Coursework

Coursework at Indiana University

 

Course Name Short Description
Introduction to Informatics Basic information representation and processing; searching and organization, evaluation and analysis of information. Internet-based information access tools; ethics and economics of information sharing.
Introduction to Bioinformatics Sequence alignment and assembly; RNA structure, protein and molecular modeling; genomics and protenomics; gene prediction; phylogenic analysis; information and machine learning; visual and graphical analysis bioinformatics; worldwide biologic databases; experimental design and data collection techniques; scientific and statistical data analysis; database and data mining methods; and network and Internet methods.
Genome Biology for Physical Scientists An introduction to Genome Biology including cell chemistry, protein structures and functions, cell and protein transportation, DNA and RNA structures, chromosome and DNA packaging, genome evolution, protein modification, regulation of gene expression, molecular phylogeny, studying genomes and high throughput techniques, and scientific paper reviews.
Informatics Management Survey of data management issues in medical, health, chemical, and biology related areas, basic techniques of physical database structures and models, data access strategies, management and indexing of massively large files.
Biologically Inspired Computing Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. The goal is to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on Biology, Computer Science, Informatics, Cognitive Science, and robotics. In this course we study bio-inspired algorithms in security, information retrieval, computational intelligence, robotics, modeling and simulation, machine learning, and biology itself.
Pandemics Infectious diseases appear to be very often in the news lately, with the emergence or re-emergence of viruses such as SARS, avian influenza, HIV-AIDS, Lyme disease, West Nile virus, “mad cow” disease, and the evolution of previously known strains developing e.g. resistance to specific drugs. In this context, tools and methods borrowed from different disciplines are becoming increasingly important in the study and analysis of the spread and control of infectious diseases. Computational epidemiology has recently emerged as a new area of research that integrates mathematical and statistical epidemiology with computational sciences and informatics tools in order to provide a new approach to conduct scenario analysis in public health domain. The course will provide a self-contained and integrated presentation of topics in large scale simulations, geographic information systems, networks, disease and population modeling approaches that are needed to access these new challenging areas of research and professional work focused on epidemic/population forecast/analysis and risk assessment.
Machine Learning in Bioinformatics The course covers advanced topics in Bioinformatics with a focus on machine learning. The course will review existing techniques such as hidden Markov models, artificial neural networks, decision trees, stochastic grammars, and kernel methods. Examine application of these techniques to current bioinformatics problems including: genome annotation and comparison, gene finding, RNA secondary structure prediction, protein structure prediction, gene expression analysis, proteonmics, and integrative functional genomics.
Mathematical Methods for Informatics This course, an introduction to applied mathematics, is concerned with the construction, analysis, and interpretation of mathematical models that shed light on significant scientific problems. It is intended to provide material of interest to student in informatics and the natural and social sciences at graduate level.         

Most of the applied mathematics courses present collections of useful mathematical techniques and illustrate the various techniques by solving classical problems of mathematical physics. Our approach is different. Typically, we select an important scientific problem whose solution will involve some useful mathematics. After briefly discussing the required scientific background, we formulate a relevant mathematical problem with some care. The formulation step is often difficult. Not many courses or textbooks actually demonstrate this, but we try to give due weight to the challenges involved in constructing our mathematical models. A new technique may be then introduced to solve the mathematical problem, or a technique known in simpler contexts may be generalized. In most instances we take care to determine what the mathematical results tells us about the scientific process that motivated the problem in the first place.
Systems Biology: A user’s guide Systems Biology is in vogue – it is a catch-phrase in laboratories, grant applications and scientific journals. So what exactly is systems biology? What is the best plan for students interested in a career in systems biology? In this seminar course, we will address these questions by introducing some of the major challenges in the discipline: problems of computational, experimental and modelling natures will be addressed. We will pay special attention to gene and protein networks, and cellular functions. Students will participate in the classes by presenting a seminar. They are also expected to be active in class discussions.
Introduction to Dynamical Systems in Cognitive Science Concepts from dynamical systems theory are becoming increasingly important in cognitive science, and the construction and evaluation of dynamical models requires a thorough understanding of the mathematical theory of dynamical systems in the same way that computational models in cognitive science require a thorough understanding of computation. This course provides such an introduction to dynamical systems theory, with an emphasis on the underlying mathematical ideas and tools. Although we will focus on dynamical systems formed by sets of differential equations, we will also cover discrete-time dynamical systems at several key points.
Neural and Genetic Approaches to Artificial Intelligence This course covers the most important computational mechanisms for implementing intelligent behavior that are based on biological systems, including individual animals and populations of animals.
Readings and Research in Psychology Literature review and exploration of social contagion and social epidemics.

 

Coursework at James Madison University

 

Course Name Short Description
Computer Organization An explanation of elementary computer organization and network communication by using the Unix operating system including use of a distributed hierarchic file system, other network resources and command scripting.
Object Oriented Programming Fundamental programming techniques using the C programming language to support algorithm development and procedural abstraction as a means of problem solving. Students also learn elementary data structures including character strings, records and files.
Data Structures Various advanced problem-solving strategies that use object-oriented techniques to develop algorithms in the C++ programming language. Students also learn advanced data structures including stacks, queues and lists using both static and dynamic memory allocations and including elementary performance analysis of these data structures.
Foundations of Computer Science Survey of fundamental computer science concepts such as iteration, recursion, induction, analysis of algorithms, combinations and probability, data structures, automata theory and regular expressions, context-free grammars and parsing, and propositional and predicate logic.
Operating Systems Concepts and principles of multiple-user operating systems. Memory, CPU, I/O device allocation, scheduling and security. Memory hierarchies, performance evaluation, analytic models, simulation, concurrent programming and parallel processors. Completion of a student project is a significant part of the course.
Formal Methods for Information Security A formal specification language is presented with case studies, proofs and the formal specification of software components. Additional topics may include formal security policy modeling, seminal formal systems, first-order logic, set theory, relations, functions, sequences, bags, free types, formal and rigorous proof, immanent reasoning, reification, decomposition, and Floyd-Hoare logic.
Ethics, Law and Policy in Cyberspace Study of ethical issues, legal resources and recourses, and policy implications inherent in our evolving online society. Provides an overview of the ethical challenges faced by individuals and organizations in the information age. Introduces the complex and dynamic state of the law as it applies to behavior in cyberspace.
Secure Software Engineering An overview of methodologies, tools and techniques for producing secure software systems. Students will cooperatively develop a secure software product. The course will also provide an introduction to professional resources and ethical issues for software developers.
Networks and Network Security Fundamental concepts, principles, and practical networking and internetworking issues relevant to the design, analysis and implementation of enterprise-level trusted networked information systems. Topics include networking and security architectures, techniques and protocols at the various layers of the Internet model.
Software Assurance Defines a trusted system and considers the design, evaluation, certification and accreditation of trusted systems, including hardware considerations, software considerations such as developmental controls, validation/verification, assured distribution and other assurance issues. Implementation, configuration management and systems administration of trusted systems. Trusted applications and trusted database issues. Importance of aggressive monitoring and setting traps for the intruder. Importance of understanding the psychology and successful modus vivendi of the attacker to generate and maintain a powerful defense.
Cryptograph: Algorithms and Applications Cryptographic techniques to achieve confidentiality, integrity, authentication and non-repudiation are examined. The underlying mathematical concepts are introduced. Topics to be covered include symmetric and public key encryption, hashing, digital signatures, cryptographic protocols and other recent developments in the field.
Advanced Network Security This is a project-based course. Students will learn advanced network security concepts, conduct information security research and apply what they have learned throughout the information security master’s program to better secure critical information infrastructure.
Secure Operations A course for the information system security professional emphasizing administrative roles in the audit and control of information systems. The administrator’s role in secure system accountability and documentation will be stressed.
Computer Forensics This course teaches how to perform computer crime investigations. The course covers the recovery and analysis of digital evidence, addressing legal and technical issues. Forensic examination of Windows and Unix systems are used to illustrate typical investigative processes.
Database Systems Types of physical storage and access methods; data models; relational algebra and calculus, data definition and query languages; dependencies, decomposition and normalization; database design; recovery; consistency and concurrency; distributed databases.