Milo Time

Milo Kessler died of osteosarcoma on March 11, 2022. He was just 18. He was a math major and loved tennis. I never met Milo but I think of him often. I got to know his father Daryl after Milo had passed. Daryl created this podcast about Milo. It’s very well done and gives me comfort.

Missing the trend

I have been fortunate to have been born at a time when I had the opportunity to witness the birth of several of the major innovations that shape our world today.  I have also managed to miss out on capitalizing on every single one of them. You might make a lot of money betting against what I think.

I was a postdoctoral fellow in Boulder, Colorado in 1993 when my very tech savvy advisor John Cary introduced me and his research group to the first web browser Mosaic shortly after it was released. The web was the wild west in those days with just a smattering of primitive personal sites authored by early adopters. The business world had not discovered the internet yet. It was an unexplored world and people were still figuring out how to utilize it. I started to make a list of useful sites but unlike Jerry Yang and David Filo, who immediately thought of doing the same thing and forming a company, it did not remotely occur to me that this activity could be monetized. Even though I struggled to find a job in 1994, was fairly adept at programming, watched the rise of Yahoo! and the rest of the internet startups, and had friends at Stanford and Silicon Valley, it still did not occur to me that perhaps I could join in too.

Just months before impending unemployment, I managed to talk my way into being the first post doc of Jim Collins, who just started as a non-tenure track research assistant professor at Boston University.  Midway through my time with Jim, we had a meeting with Charles Cantor, who was a professor at BU then, about creating engineered organisms that could eat oil. Jim subsequently recruited graduate student Tim Gardner, now CEO of Riffyn, to work on this idea. I thought we should create a genetic Hopfield network and I showed Tim how to use XPP to simulate the various models we came up with. However, my idea seemed too complicated to implement biologically so when I went to Switzerland to visit Wulfram Gerstner at the end of 1997,  Tim and Jim, freed from my meddling influence, were able create the genetic toggle switch and the field of synthetic biology was born.

I first learned about Bitcoin in 2009 and had even thought about mining some. However, I then heard an interview with one of the early developers, Gavin Andresen, and he failed to understand that because the supply of Bitcoins is finite, prices denominated in it would necessarily deflate over time. I was flabbergasted that he didn’t comprehend the basics of economics and was convinced that Bitcoin would eventually fail. Still, I could have mined thousands of Bitcoins on a laptop back then, which would be worth tens of millions today.  I do think blockchains are an important innovation and my former post-bac fellow Wally Xie is even the CEO of the blockchain startup QChain. Although I do not know where cryptocurrencies and blockchains will be in a decade, I do know that I most likely won’t have a role.

I was in Pittsburgh during the late nineties/early 2000’s in one of the few places where neural networks/deep learning, still called connectionism, was king. Geoff Hinton had already left Carnegie Mellon for London by the time I arrived at Pitt but he was still revered in Pittsburgh and I met him in London when I visited UCL. I actually thought the field had great promise and even tried to lobby our math department to hire someone in machine learning for which I was summarily dismissed and mocked. I recruited Michael Buice to work on the path integral formulation for neural networks because I wanted to write down a neural network model that carried both rate and correlation information so I could implement a correlation based learning rule. Michael even proposed that we work on an algorithm to play Go but obviously I demurred. Although, I missed out on this current wave of AI hype, and probably wouldn’t have made an impact anyway, this is the one area where I may get a second chance in the future.

 

 

Proving I’m me

I have an extremely difficult time remembering the answers to my security questions for restoring forgotten passwords. I don’t have an invariant favourite movie, or book, or colour. I have many best friends from childhood and they have various permutations of their names. Did I use their first name, nick name, full name? Even my Mother’s maiden name can be problematic because there are various ways to transliterate Chinese names and I don’t always remember which I used. The city I met my wife is ambiguous. Did I use the specific town per se or the major city the town is next to? Did I include the model of my first car or just the make. Before I can work my way through the various permutations, I’m usually locked out of my account forever.

As much as I appreciate and rely on computers, software, and the internet, objectively they all still suck. My iPhone is perhaps better than the alternative but it sucks. My laptop sucks. Apple makes awful products. Google, Amazon, Uber and the rest are not so great either. I don’t remember all the times Google Maps has steered me wrong. The tech landscape may be saturated but there is definitely room for something better.

J. Bryce McLeod, 1929-2014

I was given the sad news that J. Bryce McLeod died today in his home in England. Bryce was an extraordinary mathematician and an even better human being. I had the fortune of being his colleague in the math department at the University of Pittsburgh. I will always remember how gracious and welcoming he was when I started. One of the highlights of my career was being invited to a conference in his honour in Oxford in 2001. At the conference dinner, Bryce gave the most perfectly constructed speech I have ever heard. It was just like the way he did mathematics – elegantly and sublimely.

The MATLAB handcuff

The first computer language I learned was BASIC back in the stone age, which led directly to Fortran. These are procedural languages that allow the infamous GOTO statement, now shunned by the computer literati. Programming with the GOTO gives you an appreciation for why the Halting problem is undecidable.  Much of what I did in those days was to track down infinite loops. I was introduced to structured programming in university, where I learned Pascal. I didn’t really know what structured programming meant except that I no longer could use GOTO and there were data structures like records. I was forced to use APL at a summer job. I have little recollection of the language except that it was extremely terse and symbolic. It was fun to try to construct the shortest program possible to do the task. The ultimate program was the so-called “APL one liner”. APL gave me first hand experience of the noncomputability of Kolmogorov complexity. In graduate school I went back to Fortran, which was the default language to do scientific computing at that time. I also used the computer algebra system called Macsyma, which was much better than Mathematica. I used it to do Taylor expansions and perturbation theory. I was introduced to C and C++ in my first postdoc. That was an eye-opening experience as I never really understood how a computer worked until I programmed in C. Pointer arithmetic was a revelation. I now had such control and power. C++ was the opposite of C for me. Object oriented programming takes you very far away from the workings of a computer. I basically programmed exclusively in C for a decade – just C and XPP, which was a real game changer. I had no need for anything else until I got to NIH. It was only then that I finally sat down and programmed in MATLAB. I had resisted up to that point and still feel like it is cheating but I now almost do all of my programming in MATLAB, with a smattering of R and XPP of course. I’m also biased against MATLAB because it gave a wrong answer in a previous version. At first, I programmed in MATLAB as I would in C or Fortran but when it came down to writing the codes to estimate heritability directly from GWAS (see here), the matrix manipulating capabilities of MATLAB really became useful. I also learned that statistics is basically applied linear algebra. Now, when I code I think instinctively in matrix terms and it is very hard for me to go back to programming in C. (Although I did learn Objective C recently to write an iPhone App to predict body weight. But that was mostly point-and-click and programming by trial and error. The App does work though (download it here). I did that because I wanted to get a sense of what real programmers actually do.) My goal is to switch from MATLAB to Python and not rely on proprietary software. I encourage my fellows to use Python instead of MATLAB because it will be a cinch to learn MATLAB later if they already know Python. The really big barrier for me for all languages is to learn the ancillary stuff like what do you actually type to run programs, how does Python know where programs are, how do you read in data, how do you plot graphs, etc? In MATLAB, I just click on an icon and everything is there. I keep saying that I will uncuff myself from MATLAB one day and maybe this is the year that I actually do.