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Image and Signal Processing Group oooo
Image Processing Laboratory (IPL).
Universitat de València, Spain

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First words ex cathedra
Jesús Malo 
San Francisco, Starbucks at 390 Stockton St.
February 2015

Circa 2015, applications for full professorship in spanish universities (cathedra) involved writing an essay to describe your career and views on science. Here is what I wrote to get the condition of Accredited University Professor from the official National Evaluation Agency...
Now (after the positive outcome in july 2015) I upload the version with uncensored pictures, full text, and over 150 hyperlinks!. These are my first words ex-cathedra (even though my salary, as well as the salary of other 2000 colleages in the same situation, will remain the same for a while unless we do something):

  • Why a physicist would ever care about Human Vision?
  • Chronological summary of my career:                                                                                                 While Khun and Marx were (kind of) wrong, Sinatra was right: I did it my way
  • My research contributions:                                                                                                               Colored noise in a number of related disciplines and some thoughts on the h-index
  • My teaching activity:                                                                                                                               Why an optometrist (or engineer) would ever care about Maths (or Science)?
  • Economic constraints of Science in Spain:                                                                                         Why a positive evaluation for professorship does not imply an actual professorship position?

Why a physicist would ever care about Human Vision?
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The leit-motif of my research and teaching activity is the study of visual information processing in the human brain. This is a biological and subjective problem: not very appealing adjectives for a big-bang theory guy. Nevertheless, the aspects of this problem that may be of interest for physicists are the issues that determined the direction of my scientific career.

Despite the overuse of the word multidisciplinary, you have to consider that Visual Perception is a truly multidisciplinary problem. On the one hand, the input signal certainly involves physical issues such as light emission and scattering in every-day scenes (classical Radiometry) and image formation in biological systems (classical Physiological Optics). However, on the other hand, the analysis of such input signal is a problem for
Neuroscience: examples of the latter include the study of (natural) neural networks for image understanding. Human Vision is not at all limited to the laws of image formation, that basically date back to Newton classical Optics, but also include the formulation of laws that determine the organization of the sensors that make sense of these signals. And this is a quite different issue!. Regarding this analysis part, a theory that explains the visual cortex phenomena requires concepts coming from Statistics and Information Theory, or in nowadays jargon, Machine Learning. A particularly interesting feature of this problem is the fact that, as opposed to other science problems, the relation between maths and application (here Maths and Neuroscience) is not one-directional: in this case the system to be understood is actually a computing machine that may also inspire original mathematical approaches. Finally, the models coming from Theoretical Neuroscience may be applied in Electrical Engineering and Computer Science.

From a personal (and hence arguable) point of view, the Human Vision problem is interesting for a physicist not for the aspects related to classical Optics (fundamentally solved long ago), but for the study of the visual brain. Vision is not in the (well known) eye of the beholder, but in his/her (highly unknown) brain. The visual brain is a natural system with complex dynamics (the jargon physicists love), quantitative theories for partial explanations are very recent, and many of them are still under discussion. The study of Vision combines experiments, mathematical theories and technological applications, and this combination is the core of how the physicists approach the problems. It doesn't matter that the experimental methods come from the Psychology, the Optometry or the Neurophysiology (all of them use the so called Psycho-Physics) or that the applications are in Image Processing and Computer Vision: the study of the Human Visual System is certainly quite appropriate for a physicist.

The fascination for the surprising behavior of this system is what determined my scientific exploration: over the last 20 years I made some contributions (or managed to introduce some colored noise ;-) in most of the disciplines cited above.
Motion Aftereffect: an example of the surprising behavior of your visual system is Static Motion Aftereffect (or the perception of reverse motion after prolonged exposure to a slowly moving pattern -see video-).
Physicists like explanations from first principles (the so called laws), and this curiousity can be understood according to communication theory. Sensors that maximize information transmission from image sequences happen to have similar frequency tuning than neurons in V1 cortex. For the same efficiency reason, their response is nonlinear, and attenuates in the presence of high contrast moving patterns. Prolonged exposure to such patterns induces an operation regime that leads to the visual illusion while the system readapts to the new situation. Optimal Information Transfer seems to be a law of Human Vision.
[Find out more...] It took me 20 years to fully understand that apparently simple sentence. 

More on the link between Physics, Neuroscience, and Statistical Learningif you are not already convinced of the relation (since you only listen to authority arguments ;-) I have something for you. The New York University (36 Nobel Laureates, wikipedia dixit ;-) organizes its resources in this way: the Center for Neural Science and the Physics Department are in the very same building (both doors in the picture below lead to the same hall, and physiology and theoretical physics labs are interleaved). Moreover, the Courant Institute of Mathematics, famous for its research in data science is exactly at the other side of the street (Washington Place).

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Chronological summary of my career.     
Khun and Marx were (kind of) wrong, Sinatra was (kind of) right: I did it (kind of) my way

Selecting a multidisciplinary problem implies having wide range of collaborators over the years. The topics and the collaborators to address them are the parts of the scientific career that one can actually choose. Thomas Kuhn (or even Karl Marx) would certainly say that economic constraints sometimes impose their own choices. In my case, even though money sometimes determined the order in which I visited different aspects of the problem (e.g. applications before foundations), economic constraints didn't imply modifications in the selected direction since I was fortunate enough to get steady funds along these two decades (more details on economic constraints below).
Constraints are usually harder in the teaching part since it is determined by the duties of the department where you happen to develop your research. Nevertheless, with some dedication, this part can also be modulated. Similarly to the research side (where I started at the Optics Department in the Physics School, but then I looked for collaborators in Maths, Electrical Engineering and Computer Science), in the teaching side I decided to give lectures in PhD and Master programs out of my department (beyond the department-related duties). This was a way to convey the knowledge acquired in research activities to a broader audience.

Below is the list of multidisciplinary collaborators I found (or looked for) over the time. Note that in the formative years and right after getting my first permanent position, I focused on applications (e.g. image coding) to maximize funding probabilities. More recently, particularly after my second Spanish NSF Project as PI, I turned to the fundamental issues (the theory and the consideration of a higher abstraction level, as for instance in the current Explora Project -my 4th as PI-), yet still paying attention to technology transfer:

  • The Fulbright postdoc I got for the period 2000-2001 allowed me to work with world-class researches in Vision Science, both from the experimental perspective (Beau Watson at the NASA Ames Research Center) and the theoretical perspective (Eero Simoncelli at the Center for Neural Science and Courant Institute of Mathematics at the NYU). I already knew about divisive normalization [Carandini94, Watson97, Simoncelli98], but there I better understood its relation with unsupervised learning [Olshausen96, Schwartz01]. These were the inspiration for a lot of later work.
  • When I came back from the US and I got my permanent position, I started my period as PI in public-funded projects with J. Portilla (CSIC Optics Institute) working on image coding and restoration. Due to my participation in PhD programs of Applied Mathematics and Computer Science, I advised the PhDs of I. Epifanio and J. Gutiérrez, now faculty staff in different universities. Moreover, I looked for collaborators at the Electrical Engineering Department (G. Camps, J. Muñoz and L. Gómez) to set the Image and Signal Processing Group where I lead the activities related to Vision Science and Image Processing. In this period, as a result of my activity as PI of different regional and national projects I contacted with groups from national and international universities (e.g. the Computer Vision Center of UAB -X. Otazu-, the Electrical Eng. Dept. of UAB -J. Serra-, or the Mathematics and Statistics Dept. of Helsinki University with A. Hyvarinen and M. Gutmann).
  • Currently, particularly after my 3rd advised PhD (V. Laparra, currently PostDoc at NYU), and my second stay in the US at Stanford and NYU (now as Senior Visiting Researcher in 2013), I am more interested in Theoretical Visual Neuroscience. As a result, I contacted Prof. L. Martí­nez Otero (CSIC Neuroscience Institute) to be co-PI of a recently funded national project on modeling cortical circuits for neural response inversion. His experimental know-how and wider neuroscience perspective will also be critical in another national project involving fMRI for neuroaesthetics I am PI of. On top of these theoretical activities, following our industrial patent in 2008, I am also conducting technology transfer through two applied projects I am PI of: one funded by the regional goverment to cooperate with Analog Devices Inc., and the other funded by Davalor Salud, a company devoted to the automated evaluation of visual function. Moreover, M. Bertalmio's group at Univ. Pompeu Fabra is helping me to measure multi-stage vision models for digital movies using his ERC funds.
As summary, I find that, beyond the troubles coming from working on a truly multidisciplinary issue, the path has been (kind of) coherent and successful. At this point I have to thank Prof. Jose María Artigas for his lectures on Physics of Vision: a small course for the physics students which (unfortunately!) is no longer available in my university. In those lessons he told us about something completely different. Almost as fresh and educative as the Monty Python for physics students.