Pith. sign in

What Kind of Person Wins the Turing Award?

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

Computer science has grown rapidly since its inception in the 1950s and the pioneers in the field are celebrated annually by the A.M. Turing Award. In this paper, we attempt to shed light on the path to influential computer scientists by examining the characteristics of the 72 Turing Award laureates. To achieve this goal, we build a comprehensive dataset of the Turing Award laureates and analyze their characteristics, including their personal information, family background, academic background, and industry experience. The FP-Growth algorithm is used for frequent feature mining. Logistic regression plot, pie chart, word cloud and map are generated accordingly for each of the interesting features to uncover insights regarding personal factors that drive influential work in the field of computer science. In particular, we show that the Turing Award laureates are most commonly white, male, married, United States citizen, and received a PhD degree. Our results also show that the age at which the laureate won the award increases over the years; most of the Turing Award laureates did not major in computer science; birth order is strongly related to the winners' success; and the number of citations is not as important as one would expect.

citation-role summary

background 1

citation-polarity summary

fields

cs.LG 1

years

2024 1

verdicts

CONDITIONAL 1

roles

background 1

polarities

unclear 1

representative citing papers

Scalable Early Childhood Reading Performance Prediction

cs.LG · 2024-12-05 · conditional · novelty 6.0

A new public benchmark dataset of 6,916 first-grade students supports a masked MLP pre-training method that achieves the best accuracy and AUC for predicting reading progress among the compared models.

citing papers explorer

Showing 1 of 1 citing paper.

  • Scalable Early Childhood Reading Performance Prediction cs.LG · 2024-12-05 · conditional · none · ref 55 · internal anchor

    A new public benchmark dataset of 6,916 first-grade students supports a masked MLP pre-training method that achieves the best accuracy and AUC for predicting reading progress among the compared models.