Kevin V. Mack

Kevin V. Mack

PhD Student and graduate research assistant at Clarkson University

Resume

Contact: email - mackkv@clarkson.edu

Resume (pdf)

Education

Work Experience

Programming and Software Experience

Interests

Teamwork and Mentoring

Workshops and Training

Graduate Course Load (Clarkson University)

Digital Signal Processing (EE401/501)

Course Description: An introduction to discrete-time signal processing. Topics include: A review of orthogonality, Fourier series, Fourier transforms and sampling theory. Smoothing, interpolation, D/A conversion. Digital filters, windows. Design of nonrecursive filters, recursive filters. Correlation and spectra of random signals, spectral estimation.

Electronic Device IC Sim (EE441/541)

Course Description: Study of modern electronic devices, p-n junctions, bipolor junction transistors (BJTs) and metal-oxide-semiconductor field-effect transistors (MOSFETs), for integrated circuit applications. SPICE device models are introduced, and several SPICE simulation projects are given for integrated circuit design and analysis. This course provides a foundation for understanding SPICE device models and the basics of the microelectronic technology.

Coding and Information Transmission (EE470/570)

Course Description: Error detecting and error correcting codes. Encoding of signals and data compression. Huffman codes. Concepts of information. Limits on attainable data compression. Limits on data rates for reliable or errorless transmission.

Computer Vision (CS652)

Course Description: This course will cover both classical and recent progress in the field of computer vision, both on the theory and practice. Material covered will be from both the textbook and relevant research papers in the area. After taking this course, students will achieve the necessary knowledge to solve various practical computer-vision problems and build a solid background for further computer-vision research. Topics covered include: Early vision on one and multiple images (linear filters, edge detection, stereopsis), mid-level vision (segmentation, object tracking), highlevel vision (model-based vision, graph-based image segmentation) and applications (medical image analysis, image-based rendering).

Stochastic Processes in Engineering (EE529)

Course Description: Review of the theory of probability. Single and multiple random variables topics, such as distributions, moments, conditioning, central limit theorem, and Laws of Large Numbers. Stochastic processes. Stationary and nonstationary processes. Time averaging and ergodicity. Correlation and power spectrum. Langevin’s equation and Markov processes. Poisson and Gaussian processes. Response of linear systems. Approximate methods for analysis of nonlinear stochastic equations Application to engineering problems, such as random vibrations, turbulence, estimation theory, signal detection, and others.

Pattern Recognition and Neural Networks (EE574)

Course Description: Bayes decision theory, discriminant functions and decision surfaces. Supervised learning, parametric methods, Parzan windows, nearest neighbor classification, Fisher’s linear discriminant. Unsupervised learning and clustering. Neural networks, single-layer perceptron convergence algorithm, gradient descent training, generalizations to multilayer feed-forward networks. Classifier complexity and sample size. Hopfield networks for autoassociative memory, unsupervised and self-organizing networks.

Adaptive Signal Processing (EE628)

Course Description: An introduction to adaptive signal processing. Topics include; Applications of adaptive systems, adaptive linear combiner, Wiener least-squares solution, gradient search, the LMS/RLS algorithms, block time/frequency domain LMS, system identification.

Data Analysis of Complex Systems (EE520)

Course Descriptions: The goal of this course will be an integration of concepts of complex systems with big data analysis. Methodology will be drawn from statistical and Bayesian analysis, time-frequency analysis, image processing, linear algebra and principal component analysis, machine learning and image recognition, dimensionality reduction for dynamical systems, system identification, data assimilation, compressed sensing, and equation free modeling. Applications may include PDEs such as advection diffusion from atmospheric data and also steady flow, networked and social data-sets, feature identification in neurological applications, music analysis and identification, and image denoising.

Detection and Estimation Theory (EE528)

Course Description: Combines the classical techniques of statistical inference and the random process characterization of communication, radar, and other modern data processing systems.

Bayesian Data Analysis (STAT585)

Course Description: This course will introduce both the principles and practice of Bayesian methods for data analysis. This is a hands-on course that will use MATLAB software. Students will learn to write their own Bayesian computer programs to solve problems relevant to engineering, biology, chemistry, physics, earth science, ecology, economics, signal processing and machine learning. Topics that will be included are parameter estimation, model selection, time series and error analysis.

Neuromorphic Computing (EE503)

Course Description: Neuromorphic Computing was originally referred to as the hardware that mimics neuro-biological architectures, and was then extended to the computing systems that can run bio-inspired computing models such as neural networks and deep learning networks. In this course, students will learn the basic knowledge of artificial neural networks and the advanced hardware architectures/systems for efficient neural network computing. We will emphasize both the basic knowledge and practical tricks through a series of hand-on practices including paper readings and projects.

Deep Learning (CS570)

Course Description: This course will cover the principles of modern deep learning architectures from a theoretical and practical perspective. Course topics covered will include an introduction to machine learning and basic neural network architectures, and in-depth discussions on convolutional neural networks, recurrent neural networks based on units such as LSTMs and GRUs, and, if time permits, GANs. Students will be required to implement programming assignments and projects that apply deep learning architectures to solve classification and regression problems. Students will read and assess papers on current evolutions to these architectures. Graduate students will do additional work.

Applied Dynamical Systems (MA563)

Course Description: This course will emphasize applied dynamical systems, nonlinear science, and chaos theory. The dynamical systems approach emphasizes the study of long-term evolution through geometrical and topological considerations. We will emphasize applications from mechanics, engineering, physics, biology, medicine and chemistry.

Publications