Principal Investigator

As a neuroscientist I am working on improving perinatal health outcomes by deepening our understanding of brain's responses to ante- and perinatal asphyxia, inflammation/infection and stress. My lab uses modern machine learning (ML) techniques for signal analyses (e.g., see here) combined with A.I. frameworks to convert the medical data deluge into representations of physiological phenotypes carrying individualized predictive potential (e.g., see here).

I believe that such approach will render new treatment options and help prevent or mitigate long-term sequelae of prenatal or perinatal exposures to harmful stimuli resulting in brain, gut or heart injuries. My lab has been developing monitoring technologies (ECG, EEG) that can help detect babies at risk non-invasively and in a widely available manner.

In the preclinical line of research, I have been interested in how prenatal events harmful to the baby, e.g., infection, shape the brain development after birth with focus on glial biology, in particular microglia and astrocytes. Understanding this connection is relevant to research in etiology of adult neuroinflammatory and neurodegenerative diseases such as Multiple Sclerosis and Alzheimer's. You can find more about this on my neuroinflammation page. Excitingly, this preclinical work is connected to the fetal health monitoring idea my lab has pursued: bioelectronic medicine approach holds promise to monitor and manipulate the "vagus code" to detect, predict and restore pathophysiological brain states at early stages of developmental trajectories.

My team

Research Associate

  • TBD

Current students (and collaborators involved in co-supervision)

  • Yael Frank, undergraduate student [UW]
  • Ben Janoschek, undergraduate student [UW]
  • Colin Wakefield, undergraduate student [UW]
  • Deborah Nelson, undergraduate student [UW]
  • Nathan Gold (PhD candidate, with Dr. Steven Wang and Dr. Huaxiong Huang, York U | [publications preprints])

Past trainees

Collaborators (an incomplete list)