Biocomputational Engineering Major
Program Director: Ian White, Ph.D
Assistant Program Director: Lan Ma, Ph.D
Biocomputational engineering brings together the field of bioengineering, a discipline grounded in fundamentals of physics, chemistry, and biology, with computation and data science, which enhances the value of all fields. The objective of the biocomputational engineering program is to provide a breadth of fundamentals in biology and quantitative problem solving while developing skills in computation and data science that can be applied to the modeling of complex biological systems and the analysis of complex biological data sets in order to create new knowledge from the molecular to organ to system levels, and to develop innovative processes for the prevention, diagnosis, and treatment of disease. The synthesis of bioengineering, computation, and data science gives the graduates unique capabilities to solve existing and emerging challenges of the modern medical world.
Admission to the Major
Prior to being admitted to the Biocomputational Engineering major, students must complete the prerequisite math/science courses, lower-level General Education requirements (or an associate's degree), and a total of 60 credits. Students are welcome to apply as transfer students from community college or four-year institutions. For more information regarding admission to the Biocomputational Engineering major, visit http://biocomp.umd.edu/admissions/.
Program Educational Objectives
The BCE program provides students with a foundation in quantitative problem solving, engineering, and biology. In addition, the program provides students with data science skills. The students' educational outcomes position them for careers in data science, in particular in the biomedical and biotechnology fields.
Our graduates are grounded in fundamentals that will serve them throughout their professional careers. They will have an understanding of human behavior, societal needs and forces, the dynamics of human efforts, and the impact of those efforts on human health and our environment. With these underpinnings and abilities, we have defined three Program Educational Objectives we expect our graduates to attain in 3-5 years after graduation:
- Produce graduates with the scientific educational depth, technical skills, and practical experiences to be competitive for placement in Biocomputational Engineering careers or post-graduate educational pursuits;
- Produce graduates with an awareness of their field and an understanding of how they can address the data-driven computational biomedical challenges facing society in both the near and long term;
- Produce graduates with a foundation in professional ethics who will actively seek to serve their profession, to promote equity and justice through technology, and to positively impact society.
Student Learning Outcomes
- An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
- An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
- An ability to communicate effectively with a range of audiences.
- An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
- An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
- An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
- An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Prior to being admitted to the Biocomputational Engineering major, students should have completed the Engineering LEP gateway courses, basic math/science courses, lower-level general education requirements (or an associate's degree from a Maryland public institution), and 60 credits.
|Differential Equations for Scientists and Engineers
|General Physics: Mechanics and Particle Dynamics
|General Physics: Vibration, Waves, Heat, Electricity and Magnetism
|General Physics: Mechanics, Vibrations, Waves, Heat (Laboratory)
|Introduction to Engineering Design
|General Chemistry for Engineers
|General Chemistry Laboratory for Engineers
|Principles of Molecular & Cellular Biology
|Biology for Engineers
|MATLAB programming course -- e.g. BIOE241 or equivalent
|Lower-level general education requirements or A.A./A.S. degree from a Maryland public institution
|Introduction to Biocomputational Engineering
|Python for Data Analysis
|Object Oriented Programming in C++
|Machine Learning for Data Analysis
|Applied Linear Systems and Differential Equations
|Statistics, Data Analysis, and Data Visualization
|Biomolecular Engineering Thermodynamics
|Computational Fluid Dynamics and Mass Transfer
|Quantitative Molecular and Cellular Biology
|Molecular Techniques Laboratory
|(Imaging and Image Processing)
|Finite Element Analysis
|Computational Systems Biology (Computational Systems Biology)
|Senior Capstone Design in Biocomputational Engineering (Senior Capstone Design in Biocomputational Engineering)
|Professional Writing Requirement
Students are required to take four technical electives (12 credits). The courses must be selected from an approved list of engineering and biology courses; the list will be updated regularly by the program director. At least two of the elective courses must be from the category of engineering, mathematics, or programming, while at most two of the electives can be from the category of biology courses. The program will offer electives; at the same time, the program will arrange for opportunities for electives outside the program, including USG programs offered by other universities.
|Possible technical electives
|Research Methods in Biological Data Mining
|(Advanced Programming in Python)
|(Data Analysis with R)
|Applied Computer Vision
|(Computational Molecular Dynamics)
|(Multiscale Simulation Methods)
|(Modeling Protein Folding)
|(Spatial Control of Biological Agents)
|Bioinformatics Engineering (Bioinformatics Engineering)