Elisa Ngan is a transdisciplinary experience designer, operations researcher, and visual poet. Her research and practice probes the past, present, and future of data as feedback loop between humans, machines, and nature. She creates these connected environments with an openness to technological advances, a dedication to critical thinking, and a belief that a commitment to craft and collaboration is an ethical commitment to better collective futures.

At the University of Michigan, she designs curriculum to teach a new generation of technologists how to think ethically and data-first to architect and design connected environments. At Harvard, she invents collaboration methods that combine the cultural philosophy of Social Anthropology with the scientific precision of Applied Physics for proethical design. At Amodal LLC, she consults for organizations needing to bootstrap the conceptual and logical layer of their data architecture for graceful progressive scaling.

Elisa has been in the trenches scaling platforms and processes for multiple category-defining organizations. She knows the common traps and the strategic mindset needed to navigate and grow a powerful product. Prior to Design R&D, Elisa has shipped data platforms fit for both Fortune500 companies and SMBs in industries such as consumer packaged goods, automotive, industrial IoT, manufacturing, gaming, pharmaceuticals, and robotics. As a technical contributor, she is an expert in designing data systems for experiences leveraging highly heterogenous real-time data across both hardware and software sources. As a designer, she works from concept through specifications creating product lines and enterprise-ready features for highly configurable Turing-complete white-labeled platforms.

Elisa believes that work is the site of systemic change. Sitting at the tense but creative intersection between the native and the naive, her installations, publications, projects, products, and pedagogies are traversal vignettes bridging the emergent contingencies between truth, goodness, and beauty in contemporary data cultures.