Technical Electives (Select Four Courses)

BANA 7015 – Advanced Health Care Data Analytics, Business Intelligence and Reporting (3 credit hours)
This course teaches the use of healthcare data to make decisions and transform healthcare delivery and the health of individuals and populations. The course concentrates on big and small data, and structured and unstructured data. Tools, applications and approaches for health data analytics are taught. This course covers topics such as statistical approaches; data, web and textmining; data visualization, simulation, modeling and forecasting.

BE 7022 – Introduction to Biostatistics (3 credit hours)
Students will learn basic statistics such as mean, median, mode, standard deviation, variance, etc. Topics include probability, parametric statistics such as t tests and one way analysis of variance, and nonparametric statistics including both Wilcoxon tests and Kaplan-Meier estimation of survival. Bayes theorem, discrete (e.g. Binomial) and continuous probability distributions (e.g. normal distributions and one variable regression and product moment correlation and rank correlation are covered.

BE 7024 – Computational Statistics (3 credit hours)
SAS - Introduction; windows environment; techniques of entering data; importing data; creating permanent data sets; managing data; sub setting data sets; merging data sets; proc command; running SAS programs; analyzing counts and tables; analyzing quantitative data; creating graphs; controlling output. R - Downloading and installing R; packages; graphing facilities; getting data into R; downloading data sets into R from external sources; matrix function; data, frame function; list function; managing subsets of data; sorting data; exporting data; loops and functions; analyzing counts and tables; analyzing quantitative data; panel data Project - Analyze a specific internet health data Homework - 11 homework sheets.

BE 7070 – Qualitative and Quantitative Data Collection Methods for Health Services Research (2 credit hours)
In this course, students will learn concepts, methods, and practical procedures for developing and implementing quantitative and qualitative health survey instruments to answer their own research questions. Through hands-on learning, students will gain experience in instrument design and construction, sampling considerations, data collection methods, coding, processing (including automated methods), presentation, and data analysis. Each student will identify a health-related research question and design qualitative and quantitative instruments and methods for answering it. By Permission Only.

BE 7071 – Quality Improvement and Patient Safety (1 credit hour)
This course will cover the fundamentals of quality improvement and patient safety. It will use a framework of human factors to facilitate understanding complex system failures and successful strategies to reduce hazard in industrial and medical environments. The concepts are taught using a case-based format to explore common human and organizational sources of failure, such as missing or inert knowledge, communication/collaboration, clumsy technology, human computer interaction (alerts and reminders),and role of a safety culture. The second half of the course is devoted to learning approaches for implementing evidenced-based practices based on Rogers' theory, where adopting innovation in an organization is divided into two major activities: initiation and implementation.

BE 7074 – Community-Based Participatory Research (1-3 credit hours)
This class is designed to familiarize learners with the theoretical framework, methodologies, and applications of community-based participatory research and how it differs from traditional research approaches and community-placed research.

BE 7080 – Analysis of Internet Health Data (3 credit hours)
Examples of internet data: Framingham Health Data; National Inpatient Sample; Nurses Health Data; Emergency Admissions Data; Pediatric Admissions Data. Description of data sets. Analysis of Inpatient Sample Data. R package for data analysis. New research projects.

BE 8068 – Genetics of Complex Disease (2 credit hours)
The course is designed to provide basic understandings of the inherited basis of complex diseases that involve both genetic and environmental factors. With an introduction of the principles of gene mapping and their applications in non-Mendelian traits, emphasis will be placed on changes in the paradigm with rapid developments in technologies and analytical approaches to identify genetic variants influencing the risk of common diseases. Lectures will cover topics on o Fundamental principles of heredity o Principles of population genetics, measures of genetic variation, Hardy-Weinberg Law o Genetic markers - RFLPs, SNPs, CNVs o Fundamentals in gene mapping: linkage and association, linkage disequilibrium, haplotypes o Non-Mendelian inheritance, complex disease o Evolving paradigm of complex disease genetics o Human genome project, HapMap, ENCODE project, 1000 Genome project o Genome-wide association studies o Statistical concepts - statistical significance, effect sizes, multiple testing, population substructure o Choice of population - isolated versus cosmopolitan populations in complex disease studies o Pathophysiology, natural history and genetics.

BME 6012 – Biomedical Signal and Image Processing (3 credit hours)
Fundamentals of signal and image processing, Fourier analysis, and stochastic processes, with emphasis on biomedical applications. Filtering transformation and feature extraction for biomedical signals.

BME 7061 – Biostatistics in Research (3 credit hours)
In this course a number of statistical methods will be presented to analyze various types of data stemming from research. A rudimentary knowledge of probability and inferential statistics will be assumed. How to evaluate a diagnostic test will be dealt in depth. Analysis of contingency tables and log linear models will be presented to answer a number of relevant research questions. A detailed presentation of logistic regression and rudiments of Survival Analysis will be presented. Presentation of current research from journals.

BME 8064 – Advanced Statistical Methods in Biomedical Research (3 credit hours)
Summary statistics of multivariate data; principal components; factor analysis; multivariate analysis of variance; multivariate multiple regression; multidimensional scaling; heat maps; multivariate graphics; pattern recognition; cluster analysis; random forests.

CS 6033 – Artificial Intelligence (3 credit hours)
The course will cover in detail the topics of state space search, game tree search, constraint satisfaction, logic based knowledge representation and reasoning, first order predicate calculus, uncertainty handling using Bayesian probability theory, and some applications of these techniques .Applications may be selected from the area s of automated planning, natural language processing, or machine learning.

CS 6034 – Natural Language Processing (3 credit hours)
An introduction to methods to computationally represent human languages, including syntactic parsing, language modeling, semantic parsing, information retrieval, machine translation, and conversational agents. Topics covered will include: syntactic parsing, semantic parsing, statistical language models, meaning representation languages, information retrieval, machine translation, applied machine learning, conversational agents.

CS 6037 – Machine Learning (3 credit hours)
The goal of this course is to introduce students to the field of Machine Learning. The course covers traditional machine learning algorithms, and their implementations along with discussions of concrete problems where these algorithms are suitable. Topics covered by course include: Concept Learning and the General-to-Specific Ordering Decision Tree Learning Artificial Neural Networks Evaluating Hypotheses/Bayesian Learning Computational Learning Theory Instance-Based Learning Genetic Algorithms Learning Sets of Rules, Analytical Learning Combining Inductive and Analytical Learning Reinforcement Learning.

CS 6052 – Intelligent Data Analysis (3 credit hours)
This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.

CS 6065 – Introduction to Cloud Computing (3 credit hours)
The course will cover the following topics: 1. Introduction and Historical perspectives 2. Cloud Models: IaaS, PaaS, SaaS. 3. Underlying technologies: Networking, Internet Architecture, Virtualization 4. Distributed System Concepts 5. MapReduce Paradigm & Hadoop 6. Cloud Applications & Architectures 7. Modern Case Studies in Hadoop and Cloud Applications 8. Designing Applications for the Cloud 9. Cloud Security 10. Public cloud offerings.

CS 6067 – User Interface I (3 credit hours)
This course introduces the basic concepts of human computer interaction and the latest development of the technology for developing interactive systems. Major topics cover the role of computer technology, human users and human factors for designing windows-based applications, and design methodologies for building software applications.

CS 6068 – Parallel Computing (3 credit hours)
This course is designed as a dual level and senior undergraduate level course introducing the theory and practice of parallel computing. The course seeks to empower students with the computational thinking and practical programming skills needed to achieve terascale and petascale computing performance in all science and engineering disciplines. Students will study and gain experiences with several parallel algorithmic design patterns. Student will study the critical system and architectural design issues associated with parallel computing. Students will gain experience with parallel programming development environments and learn programming methodologies using a chosen platform. Students will learn analytical techniques for understanding the scalability and portability of parallel computing software. The course is lab and project oriented.

CS 6072 – Network Science (3 credit hours)
1. Graph theory, degree distribution, connected components, clustering coefficient, shortest paths, diameter 2. Random graphs, the Erdos-Renyi model, giant component, phase transitions 3. The scale-free property, power laws, small-world networks, degree-preserving randomization 4. Evolving networks, preferential attachment 5. Degree correlation, assortativity, structural disassortativity 6. Network robustness, network breakdown, random failure and attacks 7. Communities, modularity and modularity-maximization algorithms, the Bron-Kerbosch clique enumeration, algorithm, clique graphs and clique percolation, the Louvain algorithm, Informap 8. Spreading phenomena, epidemic modeling, network epidemics 9. Visualization algorithms

CS 6073 – Deep Learning (3 credit hours)
1. Deep feedforward networks: gradient-based learning, hidden units 2. Regularization for deep learning: sparse representations 3. Optimization for training deep models: adaptive learning rates 4. Convolutional networks: pooling, unsupervised features 5. Recurrent and recursive networks: long short-term memory 6. Autoencoders: semantic hashing 7. Representation learning: distributed representation, one-shot learning 8. Structured probabilistic models for deep learning: sampling 9. Deep generative models: Boltzmann machines

CS 7081 – Advanced Algorithms I (3 credit hours)
Advanced treatment of fundamental topics in algorithms that every graduate student should know and have some sophistication in. Knowledge and ability to apply the fundamental design strategies: the greedy method, divide-and-conquer, dynamic programming, to solve important problems in data encryption, efficient polynomial, integer, matrix multiplication, computing the Discrete Fourier transform, using the celebrated FFT algorithm, and so forth. In addition this course will introduce students to lower bound theory and NP-completeness.

CS 8021 – Pattern Recognition (3 credit hours)
The topics covered will include Statistical Pattern Recognition - its basics and applications, algorithms for clustering and their analysis. A flavor of different types of clustering algorithms will be given and a few algorithms will be studied in great depth. Relevance of all the above techniques for pattern discovery, classifier design, and dimensionality reduction will be investigated. A number of examples from real-life datasets will be examined in depth during the class presentations and by students during their homework assignments.

DB 9088 – Regulation of Gene Expression (2 credit hours)
Provides a fundamental knowledge of how eukaryoticgene expression is regulated, with a focus on state of the art experimental approaches. This knowledge is gained through a combination of both lecture and a discussion of the primary literature with ample opportunities for student-student and student-faculty interaction. Discussion sessions focus on the primary literature, utilizing a mix of both research publications and authoritative reviews of current trends in gene regulation research. Important areas of consideration will include the following: 1. The basics of transcription and promoters 2. DNA-binding proteins and transcription factors. 3. Cis-regulatory sequences, trans-acting factors and the assembly of transcriptional complexes 4. mRNA metabolism - processing, splicing, stability 5. Non-coding RNAs in gene regulation 6. Translation 6. Chromatin structure and epigenetics in the control of gene expression 7. Genetic mechanisms of cell and tissue differentiation 8. Global approaches to understanding the architecture of the genome 9. An overview of Bioinformatic and Computational Approaches 10. A literature review of the major research questions and results.

EECE 6042 – Digital Image Processing (3 credit hours)
Digital image foundation and characterization, discrete transforms, image enhancement, encoding, compression and restoration.

EECE 8075 – Data Warehousing and Mining (3 credit hours)
Data warehouse design with conceptual data models and physical storage techniques; data mining techniques including clustering, pattern recognition, and data visualization.

MCP 6031C – Computational Systems Biology (3 credit hours)
This course introduces techniques for constructing computational and mathematical models of biological processes at several levels of organizational scales from different points of view-from genome to whole-tissue, and from static to dynamic. Students will hear lectures, read literature, participate in discussions focused on the various modeling techniques, and build computational models using standard tools. Students will learn:·Criteria for selecting modeling techniques suited for addressing biological questions Quantitative characterization of biological properties (e.g. robustness)·Basis for valid assumption and how complexity of problems in biology can be tackled Hands on experience will be a key component of this course. Students will also work in teams to complete group modeling projects that utilize the modeling techniques specific to the particular module. Student teams, consisting of 3-5 students, will be assembled so that they maintain diversity with respect to computational, mathematical, and biological knowledge and skills, and therefore, students will also teach one another as they work together on their team to complete their projects.

MG 8011 – Advanced Fundamentals in Human Genetics (2-3 credit hours)
Human Genetics has grown dramatically in recent years mainly due to the rapid advances in molecular technologies and the explosion of genomic data and resources. This course will provide critical assessment of these cutting edge molecular technologies as well as analytical approaches and their applications in human genetic research. The course for two credit hours is as follows. Each week two classes will focus on providing students with the current state of gene discovery in human genetics. Lectures will be organized to cover cutting edge topics in human genetic research selected from recent review or research articles, with a heavy emphasis on critical reading and discussion of the assigned material. These topics will broadly reflect recent advances in either molecular/functional or statistical/computational aspects of human genetics. At the end of each major section, students will be expected to describe an experimental approach along with the potential problems and alternative solutions. Students registered for three credit hours will have an additional class each week to synthesize and apply the knowledge gained. By Permission Only.

STAT 6043 – Applied Bayesian Analysis (3 credit hours)
Foundation of Bayesian Statistics, basic theory and several applications including Monte Carlo and Markov Chain Monte Carlo Methods for computing Bayesian inference will be covered. Specific topics include: Foundation of Bayesian Approach, Prior and Posterior distributions; Choice of Priors: subjective and non-subjective or default approaches; Inference using posterior distribution for standard models; and Hierarchical models, and their applications. WinBUGS will be introduced.

STAT 8022 – Advanced Bayesian Analysis (3 credit hours)
The course will cover, choice of priors for estimation and testing, Bayes factors and calculation, model selection and related computational methods, and choice of topics.

Data Management (select one course)

EECE 6010 – Database Management (3 credit hours)
Database formal architectures emphasizing modeling and theory. Formal methods for database architectures; relational, hierarchical, object, object-relational and network; data dependencies, normalization, integrity constraints, concurrency, heterogeneous systems.

BE/PH 8093 – Introduction to Database Management Systems (3 credit hours)
This course emphasizes on hands-on experience of developing and using databases. Students will learn basic concepts of database techniques, use SQL to develop relational databases (with MySQL) and use NoSQL to develop non-relational databases (with CouchDB), and develop database applications to solve practical problems in biomedical science with big data. The course is highly interactive. Students will be trained to write R code in the classroom to interact with databases and perform data analyses.

Biomedical Informatics PhD Curriculum 2019-2020