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.
Innovative materials for environmental applications
Water and wastewater treatment
Energy and resources recovery
Energy conversion and storage
Our research centers on the application of innovative materials and devices to address global challenges related to water, energy, food, and environment. It is a two-way practice. Beginning with the challenges, we develop new materials and devices with rational design. Newly developed materials and devices are evaluated during applications, and the experience gained from these applications informs the re-design of the materials and devices. This design-and-evaluation cycle is integral to our research activities and allows continuous improvement.
Eukaryotes, including humans, are 'petri dishes', hosting an abundant and a rich prokaryotic 'microbiome'. The Garg Lab aims to understand the molecular interactions between a eukaryotic host and its microbiome, and how these molecular interactions dictate human health and disease. Using a concoction of innovative tools including bioinformatics, clinical microbiology, mass spectrometry, DNA sequencing, and mass spectrometry-based 2D and 3D spatial imaging, we aim to delineate specific molecules that modulate the dynamics of microbial involvement in our response to genetic and environmental triggers of disease. We characterize the biosynthesis of these small molecule natural products to innovate developement of new therapeutics.
His research interests lie in the fields of polymeric materials, electronic packaging and interconnect, interfacial adhesions, nano-functional material syntheses and characterizations, nano-composites such as well-aligned carbon nanotubes, grahenes, lead-free alloys, flip chip underfill, ultra high k capacitor composites and novel lotus effect coating materials.
Dr. Qi’s research falls in the general area of finite deformation multiphsyics modeling of soft active materials. The material systems include: shape memory polymers, shape memory elastomeric composites, light activated polymers, covalent adaptive network polymers (or vitrimers). Particularly, he is interested in understanding and modeling the evolution of material structure and mechanical properties of these materials under environmental stimuli, such as temperature, light, etc, and during material processing, such as 3D printing. To assist understanding of mechanical properties, his group routinely conducts thermomechanical or photo-mechanical experiments. Constitutive models developments are typically based on the observations from these experiments. The ultimate goal of the constitutive models is to integrate them with finite element through user material subroutines so that these models can be used to solve complicated 3D multiphysics problems involving nonlinear mechanics.
His current research projects include 4D printing of active materials, mechanics in 3D printing technology, active polymer design and manufacturing, reprocessing and recycling polymers. For 3D/4D printing, his group is developing 3D hybrid printing methods by using a variety of 3D printing technologies, such as inkjet, Stereolithography (SLA), Direct Ink Write (DiW), Fused Deposition Modeling (FDM), to print active and functional materials, such as shape memory polymers, liquid crystal elastomers, conductive polymers, epoxies, and cellulose nanocrystals. For reprocessing and recycling polymers, his group is developing methods and technologies to recycling thermosetting polymers and composites, such as fiber reinforced epoxy composites. These projects are conducted through supports by NSF and AFOSR, and through collaborations with Singapore University of Technology and Design (SUTD), and Air Force Research Laboratories (AFRL).
The Cognitive Motor Control Laboratory seeks to understand neurophysiology guiding skillful human-object interactions in upper extremity motor control. We use neuroimaging to identify anatomical and physiological circuits in humans that guide successful skilled behavior. Our clinical studies consider neural systems that can suffer injury or dysfunction related to deficits in skillful motor control, and how to utilize surrogate neural circuits in restorative motor therapies in stroke and upper limb amputation.