David Smith

Smith, David
  • Adjunct Full Professor
  • Academic Office

Short BIO

Dr. David B. Smith is an Adjunct Full Professor in the Department of Applied Data Science Vienna. He is also a professor of sound, music, and media technology and Dean Emeritus at New York City College of Technology (CUNY), where he served as founding chair of the Department of Entertainment Technology and helped establish the Emerging Media Technology program. Dr. Smith?s work focuses on the intersection of data-driven systems, artificial intelligence, and human-centered creative practice. He is recognized for his contributions to interactive and computational media, including the design of large-scale mediated systems integrating physical and virtual domains. He is the principal inventor of the Sinfonia® orchestral enhancement instrument, used in hundreds of thousands of performances worldwide, and a pioneer in live virtual orchestra technologies. An award-winning composer and sound designer, he has created over forty major works and contributed to nearly 150 theatrical, screen, an

Research

Dr. Smith?s research investigates how data-driven systems, artificial intelligence, and computational media mediate communication, perception, and decision-making across human and machine agents. His work spans applied data science, ethical analysis, and creative research, with particular emphasis on AI-enabled systems operating across physical, virtual, and conceptual environments. As a composer and system designer, he develops blended performance environments that integrate sensing, computation, sound, and visual media. These environments function both as artistic works and as experimental testbeds for research in human?AI collaboration, real-time decision systems, and mediated interaction. Current projects extend across cultural, governmental, and industrial contexts, including immersive simulation, training, and experiential communication systems.

Functions/Roles/Memberships at MU

  • Adjunct Full Professor 2025/10/06-2028/10/05

Courses

  • Spring 2026 Societal and Ethical Aspects of Data Science

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