ML analyses of audiological data to predict age-related declines in hearing and cognition

  • Matthew Goupell (HESP)
  • Michael P. Cummings (BIOL, UMIACS)

This project applies machine learning to untangle the diverse causes of hearing loss (including infection, environmental noise and the natural aging process), uncover patterns in hearing loss data, and assess the ability of training to partially restore hearing and cognitive abilities.

Cytoskeletal excitability and network dynamics in AD and other age-related neurological diseases

  • Kan Cao (CBMG)
  • Wolfgang Losert (PHYS, IPST)

This project proposes that cytoskeletal dynamics offer a link between cellular aging and altered neuronal network properties in order to develop a diagnostic biomarker for Alzheimer's disease that can account for the gradual change in collective neuronal firing that is characteristic of the disease's progression.

Neurocognitive mechanisms of sentence production in aging and stroke

  • Yasmeen Faroqi-Shah (HESP)
  • L. Robert Slevc (PSYC)

This project investigates the timing of neural processes involved in sentence formulation and production, focusing on the relationship between word retrieval and grammatical ordering during the formulation of language.

ML- and quantum materials-enabled early detection of AD with exosomes isolated from human iPSCs-derived hippocampal neurons

  • Xiaoming (Shawn) He (BIOE)
  • Cheng Gong (ECE)

This project proposes an accurate and minimally invasive method for early detection of Alzheimer's disease: the investigation of cell membrane-based extracellular vesicles using high-resolution Raman spectroscopy and machine learning.

Respiratory sinus arrhythmia as a biomarker of anxiety in adolescents with autism spectrum disorder

  • Heather Yarger (PSYC)
  • Angel Dunbar (AASP)
  • Elizabeth Redcay (PSYC)

This project explores whether respiratory sinus arrhythmia is a potential biological indicator of the anxiety that frequently co-occurs in adolescents diagnosed with autism spectrum disorder.