Honors & Awards

  • Nicholas J. Hoff Award for outstanding master's student, Stanford University (2022)
  • GNC Graduate Student Paper Competition, 3rd place, AIAA (2021)

Education & Certifications

  • M.S., Stanford University, Aeronautics and Astronautics (2022)
  • B.S., University of Stuttgart, Aerospace Engineering (2019)

Contact

  • Academic
    mschl@stanford.edu
    University - Student Department: Aeronautics and Astronautics Position: Graduate

Additional Info

All Publications

  • Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy. Cerebral cortex (New York, N.Y. : 1991) Balters, S., Schlichting, M. R., Foland-Ross, L., Brigadoi, S., Miller, J. G., Kochenderfer, M. J., Garrett, A. S., Reiss, A. L. 2022

    Abstract

    Assessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical "deep brain" regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N=20, mean age=15.3±1.9years) and age-matched healthy controls (N=20, mean age=14.5±2.0years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r>0.91) and hippocampus (r>0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r>0.79), as well as in youths with PTSD (r>0.75). The linear regression and neural network models provided the best predictions.

    View details for DOI 10.1093/cercor/bhac320

    View details for PubMedID 36066436

  • Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning</n> JOURNAL OF GUIDANCE CONTROL AND DYNAMICS Schlichting, M. R., Notter, S., Fichter, W. 2022

    View details for DOI 10.2514/1.G006129

    View details for Web of Science ID 000773294600001

  • LSTM-Based Spatial Encoding: Explainable Path Planning for Time-Variant Multi-Agent Systems AIAA Scitech 2021 Forum Schlichting, M. R., Notter, S., Fichter, W. American Institute of Aeronautics and Astronautics. 2021: 20

    View details for DOI 10.2514/6.2021-1860

https://orcid.org/0000-0003-1140-0563