I won the best paper award at the conference I’ve been attending this week. This was VERY unexpected. I mean it was a good paper but to win best paper. My mind is blown!
SO EXCITED!!!
WOOT!!! WOOT!!!
(I’m not acting in the way of a dignified scientist, but fork that WOOT!! WOOT!!!)
Wow! That’s fantastic!
From one (un)dignified scientist to another!
Congrats! Academia is filled with bad writing. Thanks for fighting the good fight!
Congrats! Well-done!
[Worrisome thought: does this mean that the mass mechanical annihilation of the race is one step closer: Beep…kill…beep - getting there, there… beep - beep - beep KILL!]
Congratulations.
Congratulations! Well done, you!
But I hope you don’t take it amiss that I read your OP title and immediately thought about the Old Man’s Major Award from Christmas Story…
Good on you. If its not to identifying or anything, post a link so we can all marvel at your wisdom!
Hey, congrats! Another point to add to your resumé! (And dignity in science is overrated!)
And just in case: I, for one, welcome our new AI overlords.
What was your paper about, if you don’t mind me asking? I love hearing about award winning science.
Well done!! 
Thanks everyone for the support! I appreciate it. I would link to the paper, but like most people I do value my anonymity on this board. I think if you tried hard enough you could probably figure out who I am. There are only so many relatively new researchers working on model inference using machine learning, who are also now working in a medical lab.
This paper was on inferring models of temporal processes where the mechanisms of the process have parametric activation functions (where at least one parameter in at least one function is time, hence temporal) from data observed from the process. The simpler version of this problem (model inference from observable data) has been an open problem since the 1970s. The algorithm we developed is the first one that can solve this in a general domain agnostic way for processes with activation functions (there have been previous approaches that can infer functions that modify how the process works but not how they activate, and they have to assume the process is known, our algorithm infers the process and the activation functions). The potential impact is reducing the need for human experts to derive a model from data, which can be difficult and complex. Additionally, usually such model creation by experts uses known scientific principles. With the developed algorithm it is possible to infer the model and then an expert can use that to deduce the scientific principles behind it.
See, your anonymity is safe with me, because I’ve read that post about three times and still have absolutely no idea what your paper is about. 
However, I’m happy for you.
I’m with you @Northern_Piper. I can recognize that those words have context and meaning, not a bunch of techy babble.
That’s all I got, though.
It sounds more complicated than it is. Let me give you a simple example.
Suppose you have a tree that produces branches. Suppose that the tree will produce a branch after a period of time T and after receiving an amount of sunlight S. My algorithm, if given the proper data, can determine that a tree’s growing process has a mechanism “Branch” that has two parameters t and s. It will also determine that Branch(t,s) activates if and only if t > T and s > S. Finally, it will determine that a the mechanism Branch produces a branch B that has the physical form of “insert description of a branch here” (this aspect gets technical so just pretend there’s a description of a branch) with of course any sub-mechanisms for the branch. For example, how that branch forms leaves or sub-branches.
In other words, given the right data, my algorithm can fully describe the process used by a tree to grow including the factors that control the underlying mechanisms of growth.