Courses

• ENG BE 802: Teaching Practicum II
  Practical teaching experience. 4 cr
• ENG BE 900: Research
  Participation in a research project under the direction of a faculty advisor. Includes research leading to the development of an MS thesis proposal or PhD prospectus, as well as the work necessary to generate an original MS thesis or PhD dissertation. Variable cr
• ENG BE 951: Independent Study
  A course of reading under the direction of a faculty advisor covering subject matter not available in a lecture course. Final report or examination normally required. Variable cr
• ENG BE 952: Mentored Project
  Students who are pursuing a project to satisfy their practicum requirement for the MS degree will register for up to 4 credits of this course. The course may be taken more than once for up to four credits (ex. two credits in Fall, two credits in Spring). Students will select a suitable project with a mentor that can be completed in 4 credits. The BME Graduate Committee must approve all proposed projects. Each student must write a project report and/or deliver a formal presentation at the end of the course that will be graded by their project mentor. All reports and presentation materials must be received by the BME Graduate Committee.
• ENG BF 527: Appl Bioinfmtcs
  This course description is currently under construction.
• ENG BF 541: Bioinformatics Internship
  Internships provide the bridge between classroom/laboratory study and ?real-world? employment. Each student must complete an internship with a minimum of 400 hours of on-the-job experience (e.g., 10 weeks full-time work in the summer). The format is very flexible, and part-time internships running concurrently with classes or employment are acceptable. Students must consult with their academic advisor to assess the suitability of a proposed internship.
• ENG BF 571: Dynamics and Evolution of Biological Networks
  The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks.
• ENG BF 690: Bioinformatics Challenge Project
  Project course for first year Bioinformatics graduate students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.
• ENG BF 752: LAW&Eth Bio Sci
  This course description is currently under construction.
• ENG BF 768: Biological Database Analysis
  Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data. Introduces the relational database query language SQL. Describes methods for ensuring data consistency and data retrieval efficiency. Object-oriented programming is introduced primarily as an implementation aid for constructing, loading, and accessing databases. Utilizes web-based programming tools to implement user access to databases. Emphasis will be on solving problems associated with large and complex data sets. Course includes a final project implementing a database using real data from a local biology/medical school lab.
• ENG BF 778: Physical Chemistry for Systems Biology
  This course introduces students to quantitative modeling in bioinformatics and systems biology. We begin with basic principles of statistical thermodynamics, chemical kinetics, with selected applications in biomolecular systems. Next we describe molecular driving forces in biology, and computation with biomolecular structures. Finally we discuss quantitative models of biomolecular networks, and design principles of biological circuits.
• ENG BF 810: Phd Lab Rotat'N
  This course description is currently under construction.
• ENG BF 821: Bioinfo Gradsem
  This course description is currently under construction.
• ENG EC 311: Introduction to Logic Design
  Introduction to hardware building blocks used in digital computers. Boolean algebra, combinatorial and sequential circuits: analysis and design. Adders, multipliers, decoders, encoders, multiplexors. Programmable logic devices: read-only memory, programmable arrays, Verilog. Counters and registers. Includes lab. 4 cr,
• ENG EC 327: Introduction to Software Engineering
  This course aims to introduce students to software design, programming techniques, data structures, and software engineering principles. The course is structured bottom up, beginning with basic hardware followed by an understanding of machine language that controls the hardware and the assembly language that organizes that control. It then proceeds through fundamental elements of functional programming languages, using C as the case example, and continues with the principles of object-oriented programming, as principally embodied in C++ but also its daughter languages Java, C#, and objective C. The course will conclude with an introduction to elementary data structures and algorithmic analysis. Throughout, the course develops core competencies in software engineering, including programming style, optimization, debugging, compilation, and program management, utilizing a variety of Integrated Development Environments and operating systems. 4 cr.
• ENG EC 330: Applied Algorithms for Engineers
  Introduction to the general concept of algorithms. Efficiency and run-time of algorithms. Graph algorithms, priority queues, search trees. Various approaches to design of algorithms and data structures, together with their applications to numerical and non-numerical problems. 4 cr.
• ENG EC 381: Probability Theory in Electrical and Computer Engineering
  Introduction to modeling uncertainty in electrical and computer systems. Experiments, models, and probabilities. Discrete and continuous random variables. Reliability models for circuits. Probability distributions. Moments and expectations. Random vectors. Functions of random variables. Sums of random variables and limit theorems. Signal detection and estimation. Basic stochastic processes. Discrete-time Markov chains. State-diagrams. Applications to statistical modeling and interpretation of experimental data in computer, communication, and optical systems. 4cr,
• ENG EC 401: Signals and Systems
  Cannot be taken for credit in addition to ENG BE 401. Continuous-time and discrete-time signals and systems. Convolution sum, convolution integral. Linearity, time-invariance, causality, and stability of systems. Frequency domain analysis of signals and systems. Filtering, sampling, and modulation. Laplace transform, z-transform, pole-zero plots. Linear feedback systems. Includes lab. 4 cr.
• ENG EC 402: Control Systems
  Analysis of linear feedback systems, their characteristics, performance, and stability. The Routh-Hurwitz, root-locus, Bode, and Nyquist techniques. Design and compensation of feedback control systems. 4 cr.
• ENG EC 410: Introduction to Electronics
  Principles of diode, BJT, and MOSFET circuits. Graphical and analytical means of analysis. Piecewise linear modeling; amplifiers; digital inverters and logic gates. Biasing and small-signal analysis, microelectronic design techniques. Time-domain and frequency domain analysis and design. Includes lab. 4 cr.

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