Usama Fayyad

About

Usama Fayyad is a professor of the practice in the Khoury College of Computer Sciences and the executive director of the Institute for Experiential AI at Northeastern University, based at the Roux Institute in Portland, Maine. His research interests include data mining, image processing, machine intelligence, machine learning, and inductive learning. Fayyad has received multiple awards and honors for his research, such as the ACM SIGKDD Innovation Award, an ACM fellowship, and the US Government Medal from NASA. He has been published in prominent outlets including the Harvard Data Science Review and Communications of the ACM. His educational background includes a PhD in Computer Science and Engineering, an MS in Mathematics and Computer Science and Engineering, and a BS in Electrical Engineering and Computer Engineering, all from the University of Michigan.

Research topics

• Computer Science
• Data Mining
• Sociology
• Psychology
• Economics
• Data science
• Business
• Economy
• World Wide Web
• Internet privacy

Selected publications

• Responsible AI: An Urgent Mandate

  IEEE Intelligent Systems · 2024-01-01 · 26 citations

  articleSenior author

  AI is rapidly becoming essential in various industries, raising societal expectations. AI’s societal consequences include impacts on mental health; misinformation; workforce displacement; and economic, regulatory, and law enforcement challenges. Indeed, the regulation of AI usage is on the horizon, with the European Union and China already taking big steps, while the United States drafted its first AI-related bill of rights last year. Professional associations and other nonprofits are also contr…

  PublisherDOI

• Analytics and Data Science Standardization and Assessment Framework

  Harvard data science review · 2020 · 26 citations

  1st authorCorresponding

  As the industry is racing to harness the power of data, demand for data science professionals is growing at an increasing rate. However, almost every organization has a unique way of defining roles in data science and associated skills and knowledge. This has resulted in a confusing industry landscape for employers, academic and training institutions, and existing and aspiring data science professionals. This article is the first in a series authored by Initiative for Analytics and Data Science…

  PublisherOA PDFDOI

• The Attention Economy and the Impact of Artificial Intelligence

  Springer eBooks · 2021 · 22 citations

  Senior authorCorresponding

  Abstract The growing ubiquity of the Internet and the information overload created a new economy at the end of the twentieth century: the economy of attention. While difficult to size, we know that it exceeds proxies such as the global online advertising market which is now over $300 billion with a reach of 60% of the world population. A discussion of the attention economy naturally leads to the data economy and collecting data from large-scale interactions with consumers. We discuss the impact…

  PublisherOA PDFDOI

• From Stochastic Parrots to Intelligent Assistants—The Secrets of Data and Human Interventions

  IEEE Intelligent Systems · 2023-05-01 · 17 citations

  article1st authorCorresponding

  Generative AI is all the rage nowadays—primarily driven by ChatGPT capturing the public imagination and attracting hundreds of millions of users in record time, reaching 100 million users in two months. However, there is much ambiguity from the providers about the technology, the methodology, and the way OpenAI makes it work. This compounds the mystique and speculation. I focus on what we know, with a particular emphasis on the aspects that the makers of ChatGPT avoid discussing with the public—…

  PublisherDOI

• From Unicorn Data Scientist to Key Roles in Data Science: Standardizing Roles

  Harvard Data Science Review · 2022-07-28 · 7 citations

  articleOpen access1st authorCorresponding

  The lack of an agreed-upon classification of job roles related to data science is causing much confusion that is challenging to the industry, educational sector, and practitioners. Prior work in this area has considered different aspects from different fields or points of view and has shown that more detail is needed in subcategorizing data science professionals. However, other prior work has also shown that avoiding the detailed subcategorization leads to challenging problems, for example, the…

  PublisherOA PDFDOI

Frequent coauthors

• Geoffrey I. Webb

  82 shared

• Ee‐Peng Lim

  Singapore Management University

  81 shared

• Hervé Marti

  National Institute of Informatics

  81 shared

• Gabriella Pasi

  81 shared

• Xin Yao

  81 shared

• Ravi Kumar

  81 shared

• S. G. Djorgovski

  California Institute of Technology

  32 shared

• N. Weir

  Argonne National Laboratory

  28 shared

Education

• Ph.D., Computer Science

  University of Texas at Austin

  1994

• M.S., Computer Science

  University of Texas at Austin

  1990

• B.S., Computer Science

  American University of Beirut

  1987

Awards & honors

• ACM SIGKDD Innovation Award
• ACM fellowship
• US Government Medal from NASA

Similar researchers at Northeastern University

• Yun Raymond Fuh-79
• Robin Waltersh-75
• Kenneth Churchh-62