The best digital experiences materialize when “what” users do and “why” they do it are both well understood. For the last decade, however, quantitative analytics and qualitative experience testing have been largely distinct work streams, with separate tools and stakeholders. In this talk, I’ll present recent work from academia and industry that demonstrates how data-driven technologies can bridge the qualitative/quantitative gap, supporting richer UX analysis workflows and increasing speed to insight.
Ranjitha Kumar is an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign and the Chief Research Scientist at UserTesting. Her research has won best paper awards/nominations at premier conferences in human-computer interaction, and is supported by grants from the NSF, Google, Amazon, and Adobe. She received her BS and PhD from the Computer Science Department at Stanford University, and co-founded Apropose, Inc., a data-driven design startup backed by Andreessen Horowitz and New Enterprise Associates.
The world is filled with a rich diversity of sounds ranging from mundane beeps and whirs to critical cues such as fire alarms or spoken content. These sounds can be inaccessible not only to people with auditory-related disabilities, such as those who are deaf or hard of hearing (DHH), but also to hearing people in many situations. We all find conversations difficult to hear in noisy bars, doorbells inaudible over a vacuum cleaner running, or may miss a phone ringing while in the shower.
My work advances sound accessibility by developing interactive systems that leverage state-of-the-art in machine learning, signal processing, and wearable technology to sense and provide sound feedback. To design these systems, I follow an iterative user-centric research process ranging from conducting formative studies, to designing and evaluating prototypes in controlled environments, to crucially, conducting deployments of full systems in the field. In this talk, I will discuss my lab’s ongoing research in advancing three areas of sound accessibility: providing awareness about everyday sounds, supporting speech conversations, and improving accessibility of sounds in emerging technologies such as AR and VR. I will close with discussing future grand challenges in the area of sound accessibility.
Dhruv “DJ” Jain is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan . His research lies in Human-Computer Interaction (HCI) and focuses on accessibility. His work has been published at top HCI and accessible computing venues such as CHI, UIST, and ASSETS; seven papers have been honored with best paper and honorable mention awards. His research has also been covered by the media (e.g., by CNN, New Scientist, and Forbes), has been recognized by Google and Apple, and has been publicly launched (e.g., one system has over 100,000 users). DJ completed his PhD at University of Washington, his Masters at MIT Media Lab, and has worked at Microsoft Research, Google, and Apple.
Do you ever have difficulty getting to common agreement with colleagues on important work decisions impacting your organization’s products? Do you strive for more positive ways to solve disagreements? Do you wish you could have more win-win outcomes?
Colin Swindells will discuss his processes to resolve tough product decisions in user experience teams. Specifically, Colin will share adaptations of leading system thinking management tools that he has refined for contemporary user experience teams in technology organizations ranging in size from startups to multinational corporations.
Colin Swindells (Moonraker Insights LLC) is a user experience researcher who is currently Principal of a global consulting services company. Colin has led user experience activities for a range of organizations including Apple, BCG, Boeing, BMW, CAE, Dako, Google, Microsoft, Nike and Yara. He also serves on the Maine Institute of Technology’s IT Board, reviewing and advising investments into Maine-based tech companies. Colin has a PhD in Computer Science from the University of British Columbia, a BASc in Engineering Science from Simon Fraser University and a professional certification in System Thinking from the Massachusetts Institute of Technology. He has 27 peer-reviewed publications, 11 patents and has led projects on 4 continents.