Predictive medicine, health informatics, data analytics, modeling, biocuration, neuropathology, neuroengineering
Cassie Mitchell’s research goal centers around expediting clinical translation from bench to bedside using data-enabled prediction. Akin to data-based models used to forecast weather, Cassie’s research integrates disparate, multi-scalar experimental and clinical data sets to dynamically forecast disease. Cassie is the principal investigator of the Laboratory for Pathology Dynamics, which uses a combination of computational, analytical, and informatics-based techniques to identify complex disease etiology, predict new therapeutics, and optimize current interventions. Cassie’s research has predominantly targeted neuropathology, but her research applications in predictive medicine expand across all clinical specialties.
Our lab studies the response of bacteria to antibiotics in order to develop new methods for eradicating persistent bacteria. Bacterial persistence is a form antibiotic resistance in which a transient fraction of bacterial cells tolerates severe antibiotic treatment while the majority of the population is eliminated. These ‘persisters’ can contribute to chronic infections and are a major medical problem. Despite their medical and scientific importance, presistence is not fully understood. A crucial challenge in studying bacterial persistence results from a lack of methods to isolate persisters from the heterogeneous populations in which they occur. As a result, systems-level analysis of persisters is beyond current techniques, and fundamental questions regarding their physiological diversity remain unanswered. Our lab seeks to develop methods to isolate persisters and study them with systems-wide, molecular techniques. The resulting findings will be used to engineer improved antibiotic therapies. Dr. Allison’s previous research included development of a novel method to eradicate pathogenic bacteria, including Escherichia coli and Staphylococcus aureus, by metabolic stimulation and the finding that bacteria communicate with each other to alter their tolerance to antibiotics.
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.