Jordan M. Sorokin

Research

Publications, preprints, and selected highlights

Peer-reviewed publications, preprints, and conference work — spanning circuit neuroscience and epilepsy, respiratory and arousal circuits, and machine-learning models for biology.

Neurophysiology & Epilepsy Research

I completed my PhD in Neuroscience (2019) at Stanford in the Huguenard Lab, studying the neural dynamics underlying absence epilepsy — specifically, how the thalamocortical circuit transitions into and out of seizure states.

Using large-scale extracellular recordings, optogenetics, and applied mathematics, I showed that real-time switching of thalamic firing modes is sufficient to both initiate and abort generalized seizures in freely behaving rodents. I identified pre-ictal oscillatory signatures — including β-band shifts in thalamocortical synchrony — that reliably predict seizure onset, and demonstrated that thalamic activity patterns across multiple time scales encode the imminence of ictal transitions.

Key publications include a first-author study in Neuron (2017) demonstrating bidirectional optogenetic control of generalized epilepsy networks, and a preprint on multi-scale thalamic dynamics as predictors of absence seizure onset.

Biotech, Deep Learning & Current Work

Over the past seven years I've worked at the intersection of machine learning and biology in industry. At Herophilus and then Recursion, I developed and deployed deep learning representation models for biological state characterization — across organoid imaging, transcriptomics, and high-content phenotypic screening.

At Recursion I contributed to self-supervised transcriptomics foundation models (TxFM architecture, presented at ICLR 2026 Workshop FM4Science), large-scale representation learning for cancer and neurological disease, and the design of scalable ML infrastructure for drug discovery programs. I led disease program teams and guided cross-disciplinary scientific strategy at the boundary of computational biology and translational research.

I'm now a researcher at Amae Health, where the focus shifts back to the brain — through the lens of passive digital biomarkers. My work centers on ML approaches to inferring latent mental health states from behavioral signals, including passive biometrics and speech. The goal is richer, more continuous measurement of mental health outside the clinic.

Selected highlights