Rate My Professor Hope Chidziwisano

Hope Chidziwisano is an Assistant Professor in Communications at the University of Tennessee, Knoxville, specifically in the School of Information Sciences. His research centers on human-computer interaction, ubiquitous computing, artificial intelligence, and Information and Communication Technologies for Development (ICTD). He employs human-centered design approaches and his computer science expertise to create, deploy, and evaluate technologies addressing challenges in resource-constrained communities, such as unreliable electricity and limited social services impacting household-level socio-economic development.

Born and raised in Malawi, Chidziwisano earned a BSc in Computer Science and Physics from the University of Malawi, an MA in Information and Media, and a PhD in Information and Media from Michigan State University. Prior to his appointment at UT Knoxville, he was a postdoctoral fellow in the Human-Computer Interaction Institute at Carnegie Mellon University. He also served as a fellow in the Data Science for Social Good program affiliated with the University of Washington’s eScience Institute, utilizing machine learning, natural language processing, and deep learning to identify disinformation in online news articles. Additionally, he participated in the Global Innovation Exchange program jointly run by the University of Washington and Tsinghua University, where he developed novel sensing techniques and user-friendly interfaces through human-centered design methods. Chidziwisano's research has earned recognition from Google Research and the Association for Computing Machinery (ACM). His projects include field deployments of feature phones integrated with motion sensors in Kenyan and Malawian households for home security; users repurposed these for monitoring chicken deaths, spousal movements, daughters' nighttime activities, and employee hours, revealing patriarchal norms and privacy risks. In response, he consulted women to design mitigations like smart cameras restricted to home exteriors or sensors on dog collars for livestock protection. Another effort collects images and audio of chickens to train AI models detecting diseases via symptoms such as droppings and vocalizations, aiding poultry farmers, extension services, and zoonotic disease prevention. He plans to adapt these technologies for refugees in East Tennessee and scale studies across the University of Tennessee System while continuing work in sub-Saharan Africa to foster cross-contextual insights.