JuliaCon 2019 streaming live

JuliaCon 2019 is streaming live on the Julia Channel on Youtube. Every video has its own livestream and will be linked on the channel page as it goes live.

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I just want to take a minute to appreciate the quality of the livestreams this year. I am glad you guys got this YouTube thing all figured out :slight_smile:

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With some help from @cormullion’s gist I put together a spreadsheet where I’m adding links to live streams, slides, repos, other supporting material etc. I’ll update it as I watch the talks or if anyone points me to relevant material. By the time the whole thing is over I think we should have a pretty comprehensive list :slight_smile:

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The code I used earlier this year (YouTube views and likes) to scrape viewcounts and stats doesn’t work today (Google knows why :)), so the viewcounts are obstinately stuck at 0. Will update once or twice more perhaps, rate-caps permitting.

You can use this link to directly see the current live streams and upcoming streams.

My sheet of livestream links is now more tweetable: https://tiny.cc/jc19talksummary. I’m not on Twitter so someone might want to get that out there if it seems useful. I’ll continue to update it with links to people’s slides, repos, papers etc as I see them.

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@swt30 Thanks a lot.

Yesterday, I took advantage that my girlfiend was out, and I pass the majority of the afternoon watching JuliaCon 2019 talks :slight_smile: . There are very interesting talks, and you can learn a lot (even about solving cryptic crosswords :stuck_out_tongue: ).

I have scraped the Youtube data on all the Juliacon 2019 talks and have created a “score” to help me give a quality indications of all the talks so I can prioritise which to watch. The score is based on some implementation of adjusted likes/views - adjusted dislikes/views.

Base on current data “Mining Imbalanced Big Data with Julia” is the best talk at the Juliacon 2019!

Code is here https://github.com/xiaodaigh/juliacon_youtube

titles rank score views likes disklikes
Mining Imbalanced Big Data with Julia 1 0.04201735687533807 538 30 0
Using Julia in Secure Environments 2 0.04117365656271626 305 18 0
A case study of migrating Timelineapp.co to the Julia language 3 0.03711281221940532 148 9 0
Scientific AI: Domain Models with Integrated Machine Learning 4 0.034864841138641495 1199 52 0
Probabilistic Biostatistics: Adventures with Julia from Code to Clinic 5 0.033460508243300054 209 11 0
How We Wrote a Textbook using Julia 6 0.03241592676757127 1071 44 0
JuliaCN: A Community Driven Localization Group for Julia in China 7 0.030525716747163846 109 6 0
The Unreasonable Effectiveness of Multiple Dispatch 8 0.03006807234238759 1419 53 0
Gaussian Process Probabilistic Programming with Stheno.jl 9 0.029833286180076902 286 13 0
IVIVC.jl: In vitro - In vivo correlation module for pharmaceutical modeling in Pumas 10 0.028661262049042575 116 6 0
Intelligent Tensors in Julia 11 0.028339287934526523 676 26 0
Yao.jl: Extensible_ Efficient Quantum Algorithm Design for Humans 12 0.028268918689989896 247 11 0
MLJ - Machine Learning in Julia 13 0.028167125632644348 831 31 0
SIMD and Cache-Aware Sorting with ChipSort.jl 14 0.028082308415941157 276 12 0
Brain Tumour Classification with Julia 15 0.026700591619932837 205 9 0
Towards Faster Sorting and Group-by operations 16 0.02669812286817836 233 10 0
Interval methods for scientific computing in Julia 17 0.026523838839529135 411 16 0
Porting a Massively Parallel Multi-GPU Application to Julia 18 0.025832857587495713 391 15 0
Multi-threading in Julia with PARTR 19 0.025742754214679534 1622 52 0
Sponsor Address: Intel 20 0.025143704515017885 308 12 0
OmniSci.jl: Bringing the open-source_ GPU-accelerated relational database to Julia 21 0.024864883639827706 220 9 0
Differentiate All The Things! 22 0.024665462154187846 1264 40 0
TrajectoryOptimization.jl: A Testbed for Optimization-Based Robotic Motion Planning 23 0.024160897717104314 196 8 0
Array Data Distribution with ArrayChannels.jl 24 0.023166022251449876 236 9 0
Turing: Probabalistic Programming in Julia 25 0.022880830939287222 618 20 0
Neural Ordinary Differential Equations with DiffEqFlux 26 0.022578150248787062 447 15 0
Analyzing and updating code with JuliaInterpreter and Revise 27 0.022316070798761998 488 16 0
Machine Learning for Social Good 28 0.022308660357245717 245 9 0
SemanticModels.jl: Not Just Another Modeling Framework 29 0.022235301142680228 348 12 0
Prototyping Visualizations for the Web with Vega and Julia 30 0.021746415944636523 464 15 0
High-Performance Portfolio Risk Aggregation 31 0.021373590041710782 435 14 0
Sponsor Address: Julia Computing 32 0.021259591655261086 257 9 0
Transducers: data-oriented abstraction for sequential and parallel algorithms on containers 33 0.021259591655261086 257 9 0
Smart House with JuliaBerry 34 0.021121725888545103 294 10 0
“Online” Estimation of Macroeconomic Models 35 0.020899194534159053 192 7 0
Keynote: Professor Steven G. Johnson 36 0.02085869181885888 1980 56 2
XLA.jl: Julia on TPUs 37 0.020501332958295607 434 16 1
Differentiable Rendering and its Applications in Deep Learning 38 0.020293526665246087 558 19 1
Why writing C interfaces in Julia is so easy 39 0.020205860025377845 460 14 0
Guaranteed Constrained and Unconstrained Global Optimisation in Julia 40 0.019956206939761637 201 7 0
What’s Bad About Julia 41 0.01994441206273697 3064 78 2
Debugging Code with JuliaInterpreter 42 0.019824192414789395 1093 29 0
Queryverse - Under the Hood 43 0.01978644467759544 352 11 0
Generating Documentation: Under the Hood of Documenter.jl 44 0.019614705458022682 293 12 1
Let’s Play Hanabi! 45 0.0195567379410433 178 9 1
Cleaning Messy Data with Julia and Gen 46 0.019224183872736767 650 18 0
Symbolic Manipulation in Julia 47 0.018908342997449187 618 17 0
Heterogeneous Agent DSGE Models in Julia at the Federal Reserve Bank of New York 48 0.018607092558516208 254 8 0
Gen: A General-Purpose Probabilistic Programming System 49 0.018341942294716116 637 17 0
Opening Remarks 50 0.0181714455971159 1161 33 2
Analyzing Social Networks with SimpleHypergraphs.jl 51 0.01714382984641421 193 6 0
The Linguistics of Puzzles: Solving Cryptic Crosswords in Julia 52 0.01714382984641421 193 6 0
Fitting Neural Ordinary Differential Equations with DiffeqFlux.jl 53 0.017018682687722348 454 12 0
A General-Purpose Toolbox for Efficient Kronecker-Based Learning 54 0.01700598255375005 248 10 1
Keynote: Dr. Steven Lee 55 0.01663208160225778 328 9 0
TimerOutputs.jl - a cheap and cheerful instrumenting profiler 56 0.01663208160225778 328 9 0
Julia + JavaScript = :heart: 57 0.01595981109195069 408 13 1
Non-Gaussian State-Estimation with JuliaRobotics/Caesar.jl 58 0.015760250628697365 167 5 0
A Probabilistic Programming Language for Switching Kalman Filters 59 0.01544632538582526 601 14 0
DataKnots.jl - an extensible_ practical and coherent algebra of query combinators 60 0.015235633617972836 217 6 0
Recommendation.jl: Building Recommender Systems in Julia 61 0.015095616120611157 219 6 0
Julia User and Developer Survey (2019) 62 0.015086588503018798 913 22 1
Julia’s Killer App(s): Implementing State Machines Simply using Multiple Dispatch 63 0.01489234969034202 467 11 0
State of the Data: JuliaData 64 0.014694928683545166 371 9 0
Soss.jl: Probabilistic Metaprogramming in Julia 65 0.014694928683545166 371 9 0
Modia3D: Modeling and Simulation of 3D-Systems in Julia 66 0.014693887875667087 338 11 1
GigaSOM.jl: Huge-scale_ high-performance cytometry clustering in Julia 67 0.01443243749382738 229 6 0
MendelIHT.jl: Generalized Linear Models for High Dimensional Genetics (GWAS) Data 68 0.014216724282161097 185 5 0
Sponsor Address: J P Morgan Chase & Co. 69 0.013751546044083098 291 7 0
Keynote: Professor Heather Miller 70 0.013709913600450875 1600 34 2
PackageCompiler 71 0.013391445788579481 1177 22 0
A New Breed of Vehicle Simulation 72 0.013102752852745161 360 8 0
Simulation and Estimation of Nonlinear Mixed Effects Models with PuMaS.jl 73 0.013055878886543004 253 6 0
Slow images_ fast numbers: Using Julia in biomedical imaging and beyond 74 0.013013065676093082 202 5 0
A Showcase for Makie 75 0.012759007888944247 858 21 2
Generic Sparse Data Structures on GPUs 76 0.012758894593484046 206 5 0
Re-designing Optim 77 0.012230516982381081 327 7 0
Electrifying Transportation with Julia 78 0.012164884910696554 216 5 0
Sponsor Address: Relational AI 79 0.011659132102660237 170 4 0
If Runtime isn’t Funtime: Controlling Compile-time Execution 80 0.011261696652033528 355 7 0
Sponsor Address: University of Maryland 81 0.010702165845826657 77 2 0
Differential Programming Tensor Networks 82 0.01058755139214851 248 5 0
Mimi.jl – Next Generation Climate Economics Modeling 83 0.01033693503177368 133 3 0
Keynote: Professor Madeleine Udell 84 0.01026458508311841 1469 33 5
The Julia Language Ephemeris and Physical Constants Reader for Solar System Bodies 85 0.00975922570948214 338 6 0
TSML (Time Series Machine Learning) 86 0.009536645328341163 440 10 1
FilePaths: File System Abstractions and Why We Need Them 87 0.009440037740742483 278 5 0
Keynote: Dr. Ted Rieger 88 0.009331741510062316 663 10 0
Ultimate Datetime 89 0.009079225514211092 289 5 0
Julia web servers deployment 90 0.00880390894590882 298 5 0
Keynote: Arch D. Robison 91 0.008551177352391089 710 15 2
Concolic Fuzzing – Or how to run a theorem prover on your Julia code 92 0.008424745245149775 163 3 0
Building a Debugger with Cassette 93 0.00817072656667643 321 5 0
Solving Delay Differential Equations with Julia 94 0.008141756341118082 243 4 0
Geometric algebra in Julia with Grassmann.jl 95 0.0073797503065606085 467 9 1
Implicit Geometry with Multi-Dimensional Bisection Method 96 0.006520349333162488 126 2 0
Polynomial and Moment Optimization in Julia and JuMP 97 0.006229686541624899 321 7 1
Pkg_ Project.toml_ Manifest.toml and Environments 98 0.005997236604208287 574 9 1
Computational topology and Boolean operations with Julia sparse arrays 99 0.00554718742442667 148 2 0
Modeling in Julia at Exascale for Power Grids 100 0.004698835954581646 277 6 1
The Climate Machine: A New Earth System Model in Julia 101 0.004047753860282182 321 6 1
Pyodide: The Scientific Python Stack Compiled to WebAssembly 102 0.00403454584465295 494 7 1
Counting On Floating Point 103 0.0038316015655475164 214 2 0
Targeting Accelerators with MLIR.jl 104 0.0035799702677530194 229 2 0
Open Source Power System Production Cost Modeling in Julia 105 0.003182061062326278 112 1 0
Static walks through dynamic programs – a conversation with type-inference. 106 0.0028852133057565205 284 2 0
Writing Maintainable Julia Code 107 8308806183545021e-20 807 15 5
LightQuery.jl 108 -0.0019332550507556367 326 3 1
Formatting Julia 109 -0.005804844313789168 466 6 3
Julia for Battery Model Parameter Estimation 110 -0.012547780555374439 131 1 1
Raising Diversity & Inclusion among Julia users 111 -0.03639844881267043 293 13 12
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Nice work. The trouble with the likes and dislikes is that it’s YouTube… :slight_smile: Most disliked video? “Raising Diversity and Inclusion”…

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The problem with that may be that the video is 2 hours long but video only starts one hour in.

Weird: How is “The Unreasonable Effectiveness of Multiple Dispatch” with a huge number of views and the largest number of likes number 8?

Based on the data, the ratio of likes to view is not as high as number 1.

I see now: it is the ratio (ratios) that play a role.

Nice job! Fun to see that people are eager to know what’s bad about Julia.

These are the top videos by views

How about ranking them simply on the basis of views? I find that #views also intuitively also makes sense.

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What I was going for was to uncover interesting content that may not have been that popular looking at the title or because presenter is not very well-known.

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The formula needs to be more thought out for sure.

Hmm, that seems too simple. You should make a neural net that classifies that topic based on views, likes, and the number of vowels. Then rank on predictive accuracy. This is Julia, come on!

Made a table that actually has links to every video

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