The lab is actively developing data analysis methods for learning cytoarchitectonics (layers), mapping brain areas, and distributed segmentation and analysis of large-scale neuroimaging data.
Low-dimensional signal models
Unions of subspaces (UoS) are a generalization of single subspace models that approximate data points as living on multiple subspaces, rather than assuming a global low-dimensional model (as in PCA). Modeling data with mixtures of subspaces provides a more compact and simple representation of the data, and thus can lead to better partitioning (clustering) of the data and help in compression and denoising.
Analyzing the activity of neuronal populations
Advances in monitoring the activity of large populations of neurons has provided new insights into the collective dynamics of neurons. The lab is working on methods that learn and exploit low-dimensional structure in neural activity for decoding, classification, denoising, and deconvolution.
Optimization problems are ubiquitous in machine learning and neuroscience. The lab works on a few different topics in the areas of non-convex optimization and distributed machine learning.
Biomechanics of brain injury, pediatric head injury, soft tissue mechanics, ventilator-induced lung injury, lung mechanics, pathways of cellular mechanotransduction, and tissue injury thresholds.
My research in head injury will continue to focus on how and why head injuries occur in adults and children and to improve detection and treatment strategies. At Georgia Tech, I will be continuing that research, looking at innovative biomarkers and new devices to detect mild traumatic brain injuries. At Emory, my research will be focused on animal models for diffuse as well as focal brain injuries—incorporating developments at Georgia Tech into our preclinical model. I also look forward to close collaborations with Children’s Healthcare of Atlanta and Emory University faculty to improve the outcomes after traumatic brain injuries.
The MNM Biotech Lab uses engineering expertise to assist life scientists in the study, diagnosis, and treatment of human disease. By developing better models of the body, we help advance drug discovery, increase understanding of the mechanisms of disease, and develop clinical treatments. Areas of study include:
Aqueous Two-Phase Systems
Microfluidic Logic Circuits
Interrogation and Control of Cell Signaling Mechanisms
Assisted Reproductive Technology
3D Cell Culture
Microenvironment Engineering and Materials Modifications
Size-adjustable nanochannels and DNA linearization/stretching
The Dreaden Lab uses molecular engineering to impart augmented, amplified, or non-natural function to tumor therapies and immunotherapies. The overall goal of our research is to engineer molecular and nanoscale tools that can (i) improve our understanding of fundamental tumor biology and (ii) simultaneously serve as cancer therapies that are more tissue-exclusive and patient-personalized. The lab currently focuses on three main application areas: optically-triggered immunotherapies, combination therapies for pediatric cancers, and nanoscale cancer vaccines. Our work aims to translate these technologies into the clinic and beyond.
Molecular Engineering, Tumor Immunity, Nanotechnology, Pediatric Cancer
Dr. Lindsey is interested in developing new imaging technologies for understanding biological processes and for clinical use.
In the Ultrasonic Imaging and Instrumentation lab, we develop transducers, contrast agents, and systems for ultrasound imaging and image-guidance of therapy and drug delivery. Our aim is to develop quantitative, functional imaging techniques to better understand the physiological processes underlying diseases, particularly cardiovascular diseases and tumor progression.
My research interests focus on image-based computational design and 3D biomaterial printing for patient specific devices and regenerative medicine, with specific interests in pediatric applications. Clinical application interests include airway reconstruction and tissue engineering, structural heart defects, craniofacial and facial plastics, orthopaedics, and gastrointestinal reconstruction. We specifically utilize patient image data as a foundation to for multiscale design of devices, reconstructive implants and regenerative medicine porous scaffolds. We are also interested in multiscale computational simulation of how devices and implants mechanically interact with patient designs, combining these simulations with experimental measures of tissue mechanics. We then transfer these designs to both laser sintering and nozzle based platforms to build devices from a wide range of biomaterials. Subsequently, we are interested in combining these 3D printed biomaterial platforms with biologics for patient specific regenerative medicine solutions to tissue reconstruction.
Bilal Haider’s research seeks to identify cellular and circuit mechanisms that modulate neuronal responsiveness in the cerebral cortex in vivo. During his PhD at Yale University, he identified excitatory and inhibitory mechanisms that mediate rapid initiation, sustenance, and termination of activity in the cerebral cortex in vivo. His studies also revealed that inhibitory circuits strongly increase the selectivity, reliability and precision of visual responses to natural visual scences. During his post-doctoral studies at University College London, he extended investigation of inhibitory circuits to the awake brain. His work showed for the first time that synaptic inhibition powerfully controls the spatial and temporal properties of visual processing during wakefulness. His future research will continue building on these themes and investigate mechanisms used by excitatory and inhibitory neuronal sub-types in the cortex during goal-directed behaviors. Discovering how neural networks and synapses control sensory-motor processing is a critical step towards lessening deficits common to many neurological disorders such as schizophrenia, dementia, epilepsy, and autism spectrum disorders.
Our lab’s long-term goal is to understand how neural activity both produces memories and protects brain health, while using this knowledge to engineer neural activity to treat brain diseases. Our lab studies how coordinated electrical activity across many neurons represents memories of experiences, how this activity fails in animal models of Alzheimer’s disease, and how engineering neurons to produce this activity has neuroprotective effects and engages the brain’s immune system. Integrating innovative experimental and analytical methods, this research will provide unprecedented insight into how neural activity failures lead to memory impairment and will reveal novel ways to engineer neural activity to repair brain function. Using non-invasive approaches, we translate these discoveries from rodents to humans. These insights could lead to radically new ways to treat diseases that affect memory like Alzheimer’s, for which there are no effective therapies.
Prior to joining the faculty at Emory and GA Tech in Dec. 2016, Dr. Pandarinath received his bachelor’s degrees in Computer Engineering and Physics from NC State, Ph.D. in Electrical Engineering from Cornell, and was a postdoctoral fellow in Neurosurgery and Electrical Engineering at Stanford. His work has spanned systems neuroscience and brain-machine interfaces across visual and motor systems. He was the recipient of the Stanford Dean’s Fellowship and the Craig H. Neilsen Foundation Postdoctoral Fellowship in spinal cord injury research, and was a finalist for the 2015 Sammy Kuo Award in Neuroscience from the Stanford School of Medicine.
Our work centers on understanding how the brain represents information and intention, and using this knowledge to develop high-performance, robust, and practical assistive devices for people with disabilities and neurological disorders. We take a dynamical systems approach to characterizing the activity of large populations of neurons, combined with rigorous systems engineering (signal processing, machine learning, control theory, real-time system design) to advance the performance of brain-machine interfaces and neuromodulatory devices.