Interview: Arno Candel, H2O.ai on the Journey from Physics to Machine Learning
We discuss Arno’s career path, transition from Physics to Machine Learning, talent gap in Big Data, advice and more.
Dr. Arno Candel is a Physicist & Hacker at H2O.ai. Prior to that, he was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in high-performance computing and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in U.S. DOE scientific computing initiatives and collaborated with CERN. Arno has authored dozens of scientific papers and is a sought-after conference speaker.
He holds a PhD and Masters summa cum laude in Physics from ETH Zurich. Arno was named 2014 Big Data All-Star by Fortune Magazine.
First part of interview.
Second part of interview.
Here is third and last part of my interview with him:
Anmol Rajpurohit: Q7. What motivated you towards Computational Physics and Machine Learning?
Dr. Arno Candel: I’m very fortunate that my parents and the public schools in Switzerland were very supportive of my early efforts. I’ve always liked computers and the idea of using them to simulate the real world with scientific algorithms. I remember programming an asteroid collision video game in BASIC as a teenager. And I’ve also always liked physics, especially remote-controlled airplanes and other fun “experiments”.
One day during my studies of physics at ETH Zürich, I discovered
a graduate course on scientific programming techniques using C++ and MPI and got immediately hooked. After a conference presentation of my subsequent PhD work on computational particle accelerator physics using Beowulf clusters, SLAC National Accelerator Laboratory offered me a position as a Staff Scientist, where I got deeper into HPC and had access to millions of CPU hours on some of the largest supercomputers for simulations of electromagnetic effects in particle accelerators funded by the U.S. Department of Energy.
After 6 years at SLAC, and armed with a Green Card, I decided to try my luck in the startup world. Machine Learning is a perfect fit for me, given the similarity to scientific computing and its critical importance for the world. I feel that there’s still lots of work left to do!
AR: Q8. You are a "Physicist & Hacker" - two roles that are rarely seen together. Are you more of one and less of the other?
AC: Yes, I’m more of a hacker these days. But if studying physics teaches you one thing, it's logical thinking - and that's quite useful when you’re trying to teach a computer what to do. I’m glad to see that my former peers in particle physics are taking advantage of Deep Learning as I used to collaborate with CERN on the LHC project. I’d love to introduce H2O to more scientific communities.
AR: Q9. Is "talent crunch" a real problem in Big Data? What has been your personal experience around it?
AC: Yes, it’s definitely not easy to find talented people with relevant experience in supercomputing, applied math and computer science, all at once. And that’s really what Machine Learning on Big Data boils down to, not to mention new infrastructure technologies such as Hadoop or Spark.
It seems that companies like Google, Twitter or Facebook have already gobbled up a large fraction of this talent in Silicon Valley over the past decade, but I fully expect outstanding new students of Big Data Machine Learning to emerge from universities all over the world given the enormous amount of interest shown by young people in computers and data science.
AR: Q10. What is the best advice you have got in your career?
AC: The best advice I ever got was to think about what I want to achieve, in 2 years, in 5 years and in 10 years from now. I realized that I had never thought that far out until then.
AR: Q11. What advice would you give to Machine Learning students, professionals and researchers who are aspiring long-term success?
AC: Try to do something you really enjoy doing. Being in the Flow makes a huge difference in your happiness and productivity.
AR: Q12. What was the last book that you read and liked? What do you like to do when you are not working?
AC: I really liked "Jitterbug Perfume" by Tom Robbins, and I like to keep up-to-date by reading “The Week”, but I love spending time with my gorgeous wife and our adorable baby boy (with whom I’d like to play golf someday).
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