Daniel Rubinoff

Roles & Affiliations: Professor of Entomology at the University of Hawaii, Manoa (since 2002), Director of the University of Hawaii Insect Museum, and Principal Investigator of the Biodiversity and Systematics Laboratory. Serves on the faculty of the Ecology, Evolution, and Conservation Biology Graduate Program, advising 3 PhD and 2 Master’s students.

Education:

• PhD in Environmental Science, Policy, and Management from UC Berkeley (2001)
• BSc in Natural Resources from Cornell University (1992)

Research Interests: Focuses on insect systematics, conservation biology, and evolutionary ecology. Explores phylogenetic relationships using morphological and molecular tools, with applications to invasive species management and endangered species conservation. Specializes in Hawaiian endemic insects, including aquatic case-bearing moths (Hyposmocoma), and their ecological adaptations. Conducts fieldwork on population dynamics, habitat fragmentation, and biogeographic patterns.

Publications Trends: Recent work emphasizes molecular phylogenies of moths and fruit flies, conservation genetics of endangered species, and invasive species ecology. Highlights include studies on mimicry complexes in clear-winged moths and the genetic basis of species delimitation in pest insects.

Awards & Grants:

• Recipient of the 2010 Regent’s Medal for Research Excellence
• Lead PI on NSF-funded projects studying Hawaiian moth biodiversity
• Secured grants from USDA, US Fish & Wildlife Service, and TSTAR for biological control and conservation studies

Advising & Outreach: Trains graduate students in molecular systematics and field ecology. Co-edits the Proceedings of the Hawaiian Entomological Society and serves on editorial boards. Active in public outreach via media (e.g., Scientific American editorials on suburban biodiversity).

Labs & Teams: Leads the Biodiversity and Systematics Lab at UH Manoa, collaborating with institutions like Bishop Museum and UC Berkeley. Research teams focus on integrative taxonomic approaches combining fieldwork, genetics, and computational biology.