Ben (Simiao) Ren, Ph.D.

Ben (Simiao) Ren, Ph.D.

Co-Founder at ScamAI | Ex-Meta AI | ML Ph.D. from Duke

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

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Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

first_author_pubs

Mapping solar photovoltaic arrays using unmanned aerial vehicles and deep learning

Published in Submitting, 1997

Submitting for mapping the solar panels using UAV imagery. We flew a DJI mini 2 taking imagery of solar panels similar to sizes of the solar home systems and estimate the cost of collecting drone imagery and compare them with the satellite imagery from private companies

Recommended citation: Ren, S., Hu, W., ..., Fetter, R., Bradbury, K., Malof, J. (2021). Mapping solar photovoltaic arrays using unmanned aerial vehicles and deep learning

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

Published in Submitting, 1997

Submitting. First author paper focusing on benchmarking the various deep learning models in specifically the realm of meta-material design.

Recommended citation: Ren, S., Mahendra, A., Deng, Y., Khatib, O., Padilla, W. J. & Malof, J. (2021). Inverse deep learning methods and benchmarks for artificial electromagnetic material design https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202101748

A modular view of recent deep inverse modelsfor inverse design problems

Published in Submitting, 1997

Submitting. In this work on provide a modular view for the recent deep inverse models for inverse design problem and tested their performance when we mix-n-match their individual components together

Recommended citation: Ren, S., Padilla, W., Malof, J., (2021). A modular view of recent deep inverse modelsfor inverse design problems.

Benchmarking deep inverse models over time, and the neural-adjoint method

Published in **NeurIPS 2020**: Advances in Neural Information Processing Systems 33, 2020

This paper is about deep learning and inverse problems (scientific discovery but not imagery). We benchmarked state-of-the-art deep inverse models and proposed a new algorithm called neural-adjoint that accurately and efficiently solves the inverse problems

Recommended citation: Ren, S., Padilla, W., & Malof, J. (2020). Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 38–48). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/hash/007ff380ee5ac49ffc34442f5c2a2b86-Abstract.html

Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

Published in **NeurIPS 2021**: Advances in Neural Information Processing Systems 34, 2021

In press. Co-first author paper about creating a benchmark suite for easy benchmarking of the forward mapping of the AEM property emulator

Recommended citation: Deng, Y., Dong, J., Ren, S., Khatib, O., Soltani, M., Tarokh, V., ... & Malof, J. (2021). Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials. https://openreview.net/forum?id=-or413Lh_aF

portfolio

publications

Mapping solar photovoltaic arrays using unmanned aerial vehicles and deep learning

Published in Submitting, 1997

Submitting for mapping the solar panels using UAV imagery. We flew a DJI mini 2 taking imagery of solar panels similar to sizes of the solar home systems and estimate the cost of collecting drone imagery and compare them with the satellite imagery from private companies

Recommended citation: Ren, S., Hu, W., ..., Fetter, R., Bradbury, K., Malof, J. (2021). Mapping solar photovoltaic arrays using unmanned aerial vehicles and deep learning

Lorentzian problem - pending

Published in Submitting, 1997

Submitting. Second author paper about using Lorentzian function as physics to inject into the black-box to make more efficient use of data and better generalization

Recommended citation: Khatib, O., Ren, S., Malof, J., & Padilla, W. J. (2021). Pending.

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

Published in Submitting, 1997

Submitting. First author paper focusing on benchmarking the various deep learning models in specifically the realm of meta-material design.

Recommended citation: Ren, S., Mahendra, A., Deng, Y., Khatib, O., Padilla, W. J. & Malof, J. (2021). Inverse deep learning methods and benchmarks for artificial electromagnetic material design https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202101748

A modular view of recent deep inverse modelsfor inverse design problems

Published in Submitting, 1997

Submitting. In this work on provide a modular view for the recent deep inverse models for inverse design problem and tested their performance when we mix-n-match their individual components together

Recommended citation: Ren, S., Padilla, W., Malof, J., (2021). A modular view of recent deep inverse modelsfor inverse design problems.

Benchmarking deep inverse models over time, and the neural-adjoint method

Published in **NeurIPS 2020**: Advances in Neural Information Processing Systems 33, 2020

This paper is about deep learning and inverse problems (scientific discovery but not imagery). We benchmarked state-of-the-art deep inverse models and proposed a new algorithm called neural-adjoint that accurately and efficiently solves the inverse problems

Recommended citation: Ren, S., Padilla, W., & Malof, J. (2020). Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 38–48). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2020/hash/007ff380ee5ac49ffc34442f5c2a2b86-Abstract.html

Direct Evidence of Void-Induced Structural Relaxations in Colloidal Glass

Published in Physical Review Letters (PRL), 2020

My undergraduate research in physics about structral relaxation of glass. First student author (without a PhD degree). Analyzed experimental data and compared with Molecular Dynamics (MD) simulations from my collegues.

Recommended citation: Yip, C. T., Isobe, M., Chan, C. H., Ren, S., Wong, K. P., Huo, Q., ... & Lam, C. H. (2020). Direct evidence of void-induced structural relaxations in colloidal glass formers. Physical Review Letters, 125(25), 258001. https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.258001

Neural-adjoint method for the inverse design of all-dielectric metasurfaces

Published in Optics Express, 2021

This work focuses on applying the Neural-adjoint method (that I proposed in NeurIPS 2020 paper) to the meta-material design for finding absorber in all-dielectric metasurface.

Recommended citation: Deng, Y., Ren, S., Fan, K., Malof, J. M., & Padilla, W. J. (2021). Neural-adjoint method for the inverse design of all-dielectric metasurfaces. Optics Express, 29(5), 7526-7534. https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-29-5-7526&id=448570

Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

Published in Advanced Functional Materials, 2021

Review paper about the progress, trend and limitations of the current deep-learning enabled Electromagenitc (EM) Metamaterial. Conduct comprehensive literature review and summerized the research leandscape for inverse problem in EM metamaterial. The only student author (without a PhD degree).

Recommended citation: Khatib, O., Ren, S., Malof, J., & Padilla, W. J. (2021). Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review. Advanced Functional Materials, 2101748 https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202101748

Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

Published in **NeurIPS 2021**: Advances in Neural Information Processing Systems 34, 2021

In press. Co-first author paper about creating a benchmark suite for easy benchmarking of the forward mapping of the AEM property emulator

Recommended citation: Deng, Y., Dong, J., Ren, S., Khatib, O., Soltani, M., Tarokh, V., ... & Malof, J. (2021). Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials. https://openreview.net/forum?id=-or413Lh_aF

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.