Pollutant removal in soils, for example through bioremediation, has long been touted as a potential solution to anthropogenically induced climate change impacts by improving soil health. Yet, these efforts are not often implemented at large-scales, and when they are, pollutant run-off and greenhouse gas (GHG) emissions outpace existing attempts. As atmospheric GHGs continue to rise outside of the safe operating space, it becomes crucial to search for avenues that offset them. Soils have huge potential to store carbon long-term, but when soils are polluted, it impacts their carbon storage capacity. It is clear we are in dire need of sustainable solutions that remove soil pollutants, increase soil carbon storage, and promote a healthy soil community. The physiological pathways that exist in plants, bacteria, and fungi are often interlinked, and evidence shows that certain interactions can ultimately lead to the storage of carbon in soils. Therefore, we hypothesize that the delivery of synergistic bacteria, fungi, and biochar via hydrogel beads will promote plant and soil communities’ ability to increase soil nutrients for plant uptake while removing pollutants and facilitating carbon storage. Preliminary data from our study, in which we applied mixed fungal-bacterial-char hydrogel beads to polluted soils growing Sorghum bicolor or Helianthus anuus, demonstrated that our novel biotechnology has potential to decrease heavy metal concentrations and toxic compounds. Additionally, we have begun analyzing the carbon storage potential through Loss on Ignition methodology, which provides measures of soil organic and inorganic carbon. Preliminary measurements show that soils that received hydrogels with an encased fungal-microbial-char consortia also increase soil carbon. These studies will not only inform the efficacy of hydrogel-delivered biofertilizers in terms of plant growth and productivity, but will also build a foundation for future research into how to promote soil health to mitigate the negative impacts of climate change.

Tumor budding, a feature of colorectal cancer and numerous other cancers, has gathered interest as an important prognostic marker and is defined as the presence of small clusters of up to four epithelial cells at the invasive tumor margin. However, current methods of evaluating tumor buds rely on 2D serial sectioning, and 3D reconstructions of this data demonstrate that many of the tumor buds identified in the cross-sectional views are not isolated buds, but extensions of a larger tumor. Advances in 3D microscopy, as well as tissue optical clearing and fluorescent labeling protocols, could provide a means to overcome these 2D limitations. One such method, open-top light sheet (OTLS) microscopy, has demonstrated its potential to visualize similar cancer features and generate rich 3D datasets for cancer risk assessment and analysis. We hypothesize that OTLS microscopy will provide further insight into the differences and distribution between tumor buds, poorly differentiated clusters (PDC) and the main tumor mass, which can be used to improve the diagnostic grading of cancer. Thus far, I have stained 12 colorectal and 4 lung cancer specimens with a fluorescent analog of hematoxylin and eosin (H&E) along with pan-cytokeratin antibodies, and then optically cleared, imaged, and processed them to generate false-colored volumetric atlases for analysis of the tumor invasive margin in 3D. These datasets are used in conjunction with deep learning-based tumor bud segmentation workflows for the calculation of volume, distance, and orientation metrics. The investigation into what proportion of tumor buds are actually extensions of the tumor mass could have implications for prognostic evaluation by pathologists.

A critical feature of autonomous cars is the ability to follow a road or predefined path. Classical methods often rely on extensive prior mapping with precise GPS positioning. These methods are labor intensive and struggle with changing, unstructured environments. Instead, machine learning (ML) models are trained to recognize paths and follow directions. In this work, we combine simulated and real-world data to train a neural network policy that controls an autonomous ground vehicle down a hallway, avoiding collisions. Training a ML road-following model consists of three steps: data collection and preprocessing, model training, and model evaluation. While all three steps pose challenges, collecting high-quality, real-world data can be expensive and dangerous in road environments. Because of this, simulator data is useful as it allows for data to be collected safely and inexpensively. Thus, we study how much the required amount of real-world data can be reduced to successfully train a road-following robot with the use of simulator data. So, we collected simulator data using AirSim to train a convolutional neural network that follows a path in simulation through live environment images. We then fine-tuned the model using real-world data collected from MuSHR cars through hallways of a building. Next, we test the fine-tuned model on the simulator to ensure limited degradation to the model solely trained from AirSim data. Finally, we deploy the model on a robotic car in a real-world environment and evaluate the model’s performance compared to the baseline model trained on real-world data. We demonstrate that we can successfully train a model in simulation (MSE <= 0.01radians), and we expect to show a comparable performance in reducing the number of collisions and minimizing trajectory differences between expert and learned controller from a model trained on simulator + less real-world data and a model trained solely on real-world data.

Understanding human mobility based on location data from smartphones and other digital accessories has become a fundamental part of urban and environmental planning in cities. Through collection of these geo-traces, it has become possible for the scientific community and policy-makers to model citizens’ daily mobility patterns. Because of privacy issues, collection of location based data is rare, sparse and infrequent. To this end generative mobility models are used to create synthetic data. Because this data can be used in big decisions such as resource allocation and planning, if the data is skewed towards privileged groups, existing social and structural inequities can be exacerbated. Therefore, a critical concern that arises is the extent to which such synthetic data is fair and inclusive. Our goal is to create tools that measure whether socioeconomic conditions and geographical location affect individual and group trajectory predictions made by generative models. Using Gravity, Deep Gravity, and other models to generate mobility flow dataframes, we layered census data onto the flow dataframes by census tract to analyze the high income areas against lower income areas. We used this analysis to establish and implement a set of fairness metrics for mobility datasets and models in a python package that’s distribution could identify skewed modality data and hopefully influence other researchers to measure fairness in their generative models.

The Field Programmable Cellular Arrays (FPCA) project at the Molecular Information Systems Lab (MISL) aims to improve current biocomputing systems utilizing spatial organizations of cellular components for logical operations. This can open doors for computation to be done within biological systems where artificial computation has never before been possible. This project encompasses three aims: characterizing the properties of signal propagation within E. coli, constructing biological circuit components for spatial signal processing, and optimizing bioprinting methods for circuits. Signal propagation through molecular signaling is employed to communicate the presence or absence of a signal and truth values to specific cells. We are demonstrating logical states of "1," "0," and the absence of a signal, thereby enabling differentiation between a logical "0" and a lack of signal. Two strains of bacterial cells are capable of performing the logic of a traditional "wire" and a NOR gate. Consequently, by arranging strains in spatially organized layouts, we engineer cellular arrays capable of performing diverse complex logical functions. This research is still in progress and we are in the process of optimizing NOR gate and wire strains. My role explores bioprinting circuits into hydrogels, and I have built a bioprinter with dual extruders to bioprint biological substances into containing slurries. This required designing, printing, and assembling 3D-printed parts. I am now characterizing the behavior of 3D printed materials into various containing slurries. This requires testing the ability of different bioprinting inks to encapsulate bacteria, testing various slurry methodologies, and testing interactions between combinations of these materials over space and time. I am also computationally modeling FPCA circuits at various levels of abstraction. Computational modeling serves to further broader computational goals in this project to compile a logic circuit specification into bioprinter GCODE.

In this research project, we delved into the realm of gate-based quantum computation with a focus on qudit-based quantum computation. In the era of Noisy Intermediate-Scale Quantum (NISQ) computation, there are many avenues for physical implementations of qudits, such as trapped ions, superconducting circuits, and photonic systems. We primarily studied trapped ion qudit-based computation, investigating the notion of universality and how arbitrary gate operations can be simulated by experimentally realizable transformations in such systems. More quantitatively, we analyzed the fidelity under the assumptions of rotation angle errors in trapped ion implementations of quantum gates. We proved several lower bounds for various connectivity graph designs applicable to the 5-level calcium ion under this model of assumptions. Our techniques also generalize to physical systems with more than 5 levels. Currently, our attention is directed toward understanding the impact of entanglement on the aforementioned dynamics and studying the notion of universality for multi-qudit systems. A related question we are trying to answer is how qubit circuits can be converted into qudit circuits to reduce a well-defined notion of "circuit complexity".