Confessions of a researchaholic

There are several aspects of scientific research that excite me: solving a challenging problem, publishing a paper, shipping a product, creating a demo/artwork, presenting a talk, reviewing a submission, serving on a committee, attending a conference, meeting people with shared interests, working with collaborators.

But what excites me the most is discovering new ideas and insights, especially those that are surprising or unexpected.
Looking back at my research career, here are some examples:

• My PhD thesis work on texture synthesis – fixed neighborhoods, over manifold surfaces, from multiple sources (blending and 3D volumes from 2D views), and order-independent/random-accessible synthesis.
• Tile-based texture mapping, in particular the math and algorithms for packing and random access.
• Parallel white noise and blue noise generation, analogous to the order-independent texture synthesis.
• Inverse texture synthesis, basically a reversal of traditional (forward) texture synthesis.
• Nonlinear Beam Tracing on a GPU \cite{Liu:2011:NBT} – it is possible to use rasterization to achieve some ray tracing effects.
• Nonlinear revision control for images – revision control can be applied beyond code and text!
• Differential domain analysis for non-uniform sampling – the trigonometric expansion of the power spectrum depends only on the relative sample positions, and the subsequent synthesis method of point sampling with general noise spectrum.
• Improving light field camera sample design with irregularity and aberration – randomness/noise helps with computational photography.
• Autocomplete painting repetitions, hand-drawn animations, and 3D sculpting – the use of workflows for analysis and prediction.
• Mapping virtual and physical reality and the subsequent work on leveraging temporary blindness to avoid warping at all for redirected walking \cite{Sun:2018:TVR}.

In retrospect, I tend to have more focus, and likely as a consequence, more innovation during the earlier stage of my career, and more breadth (in terms of topics and applications) towards the later stage.
But it is really the unexpected intellectual discovery that keeps me excited, which I missed.

An interactive UI that lets users manually input data and parameters to produce results is more suitable for iterative exploration (if you can wait for a few minutes) than mass production (which take take hours if not days), so I built a scripting environment from the same codebase.
I set up an experiment to run over 20+ inputs with a few values at each of the 3 parameter dimensions, and ended up with more than 4000 results which (dawned on me afterwards) are too many to examine one by one.
So I followed the path of iterating exploration as with the UI but with the already batched results without knowing for sure if I have missed any good ones.

Maybe we also need (semi)automatic tools to help us with cherry picking.