Dr. Seyed Hani Hojjati is an accomplished Instructor of Electrical Engineering in the Department of Radiology at Weill Cornell Medicine. With a robust background in mathematics, machine learning, signal processing, and image processing, Dr. Hojjati has significantly contributed to the field of neuroimaging, particularly in Alzheimer’s disease research. His innovative work integrates resting-state functional magnetic resonance imaging (rs-fMRI) to distinguish between healthy individuals and those progressing towards mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). His research has also expanded to include advanced modalities like diffusion tensor imaging (DTI) and positron emission tomography (PET), with the goal of identifying reliable biomarkers for early neuropsychological changes.
Education
- Ph.D. in Electrical Engineering (2018)
Babol Noshirvani University of Technology, Babol, Mazandaran, Iran
Dissertation: Identification of Effective Brain Areas to Predict Alzheimer’s Disease Using Resting-State fMRI and MRI
- M.Sc. in Electrical Engineering (2013)
Babol Noshirvani University of Technology, Babol, Mazandaran, Iran
Dissertation: Energy Efficient Cooperative Spectrum Sensing by Multi-Antenna Sensor Network and Soft Computing Techniques
- B.Sc. in Electrical Engineering (2011)
University of Mazandaran, Babolsar, Mazandaran, Iran
Dissertation: Harmonic Analysis on Relays
Professional Experience
Currently, Dr. Hojjati is an Instructor of Electrical Engineering at Weill Cornell Medicine (WCM) in the Department of Radiology, Brain Health Imaging Institute. His work focuses on the underlying mechanisms of remote associations between amyloid-beta and tau depositions at preclinical stages of Alzheimer’s disease. He has contributed significantly to the harmonization and processing of multimodal imaging data and has played a pivotal role in various NIH-funded research projects. Prior to his current role, he served as a Postdoctoral Associate at WCM, where he designed neuropsychological tasks for fMRI scanners and developed novel preprocessing tools for PET data. Dr. Hojjati also held a Postdoctoral Fellow position at the University of Tennessee Health Science Center, where he focused on multimodal neuroimaging approaches to identify early neuropsychological changes in Alzheimer’s disease.
Honors and Awards
- Travel Scholarship, Human Amyloid Imaging (2023)
- Winter Travel Stipend Award, University of Tennessee Health Science Center (2019)
- Outstanding Abstract Award, University of Tennessee Health Science Center (2019)
- Travel Stipend Award, Organization for Human Brain Mapping (2016)
- Top Student Award, National Elites Foundation (2016)
- Study Scholarship, Babol Noshirvani University of Technology (2014)
Research and Skills
Dr. Hojjati’s research expertise spans multiple neuroimaging modalities, including rs-fMRI, task-fMRI, MRI, PET, and DTI. He is proficient in machine learning, signal processing, and image processing, with a focus on developing innovative techniques for feature integration and selection in multimodal neuroimaging data. His technical skills include programming in Python, MATLAB, and C++, as well as using neuroimaging tools like FreeSurfer, FSL, and SPM. He is also experienced in statistical analysis and neuropsychological test design.
Publications
- Authors: Butler, T., Wang, X., Chiang, G., Pascoal, T.A., Rosa-Neto, P.
- Journal: Journal of Alzheimer’s Disease
- Year: 2024