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Machine learning
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- Description:
- Functional electronic materials are difficult to design due to the complex interplay among chemistry, atomic structure, and electrical properties. This dilemma is further amplified in transition metal compounds which can defy the band-theory description of non-correlated electrons. Exploring the vast possible design space completely with experiments or first-principles simulations is...
- Keyword:
- Transition metal oxides, Metal-insulator transition, Machine learning, and Data science
- Subject:
- Materials Science
- Creator:
- Wagner, Nicholas Adam
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 02/12/2020
- Date Created:
- 2019-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- http://dissertations.umi.com/northwestern:14800 and etdadmin_upload_685025
-
- Description:
- The field of materials discovery is undergoing an unprecedented transition from laboratory tocomputer. Behind this transition is the new ability to accurately compute material properties, especially energetic stability, from first principles with density functional theory (DFT). However, DFT remains computationally expensive, and DFT-based materials discovery is intractable, especially in high...
- Keyword:
- High throughput, Inorganic compounds, Big data, Density functional theory, Materials discovery, and Machine learning
- Subject:
- Materials Science, Condensed matter physics, and Computational chemistry
- Creator:
- Griesemer, Sean Darius
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 05/31/2023
- Date Created:
- 2023-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- http://dissertations.umi.com/northwestern:16606 and etdadmin_upload_986816
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- Description:
- “Where Do We Come From? What Are We? Where Are We Going?” is the name for one of French artist Paul Gauguin’s most influential paintings. Unsurprisingly, these very questions have occupied the minds of countless philosophers, artists, and scholars since the beginning of human civilization. These questions become especially salient...
- Keyword:
- Decision-making, Organizational intelligence, Artificial intelligence, Algorithmic bias, Decision bias, and Machine learning
- Subject:
- Management, Artificial intelligence, and Psychology
- Creator:
- Wang, Dawei
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 09/22/2022
- Date Created:
- 2022-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- etdadmin_upload_917463 and http://dissertations.umi.com/northwestern:16159
-
- Description:
- Engineering design is a systematic process of identifying needs and their translation into functional systems. This is a cyclic process that alternates between the acquisition of data and the synthesis of said data to inform design decisions. Conventionally, data from physical experiments are used to explore the efficacy of alternative...
- Keyword:
- Uncertainty quantification, Design, Decision making, Machine learning, Data acquisition, and Data synthesis
- Subject:
- Design and Mechanical engineering
- Creator:
- van Beek, Anton
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 02/02/2022
- Date Created:
- 2021-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- etdadmin_upload_861337 and http://dissertations.umi.com/northwestern:15848
-
- Description:
- Metal–organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes connected by organic linkers. Due to their high degree of synthetic tunability, MOFs have been considered for a wide range of applications, including many that rely on a change in oxidation state. While most MOFs are generally...
- Keyword:
- Computational screening, Density functional theory, Machine learning, and Metal-organic framework
- Subject:
- Chemical engineering
- Creator:
- Rosen, Andrew Scott
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 10/07/2021
- Date Created:
- 2021-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- etdadmin_upload_838355 and http://dissertations.umi.com/northwestern:15675
-
- Description:
- In the short amount of time that genetic manipulation has been possible through CRISPR technology, myriad applications have been developed. Results from one of the most promising applications of this technology, pooled screens, have shown that single guide RNAs (sgRNAs), RNA sequences used to target specific regions of the genome,...
- Keyword:
- sgRNA design, CRISPR efficiency, and Machine learning
- Subject:
- Biostatistics, Statistics, and Bioinformatics
- Creator:
- Zarate, Oscar Alberto
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 02/01/2021
- Date Created:
- 2020-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- http://dissertations.umi.com/northwestern:15353 and etdadmin_upload_773909
-
- Description:
- Sound is one of the most important mediums to understand the environment around us. Identifying a sound event in prerecorded audio (such as a police siren, a dog bark, or a creaking door in soundscapes) leads to a better understanding of the context where the sound events occurred. To do...
- Keyword:
- Human-in-the-loop interface, Sound event detection, Sound event annotation, Audio signal processing, and Machine learning
- Subject:
- Artificial intelligence and Computer science
- Creator:
- Kim, Bongjun
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 01/21/2021
- Date Created:
- 2020-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- etdadmin_upload_739320 and http://dissertations.umi.com/northwestern:15064
-
- Description:
- In machine learning, classification that assigns a label to a sample is a fundamental problem and serves a building block for various applications of artificial intelligence such as speech recognition, sentimental analysis, and image recognition. During the last years, deep learning rejuvenates artificial intelligence; in particular, it leads to tremendous...
- Keyword:
- Classification, Deep learning, and Machine learning
- Subject:
- Industrial engineering
- Creator:
- Koo, Jaehoon
- Owner:
- Scholarly Digital Publishing
- Language:
- en
- Date Uploaded:
- 01/21/2021
- Date Created:
- 2020-01-01
- Resource Type:
- Dissertation
- Alternate Identifier:
- http://dissertations.umi.com/northwestern:15036 and etdadmin_upload_729998
-
- Description:
- Machine learning has been widely applied to solve intricate problems in finance. Yet in options theory, machine learning methods are less visited due to the structural complexity of the derivatives market. This dissertation focuses on using machine learning algorithms to obtain optimal decisions for three distinct option-related problems. In the...
- Keyword:
- Financial application, Support vector regression, Reinforcement learning, Option market, and Machine learning
- Subject:
- Industrial Engineering and Management Sciences
- Creator:
- Yaxiong Zeng
- Owner:
- Scholarly Digital Publishing
- Date Uploaded:
- 10/09/2018
- Date Modified:
- 10/09/2018
- Date Created:
- 2017-01-01
- Resource Type:
- Dissertation
-
- Description:
- Cite this article: Shen, J., McFarland, A.G., Blaustein, R.A. et al. An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics. Microbiome 10, 206 (2022). https://doi.org/10.1186/s40168-022-01412-x and Background: Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples,...
- Keyword:
- Quantification, Quantitative metagenomics, Low biomass, Viability, Machine learning, Environmental surveillance, Sequencing depth prediction, and Infection prevention
- Subject:
- Metagenomics, Quantitative research, Nosocomial infections--Prevention, Machine learning, and Environmental monitoring
- Creator:
- K. Allison Perry-Dow, Jiaxian Shen, Vincent B. Young, Ryan A. Blaustein, Mary K. Hayden, Alexander G. McFarland, Anahid A. Moghadam, Laura J. Rose, and Erica M. Hartmann
- Contributor:
- Centers for Disease Control and Prevention (funding agency)
- Owner:
- Jiaxian SHEN
- Publisher:
- Microbiome
- Language:
- English
- Date Uploaded:
- 12/19/2022
- Date Modified:
- 12/19/2022
- Date Created:
- 2022-12-02
- Resource Type:
- Research Paper
- Alternate Identifier:
- DOI 10.1186/s40168-022-01412-x, PMCID PMC9716758, and PMID 36457108