Courses

Fundamentals of Transistors

Fundamental of BioMEMS and Micro-Integrated Systems

Photochemical Reactors: Theory, Methods & Applications

This class is divided into three modules. Module 1 addresses foundational issues of photochemistry and photochemical reactor theory. Module 2 addresses methods of reactor analysis, including analytical methods, numerical methods, and diagnostic procedures. Module 3 addresses applications of UV radiation, aimed at modification of composition in liquids, gases, and on solid surfaces.

Cell and Tissue Mechanics

This course develops and applies scaling approaches and simplified models to biomechanical phenomena at molecular, cellular, and tissue level.

Entrepreneurship in BME

Nanotechnology For Civil and Environmental Applications

This course will introduce students to the field of nanotechnology with a special emphasis on nanomaterials synthesis, characterizations and their applications in civil and environmental engineering. The specific applications will include, but not limited to, tailoring mechanical property, durability, self-cleaning, self-sealing, self-sensing, energy harvesting and other multi-functionality. It integrates the fields of materials science, civil engineering and electrical engineering. The basic concepts will be discussed including nano-scale effect, process-structure-property relationship, nano- and micro-structure property characterizations, multi-functional materials, nano-device fabrication and their applications for energy harvesting, water infiltrations and environmental sensing. lab will be provided to students enrolled in the course to learn nano and micro-structure characterizations skills.

Built Environment Modeling

More information coming soon.

Machine Learning I

An introductory course to machine learning, with a focus on supervised learning using linear models. The course will have four parts: (1) mathematical background on linear algebra, probability, and optimization. (2) classification methods including Bayesian decision, linear regression, logistic, regression, and support vector machine. (3) robustness of classifier and adversarial examples. (4) learning theory on the feasibility of learning, VC dimension, complexity analysis, bias-variance analysis. Suitable for senior undergraduates and graduates with a background in probability, linear algebra, and programming.

Applied Algorithms

Information Theory and Source Coding

A treatment of the basic concepts of information theory. Determination of channel capacity and its relation to actual communication systems. Rate distortion theory is introduced, and the performance of various source codes is presented. Offered in alternate years. Prerequisite: ECE 60000.

Introduction to Robotic Systems

The topics to be covered include: basic components of robotic systems; selection of coordinate frames; homogeneous transformations; solutions to kinematic equations; velocity and force/torque relations; manipulator dynamics in Lagrange's formulation; digital simulation of manipulator motion; motion planning; obstacle avoidance; controller design using the computed torque method; and classical controllers for manipulators. Basic knowledge of vector-matrix manipulations required.

Introduction to Game Theory

Reinforcement Learning: Theory and Algorithms

MOS VLSI Design

An introduction to most aspects of large-scale MOS integrated circuit design including: device fabrication and modeling; inverter characteristics; designing CMOS combinational and sequential circuits; designing arithmetic building blocks and memory structures; interconnect and timing issues; testing and verification; and system design considerations. Term projects involve the complete design of a functional logic block or system using CAD tools.

Artificial Intelligence

Introduction to the basic concepts and various approaches of artificial intelligence. The first part of the course deals with heuristic search and shows how problems involving search can be solved more efficiently by the use of heuristics and how, in some cases, it is possible to discover heuristics automatically. The next part of the course presents ways to represent knowledge about the world and how to reason logically with that knowledge. The third part of the course introduces the student to advanced topics of AI drawn from machine learning, natural language understanding, computer vision, and reasoning under uncertainty. The emphasis of this part is to illustrate that representation and search are fundamental issues in all aspects of artificial intelligence.

Failure Analysis

Introduction to failure analysis and prevention. Concepts of materials failure, root cause analysis, manufacturing aspects of failure, techniques for identifying failure, fracture, corrosion, wear, and case studies. Also includes business and entrepreneurship aspects.

Advanced Structural Steel Design

Design and behavior of plate girders; design of composite beam and column members; behavior and design of bolted and welded connections, including moment-resistant connections, seated connections, and gusset-plate connections.

Finite Elements in Elasticity

Fundamentals of theory of elasticity; variational principles; one-, two-, and three-dimensional elasticity finite elements; interpolation methods; numerical integration; convergence criteria; stress interpretation

Design Principles and Practices of Drinking Water Systems

Data Analysis, Design of Experiments and Machine Learning

This course will provide the conceptual foundation so that a student can use modern statistical concepts and tools to analyze data generated by experiments or numerical simulation. We will also discuss principles of design of experiments so that the data generated by experiments/simulation are statistically relevant and useful. We will conclude with a discussion of analytical tools for machine learning and principal component analysis. At the end of the course, a student will be able to use a broad range of tools embedded in MATLAB and Excel to analyze and interpret their data.

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