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Scientific Computing Applications (CSC 239)

 
 
 

Basic Course Information:

  • Course title: Scientific Computing Applications (SCS 239)
  • Instructor: Kostas Alexandridis, Ph.D
  • Frequency: Every Fall academic semester
  • Credits: 3
  • Max Students: 25
  • Prerequisites: none
  • General Description: the course is open to all UVI students, from all colleges and schools. The course aims to provide the content and context of using computers and computational principles in advancing scientific exploration and investigation. It further provides a general overview of the types of scientific computing applications, and the important of computational sciences and approaches to advancing knowledge and technology in our modern societies. 

Course introduction and rationale


 The use of computers and computational methods for acquiring, analyzing, and synthesizing scientific knowledge has been from the beginning one of the fundamental drivers in their historical development. The early computer systems were used almost exclusively for large scientific and military purposes, and in the 20th century, they aided in the accumulation, development, and advancement of science and technology. In the 21st century, the applications of scientific computing are an essential part of our collective human intelligence, and permeate almost every aspect of modern societies. From technology, production and engineering, to social, economic and cultural aspects of everyday life, as well as being valuable partners in addressing many of our modern challenges: energy security, environmental quality, climate change, biomedical applications and disease outbreaks, to name a few.
 This course developsa fundamental understanding of the role of scientific discovery in computer and computational systems, as well as the role of computers in scientific method and analysis. Will also set the stage for a historical and critical review of the past, contemporary and future trajectories and trends related to the applications of scientific computing, and will pose a series of interesting questions regarding morality, ethics and the nature of intelligence in human-machine interactions that form part of our scientific understanding of the world around us.
 Finally, the course provides the students with a useful skill set, knowledge and understanding of the computer science and the computer scientist in the context and across multiple scientific domains: from mathematics, engineering, social and cognitive sciences, environmental sciences, and general philosophy of science. It is aimed to be an enjoyable yet pedagogical tool for science-technology integration.

Course aims and objectives

The overall goal of the course is to expose students to the base history, principles and applications of scientific computing. It especially emphasizes the role of scientific computing in advancing fundamental human knowledge, aiding social and technological evolution, and addressing critical global, regional, national and local problems and challenges. The specific aims and objectives of the course are summarized below:
  1. Develop a principal and comprehensive understanding of the value of scientific computing applications, emphasizing the principles and basic motivations, theories and goals driving them, as well as the role that these applications play in advancing science and its relationship with society. Specifically, the course will enhance the understanding of:
    • The early motivations and foundations of scientific computing
    • Basic exposure and appreciation of  computational science principles
    • The role of data collection, analyses and information in scientific computing discovery.
    • The contributions of scientific computing in the scientific domains of engineering and technology; social, cognitive and economic domains; and natural and environmental sciences.
    • Understanding and evaluating the contemporary and future role of scientific computing applications, in advancing the boundaries of scientific investigation and providing the basis of our evolvement as individuals, societies and communities of space, place and practice.
  2. Enhance the critical thinking ability of the students in distinguishing scientific from non-scientific information and computational knowledge. Specifically:
    • Provide students with examples and case studies showcasing different applications and uses of scientific computing.
    • Provide both the rationale and the scientific quest for knowledge behind major advances in scientific computing that led to every-day applications.
    • Make students aware of the multidimensional character of the scientific method and how it can be served through computational implementations.
  3. Improve the students’ skill-set in relation to the use of computers as tools for scientific research. Specifically:
    • Understanding the role of data types and imputation methods and relationship.
    • Monitoring, evaluating and developing scientific principles for computational applications.
    • Acquiring and using resources for scientific computing applications.
  4. Expose students to contemporary and future challenges regarding scientific computing applications and their developments. Specifically
    • Discuss moral, ethics and good practices that render scientific computing applications beneficial tools for the society as a whole.
    • Promote the understanding of scientific computing applications in the context of the society and the community to which the applications are used/
  5. Synthesizing the student’s collective and individual learning by promoting an academic in-class culture of dialogue, communication, expressiveness and synergistic attitudes. The objective will be achieved by:
    • Encouraging students to participate in active dialogue/debate processes in classroom, and to combine their own experiences and knowledge in informing the discussions.
    • Assigning students critical thinking essays and evaluations of selected case studies and examples.
    • Enhancing student’s communication skills by in-classroom participation and audio-visually aided presentations of projects and assignments.
    • Providing students with multimedia-rich and technology advanced demonstrations and interactive examples

 Course content and themes

From code-breaking to strategic war-games: early history of scientific computing.

  • Week 1: introduction to course rationale, aims and objective. Course overview, learning and educational principles. Discussion of student aims and learning aspirations. Short introduction to the early history of scientific computing.
  • Week 2: Early applications of computing for fundamental science investigation. Early research and science aspirations and results. Scientific computing and advances in scientific understanding. Influences of scientific computing evolution to the contemporary scientific thought and epistemology. The epistemological advances of computational sciences. Discussion on how computing applications contribute to today’s scientific thought, theory and practice. HW#1 essay selection.

From abacus to computational intelligence: basic principles and goals of scientific computing applications.

  • Week 3: Early goals of arithmetic computing operations. Scientific software and hardware contributions to computational problem-solving abilities. Evolution of scientific computing applications for modeling and simulation. Basic computational science principles and the notion of computational machine in a mechanistic universe.
  • Week 4: Machines as a paradigm for intelligence. The Turing test and the Chinese room argument. Computational intelligence and its broader implications for societies. From arithmetic machines to thinking agents. The role of human-machine interactions in scientific investigation. The role of scientist in computations, and the Ockham’s razor. In-classroom discussion on the synergistic relationship between computational intelligence, and human individual, collective and social intelligence. The computer and the scientific method.

From punch-cards to global datasets: the role of knowledge and information management in scientific computing applications.

  • Week 5: Introduction and rationale of scientific imputation. Computer data as a paradigm of scientific observation. Introduction to the concept of information. Scientific computing and the transformation of information to knowledge. Types of scientific computing inference. The concepts of certainty, uncertainty, and incomplete information. Scientific information and computational sciences. Discussing the role of scientific computing in transforming and emitting information, and the role of information in modern digital era. HW#2 article critique.
  • Week 6: Information, data, and scientific discovery – exploring boundaries and advances. Computational applications that connect pieces of seemingly unrelated information. The boundaries of information era – discussing the role of modern computing in information-rich scientific applications. Principles of information and knowledge management in scientific computing. Intelligence data mining and machine learning as examples of modern scientific computational intelligence. Video learning / Interactive learning exercise.

From Enigma to the Matrix and the Lord of the Rings: computing applications in science and engineering domains.

  • Week 7: Early examples of scientific computing applications and their scientific, social, political and cultural contributions. The Enigma machine in WWII. The ENIAC and its contribution to science. Discussing the role of the early computing applications in scientific break-troughs in the post-WWII era.
  • Week 8: Modern examples of scientific computing applications, and discussing the ways they influence society. SETI@home and the revolution of distributed scientific computing. Modern robotic applications in science and technology. Intelligent energy efficiency systems and smart buildings. Interactive learning exercise and show-and-tell participatory session. HW#3 dreamtime competition.

From Eliza to computational neuroscience: computing applications in social, cognitive and economic domains.

  • Week 9: Understanding the evolution of intelligence in scientific computing applications: Eliza – a first attempt to computational intelligence. Kasparov vs. IBM’s Deep Blue and the importance of real-world interactions. The birth of the intelligence machine. Discussing the boundaries, limitations and visions of the intelligent machine, and the role of human-computer interactions. Play against the machine.
  • Week 10: Scientific computing and the development of next-generation intelligence. Decision-support systems for remote biomedical diagnosis and recovery. Scientific computing in pattern recognition and forensic science. Scientific information in the service of mankind. Video learning and discussion on next-generation intelligence.

From Silent Spring to An Inconvenient Truth: scientific computing in an era of global environmental change.

  • Week 11: The World 2/3 Limits to Growth models by Meadows and Meadows and the Club of Rome. Climate change research, global warming and scientific investigation. From a computer chip to ice core samples in Antarctica. Scientific computing and early warning systems. Student dreamtime competition demonstrations/presentations.
  • Week 12: Contemporary environmental issues and scientific computing. Internet and environmental alliances for scientific data exchange. Scientific collaborations and global assessment. Examples: Millenium Ecosystem Assessment; the Happy Planet Index; the Ecological Footprint Index. Discussing the value of scientific computing applications for environmental stewardship. Sharing views and experiences. Role-playing game. HW#4 group net journals.

From social engineering to social networking: Scientific computing, connectivism and collective intelligence.

  • Week 13: Early social engineering approaches and the role of scientific computing. The early days of mechanistic thinking and the psychology of social engineering. Can a computational model be moral? Scientific computing applications, privacy and ethics in an era of digital connectivism. Discussing issues of ethics and morality in scientific computing use and applications. In-class exercise: come up with a set of best-practice, rules-of-thumb for proper and ethical use of scientific computing application.
  • Wekk 14: Google’s Page Rank and Amazon’s ranking algorithms and their impacts in contemporary digital societies. From the dynamics of segregation, to the six degrees of separation to the facebook and social networking: the role of scientific computing. Discussing the relationship between computational science and networked society. Group net journals short presentations.

The future of scientific computing: a dynamic class discourse.

  • Week 15: Discussions on the future of scientific computing and technologies. Benefits and risks. Generating scientific computational knowledge and information and diffusing its value in the broader society. Examine and evaluate the role of scientific computing in individual and collective learning settings in family and society. Course evaluation and group discussions. Take-home messages, and course wrap-up. Teach the teacher exercise.

Previous Student Presentations

 My St. Croix students of the 2010 class uploaded a You Tube video of their class final project (Project Transport). Their project presents an important and innovative idea using scientific computing applications.
  • The first video is the final project presentation:

Project Transport Final Presentation

 
  • The second video shows some of the bloopers in making their presentation

Project Transport Bloopers

 
 
  • The same CSC239 St. Croix team of students, did a video-presentation of an scientific article critique. I really enjoyed the graphics.

Video-Essay: Scientific Article Critique