Ming Yin

Ming Yin is an Associate Professor in the Department of Computer Science at Purdue University. Her research bridges human-computer interaction, applied artificial intelligence, computational social science, and behavioral sciences. She focuses on leveraging human behavior data to design intelligent systems that balance machine efficiency with human understanding, trust, and engagement.

• Education: PhD in Computer Science (Harvard University, 2017), B.E. in Computer Software (Tsinghua University, 2011)
• Previous Roles: Postdoctoral researcher at Microsoft Research New York City (2017–2018)
• Teaching: Courses on AI, Human-AI Interaction, Data Mining, and Human-Centered Computing

Research Interests center on social computing, crowdsourcing, human-AI interaction, and ethical AI. She employs experimental and computational methods to study how human behavior can improve AI systems' design, fairness, and user trust. Her work has significant implications for gig economy platforms, decision support systems, and algorithmic accountability.

Scientific Contributions include over 15 recent articles in top venues like CHI, IJCAI, and ACL. These works explore topics such as LLM-driven trust calibration, adversarial social influence, and ethical AI design. Her research has been recognized with the

• NSF CAREER Award
• Siebel Scholar (Class of 2017)
• Multiple Best Paper and Honorable Mention Awards at CHI, CSCW, and HCOMP

Teaching Expertise spans courses like Introduction to Artificial Intelligence (CS 471), Human-AI Interaction (CS 592-HAI), and Data Mining (CS 573). She emphasizes project-based learning and designing systems for real-world problems, such as "learning in a new era" in her 2025 HCI course.