Browsing Faculty of Arts and Sciences by Keyword "Machine learning"
Now showing items 1-17 of 17
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Accelerating drug discovery with quantum chemistry, machine learning, and molecular dynamics
(2023-11-21)Drug discovery is a long and expensive process. Bringing a new drug to market takes an average of 12 years and costs $2.9 billion (USD, 2013). Most new candidates fail during pre-clinical development, and 90% of the remaining ... -
AI for Population Health: Melding Data and Algorithms on Networks
(2021-08-19)As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. My research aims to build computational methods which can impact such ... -
Algorithms for the People: Democracy in the Age of AI
(2022-06-06)Our society is being transformed by prediction tools like artificial intelligence and machine learning. And yet, we find ourselves chasing tech companies whose AI systems we know nothing about, condemning algorithms that ... -
Application of novel technologies to cardiovascular prevention
(2024-01-11)This dissertation aimed to employ advanced methodologies in machine learning (ML), causal inference, and metabolomics in epidemiological research to contribute to the field of CVD prevention. First, quantifying sodium ... -
Approximating the Shapley Value via Multi-Issue Decomposition
(ACM, 2014)The Shapley value provides a fair method for the division of value in coalitional games. Motivated by the application of crowdsourcing for the collection of suitable labels and features for regression and classification ... -
Detection of Neuron Membranes in Electron Microscopy Images Using Multi-scale Context and Radon-Like Features
(Springer Science + Business Media, 2011)Automated neural circuit reconstruction through electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that exploits multi-scale contextual information together with Radon-like ... -
Development and validation of computational models for efficient design of biological sequences
(2022-01-10)There is a huge surge of interest in designing a wide variety of proteins to use as molecular research tools and biotherapeutics - promising to revolutionize our capacity to design what we need at will. This is particularly ... -
Dopamine in the tail of striatum regulates post-assessment avoidance of stimulus novelty
(2021-11-16)Novelty drives animals’ active sampling of their environment and influences learning. Novel objects evoke characteristic behavioral responses such as approach, retreat, and avoidance of these objects, but how the complex ... -
Dynamics of surfaces and interfaces: From first-principles modeling to machine-learning molecular dynamics
(2022-05-10)Chemical production accounts for a substantial fraction of global energy use, and the majority of industrial processes rely heavily on precious metal heterogeneous catalysts. Fundamental knowledge of the surface structure ... -
Federated Lottery: Private and Communication-Efficient Learning of Personalized Networks
(2022-05-25)A promising approach to address privacy concerns, Federated learning (FL) enables distributed training of machine learning (ML) models where user data remains on edge devices and isn’t shared. However, classic FL paradigms ... -
Gas-Particle Interactions of Organic Aerosol
(2021-11-16)Atmospheric organic aerosols play significant roles in climate, air quality, and human health. Quantitative understanding and predicting the gas-particle interactions of organic aerosols and their role in particle formation ... -
Improving Developer’s Productivity for Heterogeneous Cloud Networks
(2022-06-06)The cloud is an integral part of our society, the basis, and actuation of our world. Cloud network evolves rapidly to provide better performance and more diverse functionalities for applications running in the cloud. ... -
Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability
(2023-05-15)The field of artificial intelligence (AI) has garnered increasing attention in the realms of public health and conservation due to its potential to characterize complex dynamics and facilitate difficult decision-making. ... -
Interpretable Machine Learning Methods with Applications in Genomics
(2020-08-10)A primary goal in biology is understanding the relationship between genomic sequence and cell state or function. Pharmacogenomic experiments, for instance, measure how different genomic profiles correlate with cell survival ... -
Large language models for biological prediction and design
(2024-01-25)Predicting the functional impact of changes to biological sequences is a central challenge in genetics and biology. Beyond genetics, sequence-to-function mapping has key applications in the design of sequences for use as ... -
Machine Learning Bayesian Force Fields and Applications to Phase Transformations
(2023-11-21)This thesis develops machine learning Bayesian force fields for efficient and accurate molecular dynamics simulations of materials. The Gaussian process regression model provides uncertainty quantification, enabling Bayesian ... -
Satellite Analysis of the Environmental Impacts of Armed-Conflict in Rakhine, Myanmar
(Elsevier BV, 2021-08)The impacts of armed conflict on the environment are extremely complex and difficult to investigate, given the impossibility of accessing the affected area and reliable data limitation. Very-high-resolution satellite ...