I'm an Assistant Professor in the Department of Computer Science, Purdue University. My research broadly connects to the fields of human-computer interaction, applied artificial intelligence and machine learning, computational social science, and behavioral sciences. I use both experimental and computational approaches to examine how to better utilize the wisdom of crowd to enhance machine intelligence (i.e., crowdsourcing and social computing), and how to better design intelligent systems that people can understand, trust and engage with effectively (i.e., human-AI interaction).

Prior to Purdue, I spent a year at Microsoft Research New York City as a postdoctoral researcher in the Computational Social Science group. I completed my Ph.D. in Computer Science at Harvard University, and received my bachelor degree from Tsinghua University, Beijing, China.

News

Publications

In most cases, my collaborators and I choose to have students as first authors and then determine the authorship alphabetically.

The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social Influence

Zhuoran Lu, Patrick Li, Weilong Wang, and Ming Yin
The 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), Online, November 2022. (Forthcoming)

Understanding the Microtask Crowdsourcing Experience for Workers with Disabilities: A Comparative View

Amy Rechkemmer and Ming Yin
The 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), Online, November 2022. (Forthcoming)

Understanding Decision Subjects' Fairness Perceptions and Retention in Repeated Interactions with AI-Based Decision Systems

Meric Altug Gemalmaz and Ming Yin
The 5th AAAI/ACM Conference on AI, Ethics, and Soceity (AIES), Oxford, UK, August 2022.


Towards Better Detection of Biased Language with Scarce, Noisy, and Biased Annotations

Zhuoyan Li, Zhuoran Lu, and Ming Yin
The 5th AAAI/ACM Conference on AI, Ethics, and Soceity (AIES), Oxford, UK, August 2022.


How Does Predictive Information Affect Human Ethical Preferences?

Saumik Narayanan, Guanghui Yu, Wei Tang, Chien-Ju Ho, and Ming Yin
The 5th AAAI/ACM Conference on AI, Ethics, and Soceity (AIES), Oxford, UK, August 2022.

A Holistic Framework for Analyzing the COVID-19 Vaccine Debate

Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, and Dan Goldwasser
The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Seattle, WA, July 2022.

When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models

Amy Rechkemmer and Ming Yin
The 40th ACM Conference on Human Factors in Computing Systems (CHI), New Orleans, LA, April 30 - May 6, 2022.
supplementary materials
Best Paper Award

Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making

Xinru Wang, Zhuoran Lu, and Ming Yin
The ACM Web Conference (WWW), Online, April 2022.

The Influences of Task Design on Crowdsourced Judgement: A Case Study of Recidivism Risk Evaluation

Xiaoni Duan, Chien-Ju Ho, and Ming Yin
The ACM Web Conference (WWW), Online, April 2022.

A Computer Vision Approach for Estimating Lifting Load Contributors to Injury Risk

Guoyang Zhou, Vaneet Aggarwal, Ming Yin, and Denny Yu
IEEE Transactions on Human-Machine Systems, 52(2), April 2022.

Exploring the Effects of Machine Learning Literacy Interventions on Laypeople's Reliance on Machine Learning Models

Chun-Wei Chiang and Ming Yin
The 27th ACM Conference on Intelligent User Interfaces (IUI), Online, March 2022.

supplementary materials

Video-based AI Decision Support System for Lifting Risk Assessment

Guoyang Zhou, Vaneet Aggarwal, Ming Yin, and Denny Yu
IEEE International Conference on Systems, Man, and Cybernetics (SMC), Online, October 2021.


Task Complexity Moderates Group Synergy

Abdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J. Watts.
Proceedings of the National Adademy of Sciences (PNAS), 118(36), September 2021.

Accounting for Confirmation Bias in Crowdsourced Label Aggregation

Meric Altug Gemalmaz and Ming Yin.
The 30th International Joint Conference on Artificial Intelligence (IJCAI), Online, August 2021.

You'd Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift

Chun-Wei Chiang and Ming Yin.
The 13th ACM Web Science Conference (WebSci), Online, June 2021.

Human Reliance on Machine Learning Models When Performance Feedback is Limited: Heuristics and Risks

Zhuoran Lu and Ming Yin.
The 39th ACM Conference on Human Factors in Computing Systems (CHI), Yokohama, Japan, May 2021.

supplementary materials

Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making

Xinru Wang and Ming Yin.
The 26th ACM International Conference on Intelligent User Interfaces (IUI), College Station, TX, April 2021.

supplementary materials

Motivating Novice Crowd Workers through Goal Setting: An Investigation into the Effects on Complex Crowdsourcing Task Training

Amy Rechkemmer and Ming Yin.
The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Hilversum, Netherlands, October 2020.
Best Paper Award

Does Exposure to Diverse Perspectives Mitigate Biases in Crowdwork? An Explorative Study

Xiaoni Duan, Chien-Ju Ho, and Ming Yin.
The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Hilversum, Netherlands, October 2020.

Crowdsourcing Detection of Sampling Biases in Image Datasets

Xiao Hu, Haobo Wang, Anirudh Vegesana, Somesh Dube, Kaiwen Yu, Gore Kao, Shuo-Han Chen, Yung-Hsiang Lu, George Thiruvathukal, and Ming Yin.
The Web Conference (WWW), Taipei, April 2020.

Understanding the Skill Provision in Gig Economy from a Network Perspective: A Case Study of Fiverr

Keman Huang, Jinhui Yao, and Ming Yin.
The 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), Austin, TX, November 2019.

Discovering Biases in Image Datasets with the Crowd

Xiao Hu, Haobo Wang, Somesh Dube, Anirudh Vegesana, Kaiwen Yu, Yung-Hsiang Lu, and Ming Yin.
The 7th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), Skamania Lodge, WA, October 2019. (Work in Progress track)


Leveraging Peer Communication to Enhance Crowdsourcing

Wei Tang, Chien-Ju Ho, and Ming Yin.
The Web Conference (WWW), San Francisco, CA, May 2019.

Understanding the Effect of Accuracy on Trust in Machine Learning Models

Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach.
The 37th ACM Conference on Human Factors in Computing Systems (CHI), Glasgow, UK, May 2019.
Best Paper Honorable Mention

Does Stated Accuracy Affect Trust in Machine Learning?

Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach.
The 3rd ICML Workshop on Human Interpretability in Machine Learning (WHI), Stockholm, Sweden, July 2018.

Running Out of Time: The Impact and Value of Flexibility in On-Demand Crowdwork

Ming Yin, Siddharth Suri, and Mary L. Gray.
The 36th ACM Conference on Human Factors in Computing Systems (CHI), Montreal, Canada, April 2018.
slides

More publications...

Teaching

Introduction to Artificial Intelligence (CS 471)

Instructor, Fall 2022, Purdue University.

Data Mining (CS 573)

Instructor, Spring 2019, Fall 2019, Fall 2020 & Fall 2021, Purdue University.

Human-Computer Interaction (CS 490-HCI)

Instructor, Spring 2021, Purdue University.

Human-Centered Computing (CS 590-HCC)

Instructor, Spring 2020, Purdue University.

Crowdsourcing and Social Computing (CS 590-CSC)

Instructor, Fall 2018, Purdue University.

Intelligent Machines: Reasoning, Actions and Plans (CS 182)

Teaching fellow with Professor Barbara Grosz, Fall 2013 & Fall 2014, Harvard University.

Introduction to Optimization: Models and Methods (AM 121)

Teaching fellow with Professor Yiling Chen, Spring 2013, Harvard University.

Misc

I'm an avid photographer. Check out some of my pictures here.