ML and NLP Postdocs at Northwestern University Medical School (working with >8 million patient data)
We are recruiting multiple postdoctoral fellows at Northwestern University’s Feinberg School of Medicine. The postdoctoral fellow is expected to conduct research under guidance from Dr. Yuan Luo, Associate Professor and Chief AI Scientist, Northwestern University Clinical and Translational Sciences Institute. Our group website: https://labs.feinberg.northwestern.edu/lyg/. The fellow will also have the opportunities to work closely with top-notch clinicians from Northwestern Memorial Hospital, and clinical, genetic and imaging data of >8 million patients from Northwestern Medicine Enterprise Data Warehouse.

The successful candidate will have PhD in EECS, Biomedical Informatics, IEMS, Physics or related fields with solid programming skills. Experiences in one or more of the following areas are desirable: Machine Learning (ML) and/or Natural Language Processing (NLP) and/or time series analysis and/or -omic analysis. The candidate should demonstrate good communication skills and ability to work in a collaborative environment, to coordinate and supervise part of the research project.

We offer a competitive salary and an initial appointment of 12 months, starting 2019/2020. Extension of the postdoctoral position for up to 3 years is possible. Northwestern University is an exceptional research institution that has a world-class medical school and is an emerging hub in biomedical AI; our department is located in downtown Chicago, one of the most vibrant cities in the US. Be part of a prestigious institution that offers great benefits, and enjoy our lakefront working environment.

Please send your application to Yuan Luo <yua...@northwestern.edu>, which should include:
- Curriculum vitae
- List of publications (attach a copy of one of your strongest papers)
- Contact details for 2 to 3 references

Northwestern University is an Equal Opportunity/Affirmative Action Employer.

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Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Nanni et al.: https://arxiv.org/abs/2004.05222
#Covid19Response #Data #Society #SocialNetworks
@ArtificialIntelligenceArticles

List of COVID-19 Resources for Machine Learning and Data Science Research
@ArtificialIntelligenceArticles
https://www.marktechpost.com/2020/04/12/list-of-covid-19-resources-for-machine-learning-and-data-science-research/

One knowledge that all DataScience person needs daily.

https://bit.ly/2K8XJA9

A new path for describing the fundamental theory of physics, by Wolfram et al. The end point could be one of mankind's largest intellectual accomplishments.

1) Explanation by Professor Wolfram (Inventor of Wolfram Language, and recipient of MacArthur Grant at only age 21):

https://writings.stephenwolfram.com/2020/04/finally-we-may-have-a-path-to-the-fundamental-theory-of-physics-and-its-beautiful/

2) The Wolfram Fundamental Physics Project page:

https://www.wolframphysics.org/

3) Registry of Notable Universe Models (one of which may turn out to represent our universe):

https://www.wolframphysics.org/universes/

AI v Covid-19
Jürgen Schmidhuber : http://people.idsia.ch/~juergen/ai-covid.html
#ArtificialIntelligence #Covid19 #Covid19Response

COVID-19 Anti-Viral Cure Using Deep Reinforcement Learning
Ifiok Charles : https://github.com/Ifiokcharles/COVID-19-Anti-viral-cure-using-deep-reinforcement-learning
#Covid19 #DeepLearning #ReinforcementLearning

Building a Powerful DQN in TensorFlow 2.0 (explanation & tutorial)
Sebastian Theiler: https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a
#ReinforcementLearning #MachineLearning #Python #TensorFlow

Monte Carlo Sampling using Langevin Dynamics
I wrote an article on the basics of Langevin Monte Carlo techniques. Please let me know if you find any errors.

Code: https://github.com/abdulfatir/langevin-monte-carlo
Visualization: https://www.youtube.com/watch?v=cVn0kru3hL8

I hope it's helpful for someone. :)

http://abdulfatir.com/Langevin-Monte-Carlo/

Google’s Dataset Search
"Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets & find links to where the data is.” — Natasha Noy
https://datasetsearch.research.google.com
#ArtificialIntelligence #Datasets #MachineLearning

"Jack London wrote 1,000 words every day before talking to anybody. He was totally, “Let me alone until I’ve got my thousand words!” Then he would drink or proofread the rest of the day. No, my scheduling principle is to do the thing I hate most on my to-do list. By week’s end, I’m very happy....

A person’s success in life is determined by having a high minimum, not a high maximum. If you can do something really well but there are other things at which you’re failing, the latter will hold you back. But if almost everything you do is up there, then you’ve got a good life. And so I try to learn how to get through things that others find unpleasant."


https://www.quantamagazine.org/computer-scientist-donald-knuth-cant-stop-telling-stories-20200416/

Got data and wonder if there's a formula describing it? There's a new physics-inspired AI Feynman algorithm, published today. It automates what took Kepler 4 years.
v/@tegmark
https://bit.ly/3esOWH3
"A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression
that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions
of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties.
In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network
fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics,
and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physicsbased test set, we improve the state-of-the-art success rate from 15 to 90%"