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Special Topics Courses

Fall 2022

Civil Engineering

CE540 – Pavement Design and Analysis

  • Instructor: Dr. Yang Lu
  • Description: Pavement design processes, materials selection and characterization methods, analysis and design of flexible pavements, analysis and design of rigid concrete pavements, pavement condition survey and ratings, distress evaluation, and maintenance and rehabilitation techniques.
  • Days and Times: Tues, Thurs, 4:30 pm – 5:45 pm

CE542 – Microstructure, Properties, and Performance of Concrete

  • Instructor: Dr. Yang Lu
  • Description: Basic properties of cements and mineral aggregates and their interactions in concrete from a microstructural perspective. Special emphasis on: properties of hydrated products and hardened concrete; modifications through admixtures; production, handling, and placement problems; specifications; quality control and acceptance testing; lightweight, heavyweight, and other special concrete mixtures. A supplemental understanding to the practical behaviors of concrete will be examined through the concrete’s microstructural characteristics. Integration of concrete’s sustainability. Project topics will include design and testing of advanced concrete concepts for durable, sustainable, and resilient infrastructure. Design projects will include lifecycle analysis of concrete such as materials selection, mix design, construction, maintenance, and final disposal.
  • Days and Times: Tues, Thurs, 1:30 pm – 2:45 pm

Electrical & Computer Engineering

ECE 662 Deep Learning with Python

  • Instructor: Dr. John Chiasson
  • Description: The first half of this course will cover deep learning using neural networks following the book of Michael Nielsen (free online). The book uses NumPy and Theano, but we will use NumPy and PyTorch. The adjective “deep” refers to using several layers of neurons in a neural network. We will first design a neural network to recognize the digits 0 through 9. This approach uses supervised learning which tries to globally minimize a cost function using the back propagation algorithm. The details of the back propagation algorithm will be studied along with various “tricks” to improve the performance of the network. The difficulties with training neural networks will be discussed and then convolutional neural networks (CNN) will be covered.
    The second half of the course will cover Attention and Transformers for image classification,
    sentiment classification, and natural language processing, all done in PyTorch.
  • Days and Times: Tues, Thurs, 6:00 pm – 7:00 pm


GEOG 561 – Remote Sensing and Image Analysis

  • Instructor: Dr. Ellyn Enderlin
  • Description: Fundamentals and
    applications of single frequency (including lidar), multispectral, and hyperspectral remote sensing for physical, natural, engineering, and social sciences. Emphasis on acquiring, processing, integrating, and interpretation of imagery. Completion of one year of college physics strongly recommended.
  • Days and Times: Tues, Thurs, 12 pm – 1:15 pm


MATH 597-002 – An Introduction to Element-Based Galerkin Methods

  • Description: Numerical methods are used in many areas of science and engineering to model the physical processes described by partial differential equations. In recent decades, there has been significant progress in developing advanced mathematical tools exhibiting high-order representation of the solution, geometrical flexibility to represent complex domains, and efficient use of massively parallel computing systems. This course introduces students to a class of high-order element-based Galerkin methods which has been successfully applied to various problems in computational sciences, including weather and climate prediction, geophysical modeling, biological and aeronautical engineering, etc.Although this course draws on a solid theoretical foundation (e.g., theory of interpolation, numerical integration, and function spaces), its main focus is on building the method, what the resulting matrices look like, and how to write algorithms for coding Galerkin methods. The methods covered are continuous Galerkin (i.e., finite/spectral element) and discontinuous Galerkin using nodal and modal basis functions. We will work with examples, including elliptic and hyperbolic partial differential equations. This course caters to students willing to expand their knowledge of numerical methods, particularly in application to science and engineering problems.
  • Days and Times: Mon, Wed 12:00 pm – 1:15 pm

Vertically Integrated Projects

VIP 500 Digital Humanities

  • Description: Students from technical and non-technical fields will work together to use computer vision, machine learning, and image processing to do research in the Humanities. Projects vary by semester but include reading historical documents, inferring print type, and converting printed sheet music.
  • Days and Times: TBA

Computer Science

CS 597 – Static Program Analysis

  • Description: Overview of fundamental static analysis concepts such as control-flow graphs, call graphs, data-flow analysis, and points-to analysis to reason about programs. Coursework includes hand-on interactions with static analysis tools and tuning them for performance and precision.
  • Days and Times: Tues, Thurs, 4:30 pm – 5:45 pm

Spring 2023


GEOPH 566 –  Snow and Ice Physics

  • Instructor: TBA
  • Description: Physics of water in its solid form at a wide range of spatial and temporal scales. Micro-scale processes including formation of solid precipitation, deposition, metamorphism, sublimation, melt, transition to firn, and ice deformation. Medium-scale processes including snow redistribution, energy balance, stratigraphy, slope stability, and avalanche dynamics. Large-scale processes including snowmelt, regional avalanche forecasting, glacier/ice sheet hydrology, ice cores, permafrost and sea ice.
  • Days and times: TBA

Computer Science

CS 569 – Human Computer Interaction

  • Instructor: Dr. Jerry Alan Fails
  • Description: Science-based theories and models of user interface design and development. Graphical user interfaces for desktop, web, and mobile devices. Usability assessment by quantitative and qualitative methods. Task analysis, usability tests, expert reviews, and continuing assessments of working products by interviews, surveys, and logging. Building of low-!delity paper mockups, and a high-!delity prototype using contemporary tools and programming environments. PREREQ: Regular admission to Doctor of Philosophy in Computing or Master of Science in Computer Science
  • Days and times: TBA