Amir Akhavan Masoumi

faculty

Amir Akhavan Masoumi

Assistant Teaching Professor

Computer & Information Science

Curriculum Vitae

Contact

508-910-6895

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Education

2015Computer Sciences, University of Science MalaysiaPhD
2009Computer Sciences, University of Science MalaysiaMSc
2004Civil Engineering, Urmia Azad UniversityBSc

Teaching

  • CIS 568/DSC 530: Data Visualization
  • EGR 111: Intro Engineering & Computing

Teaching

Programs

Teaching

Courses

Software architectural patterns and techniques for building web applications. This course is intended to expose students to theories and principles of web-based user interface design, and a wide variety of client- and server-side technologies for developing web applications.

This course explores real-world scenarios introducing software debugging, quality testing, unit testing, Whitebox and Blackbox testing, code analysis techniques, and employing automation for various stages like integration, testing, deployment, and monitoring to optimize the workflow, reduce errors, and expedite delivery. Furthermore, it explores the Agile environment-based automated software testing (DevOps), containerization, and version controlling.

Project-based course on advanced data visualization techniques. Topics may include: scalable visualization methods, multidimensional data analysis, network visualization, geospatial visualization, and interactive visualization. Ethical issues in data science.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Offered as needed to present advanced material to graduate students.

Offered as needed to present advanced material to graduate students.

Project-based course on advanced data visualization techniques. Topics may include: scalable visualization methods, multidimensional data analysis, network visualization, geospatial visualization, and interactive visualization. Ethical issues in data science.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

Teaching

Online and Continuing Education Courses

Project-based course on advanced data visualization techniques. Topics may include: scalable visualization methods, multidimensional data analysis, network visualization, geospatial visualization, and interactive visualization. Ethical issues in data science.

Project-based course on advanced data visualization techniques. Topics may include: scalable visualization methods, multidimensional data analysis, network visualization, geospatial visualization, and interactive visualization. Ethical issues in data science.

Introduction of Design and Organization of Computing Systems. This course introduces fundamental concepts of computing systems, such as circuit design, boolean equations, binary arithmetic and data representation, the operation of memory, as well as design of a processor. This course also covers the use of VHDL in designing circuits. The course has design, implementation, and analytical components.
Register for this course.

Research

Research activities

  • Modeling the Dynamics of Infectious Diseases
  • Climate Change and Extreme Events Patterns
  • Cryptanalysis of Chaos-Baded Cryptography Systems
  • Adverse Events Analytics using Clinical Data

Research

Research interests

  • Complex Systems
  • Cognitive Science
  • Cryptography
  • Chaos Theory
  • Data Science

Select publications

See curriculum vitae for more publications

  • Joseph Norman, Amir Akhavan, Chen Shen, David Aron, Luci Leykum, Yaneer Bar-Yam (2020).
    Toward Prevention of Adverse Events Using Anticipatory Analytics
    Progress in Preventive Medicine, 5, e0029.
  • Azam Majooni, Mona Masood, Amir Akhavan (2018).
    An eye-tracking study on the effect of infographic structures on viewer’s comprehension and cognitive load
    Information Visualization, 17, 257-266.
  • Amir Akhavan, Azman Samsudin, Afshin Akhshani (2017).
    Cryptanalysis of an image encryption algorithm based on DNA encoding
    Optics & Laser Technology, 95, 94-99.

Additional links