Department of Mathematics and Statistics

The following courses will satisfy the non-mathematical elective requirement for the MS degree. If you are interested in taking another course at Georgetown University or through the Consortium of Universities of the Washington Metropolitan Area, send an email to the program, with copy to your advisor.

For a complete and up-to-date list of all courses for the fall semester and up to date information on course content, instructor names, syllabi etc., go to the University Registrar's main page. Electives for fall 2009 are here.
 

Spring 2010 Electives

TR 4:15-5:30

TR 4-5:15

M 4:10-6:10

Fall 2009 Electives
 

T 4:15-6:45

Shaping National Science Policy

Students will learn techniques that shape our nation's science policy. We will start with an introduction to our political system. Then we will examine hardball politics with guest lectures from journalists, lobbyists, and congressional staff. Finally, we will focus on case studies in advocacy and examine how science affects policy at the local and national levels. Students work on one project throughout the semester. They will break into teams, identify a politically hot science-issue, develop a lobbying strategy, and take their issue to Capitol Hill. This course is cross-listed with the Physics Department. Spring.

Population Genetics

What processes shape genetic variation and genetic diversity and what patterns do we expect in genetic systems? The answer to this question will be sought in a broad examination of genetic principles that operate during the course of evolutionary change. Topics covered include genetic drift, inbreeding, effective population size, gene flow, natural selection, quantitative genetics, and molecular evolution. This course stresses computer simulation as a learning tool and seeks to illuminate connections among Hardy-Weinberg systems, continuous trait genetics, and molecular genetics. The laboratory sessions will cover practical applications including genetic hypothesis testing, computer simulations, and forensic DNA typing. Prerequisites: BIOL-152; and at least one of BIOL-251, BIOL-280 or BIOL-360.

Ecological Analysis

Ecology seeks to understand how organisms interact with both their abiotic (nonliving) and biotic environments, and how such interactions affect species distribution and abundance as well as the structure of ecological communities. To quantify these relationships and understand the underlying causal mechanisms, ecologists utilize both observational and experimental studies. Understanding how to test hypotheses and design experiments is fundamental to all branches of science, but ecology and other field sciences face unique challenges. This course will help students understand the critical importance of appropriate experimental design, and methods of data analysis when testing hypotheses in environmental biology. The course will be oriented around hands-on field and laboratory experiences with experimental design, data collection, data analysis techniques, null model formulation and hypothesis testing.

Genomics and Bioinformatics

This course is an introduction to genomics and the use of computers to analyze genomic data. Genomics is the study of the structure, content and evolution of genomes, including functional analysis of genes and proteins. Students learn applications of genomics to biomedical and biological research by performing computational exercises using genomic databases. Topics include genome sequencing and annotation, gene and regulatory motif prediction, genetic variation, sequence database searching, multiple sequence alignment, phylogenetic tree construction, protein structure prediction, microarray analysis, proteomic analysis, interaction networks.

Dynamic Process in Biological Physics

The class combines basic knowledge of thermodynamics/statistical physics, nonlinear dynamics and biology to highlight physical processes that govern the dynamics of biological systems. The class will concentrate on dynamical aspects emerging on different temporal and spatial scales, Brownian motion and diffusion, thermodynamics of biological processes, protein folding, generation of membrane potentials, spatio-temporal pattern generation in neural networks.

Principles of Epidemiology

Epidemiology overview and history; distributions of disease by time, place and person; association and causality; ecological studies; cross-sectional studies and surveys; case-control studies; analysis of case-control studies; types of bias in case-control studies; cohort studies; analysis of cohort studies; bias in cohort studies; population attributable risk; confounding factors; effect modification (interaction); analysis for confounding and interaction; multivariate analysis; sensitivity, specificity and screening; public health practice and prevention; special issues in cancer epidemiology, infectious disease epidemiology and genetic epidemiology. This course includes a discussion session.

Biostatistics for Bioinformatics

Bioinformatics is the application of computer science, statistics, and mathematics to the management and analysis of large-scale, complex biological data. This course will enable students to obtain some understanding of the statistical methods needed to analyze such data. During the first weeks of the course, we will provide a basic introduction to database management systems and an overview of important biological databases including GenBank, UniProt, and iProClass. The course will then go on to describe the underlying theories and algorithms for sequence alignment (pairwise, multiple, nucleotides, proteins, statistical evaluation), sequence analysis (correlations, profiles, PAM and BLOSUM matrices), genome comparison (dot matrices), molecular evolution, and gene prediction. For each of these topics, available tools will be introduced during hands-on laboratory sessions.

Clinical Trials

The objective of the course is to explain in practical terms the basic principles of clinical trials, with particular emphasis on their scientific rationale, organization and planning, and methodology. Issues discussed include design of randomized and non-randomized trials, size of a clinical trial, monitoring of trial progress, and some basic principles of statistical analysis. The intent is to present the methodology of clinical trials with emphasis on the practical aspects.

Case Studies in Epidemiology

3 one-unit modules, each covering a different topic: e.g., genotyping studies, biomarkers of exposure, family studies.
1. Genotyping Studies. Types of epidemiology studies using genotypes, study design issues, genotyping methods including quality control, database aspects, analysis of genotype data, field trip to genetic laboratory
2. Biomarkers of Exposure. Types of epidemiology studies using biomarkers, study design issues, biomarker detection methods including quality control, database aspects, analysis of biomarker data, field trip to biomarker laboratory
3. Family Studies. Types of epidemiology studies using family-based designs, study design issues, special methods in family studies, database aspects, analysis of family data, field trip to Familial Cancer Registry.

Quantum Chemistry

Fundamentals of one-particle quantum mechanics and their chemical applications. Fundamentals of many-electron quantum chemistry (atoms and molecules). Simple MO theory. Prerequisite: Physical Chemistry.

Chemical Dynamics

Kinetics, especially at the molecular level; relaxation techniques; interactions in reacting and non-reacting chemical systems on a molecular basis; attractive and repulsive forces, potential energy surfaces; lasers and molecular beams as experimental probes of molecular dynamics, reactions of molecules absorbed on surfaces. Prerequisite: Physical Chemistry

X-Ray Crystallography and Molecular Structure

Determination of molecular structure by diffraction methods, including pertinent theory; particular emphasis on X-ray diffraction; includes "hands-on" experience. Prerequisite: Physical Chemistry.

Nuclear Magnetic Resonance (NMR) Spectroscopy

Practical and theoretical aspects of NMR spectroscopy. Instrumental and experimental aspects of Fourier NMR including multipulse sequences, theories underlying the experiments, techniques, and hardware. Prerequisite: Physical Chemistry.

Solution Kinetics

Currently interesting aspects of kinetics and mechanisms of chemical reactions in liquid solutions. Techniques, methods, and theories that concern those reactions. The course will cover organic, inorganic, and biochemical reactions. Specific topics covered will vary from year to year. Prerequisite: Physical Chemistry.

Enzyme Kinetics

Fundamentals of chemical kinetics, enzyme catalysis and enzyme mechanisms. Kinetics includes steady state, stopped-flow, single and multi-substrate kinetics, as well as derivation of rate equations. Mechanisms includes principles of enzyme catalysis, allosteric control, and regulation. Prerequisites: Physical Chemistry and -419 or equivalent.

Computational Methods for Biological Macromolecules

Computational methods for proteins, nucleic acids, and lipids. Energy minimization and molecular dynamics simulations for structure/function studies and for NMR/X-ray structure determination. Bioinformatic analysis including sequence and phylogenetic analysis. Quantum chemistry calculations of biochemical reactions. While an introduction to the theories of the methods will be given, concentration will be on applications. Prerequisites: physical chemistry and biochemistry or permission of instructor.

Statistical Mechanics

Canonical ensemble; other ensembles and fluctuations; Boltzmann, Fermi-Dirac and Bose-Einstein statistics; classical statistical mechanics; ideal monatomic, diatomic and polyatomic gases; chemical equilibrium; quantum statistics; crystals; imperfect gases; distribution functions in classical monatomic liquids; perturbation theories of liquids; lattice models and their application to physical adsorption, liquid mixtures and liquid crystals. Prerequisite: Permission of instructor.

Information Assurance

This course introduces students to means of assuring the confidentiality, integrity, and availability of information through mechanisms of technology, policy, and education. Topics will include: encryption mechanisms; policy development and risk analysis; physical and personnel security; identity and authentication; malicious code; secure program design, development, and review; system auditing and integrity; computer forensics; TCP/IP security including DoS, intrusion detection, firewalls, and wireless security; and legal, social, and ethical issues.

Wireless Networks

Accessing information, anywhere, anyplace, and at anytime, will be the key in future information systems. In this course, we will study various technical aspects of wireless communications and network systems. Depending on time and interests, the course will cover the following topics: Communications, including, Spectrum allocation and characteristics, propagation characteristics, coding, frequency, and time division multiplication; Satellite systems; Network Systems, including, Cellular telephony, GSM system, TDMA system, CDMA system, network protocols (1G, 2G, 2.5G, and 3G), mobile IP, and ad-hoc networks; and Applications, including, Browsing through wireless devices, WAP and WEP. Prerequisite: COSC-173 or Permission of the instructor.

Programming Concepts and Tools

This class is intended for graduate students with a serious interest in learning C++ programming. Topics include: basic data types; the C++ string class; variables, constants, and their declarations; input/output (cin/cout), arithmetic, and assignment operators; conditional and repetition control structures; basic file operations; programmer-defined functions; value and reference parameters; scoping rules; name precedence; function overloading; template functions; elementary software engineering principles; the Standard Template Library (STL); the vector class; elementary searching and sorting; abstract data types; stacks; programmer-defined classes; operator overloading; pointers; self-referential classes; dynamic object creation and destruction; linked lists; recursion; abstract base classes; virtual functions; polymorphism; template classes; and exception handling.

Efficient Computing Methods

This course covers data structures and algorithms with an emphasis on "paper and pencil" analysis techniques that vastly improve the performance of computers and maximize improved technology. The topics are theoretical but have dramatic impact on practice; wherever possible, we will place the theory in context of its service to applications. The course covers worst-case, average-case, and amortized algorithmic analysis; list, stack, queue, and tree data structures; and the divide-and-conquer algorithm. Self-adjusting data structures and on-line algorithms will also be introduced. Prerequisites: COSC-503 or permission of the instructor.

Information Warfare

This course will study the nature of information warfare, including computer crime and information terrorism, as it relates to national, economic, organizational, and personal security. Students will gain an understanding of the threats to information resources, including military and economic espionage, communications eavesdropping, computer break-ins, denial-of-service, destruction and modification of data, distortion and fabrication of information, forgery, control and disruption of information flow, electronic bombs, and psyops and perception management. They will learn about countermeasures, including authentication, encryption, auditing, monitoring, intrusion detection, and firewalls, and the limitations of those countermeasures. They will learn about cyberspace law and law enforcement, information warfare and the military, and intelligence in the information age. Information warfare policy and ethical issues will be examined.

Topics in AI: Machine Learning Seminar

The purpose of this seminar is to expose graduate students to current methods and practice of research in machine learning by critically analyzing scholarly articles and conducting their own investigation on a topic of their choosing. The seminar will begin with an introduction to machine learning, challenges, algorithms, software, and data sets.
Students will learn current practice in the field by critically analyzing assigned or selected papers using criteria from peer-reviewed conferences and presenting their analysis to the class, which they will summarize in an annotated bibliography. They will replicate some aspect of the article and report on their findings. Students will undertake their own investigation of a research problem of their choosing. Toward the end of the semester, students will submit a draft report describing their project and findings, which the members of the class will review and critique. The seminar will culminate with a presentation of their study and its findings, and the final report and the annotated
bibliography will be due at the end of the semester.

Computational Linguistics: Tools for Linguistics

This course is designed to increase awareness of computational tools and their applications for linguistic research. In other words, it will provide ways to learn about various tools to at least partially automate or accelerate linguistic analysis. We cannot replace linguistic intuition, but we provide students with a greater understanding of how and when to use empirical approaches to linguistic analysis. A large emphasis of the course is in how to deal with large amounts of language data and to understand practical issues in dealing with corpora, annotation and multi-lingual data.

Intro to Statistical Natural Language Processing

Anyone wishing to work in natural language processing (NLP) must have some understanding of the statistical methods in common practice. This course will introduce you to the fundamentals of statistical NLP. Statistical NLP builds on ideas from many fields, including linguistics, probability theory, information theory, programming, and computer science. We will see how these fields provide us with tools to engage in part-of-speech (POS) tagging, parsing, word sense disambiguation, machine translation, and information retrieval.

The focus of the course is very data-driven, meaning that students will be working with large corpora and will be learning how to handle such large pieces of data. Applying statistical techniques to large corpora will also allow us to examine collocations and n-grams, along with techniques for categorizing text.

Machine Translation

This course (MT for short) will examine the methods used to automatically translate between natural languages. I note in passing that Georgetown has a long history in this area; the first public demonstration of an MT system was that of the Georgetown-IBM Russian-to-English system in 1954. The course will explore the classical approaches to MT as well as introduce more recent work in statistical MT. The format of the course will be a mix of classroom lectures (including guest lectures), discussions, a field trip, and student presentations. Students may also be given opportunities to assemble and/or crash-test MT systems.

Prerequisites: The course is open to upperclass and graduate students. There are NO other prerequisites. Computer programming skills aren't required, though students who have such skills will be given opportunities to further test and develop them.

Information Retrieval

Survey of principles and techniques in information retrieval with a focus on text databases, including automatic indexing, search techniques, query mechanisms (formal query languages, topic hierarchies, natural language queries, query-by-example), relevance feedback, and evaluation methodology. Participants will also examine performance of selected commercial systems. Prerequisite: LING-360 or LING-361.

Principles of Computational Neuroscience

Seemingly without effort, the human brain solves computationally very complex cognitive tasks such as recognizing facial expressions, understanding speech, planning and executing sequences of movements, or choosing actions from a range of competing alternatives to maximize the likelihood of reward in a given situation. What are the neural mechanisms that give rise to these behaviors? Given that many cognitive tasks are too varied to be coded genetically, how does experience serve to modify processing at a neural level to improve behavioral performance? How can cognitive processing be optimized “on the fly” for particular tasks? This class will examine these and related questions, showing how cognitive processing across a variety of areas can be grounded in a small number of key neurocomputational principles. Emphasis will be placed on showing how these principles apply to a variety of cognitive domains, including vision, audition, memory, motor control, and decision making. The goal of this introductory class is to convey the underlying computational ideas with a minimum of mathematical overhead, stressing their usefulness even for areas of cognition where data are still insufficient to constrain quantitative models.

Classes will be three hours. Each class will consist of a lecture by Dr. Riesenhuber on the topic du jour and student-led presentations and discussions of research papers (3 papers of 20-30 minutes of presentation & discussion each). These presentations should include critical analyses of the methods and results, along with a discussion of the implications of the findings. Presenters should feel free to discuss questions about their papers with the course director in advance of their presentation. All class members will be expected to have read these papers before class and to come prepared with relevant questions/comments/ideas for the discussion.

A final project will be due on the last day of the course. The final project will take the form of a research grant proposal and a 15-minute presentation on a computational cognitive neuroscience project chosen from any area of cognition. The project should aim to test (either experimentally or through simulations) a computational hypothesis about the neural bases of a particular cognitive function. The proposal needs to include specific aims of the research grant, background information about the aspect of vision studied, and a description of a series of experiments to address the specific aims. The proposal must include a discussion of what conclusions can be made depending on various outcomes of the experiments. The proposals should be between 10-15 double-spaced pages in length. An outline of the proposal must be approved by Dr. Riesenhuber before the final paper is written.
Participants’ grade will depend on three paper presentations (30%, 3 @ 10%), participation in discussions (30%), and the final project (40%; 30% write-up, 10% presentation).

Public Policy for Scientists

This interdisciplinary course will provide introductory lectures in a variety of fields that pertain to biomedical science policy & advocacy. Lectures will cover relevant federal agencies, prominent science advocacy groups and techniques, principles of health economics, funding of research activities, the interaction of science & industry, as well as some controversial issues in science policy such as biodefense, stem cell research, and climate change. Students will be left with a multi-faceted understanding of the environment that shapes biomedical science policy and the scientists’ role in this arena.

Functional Neuroimaging and Cognition

The course is designed to provide an overview of the application of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) to the study of human cognitive and sensorimotor processes. Principles of experimental design, statistical analysis and interpretation will be reviewed briefly at the beginning of the course. For the remainder of the semester journal publications of functional neuroimaging studies employing fMRI or PET will be reviewed and discussed. These are selected to cover a broad range of areas: vision, audition, olfaction, language (word naming, object naming, phonological processing), plasticity, working memory and learning, motor control, brain development and clinical applications.

The first few weeks of the course will provide a background in imaging, with an overview of the principles of imaging techniques and discussions of experimental design. Following this introduction, recent publications will be presented and discussed by the group. For several of these, weekly meetings will consist of a student preparing a brief ½ page summary and critique of a particular short paper and spending 30 minutes presenting the ideas to the group. This will be followed by a group discussion based on additional papers read by all of the students in the same area of study. For three or four of the weekly meetings, outside speakers will be asked to present their own paper and then students will have an opportunity to discuss it directly with the author.

Neurons: Vision to Behavior

Humans are visual animals: Over 50% of cortex is devoted to visual tasks (making it possible to, for instance, read the words on this page). While vision appears effortless, a closer examination of the steps necessary to go from the outside world to a focused picture on the retina, to a representation of this picture in the form of activations of millions of fibers in the optic nerve, to the percept of, e.g., ”my friend Jack, smiling” (and to do so despite large possible variations, e.g., of the position, scale, viewpoint, illumination, and facial expression of Jack’s face), shows that vision is a very complicated process for which the brain has developed extremely effective solutions. Understanding the principles underlying information processing in vision is likely relevant also for other sensory systems and other aspects of cognition. This is a challenging task, however, because it requires several levels of understanding, from the computational level, over the level of cellular and biophysical mechanisms and the level of neuronal circuits, up to the level of behavior.
This course will examine the neural bases of visual processing, motivated by the computational demands of vision in a complex natural world. Topics will range from contrast processing in the retina to the neural bases of reading and facial expression recognition, focusing on physiological and brain imaging studies of the mammalian visual system, behavioral studies of animals and humans, and neuropsychological data as well as ideas from machine vision, demonstrating how the integration of these techniques has been so successful in furthering our understanding of vision. We will also discuss parallels in sensory processing between vision and other sensory modalities (in particular audition).

Open to graduate and undergraduate students from neuroscience, psychology, computer science, and related disciplines. Undergraduate students should contact the course director for permission.

Prerequisites:
Basic knowledge of neuroscience and/or psychology. A little computer science can’t hurt. Willingness to learn whatever background is lacking. Contact the course director if unclear.

Computational Techniques

Techniques for solving practical problems with numerical computation, symbolic computation, and classical mathematical methods will be presented. Topics covered will include Kramers-Kronig analysis, root finding, numerical quadrature, Fast-Fourier transforms, approximation of functions, integration by residues, steepest descents, stationary phasae, and matrix operations.

Numerical Simulation Techniques

Topics will include classical Monte Carlo methods, multidimensional quadrature, Metropolis algorithms, and molecular dynamics. Visualization techniques and parallel computing will also be explored.

Statistical Mechanics

Basic ideas of statistical mechanics as applied to solid state systems will be considered, including the connection between thermodynamics and statistical mechanics, ensembles of harmonic oscillators, ideal Fermi and Bose gases, the one dimensional Ising model, Landau's theory of phase transitions, and magnetism.

Digital Processing and Control

A combination of lecture and laboratory work will focus on analog-to-digital and digital-to-analog conversion, combinational and sequential logic networks, counters and shift registers, and interfacing to computer hardware.

Biotechnology and Security

This course will focus on the interface between biotechnology and security. We will explore the current state of biotechnology, its potential benefits to society, and the threat of its misuse for terrorism or deliberate use for biowarfare. The course will delve into the complexities of the dual use dilemma – biotechnologies that have application for both good and harm – and discuss the ethical issue presented by the intersection of biotechnology and security. These ideas will all be examined in an international context, including impacts on global health and economics. Many topics will be investigated using real-world case studies, and students will have an opportunity to consider scientific, policy, ethical and economic considerations from a variety of perspectives.

Science, Technology, and the Future

This seminar course will consider policy issues such as precaution, privacy, research ethics, fraud, sustainability, risk communication, and the impact of intellectual property rights on developing countries. It will also explore key issues in science policy and the way in which science and environmental policy is made in a range of countries. These will depend to a certain extent on student interest, but are likely to include the United States, France, China, India, South Africa, Viet Nam and the Soviet Union.

The Future of World Energy

Energy is a key driver of human progress, economic prosperity, business opportunity and social well being, and the dominant factor affecting global climate change. Revolutionary changes are sweeping through the energy sector, with profound implications for business, technological innovation and global security. Governments are yielding power to business and market forces, and are introducing competition into electricity and gas markets previously regarded as natural monopolies. Mergers and acquisitions abound in the energy sector, and several successful business models continue after the collapse of Enron. Capital mobilization in the energy sector will come predominantly from the private sector over the next two decades and will become a daunting task, particularly in Russia and the developing world. Technological innovation in the energy sector is more vigorous and dynamic than at any time since the dawn of the industrial era, and is deeply affected by information technology advances. Energy businesses have increasingly embraced environmental sustainability as an opportunity, not a constraint. Visionary corporate leaders are fostering technological and organizational innovations that protect the environment and boost the "bottom line."

The course will review these major contemporary energy developments, including the transformation of business strategies in the energy industry, the changing relationship of governments and market forces, new concepts of energy security, unprecedented technological innovation and concern for the local and global environment. The course is designed to provide students with a sophisticated and multifaceted insight into the future of energy business and technology, government-business relationships in the energy sector, and an understanding of major international energy developments. The course will provide an analytic basis for examining and formulating energy policy. It may be taken by undergraduate and graduate students from anywhere in the university. It will be suitable for policy generalists, as well as for students with a business or science background.

GIS in Environment and Health

This course focuses upon the use of geographic information system (GIS) in studying the environment and health. From a technical point of view, this is an introductory course. No previous experience in GIS is expected. However, students are expected to bring to class knowledge of environmental, international development, and health studies.

GIS emerged in the 1960s as an innovative technology to capture, manage, manipulate, analyze, and present geographic and spatial information. The advancement of personal computer, internet, and now wireless technologies, GIS has become a cross-cutting and prevalent technologies used in the government, private, as well as the non-government, non-profit sectors. Its applications range from location-based analysis for business, environmental assessment/monitoring, hazard monitoring, demographic study, epidemiology, to homeland security.

This course introduces students to the fundamental of geographic data, GIS, and geographic analyses. Using geography as the basic framework, with real-life geographic data from various sources, students gain hands-on experience in using GIS to understand the biophysical, political, demographic, cultural, and economic factors that affect environmental and human health from the global to local scales.

Survey of Population Problems

This course is designed to provide a broad overview of the field of population studies. It introduces students to basic methods of demographic analysis and explores social science perspectives on population problems. Topics covered include: a survey of past and current trends in the growth of the population of the world and of selected regions; analysis of the components of population change and their determinants; and the social and economic consequences of population change.

Climate Science and Policy

International policies on emergent world problems increasingly depend on harmonizing scientific knowledge, inert sociotechnical systems, and shifting political demands. This course examines one specific but extensive problem—global climate change—from multiple perspectives to help illuminate aspects of this complex interplay. During the course we will develop a detailed understanding of scientific knowledge and uncertainties on climate change; investigate the international process created to link this science with policymaking; discuss the root causes of greenhouse gas emissions and possible technological trajectories to mitigate them; assess the perspectives and investment strategies of a diverse group of corporations; and finally examine the local, domestic, and international efforts to address this long-term, global threat. Throughout the course, we will pay close attention to the broader lessons of how science-based international policy can be coordinated under uncertainty, and also discuss how concerns of geographic and intergenerational equity are informed by scientific knowledge and in turn inform the debate. Classes will include lecture, discussion, group activities, and simulated negotiations. Approximately one-third of the course will be devoted to a substantive and quantitative introduction to the science of climate change.

Oceans

The major objective of this course is to understand to ocean's role in global environmental and energy issues such as biodiversity, ocean acidification, climate change, and sea level rise. In this context, course will (1) present fundamental biological, chemical and physical processes and features in modern and past oceans; (2) describe relationships between these processes and contemporary environmental and economic resource issues; (3) to develop familiarity with ocean and coastal zone policy issues on local, regional and global spatial scales. The course will cover these topics: sea-level rise and coastal zones, ecosystem degradation (dead zones, estuaries, mangroves), the carbon cycle, ocean acidification, coral reef ecosystems, oceans and energy and mineral resources, energy policy (carbon sequestration, iron fertilization), biodiversity (endangered species, extinction, invasive species), El Nino and climate variability, and polar oceans. We will cover topics related to continental shelves and slopes (gravity slides), deep-sea abyssal plains and hydrothermal vents. A global view is necessary to understand certain critical ocean-related issues, but many policy issues are pertinent at local and regional spatial scales.