The paper reflects on the practices of an interdisciplinary team consisting of researchers and activists from the field of computer science and social sciences involved in developing a user-facing, browser plug-in to detect and moderate instances of online gender based-violence, hate speech and harassment in Hindi, Indian English, and Tamil. The plug-in is based on machine learning (ML) approaches to detect and moderate online content while also offering other non-ML features that can help mitigate the experience of gender-based online violence and hate speech. There have been multiple calls within the field of Human-Computer Interaction (HCI) to include qualitative methods in one’s research design. These calls, while attuned to the importance of qualitative methods for HCI, ignore the intercurrent nature of different research methods, disciplines, and practices. The paper draws from the team discussions that were focussed on defining the nature and extent of online gendered violence, use-cases for the tool, differences and scepticism expressed in using ML approaches to reflect on how each disciplinary approach evokes a temporality within which the problem is framed. The paper borrows the concept of intercurrence from Orren & Skowronek (1996) and reorients it to explicate the practice of interdisciplinary research. It argues that to mark the intercurrence i.e., the multiple temporalities that inform different researchers and methods, is to mark the singularity of interdisciplinary practices wherein to be interdisciplinary is not a question of integration, blending, transcending—metaphors that are often invoked to introduce a research method from one discipline to another. These existing metaphors do not capture the contingencies and incongruencies that inform interdisciplinary practices wherein it is not just the epistemological concerns that are at stake but different temporalities that situate research methods and researchers.