Colloquium:
Friday, May 09
2:35pm in Wege
Reading Books and People
I will talk about two projects my lab works on. The first project concerns NLP on books: How can you quantify the similarity of different narrative texts, like the degree of faithfulness between a novel and its screen adaptation, or the quality of translation of a source book into a given language?
The second project concerns building models to predict life course outcomes from nation-scale social registry and social network data. How predictable is your future income, when you will retire, or who you will marry? We work with social scientists in Europe on these questions.
Steven Skiena is Distinguished Teaching Professor of Computer Science and Interim Director of the AI Innovation Institute at Stony Brook University. His research interests include data science, bioinformatics, and algorithms. He is the author of six books, including “The Algorithm Design Manual”, “The Data Science Design Manual”, and “Who’s Bigger: Where Historical Figures Really Rank”, and over 200 technical papers.
Skiena received his B.S. in Computer Science from the University of Virginia and his Ph.D. in Computer Science from the University of Illinois under Herbert Edelsbrunner in 1988. He is a Fellow of the American Association for the Advancement of Science (AAAS), a current and former Fulbright scholar, and recipient of the University of Virginia Engineering Distinguished Alumni Award (WahooWa!), the ONR Young Investigator Award and the IEEE Computer Science and Engineer Teaching Award. His paper on the DeepWalk approach to graph representation learning received the ten year Test of Time Award at KDD 2024.