Hi, I'm Andy Ganse. I'm an applied physicist and data scientist in the Seattle area, currently working as the principal scientist and founder of Anseres Research & Technology LLC, which focuses on federal and state funded scientific R&D projects. I was a senior research physicist doing computational geophysics for 16 years at UW's Applied Physics Laboratory (APL-UW), and recently worked as a data scientist in machine learning applications in Seattle's tech startup community. Whether in the commercial world or earlier in the academic science world, the focus of my work has been in inverse problems, machine learning, optimization, signal processing, and data analysis. (My PhD concerned inverse problems in ocean acoustics, and at APL-UW my work was in statistical inference and remote sensing via acoustics, electromagnetics, and gravity). Here on research.ganse.org are my publicly shareable research results and tools, both in data science topics and in applied physics topics. If you've seen my UW research website in past you'll recognize many of those pages in here too. You can contact me at firstname.lastname@example.org.
JUPYTER/PYTHON ON A RASPBERRY PI ZERO WITH SENSOR ATTACHMENT
The tiny version of the Raspberry Pi computer, the Pi Zero, is barely larger than a stick of gum yet is a full Linux-based computer. It's a bit less powerful than the larger Pi 2 & 3 models, but it uses much less power than they do allowing realistic battery implementation, while still being able to use the same "Hat" add-ons. So it's a great field sensor platform for some applications. Here I experiment with a simple canned sensor Hat made by Pimoroni, the "Enviro pHat", and running a Jupyter server on the Pi Zero to analyze/view the sensor data in a web-based Python notebook.
InvGN: GAUSS-NEWTON NONLINEAR INVERSION CODE
Calculate Tikhonov-regularized, Gauss-Newton nonlinear iterated inversion to solve the damped nonlinear least squares problem, using the InvGN toolkit for Matlab/Octave. While Matlab's optimization toolbox contains
lsqnonlin, it does not inherently include the Tikhonov regularization and is only the optimization component, whereas inversion also requires uncertainty quantification. My InvGN package is for weakly nonlinear, Tikhonov-regularized, inverse problems (so includes solution uncertainties and resolution quantification), handles both frequentist and Bayesian frameworks, and also works in Octave.
ELECTROMAGNETIC INVERSION OF ESTUARINE SALINITY STRUCTURE USING THE SIGMA PROFILER INSTRUMENT
The Conductivity Profiler is an instrument for remotely observing estuarine salinity profiles via electromagnetic measurements. Electromagnetic (EM) waves are attenuated in seawater as a function of frequency, and conductivity structure (closely related to salinity structure) in the water can be inferred by combining measurements of EM waves at different frequencies on a distant electric field receiver. Geophysical inversion methods are applied to estimate the estuarine salinity profile from the EM measurements. Using inverse theory techniques, we take advantage of statistical rigor and let the data determine the structure of the conductivity profile and quantify the uncertainty and resolution of the salinity profile.
MULTI-PHASE LINEAR REGRESSION
Code to fit multiple co-joined straight lines to a set of data points. Note the popular name (above, or sometimes "segmented linear regression") for this topic may be a bit misleading, as this is in fact a nonlinear regression problem due to solving for the intersection points of the co-joined lines.
RADIO SCIENCE GRAVITY INVERSION FOR ICY MOON INTERNAL STRUCTURE
2O/rock interface (seamounts).
LONG-RANGE OCEAN ACOUSTIC SCATTERING
NPAL Ocean Acoustics page.