This page outlines the skills I think one needs to be productive in my lab. Whichever parts are most relevant to your project, mastery comes from directed learning and focused persistent practice.

My Overall Philosphy.

Science educates humanity1. The practice of science is intellectually fulfilling and gives sempeternal benefits for humanity. The business of science is controlled by scientifically illiterate or indifferent administrative nabobs who commoditize ideas as they did the wool from a spinning jenny. Scientists forsook their independence long ago by relying on others to foot the bill2. Science as a career is a chimera, part avocation and part courtesanship3. To succeed in modern science one needs luck, grit, realpolitik, talent, and intellectual honesty, in that order.

I strive to create a lab where people join because they are interested and inspired in joining me on a mutual voyage of discovery. I expect those working with me not to misrepresent their talents or interests to get a spot nor to phone anything in4. This expectation comes because I think interesting things happen when smart creative people are given the license to be freely productive and partly because I don’t have the bandwidth or inclination to micromanage.

Update for 2024 The number of retracted articles and incidence of scientific fraud have dramatically increased since COVID. I am still trying to understand if this increased reflects an increase in fraud or an increase in attention to the same amount of fraud. Either way, we should reflect why the modern business of science, predominantly the areas of biomedical research and psychology, incentivizes this behavior.

Everyone Should Know or Have

  1. Facility with Python, Prolog, LiSP, Julia, or Haskell. In at least one of these languages you must be able to load a data file, do some analysis, and write the results to a graph or table.
  2. Markdown for describing issues on GitHub and drafting manuscripts.
  3. How to write a scientific paper [for MS and above].
  4. How to write a 250-word abstract [for college students and above].
  5. How to write a specific aims page. The Specific Aims page is the crystallization of your proposal for grant applications, similar to an executive summary. All grant applications hinge on the Specific Aims page.
  6. GitHub to organize and share code.
  7. The content of missing semester. This MIT course teaches you the day-to-day tools you need to do computational science, for example how to move and rename masses of files, quickly edit files with vim, or make a build system.
  8. LaTeX. LaTeX makes beautiful preprints and technical papers. I use Beamer for almost all my presentations. I write as many of my grants in LaTeX as possible. IEEE conferences accept TeX submissions. Biomedical journals sometimes do.

For Computational Linguists

My focus is on representing biomedical knowledge in a computable format and on developing tools that use those representation to contextualize (and especially assess the plausibility) of knowledge expressed in unstructured text.

Knowledge Representation

  1. Basic Formal Ontology. BFO is a framework for creating first order logic predicates that, when taken together, express a consistent picture about a “portion of reality”. It’s a commonly used standard.
  2. Markov Logic Networks. MLNs express uncertainty and are thus more expressive than BFO. It is an area of ongoing interest to join a BFO-compliant ontology and MLN schema.
  3. SpaCy and Prodigy. They are our NLP backbone.

For Mathematicians

Spatial Statistics

Agent-Based Modeling

Footnotes

  1. The original meaning of the word educates is to lead out of of darkness. Perhaps lead out of Plato’s cave. ↩

  2. Science relying on commerical success, possibly with a government intermediary, is an issue in economies where capitalists horde the money needed to accomplish things. A division between capital and labor isn’t the only way to do things. It is how the US does it. To do science in the US, we must acknowledge the rules of the game. Scientists make the administrative problem worse by forgetting the rules of the game, setting themselves up to be pawns. The book Arrowsmith nicely illustrated this. ↩

  3. The Simpsons said it best. Seymour Skinner: “Every good scientist is half B. F. Skinner and half P. T. Barnum.” Bart the Genius. ↩

  4. Phoning in is 90’s argot that means to do a poor job because you didn’t really try. Perhaps in the era of the smart phone everyone is phoning in. ↩