2022-2023 Graduate Catalog [ARCHIVED CATALOG]
Schmid College of Science and Technology
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Michael Ibba, Ph.D., Dean
Christopher Kim, Ph.D., Associate Dean of Academic Programs
Elaine Benaksas Schwartz, Ph.D., Assistant Dean of External Relations
Professors: Aharanov, Alpay, Caporaso, de Bruyn, El-Askary, Fudge, Gulian, Hill, J., Howell, Ibba, Jipsen, Jordan, Kafatos, Keller, Kim, C., Lyon, Moshier, Panza, Piechota, Piper, Prakash, Sebbar, Singh, Tollaksen, Verkhivker, Warren, Were, Yang;
Research Professor: Napoletani;
Associate Professors: Bisoffi, Bostean, Buniy, Dressel, Hellberg, Pace, Rakovski, Thrasher, Vajiac, A., Vajiac, M., Van der Vossen, Wright;
Instructional Associate Professors: Dunham, Gartner, Rowland-Goldsmith, Schwartz, Toto Pacioles, Zalman;
Research Associate Professors: Kim, S., Ouzounov;
Assistant Professors: Atamian, Castro Lopes, Durcik, Gil-Ferez, Glineburg, Goldsmith, G., Hankins, Hsu, LaRue, Leifer, Liberman-Martin, Lopez, McDavid, McQueen, Miklavcic, Ogba, Owens, Robinson, Tanner, Waldrop, Weissman;
Instructional Assistant Professors: Bonne, Chang, Goetz, Hill, K., John, Lopez Najera, O’Neill, Sherff, Waegell;
Visiting Assistant Professor: Monaghan;
Instructors: Cwik, Dudley, Hunnicutt, Tran;
Presidential Fellow: Fisher.
The Schmid College of Science and Technology prepares students for the complex world of the twenty-first century by challenging them to think critically, participate in research, and engage in outreach through clubs, internships, and volunteer work. The college offers traditional and interdisciplinary degrees and programs designed for students who aspire to become tomorrow’s scientists and leaders in science and technology. The Schmid College of Science and Technology invites students to join its dynamic community of teacher-scholars, researchers, and students.
Doctor of Philosophy
Master of Science
Joint Degree Program
Integrated Program
Computational Science
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CS 501 - Introductory Computation for Scientists
CS 501 is a graduate-level course intended to introduce modern computing tools and techniques to science-oriented students from diverse backgrounds. Assuming little prior knowledge, students will become proficient with a powerful set of inter-operable tools that are suitable for problem-oriented and data-intensive applications now common in modern science. While emphasizing the central role of data (structuring, processing, and visualization), students will use industry-best software development practices to develop efficient implementations and visualizations of numerical solutions to scientific problems. Students will be expected to complete programming assignments in freely available languages such as Python, Julia, C, and C++. (Offered as needed.) 3 credits -
CS 502 - Applied Methods in Mathematics
Prerequisite, MATH 111. In this course students will develop an understanding of the fundamental concepts, solution methodologies, technical applications, and connections of linear algebra and differential equations. (Offered as needed.) 3 credits -
CS 503 - Statistical Methods
Prerequisite, MATH 210, or equivalent. This course will provide a lower graduate level introduction to classical statistical theory. Main concepts such as probability functions, univariate, multivariate, marginal and conditional distributions of random variables, transformations of random variables as well as classical asymptotic results such as the Law of Large Numbers, the Central Limit Theorem, sampling distributions, likelihood, confidence intervals, hypothesis testing, categorical data and linear regression will be emphasized from a more mathematically solid viewpoint. Examples, data, and programming code will be provided to ascertain clarity of all concepts and underline connections with related topics and current research. Examples will be provided to clarify the concepts and underline connections with related topics and current research. Data analyses will be performed via the statistical software package R (http://www.r-project.org). (Offered as needed.) 3 credits -
CS 510 - Computing for Scientists
Prerequisites, CPSC 230 and CPSC 231 or CS 501 . This course introduces students to the fundamental math and algorithms of the basic computational methods required to succeed in advanced study in the computational and data sciences. In this course students will learn computer arithmetic, software development, and become proficient with the use of MATLAB, Python and/or other programming languages. The course will also focus on scientific computing by designing and analyzing algorithms for solving mathematical problems. Students will be able to derive information from a collection of data by designing algorithms involving data interpolation, regression analysis, data smoothing and forecasting, spectral method and dimensionality reduction. Letter grade. (Offered fall semester.) 3 credits -
CS 520 - Mathematical Modeling
Prerequisites, MATH 211 and MATH 350 or CS 502 . 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. Letter grade. (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 532 - Computational Economics
(Same as MGSC 532 .) 3 credits -
CS 533 - Computational Methods in Financial Markets
(Same as MGSC 533 .) 3 credits -
CS 536 - Neural Computation
Prerequisite, CS 510 . This course covers the many different ways that neurons and networks of neurons compute, as well as the techniques used for modeling neurons, neural networks, and neural time-series data. It will be a bridge between neuroscience, data science, and engineering disciplines. Some sections of NEUR 436 will be held with CS 536. Letter grade. (Offered fall semester, alternate years.) 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 567 - Game Theory I
(Same as ECON 567 .) 3 credits -
CS 571 - Remote Sensing Methodologies
Prerequisites, PHYS 101, PHYS 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. Letter grade. (Offered as needed.) 3 credits -
CS 595 - Computational Science Seminars
Prerequisites, CS 510 , CS 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 spring semester.) 1 credit -
CS 596 - Aspects of a Researcher
Becoming a researcher and cultivating a career in research involves more than being able to conduct good experiments. A range of secondary skills are necessary for survival in today’s academic and industrial research environment. This class is a primer on the basic support tools students will find crucial for conducting research and planning their careers. Pass/No Pass. (Offered fall semester.) 1 credit -
CS 599 - Individual Study
Prerequisites, admission to MS in computational and data sciences, 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 as needed.) 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, with an emphasis on reproducing kernel Hilbert space methods. Specific topics include information retrieval for data mining, multimedia data mining, data visualization, classification, clustering, and data cleansing. Letter grade. (Offered as needed.) 3 credits -
CS 614 - Interactive Data Analysis
Prerequisites, CS 530 , CS 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 616 - 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 spring semester.) 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 as needed.) 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
Prerequisite, 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 as needed.) 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 629 - Experimental Course
Prerequisite, CS 510 . Computational Science experimental courses are designed to offer additional opportunities to explore areas and subjects of special interest. Course titles, prerequisites, and credits may vary. Some courses require student lab fees. Specific course details will be listed in the course schedule. Letter grade with Pass/No Pass option. May be repeated for credit if the topic is different. Fee: TBD. (Offered as needed.) 1-3 credits -
CS 632 - Computational Economics II
(Same as MGSC 632 .) 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
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. Letter grade. (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, MATH 450. This course introduces students to the theory and applications of Fourier analysis. Theoretical topics include Fourier series and their convergence, generalized Fourier series based on orthogonal sets of functions, L^2 spaces, convolutions, the Schwartz class, continuous and discrete Fourier transforms, the Laplace transform. Applications include trigonometric interpolations, mean square approximations, boundary value problems for ordinary and partial differential equations, eigenfunction expansions, Heisenberg’s inequality, random walks, sampling theorems for band-limited signals, frequency analysis of time series, signal and image transformations. Students will develop their analytical and computational skills through a range of theoretical exercises and numerical projects, which are closely related to the above theoretical topics and applications. Although Mathematica will be used for numerical demonstrations in class, students can use any programming languages (Java, C, Python, R, MATLAB, etc.) for the numerical projects. Letter grade. (Offered as needed.) 3 credits -
CS 667 - Game Theory II
(Same as ECON 667 .) 3 credits -
CS 670 - Natural Language Processing
Prerequisites, admission to a graduate computational and data sciences program and CS 501 or CS 510 or consent of instructor. Natural Languages have evolved from thousands of years of human existence as they pass from generation to generation. The grammar of any natural language is complex and different from other languages. Moreover, it is evolutionary. This makes Natural Language Processing (NLP) a complex challenge. The main goal of NLP is to understand the meaning of text. Only when computers understand the real meaning of the text, can they take decisive action which must be the intended action. Sentiment analysis of text is one of the important applications of NLP. The use case of sentiment analysis is for the purpose of analyzing customer feedback and tweets. The translation of text between languages is another significant NLP application. NLP capabilities are currently used in daily lives. Personal assistant software like Siri (Apple’s iPhone), Google Assistant (search engines), Amazon’s Alexa, and robots use NLP (text and audio) to understand the user’s intent and provide an accurate response. Spelling and grammar errors flagged by word processing software packages (like MS Word, Google Docs) use NLP to improve the written text. Mobile phones use NLP to understand and transcribe audio messages. There are currently two different approaches to NLP. The first one is the analysis of words, sentences, and the semantics of text. There are various software packages that can provide these capabilities. These software packages are Natural Language Tool Kit (NLTK), TextBlob, and spaCy. The other approach to NLP is using the Machine Learning strategy to analyze the text. Neural Network models are used to train a model by feeding it a lot of text data. Google Cloud Platform (GCP) provides a Machine Learning (ML) API (Application Programming Interface) for the analysis of Natural Languages and provides translation service between languages. IBM Watson provides similar services for NLP. Letter grade. (Offered every year.) 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 685 - 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 688 - Curricular Practical Training
The course offers students an opportunity to learn professional skills “on the job”. P/NP. (Offered every semester.) 0 credit -
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 698 - Thesis
Prerequisites, admission to the MS in computational sciences and data sciences, completion of twelve 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. 6 credits required. If Thesis incomplete after 6 credits student must enroll in CS 698A . Letter grade. Course repeatable for a maximum of 6 credits. (Offered as needed.) 3 credits -
CS 698A - Thesis: Continuous Enrollment
Prerequisites, completion of 6 credits of CS 698 , 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. 1 credit maximum. P/NP. (Offered as needed.) 1 credit -
CS 698B - Thesis: Extended Continuous Enrollment
Prerequisite, completion 1 credit of CS 698A . This course is taken if after 1 credit of CS 698A the thesis is not yet successfully defended. P/NP. May be repeated for credit. (Offered as needed.) 1 credit -
CS 705 - Information Theory: From Classical to Quantum
The purpose of the course is to present the main theorems of classical information theory and explain, at each step, what is the non-commutative version, used in quantum information theory. The course is divided into three parts: • Classical information theory • Coding theory • An introduction to quantum information theory. Letter grade. (Offered as needed.) 3 credits -
CS 710 - Advanced Data Visualization
This course provides an introduction to data visualization and effective data communication. Topics covered include the biological basis of sight and cognitive processing of visual data, creating basic and advanced graphics for a variety of data types, and the fundamental principles of effective data communication in oral and written formats. Letter grade. (Offered spring semester.) 3 credits -
CS 770 - Topics in Computational Science
Prerequisites, CS 520 , CS 530 . May be repeated for credit. (Offered as needed.) 3 credits -
CS 798 - Dissertation
Prerequisite, advancement to candidacy in the Ph.D. in computational and data sciences program. Dissertation is an independent study that culminates in a doctoral dissertation. 12 credits taken in a maximum of 4 consecutive semesters. 12 credits required. Pass/No Pass. May be repeated for credit. (Offered every semester.) 1-6 credits -
CS 798A - Dissertation: Continuous Enrollment
Prerequisite, completion of 12 credits of CS 798 . This course is taken after 12 credits of CS 798 if the dissertation is not yet successfully defended. 2 credits maximum. P/NP. May be repeated for credit once. (Offered every semester.) 1 credit -
CS 798B - Dissertation: Extended Continuous Enrollment
Prerequisite, completion 2 credits of CS CS 798A . This course is taken after 2 credits of CS 798A if the dissertation is not yet successfully defended. P/NP. May be repeated for credit. (Offered as needed.) 1 credit -
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. Students may not exceed 21 credits of CS 799 Doctoral Studies during their time in the program. Letter grade. This course may be repeated for credit. (Offered as needed.) 1-9 credits
Food Science and Nutrition
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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 230. 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. Letter grade. (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. Letter grade. (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 every year.) 3 credits -
FSN 505 - Food Safety
This course will provide an overview of the policies and processes to ensure food safety such as Good Manufacturing Practices (GMP), Hazard Analysis and Critical Controls Points Plans as well as risk-based Hazard Analysis and Preventive Controls for Human Foods, and basic requirements for FSMA. Letter grade. (Offered every year.) 3 credits -
FSN 506 - Workplace Communications for Food Scientists
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. Letter grade. (Offered as needed.) 2 credits -
FSN 507 - Food Quality Management
This course serves as foundation for applying proven quality principles and practices that are used around the world. The class will cover quality terms, benefits, and philosophies, team organization, roles, responsibilities, and dynamics, continuous improvement concepts, processes, and tools, root cause analysis and risk management, and supplier and customer relationships. Letter grade. (Offered as needed.) 1 credit -
FSN 508 - Statistics for Food Scientists
Prerequisite, MATH 203. This course provides students in the food science graduate program an applied approach to statistical concepts and procedures used in food science. Fundamental statistical concepts will be discussed and common applications of statistics in food science will be presented. Statistical methods are important tools employed in both food science and sensory/consumer science applications, and this course will include topics that cover applications in both areas. All statistical calculations are going to be done using R. (Offered fall semester.) 3 credits -
FSN 509 - Topics in Food, Diet and Culture
An international study tour to explore the food systems, diet, and culture in another country. Travel location may change each time the class is offered. Some section of FSN 309 may travel with FSN 509. Letter grade. Fee: TBD. (Offered as needed.) 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 fall semester.) 3 credits -
FSN 517 - Food Analysis
Prerequisites, CHEM 230, MS in 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 519 - Travel course to Crete and Athens: Exploring the Original Mediterranean Diet
A study tour to explore the food systems, diet, and culture in Crete and Athens, Greece. Some sections of FSN 319 will travel with FSN 519. Fee: TBD. (Offered as needed.) 3 credits -
FSN 520 - Food Processing and Preservation
Corequisite, FSN 521 . Methods used for food processing and preservation, effects of processing technologies on shelf-life, nutritional value, and quality attributes. Factors that affect selection of most appropriate technology and equipment. (Offered spring semester.) 3 credits -
FSN 521 - Food Processing and Preservation Laboratory
Corequisite, FSN 520 . A laboratory study of the unit operations involved in food processing, the impact of ingredients and processing parameters on safety and quality of food, and problem solving. Letter grade. (Offered spring semester.) 1 credit -
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. A community service project is an essential component of this class. (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 317. 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. Some sections of this course may be offered as hybrid courses or online only. Letter grade. (Offered fall semester.) 3 credits -
FSN 530L - Food Microbiology Lab
Prerequisite, BIOL 317. Corequisite, FSN 530 . A laboratory study of the factors contributing to food safety, techniques used in the microbiological analysis of food safety and quality, and methods for food fermentation. Letter grade. (Offered fall semester.) 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 fall semester.) 3 credits -
FSN 539 - Life Cycle 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.) 3 credits -
FSN 543 - Medical Nutrition Therapy
Prerequisite, FSN 303. This course is designed to increase the students’ knowledge of the pathophysiology of various disease states. Principles of dietary management as a preventative and therapeutic tool in health care will be emphasized during various physiologic changes such as disease, metabolic alterations, and stress. Students will learn how to modify the normal diet for the prevention and treatment of diseases. Some sections of FSN 543 will be held with FSN 443. (Offered spring semester.) 3 credits -
FSN 551 - Food Fraud
Students study the history, regulations, analytical methods, vulnerabilities, and preventative controls associated with food fraud. (Offered spring semester, alternate years.) 3 credits -
FSN 587 - Nutrigenomics
Prerequisites, BIOL 208, BCHM 335. Nutrigenomics is the study of the interaction between food and genes. In the course, we will investigate how components of diet regulate human metabolism through molecular mechanisms and discern whether dietary requirements vary based on genotype. Further, we will explore the associated implications for clinical practice, food production, and policy development. (Offered fall semester, alternate years.) 3 credits -
FSN 594 - Food Product Development, Lecture and Laboratory
Prerequisite, food science program. 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. This course includes a lecture and required laboratory component held at different times. Letter grade. (Offered every year.) 4 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
Prerequisite, food science major. A comprehensive overview of the technical, aesthetic, and legal aspects of packaging processed foods. Some sections of this course may be offered as an online only course. Letter grade. (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 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. The course is designed to increase basic knowledge and broaden student perspectives in Food Science through both oral and written presentations and discussions among the students. It provides opportunities for students not only to locate, but to study scientific literature, organize the material, communicate and interact with other graduate students and faculty. An examination of the nature of scientific research and the steps necessary to successfully complete a research project will be discussed. Students will learn the principles of scientific research, how to survey and critique the literature, design experiments, statistically evaluate the data, and professionally communicate results. (Offered every semester.) 3 credits -
FSN 668 - Curricular Practical Training
This course offers students an opportunity to learn professional skills “on the job.” P/NP. (Offered every semester.) 0 credit -
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 691 - 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 698 - Thesis
Prerequisites, consent of instructor, cumulative GPA of 3.0. Students pursuing the thesis option conduct research leading to a scientific manuscript for publication. Students enroll with a thesis advisor for a total of six credits spread over the course of their project. Requires a minimum of 5 hours of instructor-student contact per credit hour over the semester and an estimated 6-8 hours of student work per week per credit hour. 6 credits required. P/NP. Course repeatable for a maximum of 6 credits. (Offered every semester.) 1- 6 credits -
FSN 698A - Thesis: Continuous Enrollment
Prerequisites, consent of instructor, food science major, 6 credits of FSN 698 , if the thesis is not yet successfully defended. P/NP. (Offered every semester.) 1 credit -
FSN 698B - Thesis: Extended Continuous Enrollment
Prerequisites, consent of instructor, food science major, completion of FSN 698A . This course is taken after 1 credit of FSN 698A , if the thesis is not yet successfully defended. P/NP. May be repeated for credit. (Offered as needed.) 1 credit -
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
Math
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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
Physics
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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 as needed.) 3 credits
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