Medical Product Ideation

BIOENG 2150

This class uses ethnography to draft a clear statement of a clinical problem and conceptualize potential solutions. Prototyping, ideation, affinitization, morphological analysis, and basic human factors design are covered.

Introduction to Neural Engineering

BIOENG 2615

This is an introductory neural engineering course covering principles that govern neural interface engineering (ion channel biophysics, overview of different neuronal and non-neuronal cell types and how they interact with implantable microelectrodes, the basics of cortical organization, simple signal propagation through these neuronal networks, and how these signals can be extracted with implantable microelectrodes and biological and engineering technologies and methodologies used in neural engineering research. Students are provided raw neural recording and histology datasets to use MATLAB for basic signal processing and analysis.

Medical Product Development 

BIOENG 2151

Covers principles of system engineering, the stage-gate process for medical product development, students will prioritize a set of feasible device and/or system solutions and learn to use intellectual property (IP) tools to determine suitability for further development, commercialization plan, design controls required by fad and international bodies; systems engineering methodologies; intellectual property (IP) and IP search tools; brief market analysis; size by region, growth, competition, barriers to entry, sustainable advantage; reimbursement issues for proposed medical device/system; basic financial analysis ' students will construct spreadsheets typically presented to senior business management. This will include estimates of costs, margins, break-even analysis, NPV, hurdle rates, ROI, IRR; codes, standards, and regulatory processes (FDA, IEC & ISO, UL, ministry of health (Japan), NRC, BRH, notified bodies, obtaining broad indications for use); safety, reliability, product liability considerations, manufacturability considerations (DFM ' design for manufacture, workflows)

Human Factors Engineering for Medical Devices 

BIOENG 2175

This course included site visits to Zoll Life Vest and Bayer. Students participated in a simulated usability study and learned the application of HFE in the design, development, and evaluation of medical devices.

Quantitative Systems Neuroscience 

BIOENG 2586

This course covered topics including sensory transduction, visual processing, motor control, and neural prosthetics. Students apply techniques from signals and systems, statistics, machine learning, information theory,  control theory, signal processing, and machine learning techniques to the analysis of biological data sets in Matlab. 

Neuromorphic Systems Design 

ECE 2195

Covers basic principles for modeling the retina, cochlea, locomotion, neurons, synapses, theory of learning, hardware and software infrastructure, address-event communication, and algorithms. It presents examples of practical applications of neuromorphic design principles. Students design, build, and test Neuromorphic Systems from scratch (Bottom-Up or Top-Down approach). They learn to build Spiking Neural Networks (SNN) on Intel’s Loihi platform and develop applications in Python.

Data Science with Python

STAT 2280

 Python as well as a collection of powerful, open-source tools will be explored and experienced within the context of solving data science problems: Jupyter notebook (creating reports) Numpy (data structure) pandas (data wrangling) matplotlib (data visualization) scikit-learn (machine learning) Learning Objectives Workflow of Data Science Data Wrangling and Visualization Machine Learning Models Python and Jupyter Notebook By the end of the course, you should be able to get the data, explore it, formulate a research question, use tools and techniques in data science to explore the answer to the question, and share your findings

Societal, Political & Ethical Issues in Biotechnology 

BIOENG 2241

This course covers ethical case-studies in Biotechnology. 

Introduction to Machine Learning 

BIOENG 2150

This is a graduate-level introduction to machine learning with focus on
foundational principles, models, and algorithms. Specific topics include estimation, regression,
classification, kernel methods, neural networks, and graphical models

*Calculus & Analytical Geometry II

ECE 2556

I completed this course provisionally to faciliate my graduate engineering coursework. 

Differential Equations 

ECE 2556

I completed this course provisionally to faciliate my graduate engineering coursework. 

Introduction to Matrices & Linear Algebra 

ECE 2556

I completed this course provisionally to faciliate my graduate engineering coursework. 

Neuro-Signal Modeling & Analysis

ECE 2556

This course focuses on signaling and modeling techniques or fundamentals that may be used to model neural data. 

Bioengineering seminar for Professional Masters Students 

ECE 2556

This seminar prepares students with professional skills and events.