Adish Singla: "Learning and Incentives in Human Computation"

Speaker: Adish Singla, ETH Zurich
Title:  Learning and Incentives in Human Computation

Abstract:    Human computation, a.k.a. crowdsourcing, aims to fuse human knowledge and expertise with computing to help solve problems that neither people nor machines can solve alone. The success of such human powered computing systems heavily depends on the active and effective participation of the users. This talk explores the research questions at the interplay of learning and incentives, with the goal of improving the overall effectiveness of such systems.

In particular, we discuss the challenges faced by operators of the bike sharing systems from fluctuating and unpredictable demands, leading to imbalance problems such as unavailability of bikes or parking docks at stations. We present a crowdsourcing mechanism that incentive the users in the bike repositioning process by providing them with alternate choices to pick or return bikes in exchange for monetary incentives. We deployed the proposed mechanism through a smartphone app among users of a large-scale bike sharing system operated by a public transport company in a city of Europe, and we provide results from this experimental deployment.

Biography: 

Adish Singla is 3rd year PhD student in the Learning and Adaptive Systems group at ETH Zurich, working with Prof. Andreas Krause. He is interested in research problems at the interplay of learning and incentives in human computation and crowdsourcing. Earlier, he worked as Senior Development Lead in Bing Search, Bellevue office for four years. In Bing, he was leading the team of Popularity and Freshness (with charter of using signals from massive amount of users’ query, click and browsing logs), as well as the team of Ranking Fundamentals (with charter of improving core machine learning techniques used in Bing rankers).