Courses

Data Analytics

Radio Frequency Integrated Circuits

CSE Seminar

Digital Systems Design Automation

This course will provide an introduction to the tools used to design and analyze circuits at the logic level of abstraction (where circuits are composed of gates and flip-flops). Most digital chips used in computing and electronic systems (including microprocessors, graphics processors, chips used in network routers, cell phones, digital audio/video appliances, automotive electronics) are entirely or largely designed using EDA tools. This course will focus on the foundations of logic-level EDA tools, including the design of exact and heuristic algorithms that form the basis for VLSI Computer-Aided Design. Topics covered include an overview of the IC design flow and levels of abstraction, synthesis of two-level (AND-OR / PLA) circuits, multi-level logic synthesis and technology mapping, sequential circuit synthesis, Logic-level verification using Boolean Satisfiability and BDDs, Timing Analysis, Power analysis and Reduction, and design techniques for emerging nanoscale technologies.

Numerical Electromagnetics

Machine Learning for Bioinformatics and Healthcare

Powder Storage and Flow

Combustions of Energetic Materials

Intelligent Systems

Quantum Circuits and Systems

Through five decades of continued transistor scaling, the size of unit computing has almost reached its fundamental size limit, thus creating a plateau in performance for traditional CMOS based circuits. While the speed of CMOS technology is relatively saturated, quantum computation seems to be the next landmark technology in computing. By relying on quantum principles and properties - most importantly superposition and entanglement - Quantum Computers demonstrate an almost miraculous capacity to solve seemingly insurmountable problems. However, the interfacing, readout and electronic control circuitry around the Quantum Computing Core still uses CMOS technologies at room temperature, and there is a strong need to place the electronic circuitry near the Quantum Core (at a few milli-Kelvins) for scalability and performance, which leads to an entirely new paradigm of CMOS-based circuits, which is celled Cryo-CMOS. Research and development in Quantum computing as well as Cryo-CMOS are currently flourishing, with possible implementation of quantum algorithms, circuits and systems in the foreseeable future. The purpose of this course is to prepare potential circuit and systems engineers for that future by introducing them to the sate-of-the-art Cryo-CMOS circuits. This course will build basic understanding of cryogenic CMOS circuits, and highlight their use in Quantum System Applications (Computing, Sensing, Communication), which has become increasingly important in quantum research in the last few years. Two design examples will be a key component of the course.

Computational Combustion & Propulsion

Fundamentals of thermochemistry Chemical equilibrium and its calculation Chemical kinetics and auto-ignition Laminar non-premixed flames and computation of an opposed jet flame Models for turbulent combustion (the flamelet model and the transported probability density function model) Turbulent non-premixed combustion and the modeling and simulation of a turbulent free jet flame Turbulent partial premixed combustion and the modeling and simulation of a turbulent lifted jet flame Computational propulsion and the modeling of a model rocket combustor [Tentative] Advanced topics on data-driven modeling and machine learning

Subsurface Hydrology

Part 1: Groundwater Cycle Groundwater is the single largest reservoir of available freshwater on Earth. Part 1 explores the essential processes and properties that affect underground water. Part 2: Wells Hydraulics To use the water from the ground, we first have to extract it! Part 2 introduces wells hydraulics. Part 3: Groundwater Contamination Part 3 describes the principles of transport in aquifers so that engineers can predict and plan the safe extraction of groundwater for private and public use.

Computational Methods for Power System Analysis

System modeling of power networks. Description of modern electricity markets. Analysis of the economic dispatch problem using optimality conditions. Planning of distributed energy resources. Smart grid applications. Machine learning applications to power systems (forecasting, demand-side management, and fault detection). Assigned projects will involve implementing some of the methods using realistic power system models.

High-Speed Mixed-Signal IC

Through five decades of continued transistor scaling, the size of unit computing has gone to virtually zero. In the foreseeable future, Computing will be all around us, in mostly invisible forms, leading to 50+ billions of connected devices to the Internet (Internet of Things or IoT). Increasingly, connectivity has become an indispensable part of modern computing devices. By some estimates, IoT devices will generate 3+ exabytes (one billion gigabytes) of data per day by 2018. The various communication fabrics that will handle this enormous amount of data needs to be extremely energy-efficient. The advance and prosperity of CMOS technology has enabled design of these communication fabrics using mixed-signal and digital-heavy techniques, which allows for lower power, reconfigurability and faster time-to-market. This course will build basic understanding of such mixed-signal circuits and systems and highlight their use in communication systems (wireline IO, wireless), which are becoming increasingly important in the data-driven world. A design project will be a key component of the course. The students will conduct a group design project that will help them obtain practical design knowledge and skills and exposure to Process Design Kit (PDK) and EDA tools like Cadence Schematic Editor, Layout Editor, and Simulator (Hspice or SpectreRF).

Surrogate Methods

Satellite Constellations and Formation

Applied Control in Astronautics

Datacenter & Cloud Networks

The course will cover the design, implementation, and management of modern datacenter and cloud networks. The course will also introduce students to research in related areas.

Stochastic Processes in Information Systems

This course is designed for science and engineering students who want to build solid mathematical foundations for probabilistic systems that evolve in time through random changes that occur at discrete fixed or random intervals. Instead of rigorous proofs of pure mathematics, such as using or developing measure theory, the course focuses on the mathematical principles and the intuition required to design, analyze, and comprehend insightful models, as well as how to select and apply the best models to real-world applications. The course has four parts: (1) point processes, which cover the Bernoulli process, laws of large numbers, convergence of sequences of random variables, Poisson process, and merging/splitting Poisson processes; (2) Markov chains and renewal processes, which cover finite-state Markov chains, Markov eigenvalues and eigenvectors, Markov rewards, dynamic programming, renewals, the strong law of large numbers, renewal rewards, stopping trials, Wald's equality, Little, M/G/1, and ensemble averages; (3) Markov processes, which cover countable state Markov chains and processes, the Kolmogorov differential equations, birth-death processes, reversibility, and semi-Markov processes; and (4) random walks, large deviations, and martingales.

Job Design

Task analysis, personnel selection and training, job and organization design, and criteria development and use. Human factors related to job design in order to increase job satisfaction and productivity.