Vhuhwavho (VK) Todani
Vhuhwavho Todani, who goes by VK, is a Research Associate in data analysis at the American Institute for Scientific and Social Research, working across statistical modelling, computational data analysis, and the design of the systems research data is held in.
He holds a Bachelor of Science in Information Science from the University of Maryland, College Park, and an Associate of Science in Computer Science from Montgomery College. His training runs from information science and computational method through statistics to human-centred research — which is to say he can both put a quantitative question and build the thing that answers it.
In a study of screen time and mental health he worked from publicly available datasets, cleaning and transforming them in Python and R, fitting linear regression models, testing the hypotheses, and drawing the figures the findings had to be read off. The question underneath it — what sustained use of digital technology does to psychological well-being — is one this Institute keeps meeting from the community side rather than the dataset side.
He also contributed to a student-athlete performance and wellness dashboard built for the University of Maryland Athletics Department, on the UI/UX research and design team: user-centred research, stakeholder needs assessment, and interface prototyping, alongside teams working on data governance, visualisation, and competitive analysis. The work was to turn what a department said it needed into a prototype that could actually monitor performance and assess wellbeing — the translation that decides whether an information system gets used or ignored.
On the engineering side he has designed and built a full-stack web application in JavaScript, Node.js, Express.js, and Supabase, with relational storage behind a RESTful backend. A separate database design project over Washington State's electric vehicle population data took him through normalised schemas, entity-relationship diagrams, SQL queries, and analytical views — the unglamorous half of quantitative work, and the half that decides whether a dataset can be asked anything at all.
He works in Python, R, SQL, JavaScript, and C++, with Pandas, NumPy, Scikit-learn, Matplotlib, MySQL, and Git.
At AISSR he is a Research Associate in data analysis, bringing that quantitative and computational work into the Institute's interdisciplinary research. His interests are computational social science, behavioural data analysis, digital well-being, and the application of data-driven method to contemporary social problems — empirical work meant to be solid enough that a decision can be made on it, rather than only read.