Teaching Awards
Marquette University Eta Kappa Nu (HKN) honor society EECE Teacher of the Year 2005
Marquette University Eta Kappa Nu (HKN) honor society EECE Teacher of the Year 2014
Selected Teaching, Education, and Pedagogy activities
Participate in numerous University of Kentucky Center for the Enhancement of Learning and Teaching (CELT)
Former advisory board member and regular workshop participant with Marquette University Center for Teaching and Learning (CTL), 2005-2010
Have participated in 4 ABET accreditation visits, 2 as department chair.
Led UK College of Engineering Cross-Disciplinary team to discuss teaching best practices and create a set of recommendations for the college, 2017.
Participated in UK College of Engineering Teaching Innovation Study Group, 2017 and 2018.
Member of UK Wildcat Freshman Experience Planning Committee, 2017-2019
Participant in KEEN/Lafferty Teaching Development Workshop Series, Fall 2015 and Spring 2016.
Coordinator for Marquette EECE Freshman Seminar, 2011-2015
International Teaching Experience, Tsinghua University, 2008-2009, 2011, 2013, 2014-2015
Member of Marquette Engineering Committee on Redesigning the Freshman Experience, 2007-2008
Signals and Systems Concept Inventory site coordinator for NSF study, 2003-2006
IEEE Signal Processing Education Technical Committee, early 2000’s
Participated in NSF Engineering Education Scholars (EES) program for new faculty, summer 2001
Participated in Prentice Hall Symposium on Education, Chicago, IL, 2001
Participated in NSF/ASEE Visiting Scholars Teaching Workshops, Fall 2000 and Spring 2001
Courses I’ve Taught
University of Kentucky (2016-2023)
EGR 101 Engineering Exploration 1
Engineering Exploration I introduces students to the creativity inherent to how engineers approach innovation, design and problem solving from blue sky brainstorming to implementing a solution. Students in this course are introduced to a wide variety of engineering disciplines, skills, and career opportunities and are introduced to engineering design and critical thinking processes.
UK 101 Academic Orientation
Academic Orientation introduces strategies and resources that build a strong foundation for academic success while promoting opportunities for intellectual and personal growth. The student learning outcomes address specific issues of student transition, focusing on the purpose and challenges of a college education, developing learning strategies and study skills, promoting student engagement, and increasing knowledge of campus resources.
EGR190 Understanding Leadership
This course is an introduction to the principles and practice of engineering leadership. Topics include defining leadership, characteristics of a leader, leadership models, trust and ethics, emotional intelligence, effective communication, and change management. Through this course students will build an understanding of what leadership is and begin the process of developing their own personal leadership style, goals, and plan.
EE 211 Circuits 1
This course provides and introduction to fundamental laws, principles and analysis techniques for DC and AC linear circuits whose elements consist of passive and active components used in modern engineering practice including the determination of steady state and transient responses.
EE 421 Signals and Systems
This course provides an introduction to continuous and discrete signal and system models and analyses. Topics include discrete and continuous convolution, Fourier transforms, and Laplace transforms and Z-transforms with application examples including AM modulation and the sampling theorem.
EE 422 Signals and Systems Laboratory
This course is a hands-on laboratory course where students apply the concepts of signals and systems to problems in signal processing, communications, and control systems. Topics include noise models, filter design, modulation techniques, sampling, discrete Fourier Transforms, state variable models, and feedback design with an emphasis on using computer software for analysis and simulation.
EE 599 Topics in EE: Speech Processing
This course provides an introduction to the fundamentals of speech processing, including speech production and perception, speech analysis and representation, and applications to speech coding, synthesis, recognition, and language modeling.
EE 630 Digital Signal Processing
This course is a graduate-level introduction to digital signal processing. Topics include frequency domain analysis of signals using Fourier and Z Transforms, sampling and reconstruction theory, filter design and implementation, multirate signal processing linear prediction and optimal filter theory, adaptive signal processing, and power spectral estimation.
Marquette University (2000-2016)
EECE 1953 and EECE 1954 ECE Freshman Seminar
This is an introduction to electrical engineering and computer engineering. Organized around the Roomba platform from iRobot, this course gives students an opportunity to learn problem solving, develop and carry out team projects, and interact with their peers and other members of the EECE Department.
EECE 2030 Digital Electronics
This course introduces students to the basic principles of digital circuit analysis and design. Topics covered include: Boolean Algebra, number systems, basic logic gates, standard combinational circuits, combinational design, timing diagrams, flip-flops, sequential design, standard sequential circuits and programmable logic devices.
EECE 4510 Digital Signal Processing
This course is an introduction to discrete-time signals and systems.Topics include sampling theory and linear time invariant system analysis through convolution, Fourier transforms and z-transforms. In addition, techniques for the design of digital filters are introduced, and the computation and use of the discrete Fourier transform and fast Fourier transform is discussed. Applications of these concepts is accomplished through several Matlab-based design projects.
EECE 6510 Optimal and Adaptive Digital Signal Processing
This course is an introduction to optimal and adaptive signal processing techniques, including spectral estimation, Wiener filters, linear prediction, steepest descent and least mean square algorithms, least squares and recursive least squares estimation, and Kalman filters.
EECE 6520 Digital Processing of Speech Signals
This course is an introduction to the fundamentals of speech processing, including speech production models and feature analysis, with applications in speech coding, synthesis, and recognition.
COEN 4710 Computer Hardware
This course is an overview of computer hardware systems, with emphasis on microprocessor design. Topics include performance analysis, MIPS assembly language, arithmetic logic units, datapath and control aspects of instruction set architectures, pipelining, and memory and I/O devices.
EECE 113 (EECE3020) Linear Systems Analysis
This course introduces mathematical concepts of continuous-time signals and systems. The time-domain viewpoint is developed for linear time invariant systems using the impulse response and convolution integral. The frequency domain viewpoint is also explored through the Fourier Series and Fourier Transform, and basic filtering concepts are discussed. The sampling theorem, the Z-transform, and the Discrete Fourier Transform are also introduced.
COEN 140 (COEN 4720) Embedded Systems Design
This course introduces students to embedded systems, the types of hardware that can support such systems, and the interfacing used in embedded systems. The course is a combined laboratory and lecture course, which directly applies the embedded systems techniques using hardware description and assembly languages to field programmable gate array technology.
EECE 214 Information and Coding Theory
This course is an introduction to information measure, mutual information, self-information, entropy, encoding of information, discrete and continuous channels, channel capacity, error detection, error correcting codes, group codes, cyclic codes, BCH codes, convolution codes, and advanced codes.
EECE 211 (EECE 6810) Algorithm Analysis and Applications
This course is an introduction to the analysis of algorithms. Topics covered include asymptotic complexity notation, recursion analysis, advanced data structures, sorting methodologies, dynamic programming, graph algorithms, and an introduction to several advanced topics such as NP-completeness theory and linear programming.
EECE 6830 Pattern Recognition
This course is an introduction to the theory and application of statistical pattern recognition, hypothesis testing, and parameter estimation. Topics include probability distribution models, Bayesian decision theory and hypothesis testing, classical and modern approaches to parameter estimation, parametric and non-parametric classifiers. Also covered are diagonalization and the Karhunen-Loeve transform (a.k.a. Principal Components analysis), supervised and unsupervised clustering, Expectation Maximization algorithms for Maximum Likelihood estimation, and linear discriminant analysis.
Tsinghua University (2008-2009, Summer 2011, Summer 2013, 2014-2015)
Digital Signal Processing
This course is an introduction to discrete-time signals and systems. Topics include sampling theory and linear time invariant system analysis through convolution, Fourier transforms and z-transforms. In addition, techniques for the design of digital filters are introduced, and the computation and use of the discrete Fourier transform and fast Fourier transform is discussed. Applications of these concepts is accomplished through several Matlab-based design projects.
Statistical Pattern Recognition
This course is an introduction to the theory and application of statistical pattern recognition, hypothesis testing, and parameter estimation. Topics include probability distribution models, Bayesian decision theory and hypothesis testing, classical and modern approaches to parameter estimation, parametric and non-parametric classifiers. Also covered are diagonalization and the Karhunen-Loeve transform (a.k.a. Principal Components analysis), supervised and unsupervised clustering, Expectation Maximization algorithms for Maximum Likelihood estimation, and linear discriminant analysis.
Professional Research Writing