Data Citizens: A Distinguished Lecture Series presents Neilam Ram

Data Citizens is a series of in-depth talks by experts in the field of data science on a wide variety of topics including data visualization, big data, artificial intelligence, and predictive analytics.  Join us on Thursday, May 6, 2021 as we welcome Neilam Ram Professor of Psychology and Professor of Communication from Stanford University for a talk on ‘screenomics’.

TALK TITLE: “Screenomics: A New Venue for Discovering the Dynamics of Digital Life”

Thursday, 5/6 3:30-4:30 PM | REGISTER NOW 

“We have recently developed and forwarded a new approach for capturing, visualizing, and analyzing the unique record of an individual’s everyday digital experiences—screenomics. In our quest to derive knowledge from and understand screenomes—ordered sequences of hundreds of thousands of smartphone and laptop screenshots obtained every five seconds for up to one year – the data have become a playground for learning about computational machinery used to processes images and text, machine learning algorithms, human-labeling of taxonomies, qualitative inquiry, and the tension between N = 1 and N = many approaches. Using a selection of empirical examples, we illustrate how engagement with these new data is reshaping what we know about behavioral change in a wide variety of domains and how we study the person-context transactions that drive individuals’ digital lives.”

Prof. Nilam Ram studies the dynamic interplay between psychological and media processes and how they change from moment-to-moment and across the life span.

Nilam’s research grows out of a history of studying change. After completing his undergraduate study of economics, he worked as a currency trader, frantically tracking and trying to predict the movement of world markets as they jerked up, down, and sideways. Later, he moved on to the study of human movement, kinesiology, and eventually psychological processes—with a specialization in longitudinal research methodology. Generally, Nilam studies how short-term changes (e.g., processes such as learning, information processing, emotion regulation, etc.) develop across the life span, and how longitudinal study designs contribute to the generation of new knowledge. Current projects include examinations of age-related change in children’s self- and emotion-regulation; patterns in minute-to-minute and day-to-day progression of adolescents’ and adults’ emotions; and change in contextual influences on well-being during old age. He is developing a variety of study paradigms that use recent developments in data science and the intensive data streams arriving from social media, mobile sensors, and smartphones to study change at multiple time scales.