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You searched for +publisher:"University of Michigan" +contributor:("Lam, Kwai Hung Henry"). Showing records 1 – 5 of 5 total matches.

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University of Michigan

1. 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 June 05, 2020. 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 Jun 2020.

Vancouver:

Merdan S. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jun 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

2. Meisami, Amirhossein. Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management.

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

 Incorporation of contextual inference in the optimality analysis of operational problems is a canonical characteristic of data-informed decision making that requires interdisciplinary research. In an… (more)

Subjects/Keywords: Integrated Learning and Optimization; Critical Care Admission Control; Individualized Scheduling; Sequential Learning under Uncertainty; Industrial and Operations Engineering; Engineering

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APA (6th Edition):

Meisami, A. (2018). Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145936

Chicago Manual of Style (16th Edition):

Meisami, Amirhossein. “Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management.” 2018. Doctoral Dissertation, University of Michigan. Accessed June 05, 2020. http://hdl.handle.net/2027.42/145936.

MLA Handbook (7th Edition):

Meisami, Amirhossein. “Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management.” 2018. Web. 05 Jun 2020.

Vancouver:

Meisami A. Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Jun 05]. Available from: http://hdl.handle.net/2027.42/145936.

Council of Science Editors:

Meisami A. Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145936

3. Pan, Qiyun. Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models.

Degree: PhD, Industrial & Operations Engineering, 2019, University of Michigan

 With the fast development of computing power over the last few decades, simulation models become increasingly popular in many applications when running actual experiments are… (more)

Subjects/Keywords: extreme quantile estimation, importance sampling, uncertainty quantification, adaptive algorithm; Statistics and Numeric Data; Science

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APA (6th Edition):

Pan, Q. (2019). Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/151701

Chicago Manual of Style (16th Edition):

Pan, Qiyun. “Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models.” 2019. Doctoral Dissertation, University of Michigan. Accessed June 05, 2020. http://hdl.handle.net/2027.42/151701.

MLA Handbook (7th Edition):

Pan, Qiyun. “Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models.” 2019. Web. 05 Jun 2020.

Vancouver:

Pan Q. Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2020 Jun 05]. Available from: http://hdl.handle.net/2027.42/151701.

Council of Science Editors:

Pan Q. Extreme Quantile Estimation and Uncertainty Quantification with Stochastic Simulation Models. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/151701


University of Michigan

4. Zhao, Ding. Accelerated Evaluation of Automated Vehicles.

Degree: PhD, Mechanical Engineering, 2016, University of Michigan

 Automated Vehicles (AVs) must be evaluated thoroughly before their release and deployment. The challenges of AV evaluation stem from two facts. i) Crashes are exceedingly… (more)

Subjects/Keywords: automated vehicles; evaluation; test; safety; accelerated; importance sampling; Mechanical Engineering; Engineering

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APA (6th Edition):

Zhao, D. (2016). Accelerated Evaluation of Automated Vehicles. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120657

Chicago Manual of Style (16th Edition):

Zhao, Ding. “Accelerated Evaluation of Automated Vehicles.” 2016. Doctoral Dissertation, University of Michigan. Accessed June 05, 2020. http://hdl.handle.net/2027.42/120657.

MLA Handbook (7th Edition):

Zhao, Ding. “Accelerated Evaluation of Automated Vehicles.” 2016. Web. 05 Jun 2020.

Vancouver:

Zhao D. Accelerated Evaluation of Automated Vehicles. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Jun 05]. Available from: http://hdl.handle.net/2027.42/120657.

Council of Science Editors:

Zhao D. Accelerated Evaluation of Automated Vehicles. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120657


University of Michigan

5. Chen, Zhihao. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.

Degree: PhD, Industrial and Operations Engineering, 2016, University of Michigan

 This thesis proposes three risk-averse models applied under demand uncertainty: a chance-constrained approach to network design problems (NDPs), a distributionally robust approach to NDPs, and… (more)

Subjects/Keywords: Two-stage stochastic optimization; Chance-constrained programming; Distributionally robust optimization; Carsharing; Mixed-integer programming; Industrial and Operations Engineering; Engineering

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

APA (6th Edition):

Chen, Z. (2016). Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120834

Chicago Manual of Style (16th Edition):

Chen, Zhihao. “Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.” 2016. Doctoral Dissertation, University of Michigan. Accessed June 05, 2020. http://hdl.handle.net/2027.42/120834.

MLA Handbook (7th Edition):

Chen, Zhihao. “Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming.” 2016. Web. 05 Jun 2020.

Vancouver:

Chen Z. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Jun 05]. Available from: http://hdl.handle.net/2027.42/120834.

Council of Science Editors:

Chen Z. Strategic Network Planning under Uncertainty with Two-Stage Stochastic Integer Programming. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120834

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