Office: SEEC S238C
Wednesday's from 1 - 3pm
Students can sign-up for office hours by emailing Steve at email@example.com.
*Office hours will be virtual
- Ph.D. 2015 Environmental Science and Management – University of California, Santa Barbara
- B.S. 2004 Computer Science – Stanford University
Research Interests & Biography:
Steve Miller combines tools from computer science and economics to study
1) the effective management of shared natural resources (e.g. fisheries)
2) how environmental policies stimulate the development and deployment of new technologies and processes that reduce environmental impact, and
3) the impacts of changing temperatures on natural resource use and economies.
Methodologically, he is primarily interested in the ways that machine learning and applied statistics can be combined to answer causal questions of interest that fall within the environmental realm. Trying to answer these questions naturally involves collaboration with plenty of smart people from a range of disciplines. Before moving to CU Boulder, Steve was an Assistant Professor in the University of Minnesota’s Applied Economics department, and also spent five years as a Product Manager at Google, helping launch the ocean features within Google Earth.
A Note to Prospective Graduate Students
I am currently recruiting both MS and PhD students to work with me (and others in ENVS!) on research that integrates environmental economics, computer science, and a range of natural sciences. Students from all backgrounds are encouraged to apply. Please see my personal website for up-to-date information on current opportunities.
A Note to Prospective ENVS Honors Undergraduate Students
I’d enjoy the chance to work with students who share an interest in one of two areas: 1) How do changing temperatures impact natural resources and other aspects of our economy? 2) How effective are environmental policies in achieving their intended goals? Students working with me would likely use statistical tools (econometrics, machine learning) to analyze existing datasets, though I would also be happy to help students develop and apply mathematical models.