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Guillermo Cecchi received an education in Physics (MSc, University of La Plata, Argentina, 1991), Physics and Biology (PhD, The Rockefeller University, 1994-1999), and Imaging in Psychiatry (Postdoctoral Fellow, Cornell University 2000-2001). He has been interested in diverse aspects of theoretical biology, including Brownian transport, molecular computation, spike reliability in neurons, song production and representation in songbirds, statistics of natural images and visual perception, statistics of natural language, and brain imaging. In 2001 he joined IBM Research to work on computational approaches to brain function.
In recent years, Dr. Cecchi has pioneered the use of a computational linguistics approach to quantify psychiatric conditions from short speech samples, applying it successfully to conditions as diverse as schizophrenia, mania, prodromal psychosis, and drug and alcohol intake.
Areas of research
Predicting natural language descriptions of mono-molecular odorants, Nature Comms. (2018).
Brain and psychological determinants of placebo pill response in chronic pain patients, Nature Comms. (2018).
Prediction of psychosis across protocols and risk cohorts using automated language analysis, World Psychiatry (2018).
Reverse-engineering human olfactory perception from chemical features of odor molecules, Science (2017).
Automated Analysis of Free Speech Predicts Psychosis Onset in High-Risk Youths, NPJ Schizophrenia (2015).
Loss of consciousness is associated with stabilization of distributed cortical dynamics, Journal of Neuroscience (2015).
A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects, Neuropsychopharmacology (2014).
Perceptual basis of evolving Western musical styles, PNAS (2013).
Predictive Dynamics of Pain Perception, PLoS Computational Biology (2012).
Self-organized Dynamical Criticality in Human ECoG, Frontiers Integrative Neuroscience (2012).
Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis, PLoS One (2012).
The relevance of the time domain in neural networks models, Springer (2012).
Full-brain Auto-Regressive Modeling (FARM) using fMRI, Neuroimage (2011).
A theory of loop formation and elimination by STDP, Frontiers in Neuroscience (2010).
Predictive network models of schizophrenia, NIPS (2009).
Self-tuned critical networks, Physical Review Letters (2009).
High throughput image analysis and reconstruction, Artech House (2009).
Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks, PNAS (2008).
Unsupervised segmentation with dynamical units, IEEE Transactions on Neural Networks (2008).
Scale-free brain functional networks, Physical Review Letters (2005).
Global properties of the Wordnet lexicon, PNAS (2002).
Unsupervised learning and adaptation in a model of adult neurogenesis, Journal of Computational Neuroscience (2001).
Simple motor gestures for birdsong, Physical Review Letters (2001).
On a common circle: natural scenes and Gestalt rules, PNAS (2001).
Noise in neurons is message-dependent, PNAS (2000).
Toward a Song Code: Syllabic Representation in the Canary Brain, Neuron (1998).
Efficiency of DNA Replication in the Polymerase Chain Reaction, PNAS (1996).
Negative Resistance and Rectification in Brownian Transport, Physical Review Letters (1996).
Recent Press Coverage & Talks
The Scientist (2019)
SuperDataSience podcast (2019)
Practical AI podcast (2019)
Wall Street Journal (2018)