Understanding the relationship between gene expression and phenotype is important in many areas of biology and medicine. Current methods for measuring gene expression such as microarrays however are invasive, require biopsy, and expensive. These factors limit experiments to low rate temporal sampling of gene expression and prevent longitudinal studies within a single subject, reducing their statistical power. Thus methods for non-invasive measurements of gene expression are an important and current topic of research. An interesting approach (Segal et al, Nature Biotechnology 25 (6) 2007) to indirect measurements of gene expression has recently been reported that uses existing imaging techniques and machine learning to estimate a function mapping image features to gene expression patterns, providing an image-derived surrogate for gene expression. However, the design of machine learning methods for this purpose is hampered by the cost of training and validation. My thesis shows that populations of artificial organisms simulating genetic variation can be used for designing machine learning approaches to decoding gene expression patterns from images. If analysis of these images proves successful, then this can be applied to real biomedical images reducing the limitations of invasive imaging. The results showed that the box counting dimension was a suitable feature extraction method yielding a classification rate of at least 90% for mutation rates up to 40%. Also, the box-counting dimension was robust in dealing with distorted images. The performance of the classifiers using the fractal dimension as features, actually, seemed more vulnerable to the mutation rate as opposed to the applied distortion level. / Master of Science

Patellar tendinopathy (PT) is a knee injury resulting in pain localized to the patellar tendon. One main factor that causes PT is repetitive overloading of the tendon. Because of this mechanism, PT is commonly seen in "jumping sports" like basketball. This injury can severely impact a player's performance, and in order for a timely return to preinjury activity levels early diagnosis and treatment is important. The standard for the diagnosis of PT is a clinical examination, including a patient history and a physical assessment. Because PT has similar symptoms to injuries of other knee structures like the bursae, fat pad, and patellofemoral joint, imaging is regularly performed to aid in determining the correct diagnosis. One common imaging modality for the patellar tendon is gray-scale ultrasonography (GS-US). However, the accurate detection of PT in GS-US images is grader dependent and requires a high level of expertise. Machine learning (ML) models, which can accurately and objectively perform image classification tasks, could be used as a reliable automated tool to aid clinicians in assessing PT in GS-US images. ML models, like support vector machines (SVMs) and convolutional neural networks (CNNs), use features learned from labelled images, to predict the class of an unlabelled image. SVMs work by creating an optimal hyperplane between classes of labelled data points, and then classifies an unlabelled datapoint depending on which side of the hyperplane it falls. CNNs work by learning the set of features and recognizing what pattern of features describes each class. The objective of this study was to develop a SVM model and a CNN model to classify GS-US images of the patellar tendon as either normal or diseased (PT present), with an accuracy around 83%, the accuracy that experienced clinicians achieved when diagnosing PT in GS-US images that were already clinically diagnosed as either diseased or normal. We will also compare different test designs for each model to determine which achieved the highest accuracy. GS-US images of the patellar tendon were obtained from male and female Virginia Tech collegiate basketball athletes. Each image was labelled by an experienced clinician as either diseased or normal. These images were split into training and testing sets. The SVM and the CNN models were created using Python. For the SVM model, features were extracted from the training set using speeded up robust features (SURF). These features were then used to train the SVM model by calculating the optimal weights for the hyperplane. For the CNN model, the features were learned by layers within the CNN as were the optimal weights for classification. Both of these models were then used to predict the class of the images within the testing set, and the accuracy, sensitivity and precision of the models were calculated. For each model we looked at different test designs. The balanced designs had the same amount of diseased and normal images. The designs with Long images had only images taken in the longitudinal orientation, unlike Long+Trans, which had both longitudinal and transverse images. The designs with Full images contained the patellar tendon and surrounding tissue, whereas the ROI images removed the surrounding tissue. The best designs for the SVM model were the Unbalanced Long designs for both the Full and ROI images. Both designs had an accuracy of 77.5%. The best design for the CNN model was the Balanced Long+Trans Full design, with an accuracy of 80.3%. Both of the models had more difficulty classifying normal images than diseased images. This may be because the diseased images had a well defined feature pattern, while the normal images did not. Overall, the CNN features and classifier achieved a higher accuracy than the SURF features and SVM classifier. The CNN model is only slightly below 83%, the accuracy of an experienced clinician. These are promising results, and as the data set size increases and the models are fine tuned, the accuracy of the model will only continue to increase. / Master of Science / Patellar tendinopathy (PT) is a common knee injury. This injury is frequently seen in sports like basketball, where athletes are regularly jumping and landing, and ultimately applying a lot of force onto the patellar tendon. This injury can severely impact a player's performance, and in order for a timely return to preinjury activity levels early diagnosis and treatment is important. Currently, diagnosis of PT involves a patient history and a physical assessment, and is commonly supplemented by ultrasound imaging. However, clinicians need to have a high level of expertise in order to accurately assess these images for PT. In order to aid in this assessment, a tool like Machine learning (ML) models could be used. ML is becoming more and more prevalent in our every day lives. These models are everywhere, from the facial recognition tool on your phone to the list of recommended items on your Amazon account. ML models can use features learned from labelled images, to predict the class of an unlabeled image. The objective of this study was to develop ML models to classify ultrasound images of the patellar tendon as either normal or diseased (PT present).

Gas Chromatography (GC) is a technique for separating volatile compounds by relying on adherence differences in the chemical components of the compound. As conditions within the GC are changed, components of the mixture elute at different times. Sensors measure the elution and produce data which becomes chromatograms. By analyzing the chromatogram, the presence and quantity of the mixture's constituent components can be determined. Machine Learning (ML) is a field consisting of techniques by which machines can independently analyze data to derive their own procedures for processing it. Additionally, there are techniques for enhancing the performance of ML algorithms. Feature Selection is a technique for improving performance by using a specific subset of the data. Feature Engineering is a technique to transform the data to make processing more effective. Data Fusion is a technique which combines multiple sources of data so as to produce more useful data. This thesis applies machine learning algorithms to chromatograms. Five common machine learning algorithms are analyzed and compared, including K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Convolutional Neural Network (CNN), Decision Tree, and Random Forest (RF). Feature Selection is tested by applying window sweeps with the KNN algorithm. Feature Engineering is applied via the Principal Component Analysis (PCA) algorithm. Data Fusion is also tested. It was found that KNN and RF performed best overall. Feature Selection was very beneficial overall. PCA was helpful for some algorithms, but less so for others. Data Fusion was moderately beneficial. / Master of Science / Gas Chromatography is a method for separating a mixture into its constituent components. A chromatogram is a time series showing the detection of gas in the gas chromatography machine over time. With a properly set up gas chromatographer, different mixtures will produce different chromatograms. These differences allow researchers to determine the components or differentiate compounds from each other. Machine Learning (ML) is a field encompassing a set of methods by which machines can independently analyze data to derive the exact algorithms for processing it. There are many different machine learning algorithms which can accomplish this. There are also techniques which can process the data to make it more effective for use with machine learning. Feature Engineering is one such technique which transforms the data. Feature Selection is another technique which reduces the data to a subset. Data Fusion is a technique which combines different sources of data. Each of these processing techniques have many different implementations. This thesis applies machine learning to gas chromatography. ML systems are developed to classify mixtures based on their chromatograms. Five common machine learning algorithms are developed and compared. Some common Feature Engineering, Feature Selection, and Data Fusion techniques are also evaluated. Two of the algorithms were found to be more effective overall than the other algorithms. Feature Selection was found to be very beneficial. Feature Engineering was beneficial for some algorithms but less so for others. Data Fusion was moderately beneficial.

This dissertation presents a study of machine learning methods with a focus on applications to statistical and condensed matter physics, in particular the problem of quantum state preparation, spin-glass and constraint satisfiability. We will start by introducing the core principles of machine learning such as overfitting, bias-variance tradeoff and the disciplines of supervised, unsupervised and reinforcement learning. This discussion will be set in the context of recent applications of machine learning to statistical physics and condensed matter physics. We then present the problem of quantum state preparation and show how reinforcement learning along with stochastic optimization methods can be applied to identify and define phases of quantum control. Reminiscent of condensed matter physics, the underlying phases of quantum control are identified via a set of order parameters and further detailed in terms of their universal implications for optimal quantum control. In particular, casting the optimal quantum control problem as an optimization problem, we show that it exhibits a generic glassy phase and establish a connection with the fields of spin-glass physics and constraint satisfiability problems. We then demonstrate how unsupervised learning methods can be used to obtain important information about the complexity of the phases described. We end by presenting a novel clustering framework, termed HAL for hierarchical agglomerative learning, which exploits out-of-sample accuracy estimates of machine learning classifiers to perform robust clustering of high-dimensional data. We show applications of HAL to various clustering problems.

Artificial intelligence is a swiftly growing field, and many are researching whether AI can serve as a diagnostic aid in the medical domain. However, the primary weakness of traditional machine learning for many applications is energy efficiency, and this may hamper its ability to be effectively utilized in medicine for portable or edge systems. In order to be more effective, new energy-efficient machine learning paradigms must be investigated for medical applications. In addition, smaller models with fewer parameters would be better suited to medical edge systems. By processing data as a series of "spikes" instead of continuous values, spiking neural networks (SNN) may be the right model architecture to address these concerns. This work investigates the proposed advantages of SNNs compared to more traditional architectures when tested on various medical datasets. We compare the energy efficiency of SNN and recurrent neural network (RNN) solutions by finding sizes of each architecture that achieve similar accuracy. The energy consumption of each comparable network is assessed using standard tools for such evaluation. On the SEED human emotion dataset, SNN architectures achieved up to 20x lower energy per inference than an RNN while maintaining similar classification accuracy. SNNs also achieved 30x lower energy consumption on the PTB-XL ECG dataset with similar classification accuracy. These results show that spiking neural networks are more energy efficient than traditional machine learning models at inference time while maintaining a similar level of accuracy for various medical classification tasks. With this superior energy efficiency, this makes it possible for medical SNNs to operate on edge and portable systems. / Master of Science / As artificial intelligence grows in popularity, especially with the rise of new large language models like Chat-GPT, a weakness in traditional architectures becomes more pronounced. These AI models require ever-increasing amounts of energy to operate. Thus, there is a need for more energy-efficient AI models, such as the spiking neural network (SNN). In SNNs, information is processed in a series of spiking signals, like the biological brain. This allows the resulting architecture to be highly energy efficient and adapted to processing time-series data. A domain that often encounters time-series data and would benefit from greater energy efficiency is medicine. This work seeks to investigate the proposed advantages of spiking neural networks when applied to the various classification tasks in the medical domain. Specifically, both an SNN and a traditional recurrent neural network (RNN) were trained on medical datasets for brain signal and heart signal classification. Sizes of each architecture were found that achieved similar classification accuracy and the energy consumption of each comparable network was assessed. For the SEED brain signal dataset, the SNN achieved similar classification accuracy to the RNN while consuming as little as 5% of the energy per inference. Similarly, the SNN consumed 30x less energy than the RNN while classifying the PTB-XL ECG dataset. These results show that the SNN architecture is a more energy efficient model than traditional RNNs for various medical tasks at inference time and may serve as the solution to the energy consumption problem of medical AI.

Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data gathered using geospatial information science (GIS) and remote sensing techniques. Two sets of data were collected: a BBD for a single year of 2020 and a historical BBD covering a five-year period from 2016 to 2020. Three ML algorithms, including random forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were trained using 319,404 and 1,246,261 bridge decks in the BBD and the historical BBD, respectively. Results showed that the use of historical BBD significantly improved the performance of the models compared to BBD. Additionally, random forest and XGBoost, trained using the historical BBD, demonstrated higher overall accuracies and average F1 scores than the ANN model. Specifically, the random forest and XGBoost models achieved overall accuracies of 83.4% and 79.4%, respectively, and average F1 scores of 79.7% and 77.5%, respectively, while the ANN model achieved an overall accuracy of 58.8% and an average F1 score of 46.1%. The permutation-based variable importance revealed that the hazard data related to earthquakes did not significantly contribute to model development. In conclusion, tree-based ensemble learning algorithms, such as random forest and XGBoost, trained using updated historical bridge data, including NBI, traffic, and climate data, provide a useful tool for accurately predicting the deck condition ratings of bridges in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.

PhD Thesis (Software Engineering) / In software engineering, behavioral state machine models are essential for validating system behavior and ensuring correctness. However, manually creating these models for existing implementations is highly undesirable. To address this, automata learning frameworks have been developed to automate the critical aspect of state machine model generation. Despite this, manual setup is often required to create a test harness for the system under test (SUT) and the learning algorithm. This thesis presents a new architecture for automata learning that leverages existing algorithms and incorporates a generic man-in-the-middle (MITM) component, significantly reducing manual setup effort. The architecture supports the automatic identification and annotation of potential system flaws in the learned state machine models of client-server systems. These flaws, which can arise in the implementation of clients, servers, their interactions, and even the protocols themselves, can be exploited by malicious clients, impostor servers, or MITM adversaries. Two sets of rules are introduced to automatically assist with flaw detection, visually annotating the potential issues within the learned models. The enhanced architecture also facilitates regression detection, test case generation, and guides the development of new features, thereby improving the debugging process and ensuring comprehensive system coverage. By employing the LTSDiff algorithm, the proposed method efficiently detects behavioral changes resulting from software updates to prevent unintended consequences. The automatically generated and annotated state machine models serve as valuable evidence in security, safety, and reliability assurance. They provide a robust tool for the development, testing, and maintenance of complex software systems, modeling the behavior of client-server systems. / Thesis / Doctor of Philosophy (PhD)

Neural Networks are powerful tools used in Machine Learning to solve complex problems across many domains, including biological classification, self-driving cars, and automated management of distributed systems. However, practitioners' trust in Neural Network models is limited by their inability to answer important questions about their behavior, such as whether they will perform correctly or if they can be entrusted with private data. One major issue with Neural Networks is their "black-box" nature, which makes it challenging to inspect the trained parameters or to understand the learned function. To address this issue, this thesis proposes several new ways to increase the trustworthiness of Neural Network models. The first approach focuses specifically on Piecewise Linear Neural Networks, a popular flavor of Neural Networks used to tackle many practical problems. The thesis explores several different techniques to extract the weights of trained networks efficiently and use them to verify and understand the behavior of the models. The second approach shows how strengthening the training algorithms can provide guarantees that are theoretically proven to hold even for the black-box model. The first part of the thesis identifies errors that can exist in trained Neural Networks, highlighting the importance of domain knowledge and the pitfalls to avoid with trained models. The second part aims to verify the outputs and decisions of the model by adapting the technique of Mixed Integer Linear Programming to efficiently explore the possible states of the Neural Network and verify properties of its outputs. The third part extends the Linear Programming technique to explain the behavior of a Piecewise Linear Neural Network by breaking it down into its linear components, generating model explanations that are both continuous on the input features and without approximations. Finally, the thesis addresses privacy concerns by using Trusted Execution and Differential Privacy during the training process. The techniques proposed in this thesis provide strong, theoretically provable guarantees about Neural Networks, despite their black-box nature, and enable practitioners to verify, extend, and protect the privacy of expert domain knowledge. By improving the trustworthiness of models, these techniques make Neural Networks more likely to be deployed in real-world applications.

Polyolefins are one of the most widely used commodity polymers with applications in films, packaging and automotive industry. The modeling of polymerization processes producing polyolefins, including high-density polyethylene (HDPE), polypropylene (PP), and linear low-density polyethylene (LLDPE) using Ziegler-Natta catalysts with multiple active sites, is a complex and challenging task. In our study, we integrate process modeling and data analytics for improving and optimizing polyolefin manufacturing processes. Most of the current literature on polyolefin modeling does not consider all of the commercially important production targets when quantifying the relevant polymerization reactions and their kinetic parameters based on measurable plant data. We develop an effective methodology to estimate kinetic parameters that have the most significant impacts on specific production targets, and to develop the kinetics using all commercially important production targets validated over industrial polyolefin processes. We showcase the utility of dynamic models for efficient grade transition in polyolefin processes. We also use the dynamic models for inferential control of polymer processes. Thus, we showcase the methodology for making first-principle polyolefin process models which are scientifically consistent, but tend to be less accurate due to many modeling assumptions in a complex system. Data analytics and machine learning (ML) have been applied in the chemical process industry for accurate predictions for data-based soft sensors and process monitoring/control. Specifically, for polymer processes, they are very useful since the polymer quality measurements like polymer melt index, molecular weight etc. are usually less frequent compared to the continuous process variable measurements. We showcase the use of predictive machine learning models like neural networks for predicting polymer quality indicators and demonstrate the utility of causal models like partial least squares to study the causal effect of the process parameters on the polymer quality variables. ML models produce accurate results can over-fit the data and also produce scientifically inconsistent results beyond the operating data range. Thus, it is growingly important to develop hybrid models combining data-based ML models and first-principle models. We present a broad perspective of hybrid process modeling and optimization combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach and not just the direct combinations of first-principle and ML models. We present a detailed review of scientific literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models according to their methodology and objective. We identify the themes and methodologies which have not been explored much in chemical engineering applications, like the use of scientific knowledge to help improve the ML model architecture and learning process for more scientifically consistent solutions. We apply these hybrid SGML techniques to industrial polyolefin processes such as inverse modeling, science guided loss and many others which have not been applied previously to such polymer applications. / Doctor of Philosophy / Almost everything we see around us from furniture, electronics to bottles, cars, etc. are made fully or partially from plastic polymers. The two most popular polymers which comprise almost two-thirds of polymer production globally are polyethylene (PE) and polypropylene (PP), collectively known as polyolefins. Hence, the optimization of polyolefin manufacturing processes with the aid of simulation models is critical and profitable for chemical industry. Modeling of a chemical/polymer process is helpful for process-scale up, product quality estimation/monitoring and new process development. For making a good simulation model, we need to validate the predictions with actual industrial data. Polyolefin process has complex reaction kinetics with multiple parameters that need to be estimated to accurately match the industrial process. We have developed a novel strategy for estimating the kinetics for the model, including the reaction chemistry and the polymer quality information validating with industrial process. Thus, we have developed a science-based model which includes the knowledge of reaction kinetics, thermodynamics, heat and mass balance for the polyolefin process. The science-based model is scientifically consistent, but may not be very accurate due to many model assumptions. Therefore, for applications requiring very high accuracy predicting any polymer quality targets such as melt index (MI), density, data-based techniques might be more appropriate. Recently, we may have heard a lot about artificial intelligence (AI) and machine learning (ML) the basic principle behind these methods is to making the model learn from data for prediction. The process data that are measured in a chemical/polymer plant can be utilized for data analysis. We can build ML models to predict polymer targets like MI as a function of the input process variables. The ML model predictions are very accurate in the process operating range of the dataset on which the model is learned, but outside the prediction range, they may tend to give scientifically inconsistent results. Thus, there is a need to combine the data-based models and scientific models. In our research, we showcase novel approaches to integrate the science-based models and the data-based ML methodology which we term as the hybrid science-guided machine learning methods (SGML). The hybrid SGML methods applied to polyolefin processes yield not only accurate, but scientifically consistent predictions which can be used for polyolefin process optimization for applications like process development and quality monitoring.

Phenotypic profiling systematically maps and analyzes observable traits (phenotypes) exhibited in cells, tissues, organisms or systems in response to various conditions, including chemical, genetic and disease perturbations. This approach seeks to comprehensively understand the functional consequences of perturbations on biological systems, thereby informing diverse research areas such as drug discovery, disease modeling, functional genomics and systems biology. Corresponding techniques should capture high-dimensional features to distinguish phenotypes affected by different conditions. Current methods mainly include fluorescence imaging, mass spectrometry and omics technologies, coupled with computational analysis, to quantify diverse features such as morphology, metabolism and gene expression in response to perturbations. Yet, they face challenges of high costs, complicated operations and strong batch effects. Vibrational imaging offers an alternative for phenotypic profiling, providing a sensitive, cost-effective and easily operated approach to capture the biochemical fingerprint of phenotypes. Among vibrational imaging techniques, infrared (IR) imaging has further advantages of high throughput, fast imaging speed and full spectrum coverage compared with Raman imaging. However, current biomedical applications of IR imaging mainly concentrate on "digital disease pathology", which uses label-free IR imaging with machine learning for tissue pathology classification and disease diagnosis. The thesis contributes as the first comprehensive study of using IR imaging for phenotypic profiling, focusing on three key areas. First, IR-active vibrational probes are systematically designed to enhance metabolic specificity, thereby enriching measured features and improving sensitivity and specificity for phenotype discrimination. Second, experimental workflows are established for phenotypic profiling using IR imaging across biological samples at various levels, including cellular, tissue and organ, in response to drug and disease perturbations. Lastly, complete data analysis pipelines are developed, including data preprocessing, statistical analysis and machine learning methods, with additional algorithmic developments for analyzing and mapping phenotypes. Chapter 1 lays the groundwork for IR imaging by delving into the theory of IR spectroscopy theory and the instrumentation of IR imaging, establishing a foundation for subsequent studies. Chapter 2 discusses the principles of popular machine learning methods applied in IR imaging, including supervised learning, unsupervised learning and deep learning, providing the algorithmic backbone for later chapters. Additionally, it provides an overview of existing biomedical applications using label-free IR imaging combined with machine learning, facilitating a deeper understanding of the current research landscape and the focal points of IR imaging for traditional biomedical studies. Chapter 3-5 focus on applying IR imaging coupled with machine learning for novel application of phenotypic profiling. Chapter 3 explores the design and development of IR-active vibrational probes for IR imaging. Three types of vibrational probes, including azide, 13C-based probes and deuterium-based probes are introduced to study dynamic metabolic activities of protein, lipids and carbohydrates in cells, small organisms and mice for the first time. The developed probes largely improve the metabolic specificity of IR imaging, enhancing the sensitivity of IR imaging towards different phenotypes. Chapter 4 studies the combination of IR imaging, heavy water labeling and unsupervised learning for tissue metabolic profiling, which provides a novel method to map metabolic tissue atlas in complex mammalian systems. In particular, cell type-, tissue- and organ-specific metabolic profiles are identified with spatial information in situ. In addition, this method further captures metabolic changes during brain development and characterized intratumor metabolic heterogeneity of glioblastoma, showing great promise for disease modeling. Chapter 5 developed Vibrational Painting (VIBRANT), a method using IR imaging, multiplexed vibrational probes and supervised learning for cellular phenotypic profiling of drug perturbations. Three IR-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. Supervised learning is used to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with novel mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic drug screening.