Hide Advanced Options
Courses - Spring 2023
ENBC
Biocomputational Engineering
ENBC312
(Perm Req)
Object Oriented Programming in C++
Credits: 3
Grad Meth: Reg, P-F, Aud
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Provides a solid foundation for object-oriented programming using the C++ programming language. It introduces fundamental conceptual tools and their implementation of object-oriented design and programming such as: object, type, class, implementation hiding, inheritance, parametric typing, function overloading, polymorphism, source code reusability, and object code reusability. Fundamental principles of object-oriented design and programming are stressed while covering the language details.
ENBC321
Machine Learning for Data Analysis
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: Minimum grade of C- in ENBC311 and ENBC332.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Instructs students in the fundamentals of machine learning methods through examples in the biological phenomenon and clinical data analysis. This course is designed to share knowledge of real-world data science and aid to learn complex machine learning theory, algorithms, and coding libraries in a simple way. Students will learn the machine learning theory, but they will also get hands-on practice building their models using programming tools such as Python and R.
ENBC342
(Perm Req)
Computational Fluid Dynamics and Mass Transfer
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: Minimum grade of C- in ENBC341; and minimum grade of C- in BIOE241 or approved prior study in Matlab; and must have earned a minimum grade of C- or be concurrently enrolled in ENBC331.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Credit only granted for: BIOE331 or ENBC342.
Principles and applications of fluid mechanics with a focus on bioengineering topics. Content includes conservation of mass, momentum, and energy, as well as the application of these fundamental relations to hydrostatics, control volume analysis, internal and external flow, and boundary layers. Applications to biological and bioengineering problems such as tissue engineering, bioprocessing, imaging, and drug delivery.
ENBC352
(Perm Req)
Molecular Techniques Laboratory
Credits: 2
Grad Meth: Reg, P-F, Aud
Prerequisite: Minimum grade of C- or concurrently enrolled in ENBC351.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Provides students with the opportunity to learn how biology and engineering can synergistically contribute to our understanding of biological and biomedical problems. Students will gain hands-on experience through wet lab experiments in basic techniques relevant to bioengineering.
ENBC424
AI for Biocomputational Engineering
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: Minimum grade of C- in ENBC423.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in Biocomputational Engineering major.
Introduces students to the basics and modern deep learning models in the Artificial Intelligence field applied to computer vision problems. The course will teach ResNet for image Classification/Regression, and U-Net for semantic segmentation. The course emphasizes applications of computer vision in medical imaging and cell biology, such as detecting brain tumor using semantic deep learning segmentation network and track dynamic measurements of live 3T3 cells through time using recurrent neural network. Computer vision techniques will be demonstrated using software packages implementing bio-image informatics methods, including ImageJ (FIJI), Python with Keras Tensorflow, Pytorch, and Matlab.
ENBC425
Imaging and Image Processing
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: Minimum grade of C- in ENBC332, ENBC311, and ENBC321.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in Biocomputational Engineering major.
Instructs students in the fundamentals of biomedical imaging and image processing methods through the physical principles behind major medical imaging modalities, including X-Ray, Computed Tomography (CT), and magnetic resonance imaging (MRI). This course is designed to instruct students in mathematical tools for extracting information from images. There will be real-world assignments and images, which aid in learning complex theories, applications, and coding libraries in a simple way.
ENBC441
Computational Systems Biology
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: ENBC351.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Introduces quantitative principles for studying biological systems using computational modeling and simulations. Topics include continuous modeling of systems using ordinary differential equations, discrete modeling using Boolean networks and Markov chains, probabilistic modeling through Bayesian networks, stochastic modeling via Monte Carlo and Brownian and molecular dynamics, model optimization, and parameter estimation. Simulation algorithms that implement these approaches will be introduced through MATLAB programming.
ENBC455
Bioinformatics Engineering
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: ENBC311.
Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Introduces students to the core principles of bioinformatics while encouraging students to apply their programming skills to real-world biological problems. Students will learn to utilize Python to process data sets.
ENBC491
Senior Capstone Design in Biocomputational Engineering
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: Must have completed 18 credits in ENBC courses.
Senior design project in which students work collaboratively in a Capstone team on a biocomputational topic. Under the guidance of a Capstone mentor, students will set project goal(s), propose project design, identify methodology, implement solutions, evaluate project results, and iterate among these steps if needed. Students will present their completed biocomputational projects to the public in a poster session. To complement the project design and implementation, students will also learn pressing ethics and DEI concerns emerging in artificial intelligence (AI), and recognize ethical responsibilities in biocomputational tasks.