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Thomas Heldt
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Thomas Heldt

Hermann Von Helmholtz Career Development Assistant Professor of Electrical Engineering, Electrical Engineering and Computer Science (EECS)
MIT's Thomas Heldt: Hermann Von Helmholtz Career Development Assistant Professor of Electrical Engineering, Electrical Engineering and Computer Science (EECS).
77 Massachusetts Avenue
Room E25-545
Cambridge, MA 02139
thomas@mit.edu
617.324.5005—Tel

Administrative Assistant

Caitlin Vinci
cburke@mit.edu
617.253.0009—Tel
Room E25-545

Thomas Heldt is the Hermann L.F. von Helmholtz Career Development Professor in MIT’s Institute for Medical Engineering and Science, an Assistant Professor in the Department of Electrical Engineering and Computer Science, and a Principal Investigator with MIT’s Research Laboratory of Electronics (RLE). He leads the Integrative Neuromonitoring and Critical Care Informatics Group.

Thomas studied Physics at Johannes Gutenberg University, Germany, at Yale University and MIT. He received the PhD degree in Medical Physics from MIT’s Division of Health Sciences and Technology and commenced postdoctoral training at MIT’s Laboratory for Electromagnetic and Electronics Systems. Prior to joining the MIT faculty, Thomas was a Principal Research Scientist at RLE.

Thomas’s research interests focus on signal processing, mathematical modeling, and model identification to support real-time clinical decision making, monitoring of disease progression, and titration of therapy, primarily in neurocritical and neonatal critical care. In particular, he is interested in developing a mechanistic understanding of physiologic systems, and in formulating appropriately chosen computational physiologic models for improved patient care. His research is conducted in close collaboration with colleagues at MIT and clinicians from Boston Children’s Hospital, Beth Israel Deaconess Medical Center, and Boston Medical Center.

Keywords

computational physiology, clinical inference, patient monitoring, critical care, brain injury, mechanistic models, structured model reduction, model-based signal processing, system identification, networks
computational physiology, clinical inference, patient monitoring, critical care, brain injury, mechanistic models, structured model reduction, model-based signal processing, system identification, networks

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