The five research networking groups which are meeting around the topics of Finite Earth, Computational Social Sciences, and Quantitative Biology are described below. Each group is spearheaded by two faculty leaders; a full list of faculty leaders can be found here.
Planet Earth is our only home. While Earth may have once seemed like an infinite paradise, humanity is increasingly facing the consequences of Earth’s finite dimensions and resources. The Finite Earth group mingles diverse intelligence at the intersection of active finite-earth research and expertise in data science, in an informal setting. Topics of stimulating discussion include sustainability, global climate change, water and food security, energy storage, non-equilibrium systems, environmental impact of society’s metals, sensor networks, cross-disciplinary data analyses, conservation, power grids, networks, geo-engineering, economics, and more. The scholars in the Finite Earth network emerging from this luncheon advise, enlighten, support, and stimulate each other, form new collaborations, write proposals together, and serve on each other’s faculty search committees.
The Finite Earth group includes 24 faculty members from McCormick, SESP, and Weinberg and is co-led by Suzan Van der Lee from Earth and Planetary Sciences in Weinberg and Eric Masanet from Mechanical Engineering and Chemical and Biological Engineering in McCormick.
Computational Social Sciences (Group One)
This interdisciplinary group is examining how computational social sciences impacts crime, law, and conflict. As technology is increasingly utilized to aid human decision-making, substantial opportunities exist to improve the efficiency and precision of decisions we make as individuals and as a society. However, new reliance on computational systems also entails novel challenges, such as ensuring that the systems are accurate, just, and fair, so there is much work to be done. Additionally, the group has explored tactics for resolving the structural barriers to interdisciplinary work and training in academia.
This group includes 10 faculty members from McCormick, Feinberg, Weinberg, Kellogg, SESP, and Pritzker and is co-led by Michelle Birkett from Medical Social Sciences in Feinberg and Doug Downey from Electrical Engineering and Computer Science in McCormick.
Computational Social Sciences (Group Two)
The second Computational Social Science networking group brings together researchers from a diverse set of disciplines and departments, engaged in rigorous empirical research utilizing cutting-edge computational techniques with application to social science questions. Group members share work in progress and solicit feedback via short presentations and discussion. Presentations emphasize the computational and machine learning methods used in these ongoing research programs, with approaches varying from network analysis to natural language processing to Bayesian models. Participants apply these methods to topics as varied as how to design effective matchmaking algorithms and apps, how politicians assess constituent beliefs, how consumers engage with news programming, how to assess effects of environmental marketing on brand perceptions via text analysis, and how to generate accurate early detection methods for psychosis risk.
This group includes 20 faculty members from Weinberg, Kellogg, Feinberg, SESP, School of Communication, and Medill and is co-led by Sarah Bouchat from Political Science in Weinberg and Brian Uzzi from Management and Organizations in Kellogg.
Quantitative Biology (Groups One and Two)
The Quantitative Biology-focused faculty are organized into two groups with a total of 33 faculty members from Feinberg, Weinberg, and McCormick. One group is co-led by Rosemary Braun from Preventive Medicine in Feinberg and Julius Lucks from Chemical and Biological Engineering in McCormick, and the other group is co-led by Deborah Winter from Medicine in Feinberg and Richard Carthew from Molecular Biosciences in Weinberg.
Presentation topics have included transforming ‘omics into dynamics; connecting microscopic behavior to bulk phenotypic properties in living systems; engineering protein superstructures; understanding short functional motifs in rapidly evolving intrinsically disordered proteins; coupling computer to microscope to provide new views on old questions; mapping the gene regulatory networks of macrophages; emergent metabolic dynamics in bacterial communities; and using neuroimaging to predict Alzheimer’s.