Mark Steyvers is a Professor of Cognitive Science at UC Irvine and heads the MADLAB. He is affiliated with the Computer Science department as well as the Center for Machine Learning and Intelligent Systems. His publications span work in cognitive science as well as machine learning and has been funded by NSF, NIH, IARPA, NAVY, and AFOSR. He received his PhD from Indiana University and was a Postdoctoral Fellow at Stanford University. He is currently serving as Associate Editor of Computational Brain and Behavior and Consulting Editor for Psychological Review and has previously served as the President of the Society of Mathematical Psychology, Associate Editor for Psychonomic Bulletin & Review and the Journal of Mathematical Psychology. In addition, he has served as a consultant for a variety of companies such as eBay, Yahoo, Netflix, Merriam Webster, Rubicon and Gimbal on machine learning problems. For his empirical research and computational modeling work, Dr. Steyvers received the New Investigator Award from the American Psychological Association as well as the Society of Experimental Psychologists.
I use a combination of empirical interventions and statistical modeling techniques to make Crowds Wiser. Empirically, this centers around user choice: How can we make use of metacognition to benefit the crowd? To model these data, I treat the un-selected questions as missing and use Hierarchical Bayesian methods to explain the missingness. These methods allow me to explain the latent process that leads to question selection. I am also competing in the Hybrid Forecasting Competition on the SAGE team. There, I develop statistical methods to aggregate Human and Machine forecasts of geopolitical events. The models I develop dynamically update over time in order to identify when to rely on Human or Machine expertise.
Can we increase the likelihood of someone having an “aha!” moment of insight when solving a difficult problem? Are the cognitive processes preceding these moments of insight different from those when there’s no insight and, if so, how? How is the “haha!” of getting (or creating) a good joke similar to the “aha!” of solving a problem? Why are some jokes better than others, according to crowd wisdom and consensus? If so, is there a reliable process we can use to create them? These are just some of the questions my research explores.
In my research, I aim to combine insights from AI and Cognitive Psychology. My current research in Cognitive Science focuses on modeling category learning from verbal explanations. On the machine learning side of things, I work on sentence and knowledge graph embeddings. I also advocate the use of word games for advancing Natural Language Processing systems. You should check out my online multiplayer word game at speaktoai.com
I’m a second-year student in the lab interested in using computational models to study how people learn, particularly older adults. My current projects include modeling the interactions between age and practice on Lumosity games, studying human engagement in human-AI collaborations, and investigating the effectiveness of cognitive tutors for older adults.
Former Lab Members
Associate Professor, Rutger University
I received my Ph.D. from the Department of Cognitive Science at the University of California, Irvine. I completed a post-doctoral fellowship in the Department of Psychology at Syracuse University before joining the faculty at Rutgers University in 2012. My research focuses on episodic and semantic memory, as well as decision making in naturalistic environments. Specifically, I focus on complex environments in which people make real world decisions about situations where prior knowledge of the environment can be brought to bear.
In memory I am interested in how people integrate noisy and incomplete information stored in episodic memory with prior knowledge of their environment. I experimentally quantify people’s prior knowledge for a wide range of domains, e.g., size, height, scenes and time. In a series of studies I have explored how people use their knowledge and expectations to make sense of their environment and appear to use this information optimally in a broad range of cognitive tasks.
Another direction in my research is to explore individual differences in rational models of cognition. While rational models provide qualitative predictions for human performance, these predictions are at an aggregate level and do not allow for inference about the individual participant. This necessitates the application of Bayesian analysis methods to the output from the rational model. I have used this approach to infer the proportion of participants who use an informative prior, as well as the distribution over priors at the individual subject level.
Associate Professor, Ohio State University
I received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS from The Ohio State University in statistics in 2010, and a Ph.D. from The Ohio State University in 2011. I spent one year as a postdoctoral researcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. My research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition. My current focus is on understanding how external factors such as the environment, and internal factors such as working memory interact to shape an observer’s perception of the world, and ultimately how this perception drives their decisions.
Lieberman Research Worldwide
Senior Software Engineer at BCG GAMMA
I love building data-driven products that people can use in their everyday lives. In my past work, I used statistics and machine learning to understand human behavior and decision making. These days, I am passionate about using what I have learned to build better products and to help companies better understand their users.
Senior Data Scientist at Change Healthcare
Data Scientist and Cognitive Scientist. Areas of expertise: Machine Learning, Statistics, Mathematical and Computational modeling, Bayesian Methods, Experimental Design and Analysis.
Assistant Professor at University of California, Riverside
I am an Assistant Professor of Teaching at UC Riverside, studying the scholarship of teaching & learning. I received my PhD in Cognitive Psychology from the University of California, Santa Cruz, and my undergraduate degree from UC Irvine. My PhD work examined the relationship between memory and creative problem solving through the lens of creative cognition. I have specific interest in fixation effects (the experience of having old and/or unhelpful ideas come to mind and block the production of new ideas), and how it is that we are able to realize (or fail to realize) that solutions encountered in one context are applicable to problems in another context.
Sheng Kung Yi
Technical Content Marketing Manager at Chartio
Siri UX Designer at Apple
Entrepreneur, product generalist, and designer