Directory

The founder and visionary of the TReNDS center, Dr. Calhoun develops techniques for making sense of complex brain imaging data.

Dr. Plis's research goals lie in developing computational instruments that enable knowledge extraction from observational multimodal data collected at different temporal and spatial scales.

Dr. Liu’s research focuses on exploring and identifying genetic effects on or association with brain anomalies related to mental illnesses and disorders..

My research interests fall under “what can we know from cognitive neuroimaging and genetic data,” and “how can we represent what we know from these experiments?”

I specialize in developmental and clinical neuropsychology, developmental dyslexia, reading and attention related disabilities, acquired brain injury, neurogenetic disorders.

Eric’s mission is to develop real-world applications from research at the TReNDS center.

H. Jeremy Bockholt works as the manager of application development at TReNDS.

His research interests are analysis of brain data in frequency domain, statistical signal analysis and brain lateralization.

Brittny Miller has worked with the TReNDS team in many capacities including facilitating multiple neuroimaging research studies, software development for the Collaborative Imaging and Neuroinformatics Suite (COINS) platform, project management, and more.

I am primarily interested in machine learning and its intersections with complex applications and theory.

I am a PhD student at Georgia Tech studying electrical and computer engineering.

I am interested in how apparently distinct brain networks integrate and shape sensory processing.

My research interests center on imaging genomics, aiming to characterize the genetic underpinnings of neurobiological traits that relate to phenotypic manifestations pf mental disorders.

Dr. DeRamus' work focuses on functional connectomics above traditional BOLD frequency values, and multi-modal fusion of functional MRI and model-free diffusion-weighted connectomics to analyze structure/function interactions in the brain.s.

My research interests include developing new methods for analysis of brain functional networks, finding biomarkers for clinical purposes, and exploring brain function using multi-modality images.

My research focuses on imaging genetics, with the aim to identify the underlying genetic factors related to abnormal imaging features/behavioral performances.

I am a PhD student majoring in Electrical Engineering at Georgia Institute of Technology. In my research projects, I am developing new data analysis methods using ideas from both electrical engineering and statistics fields.

My research interests include machine learning, data science, and statistical analysis of network data.

I am interested in developing novel algorithms for tracking the dynamic brain connectivity and exploring the underlying physiological meaning of brain dynamics.

My background is data science and signal processing including machine learning, data mining, image processing, and time-series analysis.

I am a Ph.D. student in Computer Science at Georgia State University and I work as a graduate research assistant at the TReNDS center under the supervision of Prof. Vince Calhoun.

Robyn is interested in brain dynamics, spatiotemporal dynamics, fMRI, dynamic connectivity, high-dimensional signal processing, statistical learning, deep learning

I help communicate and publicize the TReNDS center's research, events, and achievements across a variety of digital platforms.

I am interested in auto-analysis and pre- and post- processing pipelines of structural MRI, diffusion MRI, and functional MRI.

I enjoy building solutions in Electronics, Materials Science, and Intelligent Systems.

I am a solutions-oriented systems wrangler with a wide breadth of knowledge and experience. My primary areas of experience are on infrastructure design, systems operations and facilitating platform dedicated to improving research outcomes Research Institutions.

My research interest broadly includes high dimensional data processing, machine learning and deep learning.

The major focus of my research is developing machine learning tools and techniques to understand brain dynamics in neuroimaging (fMRI and sMRI) data.

My research interests center around neuroimaging with a focus on developing new techniques for fusion of multimodal MR data and analyzing diffusion MR images to identify biomarkers for various brain disorders.

Currently I am developing one common classification framework to select optimal model order for dynamic functional connectivity states in Resting state fMRI Data.

My interests lie in digital signal and image processing, machine learning, data science and generally any problem that can be solved using a computer.

I am a Ph.D. student double-majoring in Biomedical Engineering (BME) and Electrical and Computer Engineering (ECE) studying deep brain stimulation.

With a focus on combining novel deep learning architectures with flexible subspace structure models, Dr. Silva seeks to identify coupled brain biostructures across impaired and control populations, using heterogenous multimodal data in a truly unprecedented, synergistic fashion.

Dr. Vergara is an electrical engineer focused on neuroimaging data analysis with 10+ years of experience in research and development.