- Amazon.comSenior Manager - Supply Chain Control & Analytics, present
- Dixons Carphone GroupCommercial Mathematician
- Thalassa Autonomous RoboticsConsulting Systems Engineer (Navigation Algorithms R&D)
- BioSonics Inc. (Smart Sonar)Director of Engineering & Operations
- BioSonics Inc. (Smart Sonar)Head of Software Development
- Boeing Phantom Works (R&D Centre of Excellence)Scientific Programmer, NSF VIGRE Fellowship
Over the years and on both sides of the Atlantic, I've led a variety of quantitative & operational challenges across intelligent systems & software engineering, sonar, defense, environmental technology, robotics, and most recently the complex world of multi-channel retail.
The common thread linking passion, profession, and play is applying technology, good design, and quantitative modelling and simulation, to build better products, enable better decisions, and optimise performance.
Presently, I am Commercial Mathematician (aka data scientist) to a leading UK retailer, where I develop mathematical models and algorithms that get beneath a complex of systems, processes, and behaviours, to drive better performance for customers at a lower cost. My work is applied to demand forecasting, product replenishment, supply chain optimisation, statistical modelling of multi-channel behaviour, predictive analytics, as well as decisions around property portfolio transformation, merchandise ranging & assortments, and own-brand profitability.
Prior to this, I developed navigation & localisation algorithms for unmanned autonomous underwater robotic vehicles working for the ocean robotics R&D company Thalassa Autonomous Robotics Ltd. (Bristol, UK). This neat little animation illustrates the concept of what we designed for Undersea Oil & Gas Exploration. Videos of early prototypes in action are here, and here
In the U.S., I was the Director of Engineering & Operations at BioSonics, Inc. (Seattle, Washington), where I led the design, development and manufacturing of intelligent sonar systems and their real-time software. Applications spanned a number of industries including environmental monitoring of the world's first underwater tidal energy grid in New York City's East River, homeland defense with Sandia National Labs & the Naval Underwater Warfare Centre, and off-shore aquaculture (automated fish farming) with the Chilean government, among others.
I'm always experimenting and tinkering, so feel free to get in touch with ideas.
- UW, Swarthmore
Times change. Societies evolve in their understanding of justice. The Constitution was designed to be a living document that serves the societies whose laws it interprets, not to be a straight-jacket that allows injustices from previous times to continue unchallenged forever onwards. Hence the title of the post.
This is an interesting and well-written article by Jennifer Oullette on an area of physics that is probing the fundamental nature of space-time and therefore geometry.
The exceptions to this observation include "industries and organizations that rely on manual labor, have limited technology, and place strict standards for both minimum and maximum production" — essentially anywhere there's little opportunity to be exceptional.
The implication for entrepreneurial companies? Laszlo Bock (Google SVP People Operations): Managers at any company should ask, "How many people would you trade for your very best performer? If the number is more than five, you're probably underpaying your best person. And if it's more than ten, you're almost certainly underpaying."
 The Best and the Rest: Revisiting the Norm of Normality of Individual Performance; 2012; ERNEST O’BOYLE JR. (Longwood University) and HERMAN AGUINIS (Indiana University); Personnel Psychology, Vol. 65; pp79-119
 Inside Google's Policy to 'Pay Unfairly': Why 2 people in the same role can earn dramatically different amounts. Richard Feloni; 11.Apr.2015; Business Insider UK;
Just watching the trailers is rewarding. If you've never met a mathematician, these should give you a personal look into the very diverse worlds of some great modern mathematicians and to see the humanity behind the thinking. If you're a mathematician or love mathematics, these are inspiring.
And now, the list of 13 films, courtesy of Csicery's studio Zala Films:
 Counting from Infinity: Yitang Zhang & the Twin Prime Conjecture (2015)
The central challenge of the film was finding a way to depict Yitang Zhang's dedication to working in isolation. The qualities he embraces-solitude, quiet, concentration-are the opposites of those valued in the media. Fortunately, it is a conundrum Csicsery had faced before in other films about mathematicians. He had learned that contrary to the rules, it is okay to shoot long scenes of "the grass growing," or in this case, shots of "a person just sitting with pencil and paper and thinking. The longer the scene, the more you realize that you really can see someone thinking. The human face is very expressive. Give it time and it speaks volumes." - George Csicsery
 Porridge Pulleys & Pi: Two Mathematical Journeys (Hendrik Lenstra, Number Theory, Elliptic Curves & Cryptography) & (Vaughan Jones: Quantum Mechanics, Knot Theory, and DNA Protein Folding) (2004)
Trailer: http://www.imdb.com/video/wab/vi1773798425/ About: http://www.zalafilms.com/films/pppdirector.html
"There are several stereotypes and beliefs about mathematicians that Porridge pulleys and Pi aims to dispel. First, I wanted to show that there is no single type of person who can become a mathematician. ... given the right training, any child with the aptitude can turn into a mathematician. ... Jones and Lenstra are the opposites of the eccentric nerdy type who has come to characterize the popular conception of what mathematicians are like. Another cliche I hope to debunk is that of the tortured genius. This film contains clear evidence that mathematicians derive a great deal of pleasure from their work." - George Csiscery
 Taking the Long View: The Life of Shiing-shen Chern (one of the fathers of modern differential geometry) (2011)
View Short: http://zalafilms.com/takingthelongviewfilm/viewfilm.html
There’s a quotation from Lao Tzu, an ancient Chinese philosopher, that could have been written about Chern. ‘The master does his job and then stops. He understands that the universe is forever out of control, and that trying to dominate events goes against the current of the Tao. Because he believes in himself, he doesn’t try to convince others. Because he is content with himself, he doesn’t need other’s approval. Because he accepts himself, the whole world accepts him.’ - Alan Weinstein, UC Berkeley
The true importance of Shiing-shen Chern’s role in the development of mathematics ... His influence with Chinese government leaders helped bring Western mathematicians to China and send Chinese students to study abroad. Today’s leaders in Chinese mathematics were all beneficiaries of Chern’s vision. His greatest contribution to the restoration of Chinese mathematics, however, is the establishment of the Nankai Institute of Mathematics, today known as the Chern Institute of Mathematics. The Chern Institute provided a base for these international interactions which often led to collaborations, reciprocal visits, and joint papers.
He said, ‘my policy to operate this institute is very simple. Three words in Chinese. First, no meetings. Second, no plan. Third, do more.’ That means, just do your research work.
Molin Ge, Theoretical Physicist, Chern Institute of Mathematics
 Invitation to Discover: An Introduction to the MSRI (Mathematical Sciences Research Institute) (2002)
 I Want to Be a Mathematician: A Conversation with Paul Halmos (2009)
"When an engineer knocks at your door with a mathematical question, you should not try to get rid of him or her as quickly as possible. You are likely to make a mistake I myself made for many years: to believe that the engineer wants you to solve his or her problem. This is a kind of over simplification for which mathematicians are notorious. Believe me, the engineer does not want you to solve his or her problem. Once I did so by mistake (actually I had read the solution in the library two hours previously, quite by accident) and he got quite furious, as if I were taking away his livelihood. What an engineer wants is to be treated with respect and consideration, like the human being he is, and most of all to be listened to with rapt attention. If you do this, he will be likely to hit upon a clever idea as he explains the problem to you, and you will get some of the credit. Listening to engineers and other scientists is our duty. You may learn some interesting mathematics while doing so." - Gian-Carlo Rota, Indiscreet Thoughts 1979
 Julia Robinson and Hilbert's 10th Problem (2008)
 Navajo Math Circles (in production)
"Open-ended questions are totally new to most of the kids. Usually you have to have an answer within 20 seconds, 30 seconds, that’s what math is. Math circles are the opposite. We start with some simple questions, and we ask more questions and more questions. We get some answers along the way. The answers actually don’t matter. The more and more questions… we’re opening whole research problems, and that is something totally new to the kids. And once they like it, it’s just amazing, it’s transformative." - Matthias Kawski, Arizona State University
 Hard Problems: The Road to World's Toughest Math Contest, covering the story of the 2006 US IMO team (2008)
 N is a Number: A Portrait of Paul Erdos (1993)
Trailer: https://www.simonsfoundation.org/multimedia/n-is-a-number-a-portrait-of-paul-erdos/ About: http://www.zalafilms.com/films/nisfilm.html
 Erdos 100 (2013)
 To Prove and Conjecture: Excerpts from Three Lectures by Paul Erdos (1993)
 The Right Spin: How to fly a broken space craft (Mir), the Story of a Dramatic Rescue in Space and the Mathematics Behind It (2005)
About: http://plus.maths.org/content/right-spin-how-fly-broken-space-craft http://archive.msri.org/specials/rightspin Alternate documentary: https://www.youtube.com/watch?v=tM7fTLLmgbk
 On Mathematical Grounds: A Refresh of an Introduction to the Mathematical Sciences Research Institute (MSRI) (2009)
[a] George Csicsery, Producer & Director, Zala Films, in his own words: http://zalafilms.com/takingthelongviewfilm/directors_statement.html
[b] The Films: http://www.zalafilms.com/films/index.html
[c] The story of George Csicsery: Math Films, Yes - But So Much More: http://cinesourcemagazine.com/index.php?/site/comments/csicsery_math_films_yes_but_so_much_more/
[d] Science Lives, Simon Foundation & Zala Films, videos of interviews with living mathematicians https://www.simonsfoundation.org/category/multimedia/science-lives/alphabetical-listing/
Remember days gone by when one would have to pay to get to the nearest research library, then pay again to photocopy what was needed.
The digital dissemination of knowledge, though freely given, does require our support.
Please do your part now in this annual campaign. The average donation is $10.
The image below shows a typical example: the surveying rod was conventionally defined to be "sixteen feet". But whose sixteen feet? One standard specification from 1536 was clear: "Take sixteen men, short men and tall ones as they leave church and let each of them put one shoe after the other and the length thus obtained shall be a just and common measuring rod to survey the land with." (1536; Geometry; Jacob Kobel) [1; 48]
 Measures for Progress: A History of the National Bureau of Standards; Rexmond C. Cochrane; 1966, 1974, National Bureau of Standards, US Dept of Commerce;
Available for download from:
#historyofscience #measurement #standardization
What standard unit of measure would permit working readily with this wide dynamic range of measurements?
The decibel, which is the practical man's logarithms, and appears everywhere in oceanography.
There's a great quote by Laplace, whose tribute to the logarithm was much more glowing than the typical student reaction:
"[Logarithms: that] admirable artifice which, by reducing to a few days the labour of many months, doubles the life of the astronomer, and spares him the errors and disgust inseparable from long calculations."
Substitute oceanographer for astronomer and the sentiment, I think, remains the same.
Today it appears that Git, with GitHub, can be said to have won the DVCS rivalry, judging by captured mindshare of open source developers. That's no disrespect to Mercurial which is still used and loved by many. (Indeed, I've been one of these for almost half a dozen years now.)
But Git, well, the article describes it all.
Now, if you're reading this and are still on Subversion or heaven forbid, CVS, it might be time to consider whether a modern distributed version control systems may offer some more appealing ways of working. If you do start looking, Git and TortoiseGit are a pretty good place to start.
Paper: Revealing the density of encoded functions in a viral RNA, Proceedings of the National Academy of Sciences, 2015
Reidun Twarock: http://maths.york.ac.uk/www/rt507
A Course in Mathematical Virology: http://maths.york.ac.uk/www/node/14102
As should be expected given the wide variation in application areas, there is no "best" approach that works across all possible problem domains -- what works pragmatically is to get started, tune your recommender agent to your particular problem domain, and then see how well it performs against either practical performance measures (e.g. improved conversion) or theoretical measures of error on benchmark training data sets (e.g. the Netflix prize)
Some reasonable mathematical understanding is needed to feel comfortable with the algorithms, as most use applied mathematics, modern statistics or machine learning techniques.
However, neither a strong mathematical background nor previous software development expertise is needed to simply try out pre-existing algorithms using "canned packages" and to play around with various data sets. This is a good way to build up some intuition if you have never seen a recommender algorithm in action. This intuition will then help as you begin the process of building up the knowledge necessary to understand the technical details.
I am often asked how to get started in this area of applied mathematics. Below are some general responses with references that should get you started should you be interested.
Question 1: Where can I get rich data sets from real world situations?
There are free data sets available from a variety of sources. One good place to start is RecSysWiki 
Another is the competition site Kaggle. 
Question 2: What programming language should I use to give these ideas a try?
Use any language you know well! Python is a useful modern software language that is well worth knowing as it has a large collection of well designed numerical libraries. R is another popular choice, especially if you're interested in statistical applications and machine learning more generally.
Indeed, many languages will have existing canned packages offering recommender systems.
R has recommenderlab , and yhat has written a nice blog article on the subject. 
Python has python-recsys 
Ruby and Perl also have entries.
So, if you want something pre-canned, you can probably find something in just about any reasonably modern language. The more important question will be: can you find something that works for you in that language. And if not, should you "roll your own"...
Question 3: Should I use a pre-canned algorithm or "roll my own"?
For your learning about algorithms and implementations, roll your own. There's no better way to understand an algorithm than to attempt to write your own implementation.
For a production grade implementation however, it's probably wiser to work with a reputable package that has a wide installed user base, so that you can be sure that most of the bugs have been worked out, that the maths is sound, and that any updates will be made available to you.
Question 4: Which algorithms should I focus on? What about X?
There are at least a dozen popular algorithms that you could consider. I will not cover all of them here.
But I often get asked about Neural Networks in recommender systems. My personal opinion is that neural networks are more work than they are worth. They are often tuned very specifically to specific training data, and don't have a general theory behind why they are or are not effective, beyond the fact that neural networks are a form of multi-dimensional piecewise linear approxiation, which means a neural network can in principle be made to work well with anything, by introducing enough neurons.
My recommendation is to steer clear from Neural Networks when you're first starting out. Matrix factorisations or other direct measures of similarity are much more appealing (in my opinion) both on theoretical grounds and on their practical performance.
Question 5: Are there some useful papers I can start with to get a good overview of the subject?
Yes. Here's three that I think would have something useful for someone entering the subject at a variety of levels. All three great papers are freely available from the internet. A Google search on "Recommender Systems" will turn up a number of others.
* Matrix Factorisation Techniques for Recommender Systems (2009) 
* Mining of Massive Data Sets, Ch.9: Recommender Systems (2010) 
* Tutorial: Recommender Systems (Slides) (2013) 
Good luck! and feel free to write about your learnings in the comments.
 Datasets from RecSysWiki:
 RecommenderLab (R)
 Matrix Factorisation Techniques for Recommender Systems; Yehuda Koren, Robert Bell, and Chris Volinksy; August 2009; IEEE;
 Mining of Massive Data Sets, Ch.9: Recommender Systems; Jure Leskovec; Anand Rajamaran; Jeffrey Ullman; 2010; URL: http://www.mmds.org/ and http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
 Tutorial: Recommender Systems (Slides); August 2013; International Joint Conference on Artificial Intelligence; Beijing; Dietmar Jannach and Gerhard Friedrich
 Tutorial: Building a Recommendation System in R; July 2013; yhat
Image Credit: cover of the book Recommender Systems: An Introduction, by Dietmar Jannach et.al.