Mingren Shen

I am a M.S. Computer Sciences and Ph.D. in Materials Science student at UW-Madison, where I work on applying computer vision and machine learning methods into material and related area problems.

Before UW-Madison, I got B.S. Physics at Nanjing University and M.S. Physics at University of Chinese Academy of Sciences where I was advised by Dr. Ke Chen and Dr. Mingcheng Yang .

Email  /  CV  /  Google Scholar  /  LinkedIn

Current Research

I'm interested in computer vision, machine learning, image processing, virtual reality, and computational material and biology. Much of my research is about applying newly developed computational methods and models for the physical world including DNA, TEM images etc.


Automated Defect Recognition in Electron Microscopy Images
Minren Shen, 2018
Advisor: Prof. Dane Morgan, Department of Materials Science and Engineering, UW-Madison

I build an automated image analysis system for identifying dislocation loops in irradiated steels. Faster R-CNN module in ChainerCV is used to identify material defects in Electron Microscopy Images. The new system can detect TEM images faster and more stable compared to human level experts.


GAN for Super Resolution Simulated STEM Images
Minren Shen,Ruiqi Yin, Nick Lawrence, Cloris Feng, 2018
Advisor: Prof. Dane Morgan, Department of Materials Science and Engineering, UW-Madison

I develop a Generative Adversarial Networks ( GAN ) model( Pix2Pix ) to convert lower resolution but fast generate simulated STEM images(Convention STEM Simulation) to the higher resolution but slower generated images( Multi-slice STEM Simulation). The relative error is reduced from 10% to 1%. GAN model not only improves the mean square error of generated images but also preserves all physical meanings of the STEM images


Identifying Active Extravasation on Arteriograms with Deep Learning
Minren Shen, 2018
Advisor: Prof. Dane Morgan, Prof. Po-Ling Loh, Prof. Varun Jog, MD. Mark Kleedehn, UW-Madison

A two-stage method was used to solve the extravasation detection problem, where the first stage was used to classify whether a bleed was present and the second stage where an object detector was trained to identify the site of bleeding. ResNet-152 was used as the first stage classifier and Faster R-CNN was used as the second stage object detector. The first stage of the algorithm was 86% accuracy. The second stage of the algorithm correctly identified 5 of the 10 sites of bleeding.
The results are submitted to a radiologist conference(CIRSE2019).


N6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA
Guan-Zheng Luo, Ziyang Hao, Liangzhi Luo, Minren Shen, Daniela Sparvoli, Yuqing Zheng, Zijie Zhang, Xiaocheng Weng, Kai Chen, Qiang Cui, Aaron P. Turkewitz and Chuan He, 2018

I use large scale GPU accelerated molecular dynamics (MD) simulations to study mechanical property changes due to N6-methyldeoxyadenosine of DNA chains. The results shows knockout of a potential 6mA methyltransferase leads to a transcriptome-wide change of gene expression.

Paper Link


Chemically driven fluid transport in long micro channels
Mingren Shen, Fangfu Ye, Rui Liu, Ke Chen, Mingcheng Yang, and Marisol Ripoll, 2016

I show that a concentration drop across micro channels with periodically inhomogeneous boundary walls can laterally transport fluids over arbitrarily long distances along the micro channel. This work thus presents new insight into the fluid transport in long micro channels commonly found in nature and is useful for designing novel micro- or nanofluidic pumps.

Paper Link


Mesoscale simulation of self-diffusiophoretic micro rotor
Mingren Shen, Rui Liu, Mei-Ying Hou , Ming-Cheng Yang , Ke Chen, 2016

I employ hybrid molecular dynamics (MD) simulations and multi-particle collision dynamics (MPC) to investigate the motion of micro rotors. The rotational direction and speed of the micro rotor are determined by bead-solvent interactions, the rotor geometry, the solvent viscosity and the catalytic reaction ratio.

Paper Link(In Chinese)

MS&T19: Automated Defect Detection in Electron Microscopy with Machine Learning

Materials Science & Technology 2019 - Data Science for Material Property Interpretation(09/29/2019 - 10/03/2019,Portland, OR, USA)

Dane Morgan, Mingren Shen, Wei Li, Kevin Field

Oral Report

CIRSE 2019: Identifying active extravasation on arteriograms using artificial intelligence

Cardiovascular and Interventional Radiological Society of Europe (CIRSE)(09/07/2019 - 09/11/2019,Barcelona, Spain)

Mingren Shen, Mesut Ozturk, Po-Ling Loh, Varun Jog, Paul Laeseke, Dane Morgan, Mark Kleedehn

Oral Report

Course Projects

Query time optimized video inference system
Mingren Shen, Shuoxuan Dong, Xiuyuan He, 2018

Optimizing the latency of a two CNNs video inference system by reusing the intermediate results of first CNN(ResNet50 ) to accelerate the calculation of second CNN( ResNet152 ). We successfully achieved 18% latency decrease without sacrificing the accuracy of the model.
Final Report


Improvement of Twitter gender classifier using user mobility features
Xinyi Liu , Mingren Shen , Faust Shi, 2017

We collected Twitter data from St Louis, MO to build a user gender classifier including Naìˆve Bayes Network, Random Forest, Ada Boost and Multi-Layer Neural Network (Python: scikit-learn) and test user mobility feature effects for those classifier by adding those geo features.

Final Report

Personal Projects

BBQ:Bounding Box Quality Checker
Mingren Shen, Xiuyuan He, 2018

BBQ standing bounding box qualifier which is a web service that examines the matching between ground truth bounding boxes and the predicted bounding boxes and produces a prediction quality report and debugging tools for object detection algorithm. A lot of work of object detection methods are focusing on how to generate bounding box annotations. However, few have talked about how to debug or check where the object detection model fails. This Hackathon project is a tool that can help people actually see where the object detection algorithm fails.


Driver Test Schedule System
Mingren Shen , Yang Yang, Jiachen Shen, 2016

This project helps reminder the users when there are personalized available space for their driving test in College Town of TAMU. The project is build on Ruby on Rails,Ruby(4.2.2),Python.