Computer systems and machine learning research is often driven by empirical results; improving efficiency and pushing the boundaries of the state of the art are essential goals that are continually furthered by the vetting and discussion of published academic work. However, we observe and experience that reflection, intermediate findings, and negative results are often quietly shelved in this process, despite the educational, scientific, and personal value in airing such experiences. Given the lack of emphasis on negative results, important lessons learned and reflections are neither captured nor maintained by our research communities, further exacerbating the problem.

To this end, we aim to establish a workshop venue centered on reflective and in-depth conversations on the meandering path towards research publications, the path that science is inherently all about: iterating over failures to arrive at a more robust understanding of the world.

JOURNE will combine invited talks from prominent ML and Systems researchers on the evolution of and reflection on research trends with specific contributed examples of negative results, retrospectives, and project post-mortems in the MLSys community. We will complement this programming with opportunities for candid discussion and constructive brainstorming about how and when these reflections, intermediate findings, missteps, and negative results are useful for the research community and how they can be supported and brought to light. Our goal is to bring the fundamental principles of scientific research back to the forefront.