Abstract When traditional lecture methods prove ineffective, some professors turn to alternative teaching styles. In particular, a flipped or inverted classroom, where students watch conceptual videos before coming to class and use class time for application and fine tuning of these concepts has become popular in recent years. However, little consensus exists on the efficacy of these strategies. The purpose of this study is to determine whether a flipped classroom structure implemented in a medical school course successfully improved student performance. To do so, I analyzed exam data from the University of Nebraska Medical Center before and after implementation of the alternate method in a course, and compared to another class taken in the same semester that did not undergo any change in teaching style. In addition, I investigated differences among particular student academic and demographic groups that may benefit from learning in an inverted classroom environment. My findings suggest that the flipped classroom strategy is advantageous to student learning and can significantly increase the performance of particular divisions of students such as those with lower-than-average MCAT scores and students who performed highly in their first year of medical school.

The phenotype inference from genotype in RNA viruses maps the viral genome/protein sequences to the molecular functions in order to understand the underlying molecular mechanisms that are responsible for the function changes. The inference is currently done through a laborious experimental process which is arguably inefficient, incomplete, and unreliable. The wealth of RNA virus sequence data in the presence of different phenotypes promotes the rise of computational approaches to aid the inference. Key residue identification and genotype-phenotype mapping function learning are two approaches to identify the critical positions out of hitchhikers and elucidate the relations among them. The existing computational approaches in this area focus on prediction accuracy, yet a number of fundamental problems have not been considered: the scalability of the data, the capability to suggest informative biological experiments, and the interpretability of the inferences. A common scenario of inference done by biologists with mutagenesis experiments usually involves a small number of available sequences, which is very likely to be inadequate for the inference in most setups. Accordingly biologists desire models that are capable of inferring from such limited data, and algorithms that are capable of suggesting new experiments when more data is needed. Another important but always been neglected property of the models is the interpretability of the mapping, since most existing models behave as ’black boxes’. To address these issues, in the thesis I design a supervised combinatorial filtering algorithm that systematically and efficiently infers the correct set of key residue positions from available labeled data. For cases where more data is needed to fully converge to an answer, I introduce an active learning algorithm to help choose the most informative experiment from a set of unlabeled candidate strains or mutagenesis experiments to minimize the expected total laboratory time or financial cost. I also propose Disjunctive Normal Form (DNF) as an appropriate assumption over the hypothesis space to learn interpretable genotype-phenotype functions. The challenges of these approaches are the computational efficiency due to the combinatorial nature of our algorithms. The solution is to explore biological plausible assumptions to constrain the solution space and efficiently find the optimal solutions under the assumptions. The algorithms were validated in two ways: 1) prediction quality in a cross-validation manner, and 2) consistency with the domain experts’ conclusions. The algorithms also suggested new discoveries that have not been discussed yet. I applied these approaches to a variety of RNA virus datasets covering the majority of interesting RNA phenotypes, including drug resistance, Antigenicity shift, Antibody neutralization and so on to demonstrate the prediction power, and suggest new discoveries of Influenza drug resistance and Antigenicity. I also prove the extension of the approaches in the area of severe acute community disease.

In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dissertation.We propose a new ensemble learning framework—Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle—active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible.Lastly, we apply the proposed learning methods to a real-world bioinformatics problem—protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem.

For well over 200 years, scientists and doctors have been poking and prodding brains in every which way in an effort to understand how they work. The earliest pokes were quite crude, often involving permanent forms of brain damage. Though neural injury continues to be an active area of research within neuroscience, technology has given neuroscientists a number of tools for stimulating and observing the brain in very subtle ways. Nonetheless, the basic experimental paradigm remains the same; poke the brain and see what happens. For example, neuroscientists studying the visual or auditory system can easily generate any image or sound they can imagine to see how an organism or neuron will respond. Since neuroscientists can now easily design more pokes then they could every deliver, a fundamental question is ``What pokes should they actually use?' The complexity of the brain means that only a small number of the pokes scientists can deliver will produce any information about the brain. One of the fundamental challenges of experimental neuroscience is finding the right stimulus parameters to produce an informative response in the system being studied. This thesis addresses this problem by developing algorithms to sequentially optimize neurophysiology experiments. Every experiment we conduct contains information about how the brain works. Before conducting the next experiment we should use what we have already learned to decide which experiment we should perform next. In particular, we should design an experiment which will reveal the most information about the brain. At a high level, neuroscientists already perform this type of sequential, optimal experimental design; for example crude experiments which knockout entire regions of the brain have given rise to modern experimental techniques which probe the responses of individual neurons using finely tuned stimuli. The goal of this thesis is to develop automated and rigorous methods for optimizing neurophysiology experiments efficiently and at a much finer time scale. In particular, we present methods for near instantaneous optimization of the stimulus being used to drive a neuron.

For nearly two decades researchers have been interested in identifying what specifically is learned when individuals learn a sequence (e.g., sequence of stimuli, sequence of motor movements, etc.). Despite extensive research in the area, considerable controversy remains surrounding the locus of learning. There are three main theories concerning the nature of spatial sequence learning: sequence learning is purely perceptual, sequence learning includes a motor component and sequence learning is based on stimulus-response (S-R) rules. The present studies sought to disentangle these theories by demonstrating that sequence learning has both a perceptual and motor component and that altering S-R rules alone disrupts sequence learning. Experiment 1 results fully supported this S-R rule theory of sequence learning. Experiment 2 results provided only partial support for this theory, though the data were also inconsistent with both of the other accounts.

This research focuses to develop some new techniques on statistical learning including methodology, computation and application. We also developed statistical quantification in nanomaterials. For a large number of random variables with temporal or spatial structures, we proposed shrink estimates of covariance matrix to account their Markov structures. The proposed method exploits the sparsity in the inverse covariance matrix in a systematic fashion. To deal with high dimensional data, we proposed a robust kernel principal component analysis for dimension reduction, which can extract the nonlinear structure of high dimension data more robustly. To build a prediction model more efficiently, we developed an active learning via sequential design to actively select the data points into the training set. By combining the stochastic approximation and D-optimal designs, the proposed method can build model with minimal time and effort. We also proposed factor logit-models with a large number of categories for classification. We show that the convergence rate of the classifier functions estimated from the proposed factor model does not rely on the number of categories, but only on the number of factors. It therefore can achieve better classification accuracy. For the statistical nano-quantification, a statistical approach is presented to quantify the elastic deformation of nanomaterials. We proposed a new statistical modeling technique, called sequential profile adjustment by regression (SPAR), to account for and eliminate the various experimental errors and artifacts. SPAR can automatically detect and remove the systematic errors and therefore gives more precise estimation of the elastic modulus.

Thesis (M.I.T.)--The Evergreen State College, 2007. / Title from title screen viewed (4/10/2008). Includes bibliographical references (leaves 76-78).

Thesis (PhD)--Macquarie University, Division of Information and Communication Sciences, Computing Department, 2008. / Bibliography: p. 503-514.

Thesis (PhD) -- Australian Catholic University, 2007. / A thesis submitted in fulfilment of the requirements of the degree of Doctor of Philosophy. Bibliography p. 339 - 356. Also available in an electronic version via the internet.

Thesis (D. Ed. Min.)--Midwestern Baptist Theological Seminary, 2006. / Abstract. Includes bibliographical references (leaves 128-132).