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# Math Sex Jokes

I derived your mom last night.

It was f prime.

How is sex like math?

1. Half the time I get an odd result.

2. If my hands aren’t enough, I end up using my head.

3. I always wonder how the person next to me is doing on his work.

4. My average at each is pretty dismal.

Dinner for four..

Good sex interrupted by a period.

A: 7 hours, 57 minutes – who cares what she wants!

One of his friends remarks: “You made a really smart choice when you took the bicycle.”

“Yeah”, another friend adds, “just imagine how silly you would have looked in girls clothes – and they wouldn’t have fit you anyway!”

A: I don’t get either one.

1 male + 1 female = 1 baby

A: None.

A. Subtract her clothes, divide her legs, and square root her.

Before I root you, are you over 18?

“She no longer is my girlfriend. I caught her cheating on me.”

“I don’t believe that she cheated on you!”

“Well, a couple of nights ago I called her on the phone, and she told me that she was in bed wrestling with three unknowns…”

*Sex is like math:*

Add the bed,

Subtract the clothes,

Divide the legs,

and pray to God you don’t Multiply!

Add the bed,

Subtract the clothes,

Divide the legs,

and pray to God you don’t Multiply!

### The Secret Lives of Professors

*really*like. It is certainly not for everybody. It remains to be seen if it is even for me.

To be sure, there are some great things about this job. To first approximation you are your own boss, and even when it comes to teaching you typically have a tremendous amount of freedom. It has often been said that being a prof is like running your own startup — you have to hire the staff (the students), raise the money (grant proposals), and of course come up with the big ideas and execute on them. But you also have to do a lot of marketing (writing papers and giving talks), and sit on a gazillion stupid committees that eat up your time. This post is mostly for grad students who think they want to be profs one day. A few surprises and lessons from my time in the job…

**Show me the money.** The biggest surprise is how much time I have to spend getting funding for my research. Although it varies a lot, I guess that I spent about 40% of my time chasing after funding, either directly (writing grant proposals) or indirectly (visiting companies, giving talks, building relationships). It is a huge investment of time that does not always contribute directly to your research agenda — just something you have to do to keep the wheels turning. To do systems research you need a lot of funding — at my peak I’ve had 8 Ph.D. students, 2 postdocs, and a small army of undergrads all working in my group. Here at Harvard, I don’t have any colleagues working directly in my area, so I haven’t been able to spread the fundraising load around very much. (Though huge props to Rob and Gu for getting us that $10M for RoboBees!) These days, funding rates are abysmal: less than 10% for some NSF programs, and the decision on a proposal is often arbitrary. And personally, I stink at writing proposals. I’ve had around 25 NSF proposals declined and only about 6 funded. My batting average for papers is much, much better. So, I can’t let any potential source of funding slip past me.

**Must… work… harder.** Another lesson is that a prof’s job is never done. It’s hard to ever call it a day and enjoy your “free time,” since you can always be working on another paper, another proposal, sitting on another program committee, whatever. For years I would leave the office in the evening and sit down at my laptop to keep working as soon as I got home. I’ve heard a lot of advice on setting limits, but the biggest predictor of success as a junior faculty member is how much of your life you are willing to sacrifice. I have *never* worked harder than I have in the last 7 years. The sad thing is that so much of the work is for naught — I can’t count how many hours I’ve sunk into meetings with companies that led nowhere, or writing proposals that never got funded. The idea that you get tenure and sit back and relax is not quite accurate — most of the tenured faculty I know here work even harder than I do, and they spend more of their time on stuff that has little to do with research.

**Your time is not your own.** Most of my days are spent in an endless string of meetings. I find almost no time to do any hacking anymore, which is sad considering this is why I became a computer scientist. When I do have some free time in my office it is often spent catching up on email, paper reviews, random paperwork that piles up when you’re not looking. I have to delegate all the fun and interesting problems to my students. They don’t know how good they have it!

**Students are the coin of the realm**. David Patterson once said this and I now know it to be true. The main reason to be an academic is not to crank out papers or to raise a ton of money but to train the next generation. I love working with students and this is absolutely the best part of my job. Getting in front of a classroom of 80 students and explaining how virtual memory works never ceases to be thrilling. I have tried to mentor my grad students, though in reality I have learned more from them than they will ever learn from me. My favorite thing is getting undergrads involved in research, which is how I got started on this path as a sophomore at Cornell, when Dan Huttenlocher took a chance on this long-haired crazy kid who skipped his class a lot. So I try to give back.

Of course, my approach to being a prof is probably not typical. I know faculty who spend a lot more time in the lab and a lot less time doing management than I do. So there are lots of ways to approach the job — but it certainly was not what I expected when I came out of grad school.

**General philosophy of probability theory**

Probability is central to science, more than any other part of math. It enters statistics, physics, biology, and even medicine as we will see when and if we discuss tomography. This is the broad view.

There is also a narrow view – one needs to understand it before one can effectively apply it and it has many subtleties. Possibly this is due to the fact that probability, stochasticity, or randomness, may not actually exist! I think it mostly exists in our uncertainty about the world. The real world seems to be deterministic (of course one can never test this hypothesis). It is chaotic and one uses probabilistic models to study it mainly because we don’t know the initial conditions. Einstein said that ”god does not play dice”. My own view is that the world may be deterministic, but I like to think I have free will. I believe that probability should be regarded only as a model of reality.

From the notes of Lawrence A. Shepp

A new tool that can help you totally abandon microsoft word: R, Latex, Sweave

http://yihui.name/en/2010/10/how-to-start-using-pgfsweave-in-lyx-in-one-minute/

http://cos.name/2010/11/reproducible-research-in-statistics/

http://jeromyanglim.blogspot.com/2010/11/sweave-tutorial-using-sweave-r-and-make.html

Today I just found a nice list from xi’an’s blog of Top 15 papers for his graduate students’ reading:

”

- B. Efron (1979) Bootstrap methods: another look at the jacknife Annals of Statistics
- R. Tibshirani (1996) Regression shrinkage and selection via the lasso J. Royal Statistical Society
- A.P. Dempster, N.M. Laird and D.B. Rubin (1977) Maximum likelihood from incomplete data via the EM algorithm J. Royal Statistical Society
- Y. Benjamini & Y. Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Statistical Society
- W.K.Hastings (1970) Monte Carlo sampling methods using Markov chains and their applications, Biometrika
- J. Neyman & E.S. Pearson (1933) On the problem of the most efficient test of statistical hypotheses Philosophical Trans. Royal Statistical Society London
- D.R. Cox (1972) Regression models and life-table J. Royal Statistical Society
- A. Gelfand & A.F.M. Smith (1990) Sampling-based approaches to calculating marginal densities J. American Statistical Assoc.
- C. Stein (1981) Estimation of the mean of a multivariate normal distribution Annals of Statistics
- J.O. Berger & T. Sellke (1987) Testing a point null hypothesis: the irreconciability of p-values and evidence J. American Statistical Assoc

Which ones should I now add? First, Steve Fienberg pointed out to me the reading list he wrote in 2005 for the iSBA Bulletin. Out of which I must select a few ones:

- A. Birnbaum (1962) On the Foundations of Statistical Inference J. American Statistical Assoc.
- D.V. Lindley & A.F.M. Smith (1972) Bayes Estimates for the Linear Model J. Royal Statistical Society
- J.W.Tukey (1962) The future of data analysis. Annals of Mathematical Statistics
- L. Savage (1976) On Rereading R.A. Fisher Annals of Statistics

And then from other readers, including Andrew, I must also pick:

- H. Akaike (1973). Information theory and an extension of the maximum likelihood principle. Proc. Second Intern. Symp. Information Theory, Budapest
- D.B. Rubin (1976). Inference and missing data. Biometrika
- G. Wahba (1978). Improper priors, spline smoothing and the problem of guarding against model errors in regression. J. Royal Statistical Society
- G.W. Imbens and J.D. Angrist (1994). Identification and estimation of local average treatment effects. Econometrica.
- Box, G.E.P. and Lucas, H.L (1959) Design of experiments in nonlinear situations. Biometrika
- S. Fienberg (1972) The multiple recapture census for closed populations and incomplete 2
^{k}contingency tables Biometrika

Of course, there are others that come close to the above, like Besag’s 1975 Series B paper. Or Fisher’s 1922 foundational paper. But the list is already quite long. (In case you wonder, I would not include Bayes’ 1763 paper in the list, as it is just too remote from statistics.)

”

And this year some of his students are reading the following papers:

- W.K.Hastings (1970) Monte Carlo sampling methods using Markov chains and their applications, Biometrika
- G. Casella & W. Strawderman (1981) Estimation of a bounded mean Annals of Statistics
- A.P. Dawid, M. Stone & J. Zidek (1973) Marginalisation paradoxes in Bayesian and structural inference J. Royal Statistical Society
- C. Stein (1981) Estimation of the mean of a multivariate normal distribution Annals of Statistics
- D.V. Lindley & A.F.M. Smith (1972) Bayes Estimates for the Linear Model J. Royal Statistical Society
- A. Birnbaum (1962) On the Foundations of Statistical Inference J. American Statistical Assoc.

I think it is also a good list for my own reading.

I think the days when the “Feynman method” was all that was needed to make progress on basic problems in Discrete Geometry are over. Recently there have been a slew of results which make progress on long-standing open problems in Discrete and Computational Geometry that use techniques from a variety of areas in mathematics: algebraic geometry, algebraic topology, complex analysis and so on.

This is wonderful news for the field. Though, two things to consider:

1. So far, apart from the kind of work going in “Computational Topology“, this has mostly been a one-way street. There have been fewer cases of discrete and computational geometers going into topology, algebraic geometry etc. and making a similar impact there. Similarly, there are very few collaborations between mathematicians in other areas, and discrete geometers (*ed: Mulmuley also argues that the GCT program will only come to fruition when this reverse direction starts happening*)

2. From my experience, current graduate students, by-and-large, still seem to be stuck with the general outlook that “If I’m clever enough, and think hard enough, I can solve any problem directly”. Such optimism alwayscheers me up. I used to think that too, but the more I learn about other areas, and as the recent work reveals power of techniques, it has become clear to me that it is misguided to think that. I would really advise students to take courses in algebraic topology, differential geometry and so on.

From: The Geomblog

http://www.eecs.berkeley.edu/~wainwrig/

(this is a very nice homepage of a professor on machine learning)

http://www1.cs.columbia.edu/~jebara/index.html

(another homepage of a professor on machine learning)

http://www.athenasc.com/convexity.html

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