Taha Alhersh

As an Emerging Technology Advisor, I am leading the identification, evaluation, and strategic integration of cutting-edge technologies to advance business goals and operational efficiency. I am collaborating with cross-functional teams, provide expert guidance to stakeholders, and shape innovation roadmaps to ensure long-term competitiveness in a rapidly evolving digital landscape.

Working on the following projects: -HiGHmed (Heidelberg - Göttingen - Hannover Medical Informatics) Medical Informatics. -MITASSIST (Multi Immersive Therapy Assistance for The Treatment of Mental Illnesses). -SELFPASS (Self-Management for Patients with Depression). Provide consultations and support for various projects in the hospital.

Machine Learning and Computer Vision

Telemedicine research engagement: Personal Health Record (PHR): Provide Coordination and consultation to data center providers, system integrators, Malaysian Ministry of Health and Academia in terms of information technology infrastructure (Hardware and Software) in Hybrid cloud environment. Working on project for early detection of seizure using EEG and ECG. Mentoring research students: Supervising students with Multidisciplinary research nature (Engineering and Computing) and Medicine. Research Coordination

Finalized projects: -Artificial Intelligence for Neuroimaging: Despite of the advancements in neuroimaging modalities (EEG, fMRI, PET, MEG, NIFS ...) and techniques, many investigators use genetic, demographic, behavioral, and other data to be correlated with Neuroimages. Nevertheless, what is really wanted to be done is using neuroimages as a predictor outcome rather than an outcome. This outcome underscores the need to develop more accurate analytical measures and methodologies for image analysis. -Breast Cancer Classification: Developing a system for breast cancer classification and it is tested on the Mammographic Image Analysis Society (MIAS) dataset. The system is based on the wavelet transform for feature extraction, a two stage feature selection through calculating the statistical energy and the statistical metric then selecting the features below a certain threshold; then applying the cluster k nearest neighbor classifier to classify the selected feature to obtain a diagnostic decision. The preliminary results obtained have shown the potential of the method where 100% accuracy was achieved. -DNA Sequences Analysis: Developing new system and concept for species identification using DNA sequence classification and statistical feature extraction.

This program represents a substantial extension (in both time and content) of the Visiting Fellowship Program in Functional MRI. This program will includes a great deal of content on Functional MRI, but that content will be part of a more integrated approach that includes the entire arsenal of techniques currently in use in Functional Brain Imaging. The goal of this ambitious workshop is to demonstrate the ways in which a large variety of techniques are being applied to questions in human brain function. For example MRI, FMRI, DTI, DSI, MRS, PET, EEG, MEG, NIRS, DOT, TMS, and a variety of molecular and computational approaches to studying human brain function in vivo.

http://ccn.ucla.edu/wiki/index.php/Principles_of_Neuroimaging_-_2012-2013

fMRI visiting fellowship, Athinoula A. Martinos Center for Biomedical Imaging

 Monitor and when needed restrained discussions within the Bioinformatics / Neuroinformatics online community that is a part of NeurOnLine (Society for Neuroscience members-only online community).  Increasing and improving member engagement in the Bioinformatics / Neuroinformatics online community.