Anna Wu, established leader in building and leading high-performing data teams to drive changes impacting hundreds of millions of users. Currently as a research manager at Google, she leads a team of quantitative UX researchers applying UX methods and large scale analytics to inform Cloud product development.
Before this recent chapter, Anna had 10+ years practicing UX and data science at top IT companies and research labs as a UX researcher, data scientist, research scientist at Microsoft, IBM Research and Palo Alto Research Center. She got her PhD in HCI from Penn State and master/bachelor degrees from Tsinghua University.
Clayton Sisson is a game designer and aspiring data scientist, passionate about how data science can shed light on human behavior. For the next installment of my Interview Series, we discuss ways to use game design and UX design to develop usable and useful machine learning products and their experiences transitioning from design into data science. In this first part, we discuss the connections between data science and game design.
Here is the third part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses how the interconnections he has found between data science and garbology.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the second part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes garbology is and what kind of work he does as a data scientist garbologist.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
I interviewed Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses his mission to combine garbology, anthropology, and data science to better understand humanity and the trash we leave behind. In this first part, he describes the connections he has found between these various fields.
Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.
Here is the second part of three in my conversation with Tanner Greene. He discusses his strategies for transitioning from graduate school to UX research and his recommendations for any fellow student seeking to do the same.
Tanner Greene is a UX Researcher and Ph.D. Candidate at the University of Virginia, where he’s finishing a dissertation on the history of vaporwave, a music genre created on social media platforms. Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.
For my next installment in my Interview Series, I interviewed Tanner Greene. He recently received his doctorate from the University of Virginia for his research on the digital music genre, vapor wave. He primarily used qualitative means but has also taught himself Python to be able to employ quantitative textual analysis into his project. It is a good example of how to integrate qualitative digital ethnographic techniques with quantitative natural language processing.
In this first part, he discusses why he decided to study the vapor wave community and his experiences learning Python to conduct statistical analysis with.
Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.
This is a follow-up on my previous article about the difference between anthropology and ethnography. In this article, I discuss recent trends within anthropology to either revitalize ethnography and/or rethink its status as the primary research methodology within the discipline.
We anthropologists should consider expanding beyond the ethnographic toolkit. That could involve redefining what it means to conduct ethnography in such a way that includes other types of practices outside of the traditional ethnographic toolkit and/or rethinking the role of ethnography as our primary methodology.
For context, ethnography has been the primary tool within the discipline for the last several decades. I would define ethnography as a methodological approach that seeks to holistically understand and express the lived experiences of those in a particular sociocultural context(s) (see this article and this paper). Ethnography conventionally entails a specific set of qualitative methodologies that help to understand and analyze these lived experiences, including participant observation, interviews, qualitative coding, and so on. Anthropologists and other ethnographers have built this set of practices because they are excellent at capturing people’s lived experiences, and I agree that they are powerful for that.
I do not, however, believe that these are the only potential ways to do that. For me, ethnography is an orientation, an approach that seeks to make sense of the social world by focusing on the lived experiences of others, not necessarily some collection of qualitative methods. Seeing ethnography as an orientation, for example, would enable ethnographers to use data science and machine learning tools within ethnographies (see this and this).
My perspective here exemplifies the first way some anthropologists have sought to expand beyond the traditional ethnographic toolkit: by redefining ethnography. For us, viewing ethnography as a specific set of qualitative research techniques pigeonholes what ethnography can be. Although these techniques are powerful and useful, their exclusive deployment within anthropology stifles what ethnography can become.
Other anthropologists will seek to expand beyond this toolkit by advocating for non-ethnographic anthropological research. For them, anthropologists should cultivate other research practices in addition to or sometimes instead of ethnography. I am passionate about applying this specifically to data science and machine learning, and Morten Axel Pedersen is a counterpart to me who in this specific area. He thinks anthropologists should move beyond ethnographic research, which could include incorporating data science and machine learning research (see his talk as an example). Similar to me, he wants to see more utilization of data science and machine learning within anthropology, but he presents this as an alternative to doing ethnographic research not as a potential part of ethnographic research like I do.
The difference between the two approaches is subtle: the first advocates for reimaging ethnography and the second for reimagining anthropology and anthropological research while potentially keeping ethnography the same. On a practical level, though, they are not that different. Not only are they not mutually exclusive: one can seek to redefine ethnography and ethnography’s hold within anthropology. But they each also have their place in seeking when encouraging the expansion of the anthropological toolkit. In some situations, the promotion of redefining ethnography beyond its traditional qualitative practices is most beneficial, and other times, advocating for non-ethnographic forms of research would be.
As part of my Season 2, I interviewed Matt Artz, a design anthropologist who has been recently working as a product manager in the tech space. In Part 1, he discussed his experiences making innovative software products as an anthropologist and product manager.
Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.
For more context on my interview series in general, click here.
Resources we mentioned or other additional resources:
This is the second part of three in our conversation. In it, he described his work developing data science-based recommendation systems using the concepts of design anthropology, participatory research, and design thinking, and then how he uses his skills as an anthropologist to visualize and communicate results and then plan what to do going forward with stakeholders.
Matt Artz is a business and design anthropologist, consultant, author, speaker, and creator. He writes, speaks, and consults in user experience, product management, and business strategy. He creates products, podcasts, music, and visual art.
