Guillermo A. Cecchi
IBM
280 Papers
1.1K Citations
Guillermo A. Cecchi is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 39, co-authored 226 publications. Previous affiliations of Guillermo A. Cecchi include Rockefeller University & University of Washington.
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Papers
Scale-Free Brain Functional Networks
TL;DR: Analysis of the resulting networks in different tasks shows that the distribution of functional connections, and the probability of finding a link versus distance are both scale-free and the characteristic path length is small and comparable with those of equivalent random networks.
1.6K
Automated analysis of free speech predicts psychosis onset in high-risk youths
Gillinder Bedi,Facundo Carrillo,Guillermo A. Cecchi,Diego Fernández Slezak,Mariano Sigman,Natália Bezerra Mota,Sidarta Ribeiro,Daniel C. Javitt,Mauro Copelli,Cheryl Corcoran +9 more
- 26 Aug 2015
TL;DR: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis, as well as outperforming classification from clinical interviews.
Prediction of psychosis across protocols and risk cohorts using automated language analysis.
Cheryl Corcoran,Facundo Carrillo,Diego Fernández-Slezak,Diego Fernández-Slezak,Gillinder Bedi,Casimir C. Klim,Daniel C. Javitt,Carrie E. Bearden,Guillermo A. Cecchi +8 more
TL;DR: The findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder and identify sources of variability.
340
On a common circle: Natural scenes and Gestalt rules
TL;DR: It is shown that a very simple geometric rule, cocircularity, predicts the arrangement of segments in natural scenes, and that different geometrical arrangements show relevant differences in their scaling properties.
Predicting human olfactory perception from chemical features of odor molecules
Andreas Keller,Richard C. Gerkin,Yuanfang Guan,Amit Dhurandhar,Gábor Turu,Bence Szalai,Joel D. Mainland,Joel D. Mainland,Yusuke Ihara,Yusuke Ihara,Chung Wen Yu,Russell D. Wolfinger,Celine Vens,Leander Schietgat,Kurt De Grave,Raquel Norel,Gustavo Stolovitzky,Gustavo Stolovitzky,Guillermo A. Cecchi,Leslie B. Vosshall,Leslie B. Vosshall,Pablo Meyer,Pablo Meyer +22 more
TL;DR: Results of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule, with a predictive accuracy that closely approaches a key theoretical limit.