daleroberts

Thanks for telling me, it looks like Debian is using GDAL v1...

TV gives even more interesting results as it uses more unicode 9.0 characters:

http://www.github.com/daleroberts/tv

Tv pushes the rendering further by using more unicode characters. It also uses GDAL as a backend which allows subsampling of massive images and access to many scientific image formats (GeoTiff, NetCDF, JPEG2000, etc)

Terminal is great but a major lacking feature is 'true color' (https://gist.github.com/XVilka/8346728).

If you an Apple engineer working on Terminal and reading this post, please add it, it would be much appreciated :)

If you are running a Mac for your desktop (with iTerm2) and ssh'ing into a Linux machine. These work if you run them remotely as the image gets transferred through the SSH session:

https://github.com/daleroberts/itermplot

https://github.com/daleroberts/bv

Alternatively, for any other terminal emulator with Unicode 9.0 font support and true color support, you can use:

https://github.com/daleroberts/tv

This even includes Windows (using patched PuTTY) and remotely running 'tv' on a Linux machine.

You can checkout my other project https://github.com/daleroberts/tv which allows you to view images in any terminal supporting Unicode 9.0 fonts and true color. It uses GDAL for the backend which is easy to install on a Mac with:

    brew install gdal --without-python --HEAD
    pip install gdal

Just a quick, but functional, implementation as someone asked me if it could be done...

Thanks.

I just do the work because it's fun, that's the rewarding part for me. I just like to wander around working on random "mathy" pure and applied problems in a variety of areas...

Yeah, at least we got a quick cite. I believe they had to sacrifice a bit on the rigour of the methodology to be able to do it at scale. For example, atmospheric correction of the data, methodology, validation, ...

We've also improved a lot on our methodology since our own paper was published (using more sophisticated classification algorithms, for example).

Not too fussed, I've got a lot more cool stuff in the pipeline...

Don't you love those days when you want to cry as someone copies what you've done on a larger data set and gets it into Nature.

http://www.sciencedirect.com/science/article/pii/S0034425715...

http://www.ga.gov.au/interactive-maps/#/theme/water/map/wofs

Perhaps useful if you are interviewing, this is what I give to my undergraduate students.

Mathematical Finance Cheat Sheet:

https://github.com/daleroberts/math-finance-cheat-sheet

This one always gets me ready for the day

https://www.youtube.com/watch?v=dQw4w9WgXcQ

Any recommendations for the font size? I'm just using standard Menlo Regular 11. I didn't want to do hiptext any injustice.

No, looks fine for me (Retina display).

For those that are interested, I've also released my approach to imagery in the terminal (Python):

https://github.com/daleroberts/tv

Yep, you'll find fat cats in every industry. My main point is that not only is there "dualisation" like is proposed in the article but out of those who have made it into a tenured position there is another hard split between those academics that are creaming it and those that do ok.

Yes, Finance is the biggest and the money still flows. Look at all the recent hires in Finance at UNSW and UMelb who have been appointed at the Senior Lecturer level straight after PhD. Market loadings are paid in Medecine too. Probably Law as well... I've also heard that Statistics and doing Machine Learning consulting on side is good too. What surprised me the most is all the arts/sociology academics doing big consulting contracts connected to "foreign aid" work.

I beg to differ. You can make a significant amount of money as an academic in Australia if you are in the right discipline to get a market loading and be able to do consulting on the side. AU$200-300K a year is easy to achieve for these academics some are even pulling in $500K+ by the end of their career. Plus the 17% superannuation means you'll retire with a nice big pot of retirement money at the end. Very hard to find a comparable position in industry...

As someone who's spent some of their career in each one of these groups, I have to agree 100% with your analysis.

An interesting intersection is the Finance/Management/Business faculty. A new Assistant Professor in Finance can make significantly more in salary than a full Professor in Mathematics.

Here is some interesting data to back up my point and this is average salary. The amounts can be much higher at top institutions.

https://www.higheredjobs.com/Salary/salaryDisplay.cfm?Survey...

In what area of Mathematics? Or are you looking for a Theoretical Computer Scientist?

Contributions generally accepted if anyone wants to play...

The key point you are glossing over is: under what probability measure?

You are calculating an expected value in the sense of 'mathematical operation E' under some measure not in the sense of 'I expect the price to be...'

I don't want to pick a fight or anything, your product datanitro looks nice and it's cool you are writing articles on the topic.

Where did you learn this stuff from?

Yes, Black-Scholes is a good benchmark model, i.e., something to compare other models to. For example, through the implied volatility surface.

I think the author of the article hasn't really grasped the concept of option pricing. You are not calculating an 'expected value'.

The whole point of the theory is there is a correspondence between a 'no-arbitrage argument' and calculating an expectation under the 'risk-free' measure. The mathematical operation of expectation E is only a tool. This link between no-arbitrage and martingale theory is called the 'Fundamental Theorem of Asset Pricing'.

There is a nice (basic) explanation of this idea in the book by Baxter and Rennie "Financial Calculus" where they compare the 'expected value' approach of a bookmaker and the 'no-arbitrage' approach.

For a more advanced explanation, you can have a look at the book by Delbaen and Schachermayer (2006).

Do you think there is a market for teaching this? I've taught a couple of courses on this stuff "professionally" and have been debating for a while if I should create some nice online course on the topic.

I think you're seriously underestimating the amount of work we need to do...

I don't get that TED talk. For example, how are you meant to properly understand the ubiquitous Gaussian distribution without learning Calculus first?

True, but they don't offer a machine that can run OpenCL well. On my current Mac Pro (6-Core Xeon 3.33Ghz) the CPU runs most of my codes at the same speed as the ATI Radeon 5870. An ATI 7970 would have been a nice (minimal) addition but I really want support for an NVidia Tesla c2070 so I can develop CUDA codes to run on Amazon EC2.

Again, no Nvidia CUDA support for scientific computing. Not even faster ATI cards... I think my next workstation is going to be Linux on Dell or HP.

Maybe I can add some context. I was the OP that started this whole discussion about posing math problems / Brainteasers at interviews.

Out of the blue I got a call from a recruiter saying that bank X would like to interview me for a quant role they have. They wanted to fast-track the process so they booked me for 2.5-3 hours of back-to-back interviews. I was not looking for a new job but I thought it might be good to talk to them and explore leaving academia.

The first interview was with some quants who work for the hiring manager. They tell me that they plan to have a technical talk with me and then follow that with some math problems and brainteasers. At that point I respectfully declined saying "I wasn't aware that I was going to be asked those types of questions and I don't really believe they are a good hiring tool". The guy tells me too bad, that's the way they hire now. I say fine and we carry on with the first part of this session and have a technical talk.

We then come to the point that they want to ask me the math problems. I again say that it's silly. They disagree. Then I say "For example, who's to say you're not going to ask me problem A". They laugh. "followed by problem B, C, D". They stop laughing. "Finishing with the problem about the expected number of tosses of a coin to HH". They look at each other. "and who's to say that I don't know the answers by heart. It's luck of the draw, maybe I know your questions and maybe I don't and how would it reflect on me if I don't know". The guy who had problems ready for me turned white. They quickly ended the interview in a cordial way at that point and seemed to scramble to find the next two guys to come and interview me. Probably because the interview finished 30 mins earlier.

What they didn't realize is that I love these types of problems and that it is a small community. I know a lot of people and remembered what questions they had asked one of my friends. When they walked in the room, the guy had the solutions written out on a piece of paper and with only a brief glance I knew that I was going to get asked the same ones.

He had paper with the solutions face down during the whole interview but when he left I have never seen anyone pickup a piece of paper in a more awkward way. He did the whole fold in half as you lift thing.

Good points. That is probably why people are often not successful on their first interviews. It takes a few to get into "interview mode" where you have good answers for common questions about your CV.

I was quite dismayed when I called them out and they didn't come back with new questions or made up some on the spot. They simply decided to drop that part of the interview and move to the next section.

I've seen too many good people (friends and students of mine) miss out on jobs simply because they failed these type of problems.