Human-Robot Object Handover

Published by Deniz Erdogmus on

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Research Projects

Estimating Protein Function From Structure

Mining for Mechanistic Information to Predict Protein Function is a project funded by NSF. In collaboration with researchers from the Chemistry Department, we are using machine learning techniques to develop computational models that can predict protein function from chemical and molecular structure. Models will also be explainable in the sense that active residues will be identified and their roles will be connected to predicted protein function.

Research Projects

Predicting Epileptogenesis After TBI

Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy Prediction is a project funded by NIH. In collaboration with researchers at USC Medical School, we are using machine learning techniques to discover features from multimodal data such as EEG, fMRI, DTI, and blood chemistry, in order to build models that can predict if a traumatic brain injury (TBI) patient is susceptible to epileptogenesis – emergence of epilepsy following TBI.

Research Projects

Modeling TMS-induced Motor Evoked Potentials

Understanding Motor Cortical Organization Through Engineering Innovation to TMS-Based Brain Mapping is a project funded by NSF. In collaboration with researchers at Tunik Lab and MGH, we are developing hybrid models that combine physics based partial differential equations and deep neural networks to predict transcranial magnetic stimulation (TMS) induced muscle evoked potentials (MEPs) in the upper limbs of humans. These models will then be used to develop inverse motor cortex activation imaging methods to estimate how activations of muscle groups in the arms are represented in the motor cortex. We are also exploring active learning techniques for rapid label efficient modeling in this context.