comments from steve

2 parts:

follow templates.

CV:

  1. remove the journal from preperint
  2. commentray is a separte topic
  3. No cost extension
  4. removed expired
  5. Pending and planned
  6. invited speaking: National and international section and local in one section

7. remove senior collaborators

SOA:

  1. Use large amounts of data to predict when people
  2. Take this statement
  3. 2 pages say what you have done.. algorithms 3 topics
  4. what the challenges were, what are the follow up work shows.

5. how you anticipate funding

nugget | clustring variations | sparse data |

diagram collapse to something simpler. 2 pages page 3 for projected future work

next 3 to 5 years....

Teaching 1 page. coursework. each class. one sentence. Minimum expected amount.

Teaching you have done for UG graduate and postdoctoral students

(incubator for future scientists)

Five exemplary: 1. what paper is about, what is does. Role of author

CHip meeting (march 13)

comments

SOA:

  • very nicely layout platforms developed
  • used to predictive processes
  • biomedical and sociolofgical applications
  • welldone

But:

  • some aspect of why these pltforms why these platforms are superior? What are they?
  • Not that many people will read this statement will apreciate
  • try to explain what are teh advances in methodology that you have made
  • makes it possible to use these things

ZCoR: messy data, that could? Qnet: rare events from high lvel complex datasets

  • Try to say more so overview WHAT THEY ALLOW YOU TO DO
  • ==> prefatory page is good/ excellant ==> messy data into the prefatory... you have done a little bit. But do more.

==> Focus on individual papers too much.. instead of working through individual papers, give a narrative. Spend a third to half of a page explaininbg how ZCoR works and how it works. Shopuld not feel like magic.

  • prefatory: focus on philosophy and waht you see what are the big problems as you see it.
  • Key contribution was a bit of redundant
  • Age of AI can probably be shortened
  • WHAT YOUR BIG QUESTIONS ARE

give why how

  • Peer-reviewed:

    • narrative form is better.
    • should the paper pargraphs be there? Right now a bit too long and too detailed
    • I want to know what zcoir is, what is the the philosophy --- 4 : past data, geographical data sets. Tell me why and how. --- 5 : stochastic data generotors... philosophy of fractal net.

  • Length is fine.

  • Add philosophy in
  • 3 pages / 2 pages
  • How much money do you need... slip in that in the statement. We are fully supported, we wish to expand.

letter writers

  • If you bias their list too heavily, then it woudl appear you are hand-picking your reviewers
  • resources

education

  • make sure they get reviews of classes.
  • off a year from the norm.
  • have your department explain that.
  • WHY NOT TEACHING FROM YEAR 1

Timeline

Late august early september

Meeting with David Meltzer April 2 2023 2100 hrs

  • all good
  • one area
    • no question ML is important, AI is important
    • doing important things that havent been done fore
    • but
      • what is it you are doing that people were not doing here
      • what is it that is different
        • two algoritms thtat you developoed
        • can yiu write a few sentences (two sentences for both)
        • describe other methods work less better
        • why it always do better?

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