Informatics Program

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: Barry Boehm, PhD (Computer Science, Industrial and Systems Engineering); Ramesh Govindan, PhD (Computer Science, Electrical Engineering); Julia Higle, PhD (Industrial and Systems Engineering); Carl Kesselman, PhD (Industrial and Systems Engineering, Computer Science); Neno Medvidovic, PhD (Computer Science); Shri Narayanan, PhD (Electrical Engineering, Computer Science, Linguistics, Psychology); Viktor Prasanna, PhD (Electrical Engineering); Suvrajeet Sen, PhD (Industrial and Systems Engineering, Electrical Engineering, Computer Science); Cyrus Shahabi, PhD (Computer Science); Gaurav Sukhatme, PhD (Computer Science, Electrical Engineering); Milind Tambe, PhD (Computer Science, Industrial and Systems Engineering); Priya Vashishta, PhD (Physics, Computer Science, Biomedical Engineering)

Assistant Professor: Yan Liu, PhD (Computer Science)

Research Professors: Craig Knoblock, PhD (Computer Science); Yolanda Gil, PhD (Computer Science); William Swartout, PhD (Computer Science)

Research Associate Professor: Clifford Neuman, PhD (Computer Science)

Research Assistant Professor: Stefan Scherer, PhD (Computer Science)

Professor of the Practice: Roger Schell, PhD

Associate Professor of the Practice: Wensheng Wu, PhD

Senior Lecturers: Blaine Burnham, PhD; Mark Heckman, PhD

Programs

Master’s Degree

• •  Communication Informatics (MCI)
• •  Cyber Security Engineering (MS)
• •  Data Informatics (MS)
• •  Spatial Informatics (MS)

Courses

Informatics

• INF 510 Principles of Programming for Informatics

  Units: 3
  Programming in Python for retrieving, searching, and analyzing data from the Web. Programming in Java. Learning to manipulate large data sets.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• 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
  
  
• 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
  
  
• 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
  
  
• INF 523 Assurance in Cyberspace Applied to Information Security

  Units: 3
  Assurance as the basis for believing an information system will behave as expected. Approaches to assurance for fielding secure information systems that are fit for purpose.
  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
  Grading Option: Letter
  
  
• 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
  
  
• INF 525 Trusted System Design, Analysis and Development

  Units: 3
  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 522 ;
  Recommended Preparation: Prior degree in computer science, mathematics, computer engineering, or informatics; advanced knowledge of computer architecture, operating systems, and communications networks will be valuable.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• INF 526 Secure Systems Administration

  Units: 3
  The administrator’s role in information system testing, certification, accreditation, operation and defense from cyber attacks. Security assessment. Examination of system vulnerabilities. Policy development.
  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
  Grading Option: Letter
  
  
• 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
  
  
• INF 528 Computer and Network Forensics

  Units: 3
  Preservation, identification, extraction and documentation of computer evidence stored on a computer. Data recovery; cryptography; types of attacks; steganography; network forensics and surveillance.
  Recommended Preparation: Previous degree in computer science, mathematics, computer engineering, or informatics; a working understanding of number theory and some programming knowledge will be helpful.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• INF 529 Security and Privacy in Informatics

  Units: 3
  Terms Offered: FaSp

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  Fundamental concepts in information security and privacy; security and privacy policies, threats, and protection mechanisms; security and privacy laws, regulations, and ethics.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• 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
  
  
• INF 550 Overview of Data Informatics in Large Data Environments

  Units: 3
  Terms Offered: FaSp

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  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: Basic understanding of engineering and/or technology principles; basic programming skills; background in probability, statistics, linear algebra and machine learning.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• INF 551 Foundations of Data Management

  Units: 3
  Terms Offered: FaSp

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  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: INF 550  taken previously or concurrently; understanding of operating systems, networks, and databases; experience with probability, statistics, and programming.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  Crosslisted as ISE-551
• INF 552 Machine Learning for Data Informatics

  Units: 3
  Terms Offered: FaSp

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  Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
  Recommended Preparation: INF 550  and INF 551  taken previously or concurrently; knowledge of statistics and linear algebra; programming experience.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• INF 553 Foundations and Applications of Data Mining

  Units: 3
  Terms Offered: FaSp

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  Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies.
  Recommended Preparation: INF 550 , INF 551  and INF 552 . Knowledge of probability, linear algebra, basic programming, and machine learning.
  Instruction Mode: Lecture
  Grading Option: Letter
  
  
• INF 554 Information Visualization

  Units: 3
  Terms Offered: FaSp

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  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
  
  
• INF 555 User Interface Design, Implementation, and Testing

  Units: 3
  Terms Offered: FaSp

  ---

  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
  
  
• INF 556 User Experience Design and Strategy

  Units: 3
  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
  Grading Option: Letter
  
  
• INF 557 Foresight for Engineers

  Units: 3
  Terms Offered: FaSp

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  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
  
  
• INF 560 Data Informatics Professional Practicum

  Units: 3
  Terms Offered: FaSp

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  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
  
  
• INF 561 Engineering Data Analytics

  Units: 3
  Terms Offered: FaSp

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  (Enroll in ISE 529 )
  
• INF 562 Integration of Medical Imaging Systems

  Units: 3
  (Enroll in BME 527 )
  
• INF 563 Medical Diagnostics, Therapeutics and Informatics Application

  Units: 3
  (Enroll in BME 528 )
  
• 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
  
  
• INF 590 Directed Research

  Units: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
  Terms Offered: FaSpSm

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  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
  
  
• 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