USC Catalogue 2017-2018 [ARCHIVED CATALOGUE]
Informatics Program
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Return to: USC Viterbi School of Engineering
Henry Salvatori Computer Science Center 104
(213) 740-4494
Fax: (213) 740-7285
Email: informatics@usc.edu
Director: Cyrus Shahabi, PhD
Faculty
Professors: Michael Cody, PhD (Communication and Journalism); Ellis Horowitz, PhD (Computer Science, Eelectrical Engineering); Julia Higle, PhD (Industrial and Systems Engineering); Carl Kesselman, PhD (Industrial and Systems Engineering, Computer Science); Nenad Medvidovic, PhD (Computer Science); Viktor Prasanna, PhD (Electrical Engineering); Paul Rosenbloom, PhD (Computer Science); Cyrus Shahabi, PhD (Computer Science, Electrical Engineering); Gaurav Sukhatme, PhD (Computer Science, Electrical Engineering); Milind Tambe, PhD (Computer Science, Industrial and Systems Engineering); John Wilson, PhD (Spatial Science)
Associate Professors: Shahram Ghandeharizadeh, PhD (Computer Science); Yan Liu, PhD (Computer Science)
Assistant Professor: Aleksandra Korolova, PhD (Computer Science)
Research Professors: Craig Knoblock, PhD (Computer Science); Yolanda Gil, PhD (Computer Science)
Research Associate Professor: Clifford Neuman, PhD (Computer Science)
Research Assistant Professors: Yao-Yi Chiang, PhD (Spatial Science); Jelena Mirkovic, PhD (Computer Science); Stefan Scherer, PhD (Computer Science)
Associate Professor of the Practice: Wensheng Wu, PhD
Senior Lecturers: Joseph Greenfield (Information Technology Program); Tatyana Ryutov, PhD (Computer Science)
Bachelor’s Degree
Master’s Degree
University Certificate
Informatics
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INF 250 Introduction to Data Informatics Units: 4
Fundamentals of data informatics: representation of data and knowledge, role of a data scientist, data storage/processing/analytics, machine learning, big data, and data visualization.
Recommended Preparation: A basic understanding of engineering and/or technology is recommended.
Corequisite: ITP 115
Instruction Mode: Lecture
Grading Option: Letter
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INF 351 Foundations of Data Management Units: 4
Data modeling, data storage, indexing, relational databases, key-value/document store, NoSQL, distributed file system, parallel computation and big-data analytics.
Prerequisite: INF 250 and ITP 115
Recommended Preparation: Programming experience (e.g., Python or Java)
Instruction Mode: Lecture
Grading Option: Letter
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INF 352 Applied Machine Learning and Data Mining Units: 4
Foundational course focusing on the understanding, application, and evaluation of machine learning and data mining approaches in data intensive scenarios.
Prerequisite: INF 250 and MATH 208x
Instruction Mode: Lecture
Grading Option: Letter
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INF 429 Security and Privacy Units: 4
Basic concepts in information security and privacy; implications of security and privacy breaches; security and privacy policies, threats, and protection mechanisms; security and privacy laws, regulations, and ethics.
Instruction Mode: Lecture
Grading Option: Letter
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INF 454 Data Visualization and User Interface Design Units: 4
Design of systems for data visualization; user interface design for exploring and interacting with data.
Prerequisite: INF 250
Instruction Mode: Lecture
Grading Option: Letter
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INF 510 Principles of Programming for Informatics Units: 4
Programming in Python for retrieving, searching, and analyzing data from the Web. Programming in Java. Learning to manipulate large data sets.
Instruction Mode: Lecture, Lab
Grading Option: Letter
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INF 519 Foundations and Policy for Information Security Units: 4
Threats to information systems; technical and procedural approaches to threat mitigation; policy specification and foundations of policy for secure systems; mechanisms for building secure security services; risk management.
Recommended Preparation: Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
Instruction Mode: Lecture
Grading Option: Letter
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INF 520 Foundations of Information Security Units: 3
Threats to information systems; technical and procedural approaches to threat mitigation; secure system design and development; mechanisms for building secure security services; risk management.
Recommended Preparation: Background in computer security preferred. Recommended previous courses of study include computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
Instruction Mode: Lecture
Grading Option: Letter
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INF 521 Application of Cryptography to Information Security Problems Units: 3
Application of cryptography and cryptanalysis for information assurance in secure information systems. Classical and modern cryptography. Developing management solutions.
Recommended Preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; understanding of number theory and programming background are helpful.
Instruction Mode: Lecture
Grading Option: Letter
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INF 522 Policy: The Foundation for Successful Information Assurance Units: 3
Policy as the basis for all successful information system protection measures. Historical foundations of policy and transition to the digital age. Detecting policy errors, omissions and flaws.
Recommended Preparation: Background in computer security, or a strong willingness to learn. Recommended previous courses of study include degrees in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics.
Instruction Mode: Lecture
Grading Option: Letter
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INF 523 Computer Systems Assurance Units: 4
Assurance that an information system will behave as expected; assurance approaches for fielding secure information systems; case studies
Prerequisite: INF 519
Recommended Preparation: Prior degree in computer science, electrical engineering, computer engineering, management information systems, and/or mathematics. Some background in computer security preferred.
Instruction Mode: Lecture, Discussion
Grading Option: Letter
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INF 524 Distributed Systems and Network Security Units: 3
Fundamentals of information security in the context of distributed systems and networks. Threat examination and application of security measures, including firewalls and intrusion detection systems.
Prerequisite: INF 521 ;
Recommended Preparation: Prior degree in computer science, mathematics, computer engineering, or informatics. It is recommended that students have a working understanding of communication networks and computer architecture, and some programming facility.
Instruction Mode: Lecture
Grading Option: Letter
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INF 525 Trusted System Design, Analysis and Development Units: 4
Analysis of computer security and why systems are not secure. Concepts and techniques applicable to the design of hardware and software for Trusted Systems.
Prerequisite: INF 519
Instruction Mode: Lecture
Grading Option: Letter
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INF 526 Secure Systems Administration Units: 4
The administrator’s role in information system testing, certification, accreditation, operation and defense from cyber attacks. Security assessment. Examination of system vulnerabilities. Policy development.
Prerequisite: CSCI 530
Recommended Preparation: Previous degree in computer science, mathematics, computer engineering, informatics, and/or information security undergraduate program. Also, it is highly recommended that students have successfully completed course work involving policy and network security.
Instruction Mode: Lecture, Lab
Grading Option: Letter
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INF 527 Secure Systems Engineering Units: 3
The process of designing, developing and fielding secure information systems. Developing assurance evidence. Completion of a penetration analysis. Detecting architectural weaknesses. Case studies.
Prerequisite: INF 525 ;
Recommended Preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; moderate to intermediate understanding of the fundamentals of information assurance, and distributed systems and network security. Knowledge and skill in programming.
Instruction Mode: Lecture
Grading Option: Letter
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INF 528 Computer Forensics Units: 4
Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; File System Analysis; Investigative Techniques and Methodologies; Forensic Reports and Presentations.
Instruction Mode: Lecture, Lab
Grading Option: Letter
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INF 529 Security and Privacy in Informatics Units: 4
Covers societal implications of information privacy and how to design systems to best preserve privacy.
Recommended Preparation: General familiarity with the use of common Internet and mobile applications.
Instruction Mode: Lecture
Grading Option: Letter
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INF 549 Introduction to Computational Thinking and Data Science Units: 4
Introduction to data analysis techniques and associated computing concepts for non-programmers. Topics include foundations for data analysis, visualization, parallel processing, metadata, provenance, and data stewardship.
Recommended Preparation: Mathematics and logic undergraduate courses
Instruction Mode: Lecture
Grading Option: Letter
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INF 550 Overview of Data Informatics in Large Data Environments Units: 4
Fundamentals of big data informatics techniques. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools for storage/processing/analytics of large data set on clusters; in-data techniques.
Recommended Preparation: A basic understanding of engineering principles and programming language is desirable.
Instruction Mode: Lecture
Grading Option: Letter
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INF 551 Foundations of Data Management Units: 4
Terms Offered: FaSp
Function and design of modern storage systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm.
Recommended Preparation: Understanding of networks and databases; experience with probability, statistics, and programming
Duplicates Credit in INF 559
Instruction Mode: Lecture
Grading Option: Letter
Crosslisted as CSCI 537 -
INF 552 Machine Learning for Data Informatics Units: 4
Terms Offered: FaSp
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
Instruction Mode: Lecture
Grading Option: Letter
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INF 553 Foundations and Applications of Data Mining Units: 4
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies.
Recommended Preparation: INF 551 and INF 552 . Probability, linear algebra, basic programming, and machine learning
Instruction Mode: Lecture
Grading Option: Letter
Crosslisted as CSCI 541 -
INF 554 Information Visualization Units: 4
Terms Offered: Fa
Graphical depictions of data for communication, analysis, and decision support. Cognitive processing and perception of visual data and visualizations. Designing effective visualizations. Implementing interactive visualizations.
Instruction Mode: Lecture
Grading Option: Letter
Crosslisted as CSCI 560 -
INF 555 User Interface Design, Implementation, and Testing Units: 4
Terms Offered: Sp
Understand and apply user interface theory and techniques to design, build and test responsive applications that run on mobile devices and/or desktops.
Recommended Preparation: Knowledge of data management, machine learning, data mining, and data visualization.
Instruction Mode: Lecture
Grading Option: Letter
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INF 556 User Experience Design and Strategy Units: 4
Terms Offered: FaSp
The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services.
Recommended Preparation: Basic familiarity with web development and/or graphic design using a digital layout tool
Instruction Mode: Lecture, Discussion
Grading Option: Letter
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INF 557 Foresight for Engineers Units: 3
Terms Offered: FaSp
Applying specialized engineering skillsets to exploring/conceiving of solutions to future challenges; use of techniques for systematically imagining/analyzing diverse possible future paths for engineering products.
Recommended Preparation: Interest in technology innovation, technology policy/strategy and management, or entrepreneurship.
Instruction Mode: Lecture
Grading Option: Letter
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INF 558 Building Knowledge Graphs Units: 4
Terms Offered: Fa
Foundations, techniques, and algorithms for building knowledge graphs and doing so at scale. Topics include information extraction, data alignment, entity linking, and the Semantic Web.
Prerequisite: (INF 551 or CSCI 585 ) and (INF 552 or CSCI 567 )
Recommended Preparation: INF 553 and Experience programming in Python
Instruction Mode: Lecture
Grading Option: Letter
Crosslisted as CSCI 563 -
INF 559 Introduction to Data Management Units: 3
Function, design, and use of modern data management systems, including cloud; data management techniques; data modeling; network attached storage, clusters and data centers; relational databases; the map-reduce paradigm.
Recommended Preparation: Understanding of engineering principles, basic programming skills, familiarity with Python
Duplicates Credit in INF 551
Instruction Mode: Lecture
Grading Option: Letter
Crosslisted as ISE 559 -
INF 560 Data Informatics Professional Practicum Units: 4
Terms Offered: Sp
Student teams working on external customer data analytic challenges; project/presentation based; real client data, and implementable solutions for delivery to actual stakeholders; capstone to degree.
Recommended Preparation: Knowledge of data management, machine learning, data mining, and data visualization
Instruction Mode: Lecture
Grading Option: Letter
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INF 561 Engineering Data Analytics Units: 3
Terms Offered: FaSp
(Enroll in ISE 529 )
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INF 562 Integration of Medical Imaging Systems Units: 3
(Enroll in BME 527 )
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INF 563 Medical Diagnostics, Therapeutics and Informatics Application Units: 3
Terms Offered: Sp
(Enroll in BME 528 )
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INF 570 Foundations of Communication Informatics Units: 3
Modeling behavior and understanding network structures using graph theory and game theory. Using massive data to analyze group behavior.
Recommended Preparation: Minimum one year of calculus and background in matrix operations.
Instruction Mode: Lecture
Grading Option: Letter
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INF 590 Directed Research Units: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
Terms Offered: FaSpSm
Research leading to the master’s degree; maximum units which may be applied to the degree to be determined by the department.
Instruction Mode: Lecture
Grading Option: Credit/No Credit
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INF 599 Special Topics Units: 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8
Max Units: 8
Course content to be selected each semester from recent developments in informatics.
Instruction Mode: Lecture
Grading Option: Letter
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