Department of Chemistry, Biochemistry, and Physics

New biological modalities such as gene/mRNA therapies and peptide/recombinant proteins have the potential to improve drug efficacy, decrease off target side effects, decrease manufacturing constraints such as cost and production time, and even enable medical therapies to treat diseases otherwise incurable. However, one challenge with these novel biological modalities is in the delivery of these often larger molecules both to the target tissue and inside the target cells. For example, the SARS-CoV-2 vaccine uses a mRNA molecule to create immunity in a person, but delivery of the RNA must be enabled by lipid nanoparticles (LNPs) otherwise the efficacy to the tissue and into the immune cells is low. Although the LNPs allowed for the fastest recovery from a pandemic in human history, their ability to deliver larger biologics to cell types other than immune cells is limited. In this talk, we will review some of the exciting approaches my lab has endeavored to build to create novel solutions to the challenges of large molecule drug delivery. We show the importance of physical size to achieve localization of drugs to tissue and unique chemistries that enable cell uptake. We test our novel lipid-based technologies in a plethora of modalities including transfection of various cell types against standard LNPs in tissue culture, anti proliferative activity of cancer cells in organoids, delivery and activation of biologics in vivo tissue of mice, and delivery of commercial products that enhance efficacy of actives in live human skin models.

Department of Chemistry, Biochemistry & Physics

The molecular complexity engineered by nature has long been a source of interest to chemists and biologists alike in the search for bioactive and therapeutically relevant materials - and yet, the power of organic synthesis in the design of new-to-nature chemistry with unique function opens doors as an enabling tool the development of compounds that impact human health. Grounded in this, the main thrusts of research in our laboratory include synthetic derivatization and asymmetric total synthesis of bioactive natural products to more fully probe structure-activity relationships, development of chemical platforms for introducing fluorinated functionalities into complex chemical scaffolds including natural products and their inspired analogs, and developing creative workflows for benchtop NMR spectroscopy as a real time analytical tool for process development and reaction optimization in multistep and multicomponent organic synthesis. Towards this end, we will share current advances from our group in the chemical synthesis of natural products and their analogs, particularly those bearing bio-orthogonal elements, such as silicon and fluorine, to enable unique chemical and biological functions. In parallel, we will describe how recent advances in benchtop NMR spectrometers have been deployed directly in an organic chemistry laboratory, and how this has enabled broader access to NMR as a real time analytical tool for reaction discovery and development and as both a research and training tool for structural elucidation of complex natural products. 

Department of Computer Science & Engineering

The rapid advancement of artificial intelligence (AI) is revolutionizing aerospace simulation, offering enhanced speed, accuracy, and scalability to address longstanding challenges in aircraft design, noise reduction, and performance optimization traditionally dominated by computationally intensive methods like computational fluid dynamics (CFD). By integrating physics-informed neural networks (PINNs), AI enables physically consistent predictions and rapid iterations across diverse aerospace applications. This presentation examines the transformative potential of AI-driven simulation through preliminary results from two complementary research domains: aeroacoustic modeling, where machine learning frameworks achieve accurate sound pressure level predictions for aircraft noise assessment, and aerodynamic shape optimization, where AI dramatically accelerates design workflows by replacing iterative fluid dynamics solvers with significant computational speedups and minimal accuracy trade-offs. This work illustrates the capacity of machine learning methods to bridge the gap between computational efficiency and physical fidelity unlocking the ability to analyze more design configurations, run higher-fidelity simulations across broader parameter spaces, and explore optimization strategies that were previously computationally prohibitive. 

Department of Biological, Human & Life Sciences, and by courtesy, Department of Computer Science & Engineering

As the COVID-19 pandemic began spreading worldwide in late 2019 and early 2020, many vaccine candidates were developed to combat the disease. However, new COVID-19 variants such as Omicron and Delta continue to emerge globally despite advancements in vaccine technology, leaving certain countries and variants more vulnerable than others to future outbreaks of these variants. This research aims to analyze the susceptibility of different countries to a COVID-19 outbreak, present the first visualization of the spread of COVID-19, and predict which countries are at greater risk for future outbreaks of new variants based on various factors. We created interactive maps to understand the pandemic’s spread and identify high-risk countries based on their vaccination percentages. Then we employed binary classification, K-nearest neighbors (KNN), and neural network machine learning models to predict each country’s risk factor. The risk factor determines whether a country is safe from a new COVID-19 variant based on vaccine percentage and government stringency. The neural network achieved the highest accuracy, classifying countries as high risk or low risk with 94% accuracy. Inspired by the Albert Barabasi model, we graphed connections between countries based on vaccination percentages. These graphs illustrate the correlation between the two countries and better demonstrate how their vaccination rates relate to the probability of a new COVID-19 outbreak. 

Department of Computer Science & Engineering

The prediction of macroscopic mechanical properties of novel and existing materials in service from localized mechanical measurements remains a critical challenge for both predictive maintenance and the development of new materials. A couple of my research groups are addressing these challenges from various perspectives. Nanostructure identification and morphological characterization, supported by machine learning, accelerate the selection of promising candidates for scale-up. The Tabor factor has traditionally been applied to estimate bulk strength from localized hardness measurements; however, our results indicate that the correlation coefficient is influenced by the specific strengthening mechanism, grain size strengthening in pure metals for example. When the effect of strengthening mechanism is understood and empirically validated, a tailored Tabor factor can be applied to reliably forecast macroscopic strength from both nano- and micro-hardness data. In parallel, we are investigating the mechanical properties of 3D-printed materials to ensure their structural integrity and practical applicability. Our findings show that print orientation and, potentially, part thickness exert significant influence on mechanical strength. Finally, we present results from our ongoing studies on the effect of different copper etchants on dihedral angle measurements for interfacial free energy calculations from a pair of twin boundaries. Interfacial free energy is a fundamental physical parameter critical for properties calculation of novel materials in computational materials science. 

Department of Computer Science & Engineering

The accuracy of medical diagnoses and the quantitative measurement of biohazards is critical to maintaining a healthy body and environment. This is most frequently accomplished by direct measurement techniques, such as a radiologist reading an x-ray or MRI, or a survey measuring any biohazard present in an environment. Each of these situations present special challenges to accurate determination. In the case of medical diagnoses, we would like to increase the accuracy over normal medical practice, and in the case of biohazard mitigation, we would like to identify dangers more completely with less cost, time, and effort. In the case of the medical diagnoses, we are using a Convolutional Neural Network (CNN) based technique to improve the accuracy of readings beyond what the radiologist typically obtains, and in the case of biohazard identification we want to improve on the current ground based surveys, in time, effort and accuracy by using aerial techniques. The use of machine learning meshes vey will with both hardware engineering with drones, and with medical based diagnostics and with methods in quantum theory and quantum computing.  Our lab examines such diverse engineering topics as identifying invasive plants, mitigating the hazard of vegetation near power lines, detecting air pollution, mapping emergency escape routs and measuring algae in water supplies.  Using quantum computing in the medical field, we use machine learning to improve the diagnosis of brain tumors and spinal defects.  We also have a project to computationally generate small molecules and a companion project to determine the stability of those molecules. Finally, in the area of quantum theory, we examine how noise affects cryptographic key distribution, and finally we use quantum theory to detect and mitigate errors in quantum computing.