Simons Collaborations in Mathematics and the Physical Sciences
The Simons Foundation invites letters of intent for ambitious collaborations addressing fundamental questions of major importance in mathematics, theoretical physics, or theoretical computer science. Awards support multi-investigator teams across career stages for four years, with up to three new collaborations expected to begin in January 2028.
AI UPtake: Panel discussion on collaborative research
Artificial intelligence (AI) and machine learning (ML) can facilitate new paradigms and solutions in almost every research field. Collaboration is essential to achieve tangible and concrete progress in impactful and meaningful AI and ML research, due to its transdisciplinary nature. Come and meet University of Pretoria (UP) academics that are embracing and exploring the opportunities that AI and ML offer to transcend the conventional boundaries of their disciplines. Join the discussion to debate this new frontier of opportunities and challenges that may enable you to look beyond the obvious, and discover new directions and opportunities that we may offer for tomorrow — together!
A Manifesto for Big Team Science
Progress in psychology has been frustrated by challenges concerning replicability, generalizability, strategy selection, inferential reproducibility, and computational reproducibility. Although often discussed separately, I argue that these five challenges share a common cause: insufficient investment of resources into the typical psychology study. I further suggest that big team science can help address these challenges by allowing researchers to pool their resources to efficiently and drastically increase the amount of resources available for a single study. However, the current incentives, infrastructure, and institutions in academic science have all developed under the assumption that science is conducted by solo Principal Investigators and their dependent trainees. These barriers must be overcome if big team science is to be sustainable. Big team science likely also carries unique risks, such as the potential for big team science institutions to monopolize power, become overly conservative, make mistakes at a grand scale, or fail entirely due to mismanagement and a lack of financial sustainability. I illustrate the promise, barriers, and risks of big team science with the experiences of the Psychological Science Accelerator, a global research network of over 1400 members from 70+ countries.