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You searched for +publisher:"University of Michigan" +contributor:("Wiens, Jenna"). Showing records 1 – 6 of 6 total matches.

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University of Michigan

1. DeBruin, Samuel. Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 Energy consumption in buildings is an area of growing national concern, with almost 3,000 TWh going to residential and commercial buildings in the United States… (more)

Subjects/Keywords: Residential energy metering; Low power wireless networks; Computer Science; Engineering

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APA (6th Edition):

DeBruin, S. (2017). Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/140896

Chicago Manual of Style (16th Edition):

DeBruin, Samuel. “Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods.” 2017. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/140896.

MLA Handbook (7th Edition):

DeBruin, Samuel. “Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods.” 2017. Web. 05 Dec 2019.

Vancouver:

DeBruin S. Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/140896.

Council of Science Editors:

DeBruin S. Enabling Visibility Into Building Energy Consumption Through Novel Metering Designs and Methods. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/140896


University of Michigan

2. Das, Sayantan. Next Generation of Genotype Imputation Methods.

Degree: PhD, Biostatistics, 2017, University of Michigan

 In the past several years, we have witnessed numerous human genetic studies that have systematically evaluated the contribution of genetic polymorphisms to various complex diseases,… (more)

Subjects/Keywords: genotype imputation; Genetics; Science

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APA (6th Edition):

Das, S. (2017). Next Generation of Genotype Imputation Methods. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138466

Chicago Manual of Style (16th Edition):

Das, Sayantan. “Next Generation of Genotype Imputation Methods.” 2017. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/138466.

MLA Handbook (7th Edition):

Das, Sayantan. “Next Generation of Genotype Imputation Methods.” 2017. Web. 05 Dec 2019.

Vancouver:

Das S. Next Generation of Genotype Imputation Methods. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/138466.

Council of Science Editors:

Das S. Next Generation of Genotype Imputation Methods. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138466


University of Michigan

3. Bao, Tian. Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation.

Degree: PhD, Mechanical Engineering, 2018, University of Michigan

 Vestibular disorders and aging can negatively impact balance performance. Currently, the most effective approach for improving balance is exercise-based balance rehabilitation. Despite its effectiveness, balance… (more)

Subjects/Keywords: Balance rehabilitation; Vibrotactile; Sensory Augmentation; Machine learning; Reaction time; Biofeedback; Mechanical Engineering; Engineering

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APA (6th Edition):

Bao, T. (2018). Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/143901

Chicago Manual of Style (16th Edition):

Bao, Tian. “Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation.” 2018. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/143901.

MLA Handbook (7th Edition):

Bao, Tian. “Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation.” 2018. Web. 05 Dec 2019.

Vancouver:

Bao T. Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/143901.

Council of Science Editors:

Bao T. Vibrotactile Sensory Augmentation and Machine Learning Based Approaches for Balance Rehabilitation. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/143901


University of Michigan

4. Merdan, Selin. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer.

Degree: PhD, Industrial & Operations Engineering, 2018, University of Michigan

 Technological advances in biomarkers and imaging tests are creating new avenues to advance precision health for early detection of cancer. These advances have resulted in… (more)

Subjects/Keywords: Prostate Cancer Detection; Clinical decision making; Machine Learning; Optimization; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Merdan, S. (2018). Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/143976

Chicago Manual of Style (16th Edition):

Merdan, Selin. “Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer.” 2018. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/143976.

MLA Handbook (7th Edition):

Merdan, Selin. “Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer.” 2018. Web. 05 Dec 2019.

Vancouver:

Merdan S. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/143976.

Council of Science Editors:

Merdan S. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/143976


University of Michigan

5. Wright, Mason. Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics.

Degree: PhD, Computer Science & Engineering, 2019, University of Michigan

 In environments governed by the behavior of strategically interacting agents, game theory provides a way to predict outcomes in counterfactual scenarios, such as new market… (more)

Subjects/Keywords: simulation-based games; reinforcement learning; game theory; Computer Science; Engineering

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APA (6th Edition):

Wright, M. (2019). Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149991

Chicago Manual of Style (16th Edition):

Wright, Mason. “Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics.” 2019. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/149991.

MLA Handbook (7th Edition):

Wright, Mason. “Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics.” 2019. Web. 05 Dec 2019.

Vancouver:

Wright M. Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/149991.

Council of Science Editors:

Wright M. Stable Profiles in Simulation-Based Games via Reinforcement Learning and Statistics. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149991


University of Michigan

6. Zhang, Dejiao. Extracting Compact Knowledge From Massive Data.

Degree: PhD, Electrical Engineering: Systems, 2019, University of Michigan

 Over the past couple decades, we have witnessed a huge explosion in data generation from almost every perspective on our lives. Along with such huge… (more)

Subjects/Keywords: Subspace identification from streaming data; Neural network compression by simultaneous sparsification and parameter tying; Unsupervised learning of interpretable representations; Electrical Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, D. (2019). Extracting Compact Knowledge From Massive Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151479

Chicago Manual of Style (16th Edition):

Zhang, Dejiao. “Extracting Compact Knowledge From Massive Data.” 2019. Doctoral Dissertation, University of Michigan. Accessed December 05, 2019. http://hdl.handle.net/2027.42/151479.

MLA Handbook (7th Edition):

Zhang, Dejiao. “Extracting Compact Knowledge From Massive Data.” 2019. Web. 05 Dec 2019.

Vancouver:

Zhang D. Extracting Compact Knowledge From Massive Data. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2027.42/151479.

Council of Science Editors:

Zhang D. Extracting Compact Knowledge From Massive Data. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151479

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