Reward learning, and whether you can measure it from outside the skull.
Three positions, one question. Below: the benchmark that motivates all of it, then the projects, then everything written or presented.
Voice cleared chance, barely (random forest, AUC 0.63, permutation p = .049). Ventral striatal BOLD did not (logistic regression, AUC 0.58, p = .057). The two are statistically non-inferior to each other, which is a statement about how modest both are.
Fig. — AUC-ROC with 95% bootstrap CI, stratified 5-fold CV. Primary preregistered analysis. Voice: eGeMAPSv02, diarized participant speech · DAIC-WOZ, n=142. fMRI: Nucleus accumbens BOLD, BART · ds000030, n=234. The voice result is borderline: p = .049 uncorrected, and it does not survive Bonferroni correction for three classifiers (α/3 = .017). Independent cohorts and different anhedonia instruments (PHQ-8 items 1–2 vs Chapman), so this is a benchmark across datasets, not a within-subject comparison. Both streams overfit heavily (train AUC 1.0, gap > 0.35) on 447 features and 32 positive cases. Stream B is also pipeline-dependent: fMRIPrep 23.x gives 0.45, below chance. Preregistered at osf.io/4d6ey.
ModelRead Lab · USC
A Rescorla-Wagner simulation of how reward learning collapses when the dopaminergic learning rate falls and effort stops being worth spending.
SignalItti Lab · USC Viterbi
Whether that collapse is audible. An open-source pipeline that pulls acoustic biomarkers out of clinical interviews without the audio ever leaving the room.
CircuitNIMH · Experimental Therapeutics
What the circuit is doing while it happens. MEG source localization, gamma-band power, and signal complexity in the insula as a marker of suicidal ideation.
Building source-localization pipelines for resting-state MEG analyses of mood-disorder neural biomarkers.
Working on gamma-band spectral power and signal complexity in the insula as candidate markers of suicidal ideation.
PRAXIS
Psychiatry Research, Analytics & eXperimental Innovation Society
From theory into tools that reach patients.
An undergraduate research group at USC working where machine learning meets clinical mental health. I founded it in February 2026 and lead it: we run original studies, preregister them, and publish what we find including the results that come back null.
Founded
Feb 2026
Host
USC Dornsife
Sponsor
Dr. Laurent Itti
My role
Founder, project lead
Original research applying machine learning to clinical populations, preregistered on OSF before the data are touched.
A twenty-paper journal-club curriculum taking undergraduates through reinforcement learning and active inference.
A biweekly speaker series, including researchers from the NIMH.
Open-source code and computational psychiatry resources, so the methods can be checked by anyone who wants to check them.
Local-first, air-gapped clinical interview transcription and speech-biomarker pipeline. Whisper for transcription, pyannote for diarisation, OpenSMILE for eGeMAPS features.
Architected fully air-gapped so audio never leaves the machine, meeting IRB and HIPAA constraints.
Open-sourced; in use across PRAXIS research streams and adopted externally by the Rutledge Lab at Yale in August 2026.