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 
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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
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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 . There are very interesting talks, and you can learn a lot (even about solving cryptic crosswords 
).
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 = 
|
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… 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.
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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