Thursday, October 23, 2014

Title: LabintheWild: Conducting Large-Scale, Uncompensated Online Experiments Around the World
Speaker: Katharina Reinecke (School of Information, University of Michigan)
Date: Tuesday, October 28
Time: 12-1pm
Room: NSH 3305

Abstract:
HAn estimated 95% of research findings in psychology, behavioral economics, and related fields are based on studies with student samples from Western and industrialized countries. As a consequence, some of the knowledge derived from these usually small and locally-recruited student sample populations has been found to be non-generalizable — a finding that led researchers to call these participant samples ``WEIRD'', an acronym for Western, Educated, Industrialized, Rich, and Democratic (Henrich et al. 2010). In this talk, I will show how web-based experimentation with uncompensated samples has the potential to support the generation, replication, verification, and extension of results with larger and more diverse sample populations than previously seen. I will introduce the experimental online platform LabintheWild, which provides participants with personalized feedback in exchange for participation in behavioral studies. In comparison to conventional in-lab studies, LabintheWild enables the recruitment of participants at larger scale and from more diverse demographic and geographic backgrounds: In the last two years, LabintheWild was visited more than 2.5 million times, and nearly 850,000 visitors from more than 200 countries completed an experiment (an average of about 1,000 participants/day). I will discuss how participants hear about the platform, and why they choose to participate. Presenting the results of several experiments, I will additionally show that LabintheWild is a feasible tool to make science less WEIRD.

Bio:
Katharina Reinecke is an Assistant Professor at the University of Michigan School of Information. She received her Ph.D. in Computer Science from the University of Zurich, Switzerland, in 2010, and spent her postdoctoral years at Harvard School of Engineering and Applied Sciences. Her research focuses on cultural differences in users’ interaction with technology with the goal to create culturally intelligent user interfaces that automatically adapt to people’s preferences and perception. To achieve large-scale comparisons of various cultures, she employs datasets from companies such as Doodle and from her own experimental website LabintheWild.org.
































Wednesday, October 15, 2014

Title: Machine Learning for Personalized Clustering
Speaker: Yisong Yue (Computing and Mathematical Sciences, California Institute of Technology)
Date: Tuesday, October 21
Time: 12-1pm
Room: NSH 3305

Abstract:
How would you browse and organize attractions for a potential trip to Paris? How would you organize research articles while conducting a literature review? Such sensemaking tasks are often facilitated via rich user interfaces that enable users to interact more meaningfully with the data. In this talk, I will focus on the problem of learning personalized user models from rich user interactions, and in particular from clustering interactions (i.e., grouping recommended items into clusters). Clustering interactions enable users to express similarity or redundancy between different items, and are a natural way for users to organize and group information. I will describe a new machine learning setting, called collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users’ clustering tasks. I will also describe some (very) preliminary work on modeling the full closed-loop interactive clustering setting.

Bio:
Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.