Schmid College of Science and Technology

Andrew Lyon, Ph.D., Dean

Michael Fahy, Ph.D., Associate Dean of Operations, Facilities and Finance

Christopher Kim, Ph.D., Associate Dean of Academic Programs

Professors: Aharanov, Caporaso, Carson, de Bruyn, Fahy, Griffin, Jipsen, Kafatos, Lyon, Moshier, Ortiz–Franco, Piper, Prakash, Radenski, Singh, Struppa, Tollaksen, Verkhivker, Yang;

Associate Professors: Allali, Brownell, El–Askary, Funk, Keller, Kim, Vajiac, A., Vajiac, M., Wellman, Were, Wright, Zhao;

Assistant Professors: Bisoffi, Buniy, Hellberg, Linstead, MacPherson, Nistor, O'Neill, Prytkova, Rakovski, Rowland–Goldsmith, Schwartz, Toto;

Lecturer: Renault;
Instructors: Chang, Gartner, Goetz, Nayeri, Sherff.

Master of Science in Computational and Data Sciences

Master of Science in Food Science

Joint Master of Business Administration/Master of Science in Food Science

Ph.D. in Computational and Data Sciences

The Schmid College of Science and Technology prepares students for the complex world of the twenty–first century by challenging students to think critically, to engage in research and to become involved in outreach through clubs and volunteer work. The college offers traditional and interdisciplinary degrees and programs designed for students who aspire to become tomorrow’s scientists, health care providers and leaders in fields related to science and technology.

Schmid College of Science and Technology is organized into two schools, Computational Sciences and Earth and Environmental Sciences. Each school offers undergraduate, graduate and interdisciplinary degrees supported by dynamic scholar–teachers and each school intentionally builds connections from the undergraduate to graduate programs so that students can move seamlessly from baccalaureate degrees to dynamic, innovative and highly regarded graduate programs. The Schmid College of Science and Technology invites you to join our dynamic community of scholars–teachers and students.

School of Computational Sciences

Professors: Aharonov, Fahy, Jipsen, Moshier, Ortiz–Franco, Porter, Radenski, Rassenti, Struppa, Tollaksen, Verkhivker, Yang;

Associate Professors: Allali, Vajiac, A., Vajiac, M., Zhao;

Assistant Professors: Buniy, Linstead, MacPherson, Nayeri, Nistor, Prytkova, Rakovski, Toto;

Instructor: Goetz.

Master of Science in Computational and Data Sciences

Ph.D. in Computational and Data Sciences

Computational and data scientists construct mathematical models, develop quantitative analysis techniques and use modern computing technology to tackle big data problems in a wide variety of disciplines.

The School of Computational Sciences offers an M.S. degree and a Ph.D. degree in Computational and Data Sciences. The M.S. degree has four areas of study available in the program: analytics and applied mathematics, bioinformatics and computational biology, computational economics and earth systems science. The Ph.D. in Computational and Data Sciences is a 70–credit post baccalaureate research degree with a wide range of specializations. The curriculum places strong emphasis on each student’s background with the goal of producing high–quality doctoral research.

Computational science and data science are interdisciplinary fields in which computers are used to reduce the need for expensive and time demanding experimental scientific investigations, utilizing modeling and simulation of biological and physical scientific processes. Computational science is the science of the future. These disciplines tackle fundamental scientific and engineering problems (biological, physical, geophysical, environmental, chemical, as well as fluid and structural dynamics) through the use of advanced computing methodologies. Computational science focuses on modeling and simulation while data science is primarily concerned with knowledge discovery and predictive analytics.

Computational and data scientists work in and across all areas of the sciences (e.g., astrophysics, earth system science, biosciences) by applying high–performance computing to fuse data sets, visualize theoretical possibilities and create knowledge. The goal is to engage cutting–edge technology to answer the most perplexing scientific questions.

Master of Science in Computational and Data Sciences

In the Master of Science in Computational and Data Sciences program students will learn to apply techniques including machine learning, high performance computing, time series analysis and image processing to answer the world’s most complex questions in fields such as bioinformatics, climate modeling, drug design, economic science and predictive analytics. Students will also be prepared for a career in the exploding new field of data science.

Admission to the program and prerequisites

An undergraduate degree in computational science or data science is not required for admission.

Admission to the program may be achieved by the completion of the following requirements:

  1. Online application for admission (which includes $60 non–refundable application fee).
  2. Official transcript from degree granting institution.
  3. Graduate admission test scores – the Graduate Record Examination (GRE) general test scores are required and must have been taken within the last five years. Applicants must achieve the following minimum scores which are listed as previous version test scores and new version test scores, respectively: Verbal: 500/153, Quantitative: 550/146, Analytical Writing: 4.0/4.0.
  4. Graduate admission test scores – the Graduate Record Examination (GRE) subject test scores are required and must have been taken within the last five years. Applicants must achieve a minimum score in the 60th percentile in any subject.
  5. Letters of recommendation – two letters of recommendation are required, including one from an academic source which describes your professional and academic abilities.
  6. Statement of intent – a 750 word essay. Applicants are expected to address science topics they are interested in and how they envision applying computational science in those areas.
  7. Resume – a resume or curriculum vitae is required.
  8. TOEFL (international students only) – applicants who have completed their undergraduate degree outside of the United States are required to achieve an acceptable score on the Test of English as a Foreign Language (TOEFL), minimum 550 (paper–based), 213 (computer–based) or 80 (Internet–based).
  9. Financial certification form (international students only).

Prerequisites

All prerequisites must be met by the end of the first semester. 

  1. Differential equations.
  2. Data structures.
  3. Probability and statistics.

Requirements for the Master of Science in Computational and Data Sciences degree

core courses (13–16 credits)

CS 510*

Computing for Scientists

3

CS 520

Mathematical Modeling

3

CS 530

Data Mining

3

CS 540

High–Performance Computing

3

CS 555

Multivariate Data Analysis

3

CS 595

Computational Science Seminars

1

*CS 510 will be waived for students with strong computing backgrounds

elective courses (12 credits)

Select four elective courses including at least three courses (nine credits) in one area of study

12

research/project courses (6 credits)

Choose one of the following options. The option shall be declared when the student has been admitted and successfully completed the core requirements in the program.

option 1

CS 664

Research Topics in Computational Science

3

CS 665

Capstone Project (Directed Reading)

3

option 2

CS 697

Thesis

6

total credits

 

31–34

area of study requirements (9 credits)

complete one of the following areas of study

analytics and applied mathematics area of study (9 credits)

three of the following

CS 611

Time Series Analysis

3

CS 612

Advanced Numerical Methods

3

CS 613

Machine Learning

3

CS 614

Interactive Data Analysis

3

CS 615

Digital Image Processing

3

bioinformatics and computational biology area of study (9 credits)

three of the following

CS 621

Bioinformatics and Computational Biology I

3

CS 622

Bioinformatics and Computational Biology II

3

CS 623

Computational Systems Biology

3

CS 624

Biostatistics

3

computational economics area of study (9 credits)

three of the following

CS 531

Computational Economics

3

CS 564

Game Theory

3

CS 611

Time Series Analysis

3

CS 634

Dynamical Optimization

3

earth system science area of study (9 credits)

three of the following

PHYS 520

Physical Principles of Remote Sensing

3

CS 641

Introduction to Natural Hazards

3

CS 642

Earth System Science

3

CS 643

Satellite Image Processing

3

CS 644

Global Climate Change

3

4 + 1 Integrated Undergraduate/Master of Science in Computational and Data Sciences

The Schmid College of Science and Technology offers an integrated program for undergraduates which enables students to begin taking M.S. course work in their senior year and receive a M.S. in Computational and Data Sciences within one year of finishing their undergraduate studies. Thus, students can earn a B.S. and M.S. in five years. Students can apply to the M.S. program in their junior or senior year. Students will receive conditional admission to the program, pending completion of their B.S. degree as stipulated in the graduate catalog (see explanation for conditional admission in the graduate catalog). If accepted to the M.S. program, students can take up to 12 credits in M.S. 500–level courses during their senior year. The application process, prerequisites, GPA and graduate program requirements are as specified for the M.S. in Computational and Data Sciences.

An undergraduate student who is a senior may enroll in 500–level courses with the permission of the program director. An undergraduate student may enroll in a maximum of 12 credits of 500–level courses that will count towards both the B.S. and M.S. degrees. So, for future admission to the (4 + 1) program, by the end of the fourth (senior) year, the student should have completed at least 124 credits including the 12 graduate credits that will be double counted and have at least a 3.000 cumulative GPA and then they will be eligible to complete the master's degree in one additional year.

Ph.D. in Computational and Data Sciences

The Ph.D. in Computational and Data Sciences is a 70–73 credit post baccalaureate research degree. The curriculum places strong emphasis on each student’s background with the goal of producing high–quality doctoral research.

Computational scientists construct mathematical models, develop quantitative analysis techniques and use modern computing technology to tackle big data problems in a wide variety of disciplines. Some aspects of computational science, such as machine learning and predictive analytics, have become identified as the new field of data science. Computational and data scientists tackle fundamental scientific, engineering and business problems through the use of advanced computing methodologies.

Computational and data scientists work in and across a wide variety of disciplines (e.g., astrophysics, earth system science, biosciences, business, economics) by applying high–performance computing to fuse data sets, visualize theoretical possibilities and create knowledge. The goal is to engage cutting–edge technology to answer the most perplexing scientific questions.

Admission to the program and prerequisites

An undergraduate degree specifically in computational science is not required for admission. The program will consider applicants from a broad range of undergraduate and master's level science disciplines (e.g. biology, chemistry, computer science, biochemistry, cell and molecular biology, mathematics, physics). Admission will depend on the relationship between the student’s goals and the program’s objectives as well as the likelihood that the student will benefit from the program.

To be considered for admission, applicants must submit the following:

  1. Online application for admission to the Ph.D. in Computational Sciences program (which includes a $60 non–refundable application fee).
  2. Official transcripts from degree conferring institution(s) including all post–baccalaureate graduate course work and advanced degree (if applicable). A cumulative grade point average of 3.000 is required.
  3. Successful admission applicants will have completed undergraduate course work in differential equations and in data structures or the equivalent.
  4. Graduate Record Examination (GRE) general test scores taken within the last five years. Applicants must achieve the following minimum scores which are listed as previous version test scores and new version test scores, respectively: Verbal: 500/153, Quantitative: 550/146, Analytical Writing: 4.0/4.0.
  5. The Graduate Record Examination (GRE) subject test scores are required and must have been taken within the last five years. Applicants must achieve the following minimum score in the 60th percentile in biochemistry, cell and molecular biology, biology, chemistry, computer science, mathematics or physics.
  6. Letters of recommendation–two letters of recommendation are required, including one from an academic source which describes your professional and academic abilities.
  7. Resume–a resume or curriculum vitae is required.
  8. TOEFL (international students only)–applicants who have completed their undergraduate degree outside of the United States are required to achieve an acceptable score on the Test of English as a Foreign Language (TOEFL), minimum 550 (paper–based), 213 (computer–based) or 80 (Internet–based).
  9. Financial Certification Form (international students only).

Prerequisites

All prerequisites must be met by the end of the first semester. 

  1. Differential equations.
  2. Data structures.
  3. Probability and statistics.

Requirements for the Ph.D. in Computational and Data Sciences degree

The degree consists of core courses, elective courses, research courses and dissertation comprising a total of 70–73 credits. The curriculum, which places a strong emphasis on individual students’ backgrounds, with the goal of producing high–quality doctoral research, is structured as follows:

During the third semester of the program each student will work with the faculty to create a Doctoral Committee consisting of three faculty members. Either one faculty member will serve as the chair of the Doctoral Committee or two faculty members will serve as co–chairs. The student, together will the Doctoral Committee, will prepare an academic plan for the student that will specify the remaining elective courses and the Doctoral Research courses that the student will take and will also specify the problem or area of research that the student will explore in the student’s dissertation. The academic plan will be submitted to the Doctoral Steering Committee and must be approved by the Doctoral Steering Committee before the student can continue to the third year.

In order to advance to doctoral candidacy, a student must:

core courses (13–16 credits)

requirements

CS 510*

Computing for Scientists

3

CS 520

Mathematical Modeling

3

CS 530

Data Mining

3

CS 540

High–Performance Computing

3

CS 555

Multivariate Data Analysis

3

CS 595

Computational Science Seminars

1

*CS 510 will be waived for students with strong computing backgrounds

elective and research courses (45 credits)

15  courses selected from among the graduate courses in computer science, computational sciences, mathematics, physics and economic sciences. A minimum of 15 credits (five courses) must be at the 700 level (excluding the dissertation courses).

45

dissertation (12 credits)

CS 797

Dissertation Research

1–6

total credits

 

70–73

School of Earth and Environmental Sciences

Professors: Caporaso, Carson, de Bruyn, Griffin, Kafatos, Lyon, Piper, Prakash, Singh;

Associate Professors: Brownell, El–Askary, Funk, Keller, Kim, O'Neill, Wellman, Were, Wright;

Assistant Professors: Gartner, Heilberg, Rowland–Goldsmith, Schwartz.

Master of Science in Food Science

Joint Master of Business Administration/Master of Science in Food Science

The School of Earth and Environmental Sciences offers a graduate Master of Science in Food Science and a joint M.S. in Food Science/MBA degree program. Our curriculum develops students' knowledge of food science concepts that impact the safety, quality and nutritional value of food. Our faculty are teacher–scholars committed to developing students' communication and critical thinking skills through a combination of a comprehensive didactic education and faculty–mentored student research projects emphasizing an evidence–based empirical approach to problem solving. Our students will be equipped with the interpersonal skills and practical expertise to function effectively in the food and allied industries to address practical, real world concerns.

Master of Science in Food Science

Food science is a multidisciplinary discipline that applies scientific concepts to the understanding of the properties of food. Food science is concerned with the application of the physical, biological and allied sciences to the processing, preservation, packaging, storage, evaluation and utilization of foods. The food science graduate program at Chapman University prepares students for a variety of careers in the food, nutritional, pharmaceutical and related industries, in government and regulatory agencies, for service organizations and academic institutions.

Admission to the program and prerequisites

An undergraduate degree in food science is not required for admission; because of its basic orientation, the program encourages applicants from a broad range of disciplinary interests. Recently admitted applicants have degrees in chemistry, biology, pharmacy, business, chemical and mechanical engineering as well as food science and nutrition.

Admission to the program may be achieved by completing the following requirements:

  1. Hold a baccalaureate degree from a regionally accredited institution. Students with a B.A. or B.S. degree with a major in any of the physical or biological sciences will generally have the necessary prerequisites in chemistry, biology and mathematics. Students with an inadequate background will be required to take prerequisite subjects without credit toward their graduate degree. Prerequisite courses must be completed within the first year of enrollment.
  2. Have achieved a minimum required admission grade point average of 3.000. Graduate Record Exam (GRE) scores are required. (GMAT scores may be accepted in lieu of GRE). Applicants must achieve a minimum score of 680 or 153 (revised test) on the quantitative section,  500 or 153 (revised test) on the verbal section and a score of 3.5 on the analytical writing section of the general test.
  3. Applicants who have completed their undergraduate degree outside of the United States are required to achieve an acceptable score on the Test of English as a Foreign Language (TOEFL), minimum of 550 (paper–based) or 80 (Internet–based).

For further information, please contact the Office of Admission.

Prerequisites

  1. General chemistry with laboratory (two semesters).
  2. Organic chemistry with laboratory (two semesters or one semester organic and one semester biochemistry).
  3. Microbiology with laboratory.
  4. Statistics.
  5. Human nutrition.

Transfer policy

Students admitted to the Master of Science in Food Science degree program with an earned master’s degree may transfer up to six credits of graduate course work upon approval of a petition by the program coordinator and the dean of the school. (See the academic policies and procedures section for transfer policies.)

Continuous enrollment fee

Students who have previously registered for the thesis/project but who have not completed the course work are required to submit a continuous enrollment fee for each semester the thesis/project remains outstanding. The fee for continuous enrollment is equal to one credit of tuition charged per program and will allow students to remain in active status as well as enable them to utilize university resources for completion of the course work.

Requirements for the Master of Science in Food Science degree

1. course work

30 semester credits in food science and nutrition–related courses must be completed. Students entering the program without a degree in food science or a food science background will be required to take the food science core courses (11 credits) as part of their 30–credit degree requirements. If the core courses have been taken as an undergraduate at Chapman University or at another academic institution, a student will not be expected to repeat this material. The student would then build a program by selecting courses from the approved list of electives for graduate students in consultation with their advisor. Essentials of Food Science (FSN 500) and Research Methods (FSN 660) are required of all graduate students. Thus, a typical student will take the 11–credit core, one credit for Essentials of Food Science, three credits for Research Methods and 15 elective credits.

Students who earn a C– or lower in any of the three core classes must retake the class and earn a higher grade before taking the comprehensive exam and/or completing the program.

core courses (11 credits)

FSN 501

Food Chemistry

3

FSN 502

Food Chemistry Lab

1

FSN 520

Food Processing and Preservation

3

FSN 530/530L

Food Microbiology/Food Microbiology Lab

3,1

requirements (4 credits)

FSN 500

Essentials of Food Science

1

FSN 660

Research Methods

3

electives (15 credits)

FSN 503

Government Regulation of Foods

3

FSN 505

Quality Control and Assurance

3

FSN 506

Effective Communications for the Real World Scientist

3

FSN 510

Food Industry Study Tour

3

FSN 512

Sensory Evaluation of Foods

3

FSN 515

Food Ingredients

3

FSN 517

Food Analysis

3

FSN 522

Community Nutrition

3

FSN 538

Nutrition and Human Performance

3

FSN 539

Life Cycle and Clinical Nutrition

3

FSN 540

Food Engineering

3

FSN 560

Current Topics in Food Science and Nutrition

3

FSN 580

Management and Marketing Fundamentals for Food Scientists

3

FSN 594

Food Product Development

3

FSN 600

Advanced Food Science: Selected Topics

3–12

FSN 601

Food Packaging

3

FSN 602

Food Flavors

3

FSN 603

Chemistry and Technology of Fats and Oils

3

FSN 606

Dietary Supplements and Functional Foods

3

FSN 690

Internship for Graduate Students

½–3

FSN 695

Thesis I

3

FSN 696

Thesis II

3

FSN 697

Thesis III

1–3

FSN 699

Independent Research

1–3

total credits (excluding prerequisites)

30

2. thesis and nonthesis options

A non–thesis course work option or thesis/research project must be completed.

A.

Students must have a cumulative GPA of 3.000 "B" (See the academic policies and procedures section for additional guidelines). Students must complete Research Methods (FSN 660) in which they will develop a research proposal or comprehensive review of the literature on a food science topic. Students must also successfully pass an oral comprehensive exam with a faculty panel. The exam will gauge the ability of the student to coherently and analytically integrate knowledge gained from course work and relate it cogently to the selected research topic. Successful completion of course work alone does not assure the candidate of passing the comprehensive exam. Students will have two opportunities to pass the oral comprehensive exam. In addition, students pursuing the non–thesis option need to complete one semester of either Product Development (FSN 594) or an independent study research project.

B.

Students must have a cumulative GPA of 3.000 "B" (See the academic policies and procedures section for additional guidelines) and be accepted by a faculty member as a research advisee to enroll in the thesis option. Students must complete Research Methods (FSN 660) in which they will develop a research proposal or thesis. Students in the thesis option must complete 30 credits to graduate, including 24 credits of course work and six thesis credits (FSN 695 and FSN 696). If additional time is required to complete the thesis, students must register for one credit of FSN 697 for each semester the thesis remains outstanding. Students must submit a manuscript for publication and pass a final oral exam given by the oral examination committee. The advisor and graduate student will select three faculty to serve as the oral examination committee. Committee members should be chosen to reflect breadth in the food science discipline and can include appropriate colleagues from outside the program who are familiar with the field of study.

4 + 1 Integrated Undergraduate/Master of Science in Food Science

The food science program offers a 4 + 1 program that enables undergraduate students to begin taking M.S. course work in their junior or senior year and receive a Master of Science in Food Science within one year of finishing their undergraduate degree. The program is open to all undergraduate majors as long as they have satisfied the prerequisites for the program and meet admission requirements.

Chapman students can apply to a graduate program in their junior or senior year. Students will receive conditional admission to the program, pending completion of their bachelor's degree as stipulated in the graduate catalog (see explanation of conditional admission in the graduate catalog). If accepted into a graduate program, undergraduate students may take up to 12 graduate credits once they have completed 90 undergraduate credits. These 12 credits may also count towards their undergraduate degree credit requirement. Students would complete the remaining credit hours of graduate course work beginning in the semester after receiving the undergraduate degree. The application process, prerequisites, GPA and graduate program requirements are as specified for the M.S. in Food Science.

Joint Master of Business Administration/Master of Science in Food Science

In conjunction with the Argyros School of Business and Economics, the Chapman University Schmid College of Science and Technology offers a joint program leading to both the MBA and M.S. in Food Science degrees. Offered to full–time and part–time students, the program requires acceptance into the Professional MBA program at the Argyros School of Business and Economics and the M.S. in Food Science program in the Schmid College of Science and Technology.

Students interested in the dual degree program must meet all admission requirements for each school. Students will make only one financial aid application. Scholarships and grants applicable to each degree will be decided by the separate schools.

Students may apply to the joint program, alternatively, students may also apply to the MBA program during their first year in the food science program and students may apply to the M.S. in Food Science program during their first year of study in the Professional MBA program.

Students must satisfy the minimum requirements for each degree program including course requirements, residency and other requirements listed in the graduate catalog. Students must maintain a cumulative grade point average of 3.000 "B" or higher in each program. A non–thesis course work option or thesis/research project must be completed.

The dual degree program requires the completion of 65 total credits (as opposed to 80 credits if the two degrees are sought separately and outside the joint program). The Argyros School of Business and Economics will accept up to nine of the M.S. in Food Science credits toward completion of its 50–credit requirement; thus students must complete 41 MBA credits in the Professional MBA program.

The Schmid College of Science and Technology will accept six of the MBA credits toward completion of its 30–credit requirement, thus reducing the M.S. in Food Science requirements to 24 credits (15 credits of required courses and nine credits of electives).

Food science courses eligible for credit towards the MBA degree (limit of seven credits applied towards the MBA):

FSN 503

Government Regulation of Foods

3

FSN 510

Food Industry Study Tour

3

FSN 560*

Current Topics in Food Science and Nutrition

3

FSN 580

Management and Marketing Fundamentals for Food Scientists

3

FSN 594

Food Product Development

3

FSN 600*

Advanced Food Science: Selected Topics

3–12

FSN 690*

Internship for Graduate Students

½–3

FSN 695*

Thesis I

3

FSN 696*

Thesis II

3

FSN 697*

Thesis III

1–3

FSN 699*

Independent Research

1–3

*Requires approval of the Argyros School of Business and Economics Graduate Committee for credits to be applied towards the MBA.

Business courses eligible for credit towards the M.S. in Food Science degree are BUS 605 (Marketing Management) and BUS 606 (Operations and Technology Management), offered by the Argyros School of Business and Economics.

Course Descriptions – Computational Science

CS 510 Computing for Scientists

Prerequisites, CPSC 230, 231. This course provides students with the necessary computer programming and software engineering background required to succeed in advanced study in the computational sciences. The course is organized into three main parts. In the first part of the course students will become proficient with the C++ programming language. The second part of the course will focus on high-performance computing techniques using multiprocessing and multithreading. Finally, the last part of the course will discuss software engineering process and the software development lifecycle. (Offered fall semester.) 3 credits.

CS 520 Mathematical Modeling

Prerequisites, MATH 211, 350. Mathematical modeling will concentrate on the process of developing mathematical descriptions of physical phenomenon. The main goal of this course is to learn how to make a creative use of some mathematical tools, such as difference equations, ordinary differential equations, and numerical analysis, to build a mathematical description of some physical problems. (Offered fall semester.) 3 credits.

CS 530 Data Mining

Prerequisite, CS 510. This course provides an overview of standard techniques and algorithms for data mining and machine learning. Students will be exposed to exploratory data analysis and data cleaning before surveying standard algorithms for classification and clustering. Additionally, students will learn the types of problems each algorithm is best suited to solve. Special attention will be given to efficiency and scalability. Students will apply algorithms to data sets from biology, chemistry, social media, and industry (Netflix Grand Challenge, etc). (Offered spring semester.) 3 credits.

CS 531 Computational Economics

(Same as CPSC 430, MGSC 530.) Prerequisites, MATH 110 and either CPSC 230, or 236, or consent of instructor. This course will introduce students to the computational tools required to understand electronic exchange systems and implement economic experiments. Students will be required to become familiar with numerical analysis, computer simulation, and programming of experiments. (Offered every year.) 4 credits.

CS 540 High-Performance Computing

Prerequisite, CS 510. This course covers the basic concepts and techniques needed for problem solving using parallel computers. It will introduce the students to high-performance computer architectures, their taxonomies and performance issues. The design and analysis of parallel algorithms will be covered. Techniques for data and workload partitioning for parallel execution will be discussed. It will also introduce parallel programming models and contemporary parallel programming techniques including message passing and shared memory. Cluster, grid and cloud computing will be introduced. (Offered every year.) 3 credits.

CS 555 Multivariate Data Analysis

Prerequisite, MATH 361, or consent of instructor. This course will provide a graduate level introduction to theory and applications of classical and modern methods for Multivariate Data analysis. Main concepts such as multivariate distributions, matrix algebra, inference, convergence, and estimation will be studied from a more mathematically solid viewpoint. Examples and real-life datasets will be provided to clarify the concepts and underline connections with related topics and current research. Data analyses will be performed using the R statistical software package. (Offered fall semester.) 3 credits.

CS 560 Applied Partial Differential Equations

Prerequisites, MATH 210, 350. Students will learn how to solve certain types of Partial Differential Equations. They will study the general theory of PDEs, as well as methods of solving linear and non-linear PDEs. Students will also learn how to solve equations that come from the world of physics and other sciences. (Offered as needed.) 3 credits.

CS 564 Game Theory

(Same as ECON 564.)

CS 595 Computational Science Seminars

Prerequisites, CS 510, 520, or consent of instructor. Students are introduced to various topics covering computational science and other related topics by attending research oriented seminars. This seminar series is intended to be capstone experience. Seminars presented by faculty, invited speakers and students; topics vary from semester to semester. (Offered every year.) 1 credit.

CS 599 Individual Study

Prerequisites, admission to CS MS program and consent of instructor. Directed reading and/or research designed to meet specific needs of graduate students. Topics to be selected by mutual agreement of students and faculty. (Offered every semester.) 1–6 credits.

CS 610 Models of Computing

Prerequisites, equivalent of MATH 211, CPSC 406. In this course, students will study the mathematical models of computing from a contemporary perspective. The course will explore the connections between classical automata, operational and denotational semantics, and contemporary models of quantum computing. The theory developed in the course will be applied to specific known problems, e.g., in control theory (finite automata), real number computing (operational and denotations models), and cryptography (quantum computing). (Offered as needed.) 3 credits.

CS 611 Time Series Analysis

Prerequisite, MATH 361, or equivalent. This course will provide a graduate level introduction to theory and applications of classical and modern methods for Time Series analysis. Main concepts such as stochastic processes, stationarity, invertibility, convergence, prediction and estimation will be studied from a more mathematically solid viewpoint. Examples and real-life datasets will be provided to clarify the concepts and underline connections with related topics and current research. Data analyses will be performed using the statistical software package R (http://www.r-project.org). We will be emphasizing the statistical knowledge, software implementation and scientific problem selection that would assist you to write publication quality research papers. (Offered as needed.) 3 credits.

CS 612 Advanced Numerical Methods

Prerequisite, MATH 350. Students study and come to understand several advanced methods of numerical computation as used in 3d modeling, simulations, and solution of partial differential equations. (Offered as needed.) 3 credits.

CS 613 Machine Learning

Prerequisite, CS 530. An introduction to the core algorithms and techniques of machine learning and data mining with emphasis on contemporary big data challenges. Specific topics include information retrieval for data mining, multimedia data mining, data visualization, classification, clustering, and data cleansing. (Offered as needed.) 3 credits.

CS 614 Interactive Data Analysis

Prerequisites, CS 530, 540, 555. This course introduces novel ideas and techniques for interactive data analysis. Students will explore concepts related to data interaction, data preparation, data transformation, data modeling and computation, and data presentation. Students will practice interactive data analysis with Python-based frameworks. Individual term projects will permit students to identify and pursue new research opportunities. Although based on intensive hands-on exploration, this course will be interdisciplinary in nature and cover various data analysis case studies. (Offered as needed.) 3 credits.

CS 615 Digital Image Processing

Prerequisites, MATH 210, 211. This course provides an overview of the main concepts, results, and techniques that are the foundations of current academic research and industry practice in digital image processing. (Offered as needed.) 3 credits.

CS 620 Foundations in Mathematical Bioscience

Prerequisites, MATH 110, BIOL 208, CHEM 330, or consent of instructor. Computational science is an emerging field of the sciences, computer science, and mathematics. This course is to provide the fundamentals of computational science, and introduce a variety of scientific applications in bioscience. We will examine how scientific investigations involve computing in basic biosciences such as physics, chemistry, medicine, and particularly biosciences. It covers selected topics in physiology, biochemistry, and behavior. It may include biochemical reaction kinetics, the Hodgkin Huxley model for cellular electrical activity, continuous and discrete population interactions, and neural network models of learning. Techniques utilized include ordinary differential equations, difference equations, algebraic equations, and computer simulations. The student will be offered examples of computer simulations and data analysis. (Offered fall semester.) 3 credits.

CS 621 Bioinformatics and Computational Biology I

Prerequisite, BIOL 208, or CHEM 230. Students will be introduced to the basic concepts behind Bioinformatics and Computational Biology tools. Hands-on sessions will familiarize students with the details and use of the most commonly used online tools and resources. This course introduces students to the practical application of structure and sequence analysis, database searching and molecular modeling techniques to study protein sequence, structure and function. Amino acid properties and protein secondary structures will be reviewed as supporting information for understanding the importance of protein sequence. Internet resources, molecular visualization software, and computational algorithms will be introduced to the student for structure analysis. (Offered as needed.) 3 credits.

CS 622 Bioinformatics and Computational Biology II

Prerequisite, CS 621. Students will be introduced to the advanced concepts behind Bioinformatics and Computational Biology tools. Hands-on sessions will familiarize students with the details and use of the most commonly used online tools and resources related to developing and building websites, machine learning, data mining and genomics applications. Students will gain practical knowledge in using software techniques and internet resources to handle and compare biological, genomic and medical information. search databases and interpret protein structure. (Offered as needed.) 3 credits

CS 623 Computational Systems Biology

Prerequisites, BIOL 208, or equivalent, or consent of instructor. Computational Systems Biology is to understand complex biological systems that require the integration of experimental and computational research. This course aims to develop and use efficient algorithms, data structures, and visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling of biological systems. Students will learn how to use computer simulations of biological systems to analyze as well as visualize the complex connections of such systems and cellular processes. (Offered spring semester.) 3 credits.

CS 624 Biostatistics

Prerequisite, MATH 203, or equivalent. This course will provide an intermediate-level introduction to various statistical methods with emphasis on applications in Biology, Medicine, and Public Health. Main concepts such as sampling distributions, contingency tables, survival analysis, linear, logistic, and Poisson regressions will be studied from a more mathematically solid viewpoint. Examples and real datasets will be provided to clarify the concepts and underline connections with related topics and current research. Data analyses will be performed using the statistical software package R. (Offered as needed.) 3 credits.

CS 625 Bioinformatics Algorithms

Prerequisites, BIOL 330, CPSC 406, or equivalent. Bioinformatics is the study of living organisms viewed as information processors. Students will study some of the major algorithms used in bioinformatics: sequence alignment, multiple sequence alignment, phylogeny, gene identification, and analysis of gene expression data. (Offered as needed.) 3 credits.

CS 634 Dynamic Optimization

Prerequisite, CS 555. This course will introduce you to the theory and practice of stochastic and dynamic optimization. Stochastic programming techniques will be utilized along with Bayesian networks and Markov processes. (Offered as needed.) 3 credits.

CS 635 BioMedical Informatics

(Same as CPSC 435.) Prerequisite, CS 510. Students are introduced to contemporary research topics in medical informatics, including computational techniques for the collection, management, retrieval, and analysis of biomedical data. (Offered as needed.) 3 credits.

CS 641 Introduction to Natural Hazards

Students are introduced to earth system sciences, earth processes, various natural hazards associated with land, ocean, atmosphere and cryosphere and their impacts on society and environment, as well as to different types and impacts of natural and anthropogenic hazards and resultant disasters worldwide. Connection of climate change and global change to hazards, the effects of pollution and land use change will be discussed and conclusions of how societies may face them will be drawn, Computer exercises/demonstrations will be given to see the changes of natural hazards on land, ocean, atmosphere and cryosphere. (Offered as needed.) 3 credits.

CS 642 Earth System Science

Prerequisite, CS 641. Introduction to Earth Systems- Lithosphere, Hydrosphere, Atmosphere, Biosphere and Crysophere. Processes associated with Lithosphere, Hydrosphere, Atmosphere, Biosphere and Crysophere. Biogeochemical cycle. Coupling between Lithosphere-Hydrosphere-Biosphere-Atmosphere and associated impact on Global Climate Change and Natural Hazards (all types: Land, Biosphere, Atmosphere, Crysophere, Hydrosphere), Extreme Events. (Offered as needed.) 3 credits.

CS 643 Satellite Image Processing

Prerequisite, consent of instructor. This course will emphasize digital processing of earth observing imagery. Students will be introduced to digital image processing techniques and their applications to earth observing remote sensing data. Topics include radiometric and geometric corrections, image enhancement, transformation, segmentation, and classification. Image acquisition sensors and platforms and commonly used data formats for remote sensing data are introduced. This course provides an opportunity to students to explore various applications of remote sensing data to earth system understanding. Strong math skills required. (Offered as needed.) 3 credits.

CS 644 Global Climate Change

Prerequisite, CS 641, or consent of instructor. This course will emphasize global climate change and associated impacts. Students will be introduced to climate change, including changes in the human and natural drivers of the climate, space observations of changes, modeling and the simulations as projections of future climate change and key findings and uncertainties and the relationship of natural hazards to changing climate. The connection of climate change to economy, health, energy and food production will be briefly studied in law, science, education and policy. This course will provide an opportunity to observe applications of remote sensing data and numerical models. (Offered as needed.) 3 credits.

CS 650 Advanced Linear Algebra and Digital Signal Processing

Prerequisites, MATH 210, 211. This course gives students an exposure to advanced topics in linear algebra and their applications to digital signal processing. Using vector space methods, this course provides an overview of the main concepts, results, and techniques that are the foundations of current academic research and industry practice in digital signal processing. (Offered as needed.) 3 credits.

CS 660 Fourier Analysis

Prerequisites, MATH 211, 450. Periodic functions and Fourier series, convergence of Fourier series, Fourier transform of rapidly decreasing functions and L2 functions, Inversion formula and Plancherel theorems, application of the Fourier transform to differential equations, Multiresolution analysis, and orthonormal wavelet bases, signal, and image compression. (Offered as needed.) 3 credits.

CS 664 Research Topics in Computational Science

Prerequisites, admission to CS MS program, CS 510, 530, 540, or consent of instructor. This course will be an applied project chosen and completed under guidance of graduate faculty member resulting in acceptable paper in a major conference in Computational Science or preferably a journal paper. (Offered spring semester.) 3 credits.

CS 665 Capstone Project (Directed Reading)

Prerequisites, admission to CS MS program, CS 510, 530, 540, or consent of instructor. This course will be reading project chosen and completed under guidance of graduate faculty member resulting in acceptable technical report. (Offered spring semester.) 3 credits.

CS 680 Computational Algebra I

Prerequisite, MATH 211. A course in multivariate polynomials, their algebraic properties, and related algorithms for effective computations. After an introduction of the main concepts of the ring of single variable polynomials (polynomial ideals, unique factorization, division algorithm, similarities with the ring of integers), multivariable polynomials are defined. The course addresses the problem of defining order relations on the set of multivariate terms, and moves to the basic concepts of the theory of Gröbner bases. These include: the multivariate division algorithm as a generalization of the Gauss reduction algorithm for vector spaces; the Macaulay Basis theorem; viewing polynomials as rewrite rules; Buchberger's algorithms for the construction of Gröbner bases for polynomial ideals; and the notion of syzygy. Throughout the course, students learn how to use a computer algebra software program to compute with polynomials and to implement the algorithms presented in class. (Offered as needed.) 3 credits.

CS 690 Internship

Prerequisite, consent of instructor. Offers students an opportunity to gain work experience. A minimum of 40 hours of work for each credit. P/NP. May be repeated for credit. (Offered as needed.) ½–3 credits.

CS 697 Thesis

Prerequisites, admission to MS CS program, completion of 12 graduate credits, consent of instructor. Students will complete a research project chosen and completed under guidance of a faculty member and/or faculty committee. The project will result in an acceptable technical report (Thesis) and an oral defense. May be repeated for credit. (Offered as needed.) 3–6 credits.

CS 711 Bayesian Data Analysis

Prerequisite, MATH 361, or equivalent. The main concepts covered in this class include the following: Bayes' theorem and the Bayesian inferential framework (model specification, model fitting, and model checking), computational methods for posterior simulation integration, regression models, hierarchical models, ANOVA, the Gibbs sampler, Markov chain simulations and other numerical methods, (Offered as needed.) 3 credits.

CS 770 Topics in Computational Science

Prerequisites, CS 520, 530, 540. May be repeated for credit. (Offered as needed.) 3 credits.

CS 797 Dissertation Research

Prerequisite, advancement to candidacy in the Ph.D. in computational science program. Dissertation research is an independent study that culminates in a doctoral dissertation. Students must be enrolled continually for at least 1 credit of CS 797 for their dissertation defense. Grading: P/NP. May be repeated for credit to a maximum of 12 credits. (Offered every semester.) 1–6 credits.

CS 799 Doctoral Studies

Prerequisite, advancement to candidacy. This is an individual study course for doctoral students. Content to be determined by the student and the student's Doctoral Committee. May be repeated for credit. (Offered as needed.) 1–3 credits.

Course Descriptions – Environmental Science

ENV 550 Principles of Sustainability

Astronauts are awed by both Earth’s beauty and its vulnerability. Can we become better environmental stewards? Is it necessary? This course will address environmental “sustainability” and develop student interdisciplinary skills in connecting ideas, thinking critically, and tolerating the ambiguity that results from alternative views. (Offered as needed.) 3 credits.

Course Descriptions – Food Science

FSN 500 Essentials of Food Science

Prerequisite, admission to the food science graduate program. An introduction to the multidisciplinary nature of the food science via analysis of relevant case studies. The role of industry, government agencies, service organizations, and academic institutions in supplying safe and wholesome foods to consumers is explained. Relevant career paths for graduates are explored. To be completed during the first year of study. P/NP. (Offered every semester.) 1 credit.

FSN 501 Food Chemistry

Prerequisite, CHEM 330. Corequisite, FSN 502. Students study the chemistry of proteins, lipids, enzymes, carbohydrates, etc. as it relates to the composition, preservation, processing, stability, flavor, and nutritional characteristics of foods. (Offered spring semester.) 3 credits.

FSN 502 Food Chemistry Lab

Corequisite, FSN 501. A laboratory study of the chemistry of proteins, lipids, enzymes, carbohydrates, etc. as it relates to the composition, preservation, processing, stability, flavor, and nutritional characteristics of foods. Fee: $75. (Offered spring semester.) 1 credit.

FSN 503 Government Regulation of Foods

Students examine the rules and regulations of various governmental agencies with regard to the processing, packaging, labeling, and marketing of food products. (Offered as needed.) 3 credits.

FSN 505 Quality Control and Assurance

Students apply physical, chemical, microbiological, organoleptic, and statistical methods to the evaluation of critical properties (i.e., color, flavor, texture, nutrients, stability, and safety) of ingredients and commercial food products. (Offered every third semester.) 3 credits.

FSN 506 Effective Communications for the Real World Scientist

This hands-on course is designed to improve the oral and written communication skills required of a scientist throughout their career. Students will write and critique peer-reviewed publications, practice grant writing, and explore a scientist’s role in effective advertisements, journalism, and consumer dialogue. Effective, efficient, and appropriate use of technical communication tools, including emails, product specifications, product recalls, agendas, and team meetings will be reviewed. (Offered every year.) 3 credits.

FSN 510 Food Industry Study Tour

A study tour of Southern California food processors and allied industries to develop a more thorough understanding of how basic food technology principles are applied to the manufacture of commercial food products. Lecture, laboratory. (Offered interterm.) 3 credits.

FSN 512 Sensory Evaluation of Foods

Prerequisite, MATH 203. Students learn the principles and methodology involved in the sensory testing of food products. (Offered every third semester.) 3 credits.

FSN 515 Food Ingredients

Students evaluate food supplements, preservatives, and other additives designed to improve the acceptability, stability, and nutritional properties of processed food products. Practical aspects of improving existing products and formulating new food products are emphasized. (Offered every third semester.) 3 credits.

FSN 517 Food Analysis

Prerequisites, CHEM 230, food science major. Designed to acquaint the students with the principles and application of physical and chemical methods for the separation, characterization, and quantitative analysis of food constituents. (Offered as needed.) 3 credits.

FSN 520 Food Processing and Preservation

Microbiology and biochemistry of food spoilage, engineering techniques of food processing and preservation, and food plant sanitation; representative methods of food processing and preservation. (Offered every third semester.) 3 credits.

FSN 522 Community Nutrition

Prerequisite, FSN 200. Study of the roles and resources of community/public health nutrition professionals promoting wellness in the community. Assessment of community nutritional needs, and planning, implementing and evaluating nutrition education programs for various age groups under different socio-economic conditions. The legislative process, health care insurance industry, and domestic food assistance programs will also be covered. (Offered spring semester, alternate years.) 3 credits.

FSN 529 Experimental Course

Experimental courses are designed to offer additional opportunities to explore areas and subjects of special interest and may be repeated for credit if course content is different. Course titles, prerequisites, and credits may vary. Some courses require student lab fees. Specific course details will be listed in the course schedule. May be repeated for credit. (Offered as needed.) 1–3 credits.

FSN 530 Food Microbiology

Prerequisite, BIOL 417. Corequisite, FSN 530L. Students study the microorganisms specifically related to the fermentation, preservation, stability, safety, and flavor of foods. Three hours of lecture and three hours of laboratory per week. (Offered every fall.) 3 credits.

FSN 530L Food Microbiology Lab

Prerequisite, BIOL 417. Corequisite, FSN 530. Lab component of FSN 530. Fee: $75. (Offered every fall.) 1 credit.

FSN 531 Special Topics in Nutrition

Prerequisite, depends on the topic being offered. Students discuss current issues in the field of nutrition. Topics may include concepts and controversy, eating disorders, cultural aspects of foods, nutrient interactions, and effects of processing on foods. May be repeated for credit. (Offered as needed.) 3 credits.

FSN 538 Nutrition and Human Performance

Prerequisite, FSN 200. Designed to provide a more in depth view of nutrition, metabolism, and human performance. Ergogenic aids, blood doping, and nutritional needs of the athlete will be emphasized. The methodologies and current topics related to nutrition and human performance will be evaluated. Mechanisms of nutrition will be presented to better understand the cause and effect of human nutrition. (Offered spring semester, alternate years.) 3 credits.

FSN 539 Life Cycle and Clinical Nutrition

Prerequisite, FSN 200. The human body has different nutrient requirements at different times during the life-cycle and when in a diseased state. This course explores the physiological changes, adaptations, and stresses that affect nutritional status and explains the influence of dietary practices in maximizing growth, maintenance, and health. Nutrition counseling and diet analyses are included. (Offered fall semester, alternate years.) 3 credits.

FSN 540 Food Engineering

A survey of engineering concepts and unit operations as applied to food processing. Students examine conveying and washing of foods, fluid flow, evaporation, drying, extraction, mixing, freezing, distillation, and filtration. Two hours of lecture and three hours of laboratory per week. (Offered as needed.) 3 credits.

FSN 560 Current Topics in Food Science and Nutrition

Food science and nutrition are dynamic fields of inquiry and every year new areas of research emerge. The safety of our food, the environmental impacts of processing, and the sustainability of our food supply are being questioned. This course will provide an in-depth examination of current topics of interest in the areas of food safety, quality, processing, and nutrition. (Offered alternate years.) 3 credits.

FSN 580 Managing and Marketing Fundamentals for Food Scientists

An introductory course in the fundamentals of management and marketing, designed for those food science majors who have no academic background in these areas. The objectives of the course include the accelerated learning of introductory management theory and a survey of basic marketing structures and functions as they apply to the food industry. (Offered as needed.) 3 credits.

FSN 594 Food Product Development

Students incorporate the principles taught in the food science and nutrition core courses and apply them to the theoretical and practical considerations of commercial food product development. Teams of students will complete real food product development projects solicited from the food industry. (Offered every year.) 3 credits.

FSN 600 Advanced Food Science: Selected Topics

Current advanced food science course topics are offered as needed (e.g., Food Proteins, Food Carbohydrate Chemistry, Cereal Technology, Fruit and Vegetable Processing, Effects of Processing Foods.) May be repeated for up to twelve credits. (Offered as needed.) 3–12 credits.

FSN 601 Food Packaging

A comprehensive overview of the technical, aesthetic, and legal aspects of packaging processed foods. (Offered as needed.) 3 credits.

FSN 602 Food Flavors

Students study chemical properties, isolation, separation, identification, formation and interaction mechanisms, and applications of flavor compounds. (Offered alternate years.) 3 credits.

FSN 603 Chemistry and Technology of Fats and Oils

Students study the chemical properties, isolation, identification, and degradation mechanisms of fats and oils, and the technology of the processing and uses of fats and oils in the food industry. (Offered alternate years.) 3 credits.

FSN 606 Dietary Supplements and Functional Foods

This course is designed to acquaint students with current trends and regulations in the supplement and functional foods industry. Students will evaluate evidence for claims made, and the efficacy and adverse effects of supplement use. The effect of processing on the stability of dietary supplement and functional foods will be discussed. (Offered alternate years.) 3 credits.

FSN 660 Research Methods

Prerequisites, MATH 203, completion of at least 12 graduate credits. A complete orientation to research and an examination of the nature of scientific research and the steps necessary to successfully complete a research project. Students learn the principles of scientific research, how to survey and critique the literature, operationalize their hypothesis, design experiments, statistically evaluate the data, and professionally communicate results. (Offered every semester.) 3 credits.

FSN 682 Student-Faculty Research

Prerequisite, consent of instructor. Students engage in independent, faculty-mentored scholarly research/creative activity in their discipline which develops fundamentally novel knowledge, content, and/or data. Topics or projects are chosen after discussions between student and instructor who agree upon objective and scope. P/NP or letter grade option with consent of instructor. May be repeated for credit. (Offered every semester.) 1–3 credits.

FSN 690 Internship for Graduate Students

Prerequisite, consent of instructor. Offers students an opportunity to gain work experience. A minimum of 40 hours of work for each credit. P/NP. May be repeated for credit. (Offered every semester.) ½–3 credits.

FSN 695 Thesis I

Prerequisite, cumulative GPA of 3.00 (B) to meet the minimum eligibility requirements to enroll in the thesis option (see the Academic Policies and Procedures section for additional guidelines). Students do research leading to the preparation and completion of a scientific manuscript for publication. Students enroll with a thesis advisor for FSN 695, 696, and 697 over three semesters for a total of 9 credits. Students must have a written thesis proposal approved by their FSN Graduate Committee during the first semesters of the course. P/NP. (Offered every semester.) 3 credits.

FSN 696 Thesis II

Prerequisite, cumulative GPA of 3.00 (B) to meet the minimum eligibility requirements to enroll in the thesis option (see the Academic Policies and Procedures section for additional guidelines). Students do research leading to the preparation and completion of a scientific manuscript for publication. Students enroll with a thesis advisor for FSN 695, 696, and 697 over three semesters for a total of 9 credits. Students must have a written thesis proposal approved by their FSN Graduate Committee during the first semesters of the course. P/NP. (Offered every semester.) 3 credits.

FSN 697 Thesis III

Students do research leading to the preparation and completion of a scientific manuscript for publication. Students enroll with a thesis advisor for FSN 695, 696, and 697 over three semesters for a total of 9 credits. Students must have a written thesis proposal approved by their FSN Graduate Committee during the first semester of this course. P/NP. May be repeated for credit. (Offered every semester.) 1–3 credits.

FSN 699 Independent Research

Prerequisite, consent of instructor. Selected research projects involving either literature studies or laboratory research which develops new information, correlations, concepts, or data. Topics or projects are chosen after discussions between student and instructor who agree upon objective and scope. May be repeated for credit. (Offered every semester.) 1–3 credits.

Course Descriptions – Hazards, Global and Environmental Change

HGEC 500 Hazards, Climate and Earth System Science Seminars

Prerequisites, HGEC 510, 530, 540, or consent of instructor. Students are introduced to various topics covering earth system sciences, hazards, global change, advanced processing techniques and other related topics by attending research oriented seminars. Seminars presented by faculty, invited speakers and students; topics vary from semester to semester. (Offered as needed.) 1 credit.

HGEC 550 Principles of Sustainability

This course covers environmental sustainability and develops interdisciplinary skills in connecting ideas, thinking critically and considering multiple viewpoints. This course will guide students toward understanding environmental sustainability from an interdisciplinary and a practical standpoint. Students will develop theoretical and applied skills to connect ideas, think critically, and tolerate ambiguity that results from alternative perspectives, through such contemporary concepts as “the global commons” and in efforts to influence positive action for a sustainable environment, such as The Club of Rome and “limits to growth,” the Brundtland Commission and “our common future,” and the United Nations and “climate change.” (Offered as needed.) 3 credits.

HGEC 610 Earth Observation and Modeling

Prerequisite, PHYS 520. Students are introduced to different Earth observing sensors, satellite platforms and Earth modeling systems. The usage of these modern systems provides critical information for climate and global changes, and applications to hazards. Specifics for instruments and satellite platforms, and the corresponding parameters obtained from remote sensing, will be discussed. Observing of the Earth systems applications for these instruments will be discussed. Infrastructure support and satellite data bases, algorithms and methods for accessing the data will also be discussed. The course will cover data systems, their evolution and understanding and what information observational and modeling data provide for Earth system, climate and hazards. Includes discussion of current efforts by agencies such as NASA and NOAA, international agencies and others to provide integrated data gathering and dissemination systems, such as the NASA DAACs, GEOSS, etc. (Offered spring semester.) 3 credits.

HGEC 630 Meteorology, Weather and Climate

Prerequisites, PHYS 520, HGEC 540. This course will emphasize the atmospheric environment by conveying meteorological concepts in a visual and practical manner with a thorough background on basic meteorology. Topics of the course will include global warming, ozone depletion, El-Niño, and weather events such as devastating fires associated with Santa Ana winds. (Offered fall semester.) 3 credits.

HGEC 640 Environmental Impact Assessment

Prerequisites, HGEC 510, and either ENV 101, or 112, or consent of instructor. This course will provide a general overview for the need of EIA, identify, predict and evaluate the potential biological, physical, social, and health effects of projects and other development actions. (Offered fall semester, alternate years.) 3 credits.

HGEC 650 Remote Sensing of the Environment

Prerequisite, PHYS 520. Students are introduced to a thorough introduction on how to utilize remotely sensed data to its full potential so that the user would know how to extract useful information from satellite data. The main emphasis of this course is different application topics like remote sensing of vegetation, water, soils, minerals, geomorphology, and urban landscaping. (Offered fall semester.) 3 credits.

HGEC 664 Research Topics in Global Hazards

Prerequisites, admission to HGEC MS program, HGEC 510, 530, 540, or consent of instructor. This course will be an applied project chosen and completed under guidance of graduate faculty member resulting in acceptable paper in a major conference in Geosciences or preferably a journal paper. (Offered spring semester.) 3 credits.

HGEC 665 Directed Reading

Prerequisites, admission to HGEC MS program, HGEC 510, 530, 540, or consent of instructor. This course will be reading project chosen and completed under guidance of graduate faculty member resulting in acceptable technical report. (Offered spring semester.) 3 credits.or consent of instructor. This course will be reading project chosen and completed under guidance of graduate faculty member resulting in acceptable technical report. (Offered spring semester.) 3 credits.

HGEC 697 Thesis

Prerequisites, admission to HGEC MS program, completion of 12 graduate credits, consent of instructor. This course will deal with a research project chosen and completed under guidance of a faculty member and/or faculty committee. The project will result in an acceptable technical report (Thesis) and an oral defense. May be repeated for credit. (Offered as needed.) 3–6 credits.

HGEC 699 Individual Study

Prerequisites, admission to HGEC MS program, HGEC 510, 530, 540, or consent of instructor. This course will be an applied project chosen and completed under guidance of graduate faculty member resulting in acceptable paper in a major conference in Geosciences or preferably a journal paper. (Offered spring semester.) 3 credits.

Course Descriptions – Math

MATH 580 Modern Algebra I

Prerequisite, MATH 380, or 460. A first semester graduate course in algebra. Group Theory (solvable groups, Sylow Theorems, free groups, finitely presented groups, permutation groups, orbits, stabilizers, G-sets, applications to combinatorics, representation theory, character tables), (noncommutative) rings, polynomial rings, Groebner bases, modules, Hilbert’s Nullstellensatz, fields, Galois Theory, fundamental theorem of algebra, commutative algebras, Lie groups and Lie algebras, classification of finite simple groups, and applications. (Offered as needed.) 3 credits.

Course Descriptions – Physics

PHYS 520 Physical Principles of Remote Sensing

Prerequisites, PHYS 101, 102, or consent of instructor. Students get a thorough introduction to gathering the basic concepts and procedures of fundamentals of physical principles of remote sensing. The main emphasis is on the physical and mathematical principles underlying the techniques, such as the atmospheric radiative transfer, satellite orbit, and geo-location simulation, and science algorithm designing, calibration, and atmosphere corrections. Other computational methods will be emphasized. (Offered spring semester.) 3 credits.