Workshop on Collective Decision-making, Learning and Problem Solving

May 16-17, 2019
University of Maryland
Iribe Center for Computer Science and Engineering, Room 4105
8125 Paint Branch Drive, College Park MD 20740


Thursday, May 16
9:00 Pedja Neskovic, ONR
9:30 Juho Kim, KAIST (Web presentation from Seoul, S. Korea)
Supporting Learning & Collaboration in Online Discussion
10:00 Rich Baraniuk, Rice University
Human-In-The-Loop Machine Learning
10:30 BREAK
11:00 Jacob Whitehill and Neil Heffernan, WPI
A Bayesian Multi-Scale Personalized Learning Approach
11:30 Joseph Williams, University of Toronto
Enhancing Educational Resources using Multi-Armed Bandits for Crowdsourced & Dynamic Experimentation
12:00 Sharad Goel, Stanford University
Personalized Conversational Agents for Education
12:30 LUNCH
2:00 Ariel Procaccia, CMU
Putting Ethical AI to the Vote
2:30 Chinmay Kulkarni, CMU
Peer Feedback in Online Labor Market
3:00 Lirong Xia, RPI
A Mathematical Model For Optimal Decisions In A Representative Democracy
3:30 BREAK
4:00 Steve Dorton, Sonalyst
Visual Argumentation for Resolving Inefficiencies (VARI)
4:30 Mike McCloskey and Julio Mateo, 361 Inc.
Meet GECKOS - Generating Employee Crowdsourced Knowledge for Organizational Solutions
5:00 Leonard Eusebi, Charles River Analytics
Group Learning and Optimization of Collaborative Workflows (GLOW)
Friday, May 17
9:00 Mark Klein, MIT, (Web presentation from Tokyo)
Enabling Crowd-Scale Deliberation For Complex Problems
9:30 Ashish Goel, Stanford University
Markets for Public Decision Making
10:00 Anna de Liddo, Open University, UK
Structured and Decentralised Discussion Systems for Distributed Decision Making
10:30 BREAK
11:00 Michael Bernstein, Stanford University
Solving Complex Tasks with Team-Based Crowdsourcing
11:30 Dan Weld, University of Washington
Context & Explanations in Collective Decision-Making
12:00 Niki Kittur, CMU
Distributed Sensemaking: Externalizing and Aggregating Structured Mental Representations
12:30 LUNCH
2:00 Rohit Vaish, RPI
Minimizing Time-to-Rank: A Learning and Recommendation Approach
2:30 Joan Feigenbaum, Yale University
PriFi: Tracking-Resistant, Intra-Organizational Networking
3:00 Bryan Ford, Swiss Federal Institute of Technology in Lausanne (EPFL)
3:30 Aaron Johnson, Navy Research Lab (NRL)
Privacy in Communication Networks