Data Science and Computation engineering, which includes computational synthetic biomedical engineering, is an emergent field that combines experimental, computational, and theoretical methods to solve challenging biomedical problems. Data Science and Computation engineering is based on a holistic approach of integrating large amounts of molecular information to elucidate the relationships between genotype and phenotype. This multiscale understanding of biological systems will help answer important questions about physiological systems, human disease, and therapeutic strategies. Data Science and Computation engineering is the design and construction of biological systems from molecular biological components for useful purposes. Such systems have applications in a wide range of complex biomedical problems.
Among the greatest challenges in these fields are how to obtain, manipulate, and interpret massive datasets. Research in this area also requires a multi-scale understanding of the system of interest, from molecules to cells, to organisms to ecosystems. Data Science and Computation engineering draw from a wide range of specialties – including mathematical modeling, scientific computing, signal processing, molecular biology, and high-throughput technologies – to provide a unique approach to solving biomedical problems.

Masters Students

M.S. and M.E. students in the Data Science and Computation track must successfully complete the core course requirements outlined below, as well as the total course credit hour requirement of the M.S. or M.E. degree programs.

  • More Information on the M.S. Program

Ph.D. Students

Ph.D. Qualifying Exam

Ph.D. students in the Data Science and Computation track are expected to have general knowledge in computational and numerical methods as well as in systems and synthetic biomedical engineering, with a specific focus in one biomedical engineering application. A student who, for example, applies computational methods to problems in cancer genomics, should have knowledge in both areas. The material for the exam will be based primarily on topics covered in the core courses. However, there will be a strong emphasis on the integration of computational approaches and the target area of application, material not likely to be covered explicitly in any course or textbook.

Program of Study

The course selection that will be appropriate for each student in the Data Science and Computation track will vary and depend highly on the specific research project in which the student participates. It will be especially important to choose courses that provide both the scientific background and the technical skills required to carry out this research. The Program of Study is a list created by the student and the supervisory committee of all courses to be completed by the student as part of the requirements for the Ph.D. The Program of Study requires formal approval by the student’s advisor, Dissertation Supervisory Committee, and Director of Graduate Studies. More Information on the Ph.D. Program

Life-Science Fundamentals

Students in this track must follow the standard guidelines relevant to their Life-Science Fundamental courses.

Computational Track Fundamentals

Selected: 2 of 4 required

  • BME 6002 – Molecular Biophysics
  • BME 6003 – Cellular Biophysics and Electrophysiology
  • BME 6250 – Biomechanics II
  • BME 6302 – Biomaterials II
  • BME 6401 – Medical Imaging Systems
  • BME 6440 – Neural Engineering

Computational Engineering Advanced Electives

Biological Chemistry

  • BLCHM 6400 – Genetic Engineering


  • BIOL 5110 – Molecular Biology and Genetic Engineering
  • BIOL 5120 – Gene Expression
  • BIOL 5140 – Genome Biology
  • BIOL 5920 – Advanced Eukaryotic Genetics
  • BIOL 6500 – Advanced Statistical Modeling for Biologists

Biomedical Informatics

  • BMI 6030 – Foundations of Bioinformatics
  • BMI 6420 – Advanced Biomedical Computing
  • BMI 6530 – Bioinformatics Data Integration and Analysis

Computer Science

  • CS 6140 – Data Mining
  • CS 6150 – Advanced Algorithms
  • CS 6220 – Advanced Scientific Computing II
  • CS 6350 – Machine Learning
  • CS 6530 – Database Systems
  • CS 7120 – Information-Based Complexity

Electrical and Computer Engineering

  • ECE 6520 – Information Theory
  • ECE 6530 – Digital Signal Processing
  • ECE 6540 – Estimation Theory
  • ECE 6550 – Adaptive Filters
  • ECE 6570 – Adaptive Control

Family and Preventive Medicine

  • PBHLT 6107 – Survival Analysis
  • PBHLT 7120 – Linear and Logistic Regression Models

Human Genetics

  • H GEN 6500 – Human Genetics
  • H GEN 6503 – Clinical Cancer Genetics


  • MATH 6770 – Mathematical Biology I/II
  • MATH 6810 – Stochastic Processes and Simulation I/II
  • MATH 6845 – Ordinary Differential Equations and Dynamical Systems
  • MATH 6855 – Survey of Numerical Methods
  • MATH 6860 – Introduction to Numerical Analysis I/II

Medicine Clinical Research Center

  • MDCRC 6150 – Foundations in Personalized Healthcare
  • MDCRC 6420 – Genetics of Complex Diseases

Molecular Biology

  • MBIOL 6420 – G3: Genetics, Genomes, and Gene Expression


Please contact Dr. Orly Alter or Dr. Tamara Bidone.