
ORL 6501 Qualitative research methods in organizations: Data analysis and reporting.
ORL 6518 Methods of case study and analysis.
B9506-001 (PhD) Organizational behavior


Students should be able to apply general methods and theories of informatics to one or more areas of specialization: data science, clinical informatics, translational informatics, bioinformatics, and public health informatics. MDs and Nurses are exempt from the clinical domain requirement and will pick substitute courses with their academic and/or research advisor.

Clinical Informatics/Clinical Research Informatics

B8221 Economics of Healthcare and Pharmaceuticals

BINF G4011 Acculturation to Medicine and Clinical Informatics
BINF G4018 Microbiome Data Analysis
BINF G5000 Quality in Health Care
BINF GU4008 003 Special Projects: Advanced Machine Learning for Health and Medicine
BIST P8133 Bayesian Analysis and Adaptive Designs
BIST P8140 Introduction to Randomized Clinical Trials
BIST P8144 Pharmaceutical Statistics
EPID P8450 Clinical Epidemiology
EPID P8477 Epi Modeling for Infectious Diseases
PATH G6003 Mechanisms in Human Disease

BINF G4006 Translational Bioinformatics
BINF G4013 Biological Sequence Analysis
BINF G4017 Deep Sequencing
BINF G4018 Microbiome Data Analysis
BIOL GR5073 Cellular/Molecular Immunology
BIOL GU4002 Macromolecular Structure and Interactions
BIOL W4510 
Genomics of Gene Regulation
BIOL W4799
Molecular Biology of Cancer
BIOL W6560
Human Evolutionary Genetics

BIOT W4200 Biopharmaceutical Development & Regulation
BIST P8119 Advanced Stat/Comp Methods Genetics/Genomics
BIST P8149 Human Population Genetics
BMEB W4020 Comp Neuro: Circuits in Brain
BMEN E4480 Statistical Machine Learning for Genomics

CHEN 4800 Protein Engineering
CMBS G5301 Topics in Math Genomics – Applications
CMBS 5305 Topics in Mathematical Genomics and Biology
COMS W4761 Computational Genomics
COMS W4762 Machine Learning for Functional Genomics
COMS E6998 Computational Methods/High Throughput Sequencing
ELEN E6010 Design Principles for Biological Circuits
EPID 8443 Microbiome & Health
PATH 4500 Cancer Cell Biology
PATH G6003 Mechanisms in Human Disease
PHAR G8001 Principles of System Pharmacology

Consumer Health Informatics and Public Health Informatics
BINF G4008 001 Special Topics in Biomedical Informatics: Intelligent Decision Support: History, Paradigms, Applications
BIST P6530 Issues & Approaches in Health Policy & Management
EHSC P6385/6
Principles of Genetics and the Environment I and II

EPID P6400/02 Epidemiology
EPID P8471 Social Epidemiology
HPMN P6503 Introduction to Health Economics
HPMN P8575 Cross National Health Policy
SOSC P8795 New Media and Health


Students should conduct independent research in Biomedical Informatics; including the ability to formulate a hypothesis, design a suitable experiment, and carry it out with sensitivity to ethical standards.

BINF G4099 Research Seminar;  ColloquiaIs familiar with investigators, institutions, projects, methods and theories in the field locally and at other institutions.
BINF G6001 Projects in Biomedical Informatics. Taken at least once for MA students and every fall and spring term for PhD students until the successful passage of Oral II/Depth Exam. MA students enroll for 3 points. First-year PhD students enroll for 6 points fall and spring terms; 9 points fall and spring terms in 2nd year; 12 points fall and spring terms each subsequent year until enrollment in BINF G9001. NLM funded postdoctoral MA students enroll for 6 points fall and spring terms of the first year, and 9-12 points for fall and spring terms of their 2nd and 3rd years, depending upon their course load.
BINF G8001 Independent Readings
BINF G8010 Teaching Experience; Teaching can prepare educational materials, deliver lectures, and evaluate students.
BINF G9001 Doctoral Research in Biomedical Informatics. Taken the term following the successful passage of the 2nd preliminary PhD exam, the Oral II/Depth Exam.
BINF G9999 Doctoral Dissertation. Taken in the final term of enrollment for PhD students along with BINF G9001.
CMBS G4010 Responsible Conduct of Research and Related Policy Issues

Other Requirements

Ethics Course
All doctoral and postdoctoral students are required to take the Ethics Course (CMBS G4010 Responsible Conduct of Research and Related Policy Issues, 1 pt) during the Spring semester of their first year in the program. The ethics course satisfies a National Institutes of Health requirement.

MPhil Course 
BINF G8010 MPhil Teaching Experience, 2 pts
Serving as a Teaching Assistant (TA) is a GSAS degree requirement for PhD students and a DBMI degree requirement for postdoctoral students. PhD students TA for 2 courses. (BINF G8010 MPhil Teaching Experience, 2 pts) MD-PhD students TA for 1 course. Postdoctoral MA students TA for one (two-year postdoctoral students) or two courses (three year postdoctoral students). Students are solicited for TA preferences over email Spring term. Final assignments are made by the Graduate Program Director.

Research Seminar
BINF G4099 Research Seminar, 1 pt, P/F
Enrollment in the Research Seminar is required for PhD students. Bioinformatics students may attend the C2B2 seminar in their second year and each subsequent year in lieu of the Research Seminar. Full-time MA students are expected to enroll in the Research Seminar. Part-time MA students are not required to enroll if doing so would cause them to enter the next residence unit category. However, they are expected to attend whenever feasible. Passing the Research Seminar is dependent upon attendance.