I've been working since 1992 at Lawrence Berkeley National Laboratory (which is not the same as Lawrence Livermore National Laboratory). Most of my current work is related to commercial building energy efficiency. In the past I worked on indoor radon (a naturally occurring radioactive gas) and on indoor airflow (with applications to protecting buildings from chemical or biological attack). I've also done some work on statistical methods and data analysis methods, for analyzing data with various unusual features. I was elected Fellow of the American Physical Society in 2003.
Commercial Building Energy Efficiency
Starting in 2008, I've been working on several projects related to commercial building energy efficiency. For instance, I'm working on recognizing when a building is using more energy than it should (often, buildings operate with faults in their mechanical systems that go undetected for years).
Since 1999, my group, the airflow and pollutant transport group has been working on predicting airflow within, into, and out of, buildings. This work suddenly became more relevant, or at least more urgent, due to anthrax attacks and terrorism in the U.S. in late 2001. Some of my group's advice related to these issues can be found at the "secure buildings" website.
Radon is a naturally occurring radioactive gas that can reach dangerous concentrations indoors. Although much over-hyped by the EPA, it really is a danger to a small fraction of people in the U.S. My first project involved several aspects of radon prediction and mapping, such as determining how effectively various types of geologic information can be used to predict radon concentrations. If you want, you can see whether I think you should check your house for radon: my friend Andrew Gelman and I put together a web site to make a recommendation based on information about your house and your risk tolerance.
The radon research got me involved in various other issues, leading to publications on issues such as: how to incorporate uncertainties when performing cost-benefit analyses; misleading characteristics inherent in maps of parameter estimates; and including spatial information in statistical models.
Computed tomography of gases in air
If you shine a beam of infrared light through the air, pollutants in the air will absorb some of it. Different gases absorb different wavelengths of light. The amount of absorption at a given wavelength can tell you how much of a given gas (methane, say) is present along the light beam.
If you measure along a bunch of different beams (e.g., a bunch of intersecting beams) then it is sometimes possible to deduce the spatial distribution of the gas that gives rise to the resulting absorption measurements. That is, if you know the total amount of gas along each path, you can sometimes figure out how the gas must be arranged in space. The process of determining a spatial distribution from a bunch of path integrals is called computed tomography. You might be familiar with it from the medical technique called CAT scans, which just stands for Computer Aided Tomography. Anyway, some colleagues and I invented a new method of solving the mathematical problems that arise in computed tomography, that work for the particular type of distributions that come up in gas concentrations in air.