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About Monte314


  • MBTI
  • Astrology Sign


  • Biography
    Jedi Math Dog!
  • Location
    Melbourne, Florida
  • Occupation
    Chief Scientist, Professor
  • Interests
    Math, Astronomy
  • Gender
  • Personal Text
    Chief Scientist; Adjunct Full Professor of Computer Science; six times Who's Who in the World; national, state Advisory Panels; author of two books, many papers; Jedi Math Dog

Recent Profile Visitors

165,160 profile views
  1. What do you use to clean cracker grease?
  2. Yes, it is wrong. In English, the colon, ":", is used to indicate that what follows is a further specification or explanation of what was just said. It is often followed by a list, or example.
  3. Your safe bet is probably to take the Intermediate course next. In Fourier Analysis, measuring "angles" requires Integral Calculus.
  4. SirJac has provided a complete solution. Anyone else?
  5. Conventional Fourier Analysis *IS* Linear Algebra in a particular infinite-dimensional function space... jndiii and I do not agree about the differences between Mathematics and Physics. And... that's OK.
  6. You woudn't be the first...
  7. ...... Huh? What are you talking about... *goes back to sleep*
  8. *eyes shift rapidly back and forth*
  9. I have gobbled up lots of other dogs...
  10. True... but...
  11. Thanks for the useful info, vertebrate! I hope I don't need it.
  12. SirJac got it... still, it is any easy gap to fill...
  13. jndiii has explained the essential element of the result, but there is a tiny technical detail he did not address... ...anyone want to take a stab at it?
  14. True... I would like to build a top-notch data science group, and I could do that in, say, three years... that would be a worthwhile goal.
  15. As usual, thod brings it home. I don't need the money. But the meeting I had today in their facility went tremendously well, as I knew it would. It was 6 seniors and me; most of the time was me speaking extemporaneously about the business and technology of data science, and fielding answers to questions with detailed and specific answers... some with significant technical depth: NP-complete problems, optimal automatic feature selection, hierarchical machine learning architectures, automatic problem segmentation, Spiral Development methodologies, selecting objective functions, regression techniques for anomaly detection, information binding across data, information-friendly feature winnowing in Banach Spaces... stuff that I know inside out, most of which I have published work in. I also described an organizational plan I developed for jump-starting their data science technology group. I guess I am hoping right now that I will get a low-ball offer (<$200k), so I will have a decent excuse for just forgetting this whole thing. This 4-day stint in the job market has been vexing.