Wednesday, March 22, 2017

Title: Cognitive modeling explorations with crowdsourced predictions and opinions
Speaker: Michael Lee, Professor of Cognitive Sciences, University of California Irvine
Time: 12:30-1:30
Room: NSH 1507

The analysis of crowdsourced data can be treated a cognitive modeling problem, with the goal of accounting for how any why people produced the behaviors that were observed. We explore this cognitive approach in a series of examples, involving Thurstonian models of ranking, calibration models of probability estimation, and attention and similarity models of category learning. Many of the demonstrations use crowd-sourced data from Some involve "wisdom of the crowd" predictions, while others aim to describe and explain the structure of people's opinions. Throughout the talk, we emphasize the tight interplay between theory and application, highlighting not just when existing cognitive theories and models can help address crowd-sourcing problems, but also when real-world applications demand solutions to new basic research challenges in the cognitive sciences.

Michael Lee is a Professor of Cognitive Sciences at the University of California Irvine. His research focuses on modeling cognitive processes, especially of decision making, and the Bayesian implementation, evaluation, and application of those models. He has published over 150 journal and conference papers, and is the co-author of the graduate textbook "Bayesian cognitive modeling: A practical course". He is a former President of the Society for Mathematical Psychology, a winner of the William K. Estes award of that society, and a winner of the best applied paper from the Cognitive Science Society. Before moving the U.S., he worked as a senior research scientist for the Australian Defence Science and Technology Organization, and has consulted for the Australian and US DoD, as well as various universities and companies, including the crowd-sourcing platform Ranker.

Friday, March 17, 2017

Title: Human-in-the-loop Analytics
Speaker: Michael Franklin, Liew Family Chair of Computer Science, University of Chicago
Time: 2:30 -3:30pm
Room: NSH 1507

The “P“ in AMPLab stands for "People" and an important research thrust in the lab was on integrating human processing into analytics pipelines. Starting with the CrowdDB project on human-powered query answering and continuing into the more recent SampleClean and AMPCrowd/Clamshell projects, we have been investigating ways to maximize the benefit that can be obtained through involving people in data collection, data cleaning, and query answering.  In this talk I will present an overview of these projects and discuss some future directions for hybrid cloud/crowd data-intensive applications and systems.

Michael J. Franklin is the Liew Family Chair of Computer Science and Sr. Advisor to the Provost for Computation and Data at the University of Chicago where his research focuses on database systems, data analytics, data management and distributed computing systems.  Franklin previously was the Thomas M. Siebel Professor and chair of the Computer Science Division of the EECS Department at the University of California, Berkeley.   He co-founded and directed Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), which created industry-changing open source Big Data software such as Apache Spark and BDAS, the Berkeley Data Analytics Stack.   At Berkeley he also served as an executive committee member for the Berkeley Institute for Data Science.  He currently serves as a Board Member of the Computing Research Association and on the NSF CISE Advisory Committee.  Franklin is an ACM Fellow and a two-time recipient of the ACM SIGMOD “Test of Time” award. His other honors include the Outstanding Advisor award from Berkeley’s Computer Science Graduate Student Association, and the “Best Gong Show Talk” personally awarded by Andy Pavlo at this year’s CIDR conference.

For more information about Dr. Franklin, visit and

Friday, March 3, 2017

Constructing Visual Metaphors: Using the Design Process to Crowdsource a Creative Task

Title: Constructing Visual Metaphors: Using the Design Process to Crowdsource a Creative Task
Speaker: Lydia Chilton, Stanford University (Columbia University starting in Fall 2017)
Date: Tues, March 7
Time: 12:30-1:30pm
Room: NSH 1507

Visual Metaphors are a communication tool used to draw users' attention in print media, ads, public service announcements and art. They involve blending two symbols together visually to convey a new meaning. This is a creative problem with many solutions, but some solutions have more impact and meaning to readers than others.

I will introduce the problem of visual metaphors, and describe our early stages in crowdsourcing this problem. I will discuss how we had to adapt the design process to apply to microtasks and the lessons we have learned so far about designing media that speaks directly to reader’s low-level perceptual processing.

Lydia Chilton is an assistant professor in the Computer Science Department of Columbia University in the City of New York. Actually, she won't technically start that position until July. She is currently a post-doc working with Maneesh Agrawala at Stanford University at the intersection of graphics, HCI and crowdsourcing. She has been doing crowdsourcing for ten years and is excited to see how the original goals of crowdsourcing are being realized by a large community of talented researchers. 

Monday, February 20, 2017

Real-Time Crowdsourcing for Complex Tasks

Title: Real-Time Crowdsourcing for Complex Tasks
Speaker: Walter Lasecki, Assistant Professor, University of Michigan
Date: Tues, Feb 21
Time: 12:30-1:30
Room: NSH 1507
Creating robust intelligent systems that can operate in real-world settings at super-human performance levels requires a combination of human and machine contributions. Crowdsourcing has allowed us to scale the ubiquity of these human computation systems, but the challenges in mixing human and machine effort remain a limiting factor of these systems. My lab’s work on modeling crowds as collective agents  has helped alleviate some of these challenges at a system level, but how we can create cohesive ecosystems of crowd-powered tools that together solve more complex and diverse needs remains an open question. In this talk, I will discuss some initial and ongoing work that aims to create complex crowdsourcing systems for applications that cannot be solved using only a single tool. 

Walter S. Lasecki is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he directs the Crowds+Machines (CROMA) Lab. He and his students create interactive intelligent systems that are robust enough to be used in real-world settings by combining both human and machine intelligence to exceed the capabilities of either. These systems let people be more productive, and improve access to the world for people with disabilities. Dr. Lasecki received his Ph.D and M.S. from the University of Rochester in 2015 and a B.S. in Computer Science and Mathematics from Virginia Tech in 2010. He has previously held visiting research positions at CMU, Stanford, Microsoft Research, and Google[x].​

Tuesday, February 7, 2017

Perpetually enhancing user experience through collaborative, dynamic, personalized experimentation

Title: Perpetually enhancing user experience through collaborative, dynamic, personalized experimentation
Speaker: Joseph Jay Williams, Harvard University
Date: Tues, Feb 14
Time: 12:30-1:30pm

There is a proliferation of websites and mobile apps for helping people learn new concepts (e.g. online courses), and learn how to change health habits and behavior (e.g. websites for reducing depression, apps for quitting smoking). How can we use data from real-world users to rapidly enhance and personalize these technologies? I show how we can build self-improving systems by reimagining randomized A/B experimentation as an engine for collaboration, dynamic enhancement, and personalization. I present a novel system that enhanced learning from math problems, through crowdsourcing explanations and automatically experimenting to discover the best. My second application boosted responses to an email campaign, by experimentally discovering how to personalize motivational messages to a user's activity level. These self-improving systems use experiments as a bridge between designers, social-behavioral scientists and researchers in statistical machine learning.

Joseph Jay Williams is a Research Fellow at Harvard's Office of the Vice Provost for Advances in Learning, and a member of the Intelligent Interactive Systems Group in Computer Science. He completed a postdoc at Stanford University in the Graduate School of Education in Summer 2014, working with the Office of the Vice Provost for Online Learning and the Open Learning Initiative. He received his PhD from UC Berkeley in Computational Cognitive Science, where he applied Bayesian statistics and machine learning to model how people learn and reason. He received his B.Sc. from University of Toronto in Cognitive Science, Artificial Intelligence and Mathematics, and is originally from Trinidad and Tobago. More information about his research and papers is at

Sunday, February 5, 2017

Crowdbotics: Writing Software with Crowds

Title: Crowdbotics: Writing Software with Crowds
Speaker: Anand Kulkarni, Founder of Crowdbotics, previous Chief Scientist of LeadGenius
Date: Tues, Feb 7
Time: 12:30 - 1:30pm
Room: NSH 1507

Crowd computing systems are now capable of doing more sophisticated work than ever before, accelerating the pace of creative work such as creating movies, carrying out research, and writing stories.  How can crowds be used to automate software creation? This is a complex problem, requiring good methods for expert selection, program synthesis and collaboration.  We’ll discuss strategies for accelerating software creation using crowds of geographically-diverse communities of software engineers, as well as the cultural and technical challenges that emerge.

We'll also discuss several open problems in crowd computing, including efforts by our team and others to create a crowdsourcing compiler, a hypothetical system that can optimally divide an open-ended task between artificial intelligence systems and teams of humans.  These systems blur the line between crowd-powered software and real-world organizations, raising important questions about the future of work.

Anand is founder of Crowdbotics, a startup using crowdsourcing and machine intelligence to accelerate the process of software development. Crowdbotics brings developers from new-to-coding communities worldwide into a global community of software engineers that collaborate 1-1 with technology companies.  Prior to that, Anand was Chief Scientist of LeadGenius, a Y Combinator, Sierra Ventures, and Lumia Capital-backed startup using human computation and deep learning to automate account-based marketing (ABM). LeadGenius has raised over $20M in venture funding and developed best-in-class marketing automation technology used by Fortune 500 customers like Google, eBay, and Box. In conjunction with nonprofits like the World Bank, LeadGenius generates fairly-paid digital employment for over 500 individuals in 40 countries.  Anand has been named as one of Forbes Magazine's "30 under 30" Top 30 Entrepreneurs Under 30. Anand has published over a dozen papers in ACM, AAAI and IEEE magazines, journals, and conferences. Anand previously held a National Science Foundation graduate research fellowship in mathematics. He holds degrees in Industrial Engineering and Operations Research, Mathematics, and Physics from UC Berkeley.

Sunday, January 29, 2017

Collective Creativity Systems: Design families and remix landscapes

Title: Collective Creativity Systems: Design families and remix landscapes
Speaker: Jeffrey Nickerson, Professor, School of Business, Stevens Institute of Technology
Date: Tues, Jan 31
Time: 12:30-1:30pm
Room: NSH 1507

Collective creativity systems often include modification and recombination mechanisms, forms of remixing. In addition, metasystems can accelerate the exploration of design space. For example, customizers create design families by defining parameter ranges. These customizers can in turn be modified. The effect of this reuse for customization is analyzed in the 3D printing community Thingiverse. The study focuses on the design artifacts and their inheritance history, as well as their shape and semantic distance from each other. Some ways of using coordination and computation to catalyze design space exploration are discussed, with examples from several online communities.

Jeffrey Nickerson is a professor in the School of Business at Stevens Institute of Technology. His current research focuses on collective creativity: through observation and experiment, he seeks to understand how systems composed of humans and machines can explore design space. He has a Ph.D. from New York University in Computer Science, as well as an M.F.A. in Graphic Design from Rhode Island School of Design. He is now working on two NSF-funded projects related to online community-based design and problem solving.