An up-to-date list of publications can be found on Google Scholar
▨ (Published) | ◈ (Accepted & Forthcoming) | ◭ (Revise & Resubmit) | ◮ (In Review) | ◕ (In Preparation)

My current work is about epistemic trust in Artificial Intelligence in scientific contexts. I attempt to articulate the fundamental nature of epistemological challenges presented by the use of AI and seek to specify what plausible and satisfying solutions might look like. Additionally, I have begun to work on social and collective epistemology of science where I attempt resolve epistemological puzzels that arise when we reflect on certain forms of inference. More recently, my research has expanded to treat questions in the applied ethics of technology (principally AI)

Recently Published, In Press, or Accepted
Eamon Duede. "Deep Learning Opacity in Scientific Discovery". Philosophy of Science
Eamon Duede. "Instruments, Agents, and Artificial Intelligence: Novel Epistemic Categories of Reliance." Synthese

Manuscripts in Review
Eamon Duede. "Deep Learning and Representational Models" (Revise & Resubmit)
Eamon Duede & Richard Jean So. "A Humanistic Case for AI Optimism"
Eamon Duede & James A. Evans. "Social Limits of Scientific Certainty"

Manuscripts in Preparation
Eamon Duede. "Some AI Ethics Problems are Epistemological Problems"
Eamon Duede & James A. Evans. "Social Abductive Inference"
Eamon Duede. "Inductive Risk and Autonomous Science"
◕ Britt Skaathun & Eamon Duede. "Ethical Frameworks for Balancing Bystander Risk with Public Health Priorities in Social Network Research"

My current empirical work investigates the role of data science and artificial intelligence in enhancing the effectiveness and scalability of innovative, data-driven, and algorithm-assisted operational frameworks. I also evaluate whether and when it is suitable for algorithms to function autonomously, with or without human supervision. Additionally, I am concerned with the dynamics of discovery across disciplines, geographic space, and time. Here, I use the computational techniques of ‘data science’ to study the flow of knowledge and influence between communities of experts and other audiences, and study how these can be catalyzed or stymied by institutional and technological factors.

Recently Published, In Press, or Accepted
▨ Misha Teplitskiy, Eamon Duede, Michael Menietti, & Karim R. Lakhani. "How status of research papers affects the way they are read and cited" Research Policy
Eamon Duede, Misha Teplitskiy, Karim R. Lakhani, & James A. Evans. "Being Together in Place as a Catalyst for Scientific Advance" (Forthcoming at Research Policy)

Manuscripts in Preparation
Eamon Duede, Richard Jean So, Karim Lakhani. "Interpolating the Lost: A Generative Model Approach to Restoring Incomplete Historical and Cultural Records"
Eamon Duede, William Dolan, Ian Foster. "Quantifying AI in Science"
Eamon Duede & James A. Evans. "Digital Doubles of Everything (Even Your Morals)"

Selected Prior Publications
▨ Feng Shi, Misha Teplitskiy, Eamon Duede, & James A Evans. "The Wisdom of Polarized Crowds" Nature Human Behavior
Eamon Duede & Victor Zhorin. "Convergence of Economic Growth and the Great Recession as Seen From a Celestial Observatory" EPJ Data Science
▨ Misha Teplitskiy, Grace Lu, & Eamon Duede. "Amplifying the impact of open access: Wikipedia and the diffusion of science" JASIST
▨ Aaron Gerow, Bowen Lou, Eamon Duede, & James Evans. "Proposing Ties in a Dense Hypergraph of Academics" International Conference on Social Informatics
▨ Misha Teplitskiy, Grace Lu, & Eamon Duede. "The Transmission of Scientific Knowledge to Wikipedia" AAAI Conference on Web and Social Media 2015


Recently Published, In Press, or Accepted
◈ Zhi Hong, J. Gregory Pauloski, Aswathy Ajith, Eamon Duede, Kyle Chard & Ian Foster. "The Diminishing Returns of Masked Language Models to Science" Findings of the ACL 2023

Selected Prior Publications
▨ Yadu N Babuji, Kyle Chard, Eamon Duede, & Ian Foster. "Safe Collections and Stewardship on Cloud Kotta" IEEE eScience 2017
▨ Yadu N Babuji, Kyle Chard, & Eamon Duede. "Enabling Interactive Analytics of Secure Data using Cloud Kotta" Proceedings of the 8th Workshop on Scientific Cloud Computing
▨ Yadu N Babuji, Kyle Chard, Aaron Gerow, & Eamon Duede. "Cloud Kotta: Enabling Secure and Scalable Data Analytics in the Cloud" IEEE BigData 2016
▨ Yadu N Babuji, Kyle Chard, Aaron Gerow, & Eamon Duede. "A Secure Data Enclave and Analytics Platform for Social Scientists" IEEE eScience 2016

Recent Research Grants and Awards

AI-enabled Molecular Engineering of Materials and Systems (AIMEMS) for Sustainability
Molecular engineering is a dynamic and evolving field that applies molecular-level science in experimental, theoretical, and computational approaches to engineer advanced materials, processes, devices, and systems. Starting from molecular level concepts and principles offers exciting opportunities for targeted design and exquisite tuning of material/system properties for specific applications. Focusing on use-inspired research driven by sustainability will lead to substantial societal benefits. Artificial Intelligence (AI) is a powerful tool that is rapidly transforming almost every aspect of society. By integrating AI with molecular engineering, this research traineeship will open new horizons for engineering complex, multifunctional materials, processes, and systems within a modern framework of sustainability, and will dramatically accelerate scientific discovery and technological innovation. This National Science Foundation Research Traineeship (NRT) award will train a new generation of graduate student leaders at the frontiers of knowledge in AI-enabled molecular engineering of materials and systems for sustainability. The program funds twenty (20) students and intentionally integrates transferrable professional skills with interdisciplinary technical skills ranging from materials science to computer science and social science into the curricular design and thesis research projects. Through a strategic partnership with Argonne National Laboratory and with industrial collaborators, the program will further leverage world-class expertise and unique facilities to train students toward a range of research and research-related career pathways, both within and outside academia.

Personnel: Eamon Duede (UChicago).
Funding provided by: National Science Foundation

Latent Knowledge Capture on Covid-19 Literature
As the published (and preprint) literature on Covid-19 explodes, no individual researcher, team, or institution can cognitively process and understand what we now collectively know about this disease. Many connections and relationships between and across properties of the virus, its interaction with hosts, its transmission within populations, as well as health and economic outcomes are missed precisely because the expertise needed to make these connections spans otherwise disparate and cognitively disjoint disciplines (microbiology, virology, epidemiology, medicine, and public policy). As a result, we may know more about this disease than we are collectively aware. As the number of these “unknown knowns” piles up, we are missing opportunities for breakthroughs. This suggests the need for computational approaches to reveal the correlations latent in our collective understanding. However, because that understanding is codified as text (as opposed to, say, structured data), it lacks the kind of statistical tractability necessary for traditional machine learning approaches to modeling. Here I propose to develop novel unsupervised word embedding models to capture and extract knowledge about Covid-19 that is otherwise latent in and across the blooming literature.

Personnel: Eamon Duede (UChicago).
Funding provided by: Google

When Technology Transforms Society: Considering the Societal and Ethical Impacts of Quantum Computing and AI Quantum computing and artificial intelligence are currently making significant technical progress, with commensurate interest from the public, media outlets, funding agencies, and corporate partners. Stakeholders frequently point to the potential of these technologies to “transform society,” but what does this mean, practically? Should we, as researchers, anticipate the social, political, and ethical consequences of our work and steer our research programs accordingly? Can we draw from scholarship in the social sciences and the humanities to inform understanding of the distributional impacts of our programs? This workshop will explore these questions and develop collaborations across disciplines, institutions, and key stakeholders who may be able to help responsibly steer the evolution of these revolutionary technologies in ethical and socially beneficial ways.

Personnel: Daniel Bowring (Fermilab), Chihway Chang (UChicago), Eamon Duede (UChicago), and Brian Nord (Fermilab).
Funding provided by: Center for Data and Computing at the University of Chicago and the Kavli Foundation