John Meluso, PhD

Organization Scientist | Computational Social Scientist | Qualitative Researcher

Contact
john.meluso (at) cornell.edu

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Hello and welcome! I'm John Meluso (he/him, they/them). I study collective intelligence and how networks, communication, and social psychology shape collaborations.

I'm an Ezra Systems Postdoctoral Associate at Cornell Systems Engineering where I work with Oliver Gao. Previously, I was the Sloan Postdoctoral Fellow and the OCEAN Postdoctoral Fellow at the Vermont Complex Systems Center at the University of Vermont where I worked with Laurent Hébert-Dufresne. I completed my Doctorate of Philosophy at the University of Michigan in Design Science. At U-M, I studied Complex System Design with Jesse Austin-Breneman and Management & Organizations with Jose Uribe. Also on my committee were Scott Page and Lynette Shaw.

For overviews of my publications, research, and teaching, take a look at my summaries below. To see my previous job market documents, check out my job market page.


Disciplines: management & organizations, systems engineering, complex systems, communication, social psychology, information science, design

Methods: network science, agent-based modeling, optimization, data science, interviews, surveys, diaries, ethnography

Topics: collective performance, innovation processes, data science, inclusion, organizational culture, open source, openness, virtual collaboration, information & communication technology, miscommunication

Industry Experience: aerospace, automotive, consumer goods, software, management

Personal interests: podcasts, bunnies, coffee, science fiction, community, authenticity, mindfulness, balance

Publications

Ongoing Work


Refereed Publications


Lightly-Refereed Publications


Invited Publications

Research Summary

Networks of interactions underlie all modern work. Whether face-to-face, through videocalls, via documents, or other artifacts—the complex structures and contents of these interactions shape how groups learn, with significant consequences for performance and social issues alike. To truly disentangle the causes of macro-level outcomes from feedback loops and morasses of data, we need an interdisciplinary approach that draws on data and information science as much as organizational theory, social psychology, and communication. To that end, my approach identifies “what’s happening” in real workplace interactions through qualitative methods and “why it matters” through trailblazing techniques from computational social science. These methods inform one another, creating a powerful suite of tools for answering: How do learning processes shape networked collaborations? And how can we design interactions that benefit everyone involved? For example, my approach revealed that decentralizing multidisciplinary teams improves performance while empowering workers. My research follows three branches: (1) collective adaptation, (2) issues in social networks, and (3) thriving in open collaborations. I hope to break new ground in theories of innovation searches, cultural evolution, and open organizing while advancing organizations toward thriving—ever improving performance and inclusion. 

To learn more, take a look at my Research Statement!

Teaching Summary

As an instructor, I strive to create a learning environment that provides every student the best opportunities to construct their own knowledge and to acquire practical skills relevant to their interests. I achieve these outcomes by centering research-based principles of learning that create inclusive atmospheres for students of diverse backgrounds. These principles include now-classic techniques such as active learning, but also involve establishing feelings of belonging, creating opportunities to form connections through practical examples, and facilitating deliberate practice. I hope to apply these combined approaches to advance every student toward success.

For more, see my Teaching Statement.