nhaliday + phd   94

GOP tax plan would provide major gains for richest 1%, uneven benefits for the middle class, report says - The Washington Post
https://twitter.com/ianbremmer/status/913863513038311426
https://archive.is/PYRx9
Trump tweets: For his voters.
Tax plan: Something else entirely.
https://twitter.com/tcjfs/status/913864779256692737
https://archive.is/5bzQz
This is appallingly stupid if accurate

https://www.nytimes.com/interactive/2017/11/28/upshot/what-the-tax-bill-would-look-like-for-25000-middle-class-families.html
https://www.nytimes.com/interactive/2017/11/30/us/politics/tax-cuts-increases-for-your-income.html

Treasury Removes Paper at Odds With Mnuchin’s Take on Corporate-Tax Cut’s Winners: https://www.wsj.com/articles/treasury-removes-paper-at-odds-with-mnuchins-take-on-corporate-tax-cuts-winners-1506638463

Tax changes for graduate students under the Tax Cuts and Jobs Act: https://bcide.gitlab.io/post/gop-tax-plan/
H.R.1 – 155th Congress (Tax Cuts and Jobs Act) 1 proposes changes to the US Tax Code that threatens to destroy the finances of STEM graduate students nationwide. The offending provision, 1204(a)(3), strikes section 117(d) 2 of the US Tax Code. This means that under the proposal, tuition waivers are considered taxable income.

For graduate students, this means an increase of thousands of dollars in owed federal taxes. Below I show a calculation for my own situation. The short of it is this: My federal taxes increase from ~7.5% of my income to ~31%. I will owe about $6300 more in federal taxes under this legislation. Like many other STEM students, my choices would be limited to taking on significant debt or quitting my program entirely.

The Republican War on College: https://www.theatlantic.com/business/archive/2017/11/republican-college/546308/

Trump's plan to tax colleges will harm higher education — but it's still a good idea: http://www.businessinsider.com/trump-tax-plan-taxing-colleges-is-a-good-idea-2017-11
- James Miller

The Republican Tax Plan Is a Disaster for Families With Children: http://www.motherjones.com/kevin-drum/2017/11/the-republican-tax-plan-is-a-disaster-for-families-with-children/
- Kevin Drum

The gains from cutting corporate tax rates: http://marginalrevolution.com/marginalrevolution/2017/11/corporate-taxes-2.html
I’ve been reading in this area on and off since the 1980s, and I really don’t think these are phony results.

Entrepreneurship and State Taxation: https://www.federalreserve.gov/econres/feds/files/2018003pap.pdf
We find that new firm employment is negatively—and disproportionately—affected by corporate tax rates. We find little evidence of an effect of personal and sales taxes on entrepreneurial outcomes.

https://www.nytimes.com/2017/11/26/us/politics/johnson-amendment-churches-taxes-politics.html
nobody in the comments section seems to have even considered the comparison with universities

The GOP Tax Bills Are Infrastructure Bills Too. Here’s Why.: http://www.governing.com/topics/transportation-infrastructure/gov-republican-tax-bills-impact-infrastructure.html
news  org:rec  trump  current-events  wonkish  policy  taxes  data  analysis  visualization  money  monetary-fiscal  compensation  class  hmm  :/  coalitions  multi  twitter  social  commentary  gnon  unaffiliated  right-wing  backup  class-warfare  redistribution  elite  vampire-squid  crooked  journos-pundits  tactics  strategy  politics  increase-decrease  pro-rata  labor  capital  distribution  corporation  corruption  anomie  counter-revolution  higher-ed  academia  nascent-state  mathtariat  phd  grad-school  org:mag  left-wing  econotariat  marginal-rev  links  study  summary  economics  econometrics  endogenous-exogenous  natural-experiment  longitudinal  regularizer  religion  christianity  org:gov  infrastructure  transportation  cracker-econ  org:lite  org:biz  crosstab  dynamic  let-me-see  cost-benefit  entrepreneurialism  branches  geography  usa  within-group 
september 2017 by nhaliday
The GRE is useful; range restriction is a thing – Gene Expression
As an empirical matter I do think that it is likely many universities will follow the University of Michigan in dropping the GRE as a requirement. There will be some resistance within academia, but there is a lot of reluctance to vocally defend the GRE in public, especially from younger faculty who fear the social and professional repercussions (every time a discussion pops up about the GRE I get a lot of Twitter DMs from people who believe in the utility of the GRE but don’t want to be seen defending it in public because they fear becoming the target of accusations of an -ism). My prediction is that after the GRE is gone people will simply rely on other proxies.
gnxp  scitariat  commentary  trends  academia  grad-school  phd  psychometrics  progression  prediction  hmm  egalitarianism-hierarchy  general-survey 
september 2017 by nhaliday
Career Options for Scientists
Most PhD students in the biological sciences will not go on to become academics. For these individuals, choosing the best career path can be difficult. Fortunately, there are many options that allow them to take advantage of skills they hone during graduate and postdoctoral work.

The declining interest in an academic career: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184130
study  essay  advice  career  planning  long-term  higher-ed  academia  science  uncertainty  regularizer  supply-demand  data  visualization  trends  grad-school  phd  🔬  success  arbitrage  progression  multi  longitudinal  values  poll  flux-stasis  time  correlation 
september 2017 by nhaliday
Taulbee Survey - CRA
- about 30% academic, 10% tenure-track for both ML and theory
- for industry flow, it's about 60% research for ML and 40% research for theory (presumably research in something that's not theory for the most part)
- so overall 60-70% w/ some kind of research career
grad-school  phd  data  planning  long-term  cs  schools  🎓  objektbuch  poll  transitions  progression 
february 2017 by nhaliday
Information Processing: Boom, Bust, and the Global Race for Scientific Talent
Falling Behind? is a recent (March 2014) book by Michael Teitelbaum of the Sloan Foundation, a demographer and long time critic of STEM (Science, Technology, Engineering and Mathematics) shortage claims. Falling Behind? is an excellent book with a wealth of data and information on the history of booms and busts in science and engineering employment since World War II, STEM shortage claims in general, and lobbying for “high-skilled” immigration “reform”. Although I have been a student of these issues for many years, I encountered many facts and insights that I did not know or had not thought of. Nonetheless the book has a number of weakenesses which readers should keep in mind.

... The evidence assembled in this book leads inescapably to three core findings:

o First, that the alarms about widespread shortages or shortfalls in the number of U.S. scientists and engineers are quite inconsistent with nearly all available evidence;

o Second, that similar claims of the past were politically successful but resulted in a series of booms and busts that did harm to the U.S. science and engineering enterprise and made careers in these fields increasingly unattractive; and

o Third, that the clear signs of malaise in the U.S. science and engineering workforce are structural in origin and cannot be cured simply by providing additional funding. To the contrary, recent efforts of this kind have proved to be destabilizing, and advocates should be careful what they wish for. ...

https://blogs.wsj.com/cio/2016/08/12/is-there-a-stem-crisis-or-a-stem-surplus/
- “In the academic job market, there is no noticeable shortage in any discipline. In fact, there are signs of an oversupply of Ph.D.’s vying for tenure-track faculty positions in many disciplines (e.g., biomedical sciences, physical sciences).”
- “In the government and government-related job sector, certain STEM disciplines have a shortage of positions at the Ph.D. level (e.g., materials science engineering, nuclear engineering) and in general (e.g., systems engineers, cybersecurity, and intelligence professionals) due to the U.S. citizenship requirement. In contrast, an oversupply of biomedical engineers is seen at the Ph.D. level, and there are transient shortages of electrical engineers and mechanical engineers at advanced-degree levels.”
- “In the private sector, software developers, petroleum engineers, data scientists, and those in skilled trades are in high demand; there is an abundant supply of biomedical, chemistry, and physics Ph.D.’s; and transient shortages and surpluses of electrical engineers occur from time to time.”

The STEM Crisis is a Myth: An Ongoing Discussion: http://spectrum.ieee.org/static/the-stem-crisis-is-a-myth-an-ongoing-discussion
https://news.ycombinator.com/item?id=6305671

STEM: Still No Shortage: https://medium.com/i-m-h-o/stem-still-no-shortage-c6f6eed505c1
- Freddie deBoer
https://www.wsj.com/articles/where-college-seniors-are-falling-short-1493118000

Where the STEM Jobs Are (and Where They Aren’t): https://www.nytimes.com/2017/11/01/education/edlife/stem-jobs-industry-careers.html
The number of graduates with technical majors (shown: bachelor, master and Ph.D. degrees awarded in 2015-16) tends to outpace job openings (shown: 2014-24 projections, annualized). Computer science is the exception.
hsu  scitariat  books  review  science  supply-demand  academia  phd  labor  cycles  quotes  malaise  rot  multi  career  planning  data  trends  macro  economics  org:rec  working-stiff  links  tech  sv  grad-school  compensation  long-term  uncertainty  news  org:sci  progression  wonkish  commentary  hn  hmm  org:med  unaffiliated  left-wing  education  higher-ed  regularizer  arbitrage  innovation  visualization  scale  human-capital 
january 2017 by nhaliday
What was the hardest part of doing your Ph.D.? - Quora
I think it’s a 5-way tie, each hard in its own way:
- Picking a good topic.
- Figuring out how to bound it.
- Actually getting started.
- Going on when nothing works as you had planned.
- Knowing when to stop.

A good advisor can make some of these things easier, but you’re the one who has to do them all.
q-n-a  qra  grad-school  phd  planning  scholar  success  advice  prioritizing 
january 2017 by nhaliday
Information Processing: Advice to a new graduate student
first 3 points (tough/connected advisor, big picture, benchmarking) are key:

1. There is often a tradeoff between the advisor from whom you will learn the most vs the one who will help your career the most. Letters of recommendation are the most important factor in obtaining a postdoc/faculty job, and some professors are 10x as influential as others. However, the influential prof might be a jerk and not good at training students. The kind mentor with deep knowledge or the approachable junior faculty member might not be a mover and shaker.

2. Most grad students fail to grasp the big picture in their field and get too caught up in their narrowly defined dissertation project.

3. Benchmark yourself against senior scholars at a similar stage in their (earlier) careers. What should you have accomplished / mastered as a grad student or postdoc in order to keep pace with your benchmark?

4. Take the opportunity to interact with visitors and speakers. Don't assume that because you are a student they'll be uninterested in intellectual exchange with you. Even established scholars are pleased to be asked interesting questions by intelligent grad students. If you get to the stage where the local professors think you are really good, i.e., they sort of think of you as a peer intellect or colleague, you might get invited along to dinner with the speaker!

5. Understand the trends and bandwagons in your field. Most people cannot survive on the job market without chasing trends at least a little bit. But always save some brainpower for thinking about the big questions that most interest you.

6. Work your ass off. If you outwork the other guy by 10%, the compound effect over time could accumulate into a qualitative difference in capability or depth of knowledge.

7. Don't be afraid to seek out professors with questions. Occasionally you will get a gem of an explanation. Most things, even the most conceptually challenging, can be explained in a very clear and concise way after enough thought. A real expert in the field will have accumulated many such explanations, which are priceless.
grad-school  phd  advice  career  hi-order-bits  top-n  hsu  🎓  scholar  strategy  tactics  pre-2013  scitariat  long-term  success  tradeoffs  big-picture  scholar-pack  optimate  discipline  🦉  gtd  prioritizing  transitions  s:***  benchmarks  track-record  s-factor  progression 
november 2016 by nhaliday
Thoughts on graduate school | Secret Blogging Seminar
I’ll organize my thoughts around the following ideas.

- Prioritize reading readable sources
- Build narratives
- Study other mathematician’s taste
- Do one early side project
- Find a clump of other graduate students
- Cast a wide net when looking for an advisor
- Don’t just work on one thing
- Don’t graduate until you have to
reflection  math  grad-school  phd  advice  expert  strategy  long-term  growth  🎓  aphorism  learning  scholar  hi-order-bits  tactics  mathtariat  metabuch  org:bleg  nibble  the-trenches  big-picture  narrative  meta:research  info-foraging  skeleton  studying  prioritizing  s:*  info-dynamics  chart  expert-experience  explore-exploit 
september 2016 by nhaliday
CSRankings: Computer Science Rankings (beta)
some missing venues: ITCS, QCRYPT, QIP, COLT (last has some big impact on the margins)
data  higher-ed  grad-school  phd  cs  tcs  list  schools  🎓  top-n  database  conference  ranking  publishing  fall-2016  network-structure  academia  objective-measure  let-me-see  nibble  reference 
july 2016 by nhaliday
Mental Wilderness | Holden Lee
Holden Lee takes a lot of amazing notes (both live-TeXed and in Workflowy): https://pinboard.in/u:nhaliday/b:ca7feec10ca8

also he wrote that pretty inspirational essay a while back
people  students  princeton  math  tcs  notetaking  homepage  blog  todo  phd  oly  mathtariat  multi 
june 2016 by nhaliday
10 reasons Ph.D. students fail
Once a student has two good publications, if she convinces her committee that she can extrapolate a third, she has a thesis proposal.

Once a student has three publications, she has defended, with reasonable confidence, that she can repeatedly conduct research of sufficient quality to meet the standards of peer review. If she draws a unifying theme, she has a thesis, and if she staples her publications together, she has a dissertation.
advice  grad-school  phd  techtariat  planning  gotchas  scholar  🎓 
may 2016 by nhaliday
For potential Ph.D. students
Ravi Vakil's advice for PhD students

General advice:
Think actively about the creative process. A subtle leap is required from undergraduate thinking to active research (even if you have done undergraduate research). Think explicitly about the process, and talk about it (with me, and with others). For example, in an undergraduate class any Ph.D. student at Stanford will have tried to learn absolutely all the material flawlessly. But in order to know everything needed to tackle an important problem on the frontier of human knowledge, one would have to spend years reading many books and articles. So you'll have to learn differently. But how?

Don't be narrow and concentrate only on your particular problem. Learn things from all over the field, and beyond. The facts, methods, and insights from elsewhere will be much more useful than you might realize, possibly in your thesis, and most definitely afterwards. Being broad is a good way of learning to develop interesting questions.

When you learn the theory, you should try to calculate some toy cases, and think of some explicit basic examples.

Talk to other graduate students. A lot. Organize reading groups. Also talk to post-docs, faculty, visitors, and people you run into on the street. I learn the most from talking with other people. Maybe that's true for you too.

Specific topics:
- seminars
- giving talks
- writing
- links to other advice
advice  reflection  learning  thinking  math  phd  expert  stanford  grad-school  academia  insight  links  strategy  long-term  growth  🎓  scholar  metabuch  org:edu  success  tactics  math.AG  tricki  meta:research  examples  concrete  s:*  info-dynamics  s-factor  prof  org:junk  expert-experience 
may 2016 by nhaliday
Department of Computer Science, Columbia University | Ph.D.
Rocco Servedio, Xi Chen

a bit of an analysis/property-testing flavor

Daniel Hsu does data privacy and machine learning stuff
grad-school  phd  schools  cs  columbia  fall-2016 
may 2016 by nhaliday
Deliberate Grad School | Andrew Critch
- find a flexible program (math, stats, TCS)
- high-impact topic
- teach
- use freedom to visibly accomplish things
- organize seminar
- get exposure to experts
- learn how productive researchers work
- remember you don't have to stay in academia
academia  grad-school  advice  phd  reflection  expert  long-term  🎓  high-variance  aphorism  hi-order-bits  top-n  tactics  strategy  ratty  core-rats  multi  success  flexibility  metameta  s:*  s-factor  clever-rats  expert-experience 
may 2016 by nhaliday
Graduate Program in Computer Science | Harvard John A. Paulson School of Engineering and Applied Sciences
Boaz Barak, Salil Vadhan, Ryan Adams, Madhu Sudan, Jelani Nelson, Michael Mitzenmacher

wow

also Yaron Singer does algorithmic econ stuff
phd  grad-school  cs  schools  fall-2016 
april 2016 by nhaliday
Computer Science Ph.D. Program | Georgia Tech - College of Computing
Richard Peng, Vijay Vazarini, a couple others

maybe: Santosh S. Vempala

lotsa stat mech, markov chains, and spectral graph theory: Eric Vigoda, Dana Randall, Milena Mihail
georgia  grad-school  cs  phd  schools  fall-2016 
april 2016 by nhaliday
Graduate Studies in CSE at UCSD | Computer Science and Engineering
theory: Daniel Kane (frankly seems too intense for my talent level), Russell Impagliazzo (only vaguely related), Shachar Lovett (same)
ml: Yoav Freund, Sanjoy Dasgupta
phd  grad-school  cs  schools  fall-2016 
april 2016 by nhaliday
Graduate Program | Computer Science
Daniel Spielman. seems like a tiny department
phd  grad-school  schools  cs  fall-2016 
april 2016 by nhaliday
Full-Time Ph.D. Program | Computer Science & Engineering
Anup Rao (advised by David Zuckerman), James R. Lee, Shayan Oveis Gharan, Anna R. Karlin (algorithmic econ)

seems to have a lot of a sorta ACM/Theory blend

Sham M. Kakade does some interesting interdisciplinary stuff
phd  grad-school  cs  schools  fall-2016 
april 2016 by nhaliday
Graduate Program | Computer Science Department at Princeton University
Sanjeev Arora and his new research group, Mark Braverman (algorithmic econ+ML-type stuff), Moses Charikar, Elad Hazan (the kind of ML i like), and Zeev Dvir (more of a mathematician)
schools  phd  princeton  grad-school  cs  fall-2016 
april 2016 by nhaliday
TTIC Prospective Students
Yury Makarychev, Madhur Tulsiani (seems to advise UChicago students tho), Srinadh Bhojanapalli

also some more ML-ish faculty that look decent
phd  schools  academia  tcs  machine-learning  grad-school  chicago  cs  fall-2016 
april 2016 by nhaliday
« earlier      
per page:    204080120160

bundles : academeed

related tags

80000-hours  :/  academia  acm  acmtariat  advice  akrasia  algorithms  allodium  ama  analysis  anomie  aphorism  arbitrage  backup  benchmarks  berkeley  best-practices  big-picture  big-surf  blog  books  branches  california  capital  career  chart  cheatsheet  checklists  chicago  christianity  class  class-warfare  clever-rats  cmu  coalitions  columbia  comics  commentary  compensation  concrete  conference  core-rats  corporation  correlation  corruption  cost-benefit  counter-revolution  courage  cracker-econ  critique  crooked  crosstab  cs  curiosity  current-events  cycles  data  data-science  database  discipline  discovery  discussion  distribution  dynamic  econometrics  economics  econotariat  education  effective-altruism  egalitarianism-hierarchy  eh  elite  endogenous-exogenous  entrepreneurialism  essay  examples  expert  expert-experience  explore-exploit  failure  fall-2016  fellowship  finance  flexibility  flux-stasis  focus  forum  frontier  gelman  general-survey  geography  georgia  germanic  gnon  gnxp  gotchas  gowers  grad-school  growth  growth-econ  gtd  guide  habit  hanson  haskell  hi-order-bits  high-variance  higher-ed  hmm  hn  homepage  howto  hsu  human-bean  human-capital  idk  impact  increase-decrease  industrial-org  inequality  info-dynamics  info-foraging  infrastructure  init  innovation  insight  interview  jobs  journos-pundits  labor  learning  left-wing  len:long  lens  let-me-see  links  list  long-short-run  long-term  longitudinal  machine-learning  macro  malaise  marginal-rev  math  math.AG  mathtariat  meta:research  meta:science  metabuch  metameta  michael-nielsen  micro  mobility  monetary-fiscal  money  multi  narrative  nascent-state  natural-experiment  network-structure  news  nibble  noahpinion  notetaking  objective-measure  objektbuch  oly  optimate  org:biz  org:bleg  org:edu  org:gov  org:junk  org:lite  org:mag  org:med  org:nat  org:rec  org:sci  overflow  p:whenever  pdf  people  personal-finance  phd  planning  poast  policy  politics  poll  pragmatic  pre-2013  prediction  presentation  princeton  prioritizing  pro-rata  productivity  prof  progression  psychometrics  publishing  q-n-a  qra  quotes  ranking  rant  ratty  recruiting  reddit  redistribution  reference  reflection  regularizer  religion  research  retention  review  rhetoric  rhythm  right-wing  rot  s-factor  s:*  s:**  s:***  scale  scholar  scholar-pack  schools  science  scitariat  skeleton  slides  social  social-science  soft-question  speaking  ssc  stamina  stanford  startups  stats  status  strategy  stream  stress  students  study  studying  stylized-facts  success  summary  supply-demand  sv  systems  tactics  talks  taxes  tcs  tcstariat  teaching  tech  techtariat  texas  the-trenches  thinking  time  time-use  todo  top-n  track-record  trade  tradeoffs  transitions  transportation  trends  tricki  trump  tutorial  twitter  unaffiliated  uncertainty  unit  usa  values  vampire-squid  visualization  vitality  winner-take-all  wire-guided  wisdom  within-group  wonkish  working-stiff  yvain  zero-positive-sum  🎓  🔬  🦉 

Copy this bookmark:



description:


tags: