Aaron Halfaker

Microsoft Research

Date: 4 April 2022

Time: 17:00 (CEST)

Title: Designing to Learn - Aligning Design Thinking and Data Science to Build Intelligent Tools That Evolve

Abstract: “Design to learn" is a collaborative approach to developing intelligent systems that leverage the complementary capabilities of designers and data scientists. Data scientists develop algorithms that work despite the noisy, messy realities of human behavior patterns, and designers develop techniques that reduce noise by aligning interactions closely with how users think about their work. In this talk, I'll describe a set of shared concepts and processes that are intended to help designers and data scientists communicate effectively throughout the development process. This approach is being applied and refined within various product contexts in Microsoft including email triage, meeting recap, time management, and Q&A routing.

Speaker Biography: Aaron Halfaker is a principal applied research scientist working in the Office of Applied Research in Microsoft’s Experiences and Devices organization. He is also a Senior Scientist at the University of Minnesota. Dr. Halfaker’s research explores the intersection of productive information work and the application of advanced technologies (AI) to support productivity.

In his systems building research, he’s worn many hats from full stack engineer, ethnographer, engineering manager, UX designer, community manager, and research scientist. He’s most notable for building an open infrastructure for machine learning in Wikipedia called ORES. His research and systems engineering have been features in the tech media including Wired, MIT Tech Review, BBC Technology, The Register, and Netzpolitik among others.

Dr. Halfaker reviews and coordinates for top-tier journals in the social computing and human center-AI space including ACM CHI, ACM GROUP, ACM CSCW, Transactions on Social Computing, WWW, and JASIST.

Homepage: https://www.microsoft.com/en-us/research/people/ahalfaker/.

Video Recording