Follow your star” by Theoretical physicist Ulf Leonhardt (thanks to Prof. Martin Vetterli’s tweet) is remarkable in many ways. I will quote few inspiring portions, which are highly significant for early-career researchers like me. What qualities are necessary to succeed as a scientist? “Be stubborn. Believe in yourself. Don’t do what others are saying. Also very important is to stand up again and again. You will fall all the time. There will be disasters, small and great. Usually the first attempt fails, but you learn from it. Maybe you don’t even learn all the time, but from the beginning, you should not be afraid of failure.” Advice for young scientists? “What I have observed in some very good young scientists is that they think too much about their careers. This is all wrong: Build yourself, not your CV. Otherwise, you succeed in the short term, but then you may burn out and lose the fun in science. Young scientists should first think about what they want to do.” “Know how science and the scientific establishment works, but don’t take it too seriously. Listen to what other people are saying, but don’t apply it automatically. Other people may see some aspects of your situation, but they don’t have the knowledge of it all. Only you have that.” Have people tried to dissuade you from following your ideas? “Of course, all the time. Whether it affects me or not depends on the people and the style of the discussion. If people criticize me in a nonscientific way, I completely ignore them because it’s not an argument. If it’s a scientific attack I take it seriously, and then I respond and I learn from it.”]]>
Blog
Deep Association with Manny Parzen: 2009-2016
Each mind to achieve its full potential needs a SPARK. The spark of enquiry, excitement, and passion. Often that the spark comes from a teacher. There was teacher behind every great artist, every great philosopher, every great scientist. However difficult life can be, teachers have always been there, behind the scenes, showing us the way forward” — Stephen Hawking, 2016
Manny Parzen, my greatest mentor and my hero, was that SPARK in my life. He changed my whole outlook by opening a new window for viewing the landscape of Statistical Science — The “Parzen’s window.” I was hooked by discovering the joy of “connecting the dots.” It was the turning point in my career. A lot what I do, how I do, and why I do is heavily influenced by those wonderful years I spent with Manny. I salute my real master Manny Parzen, who infused in me a sense of purpose, which drives me to do meaningful research; who taught me the art of asking the right question whose solution really matters. THANK YOU for igniting the passion for the study of fundamental laws of numbers. I hope someday we’ll be able to fulfill your dream and vision for “United Statistical Theory and Algorithms” to reboot 20th Century fragmented Statistics.
To access the scientific impact of a researcher, I often run a quick thought experiment by asking what will happen if I remove this person from the history of that discipline. If we dare to do that experiment for Manny, there will be a multidisciplinary collapse: Statistics, Econometrics, Machine Learning, Signal processing (and Data Science, which will soon be clear). His innovations were pillars for modern data analysis.
In my last telephone conversation on Jan 7th, I told him: we should catch up soon, and his reply was “I will send you a note”; Feb 6th will be remembered as the saddest day in my professional life. I will miss my HERO dearly, but the friendship we had will never be forgotten from this day until we meet again. You will always remain Deep in my heart.
I am still recovering from the shock. I am now more focused and determined than ever to run the show; I’m looking forward to the day when you will welcome me saying: “It was a good show, my boy. Let’s raise a toast together.”
In eternal gratitude and love, Deep
The Scientific Core of Data Analysis
Richard Courant‘s view: “However, the difficulty that challenges the inventive skill of the applied mathematician is to find suitable coordinate functions.” He also noted that “If these functions are chosen without proper regard for the individuality of the problem the task of computation will become hopeless.” This leads me to the following conjecture: Efficient nonparametric data transformation or representation scheme is the basis for almost all successful learning algorithms–the Scientific Core of Data Analysis–that should be emphasized in research, teaching, and practice of 21st century Statistical Science to develop a systematic and unified theory of data analysis (Foundation of data science).]]>
Jacob Lurie
Two Kinds of Mathematical Statisticians: Connectionist and Confirmatist
Connectionist: Mathematicians who invent and connect novel algorithms based on new fundamental ideas that address real data modeling problems. Confirmatist: Mathematicians who prove why an existing algorithm works under certain sets of assumptions/conditions (post-mortem report). Albeit, the theoreticians of the first kind (few examples: Karl Pearson, Jerzy Neyman, Harold Hotelling, Charles Stein, Emanuel Parzen, Clive Granger) are much more rare than the second one. The current culture has failed to distinguish between these two types (which are very different in their style and motivation) and has put excessive importance on the second culture – this has created an imbalance and often gives a wrong impression of what “Theory” means. We need to discover new theoretical tools that not only prove why the already invented algorithms work (confirmatory check) but also provide the insights into how to invent and connect novel algorithms for effective data analysis – 21st-century statistics.]]>