ML and AI Postdoc Opportunity at Northwestern University Feinberg School of Medicine

We are recruiting a postdoctoral fellow in the Division of Health and Biomedical Informatics at Northwestern University’s Feinberg School of Medicine. The postdoctoral fellow is expected to conduct research under guidance from Dr. Yuan Luo, 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 strong supporting staff from Northwestern Medicine Enterprise Data Warehouse Team.

The successful candidate will have PhD in EECS, Biomedical Informatics, IEMS, Physics or related fields with solid programming skills. Experiences in some 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. 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 informatics; 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 <[email protected]>, 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.

There is a really cool tool called SEER

It recently obtained real-time face-mirroring ability.
SEER is created by Takayuki Todo
Link: https://www.takayukitodo.com/

How to Segment Buildings on Drone Imagery with Fast.ai & Cloud-Native GeoData Tools
Blog by Dave Luo : https://medium.com/@anthropoco/how-to-segment-buildings-on-drone-imagery-with-fast-ai-cloud-native-geodata-tools-ae249612c321
#DeepLearning #Geospatial #Drones #MachineLearning #Tutorial

Lifelong GAN: Continual Learning for Conditional Image Generation
Zhai et al.: https://arxiv.org/abs/1907.10107
#deeplearning #generativemodels #GAN

A paper posted online this month has settled a nearly 30-year-old conjecture about the structure of the fundamental building blocks of computer circuits. This “sensitivity” conjecture has stumped many of the most prominent computer scientists over the years, yet the new proof is so simple that one researcher summed it up in a single tweet.
“This conjecture has stood as one of the most frustrating and embarrassing open problems in all of combinatorics and theoretical computer science,” wrote Scott Aaronson of the University of Texas, Austin, in a blog post. “The list of people who tried to solve it and failed is like a who’s who of discrete math and theoretical computer science,” he added in an email.
The conjecture concerns Boolean functions, rules for transforming a string of input bits (0s and 1s) into a single output bit. One such rule is to output a 1 provided any of the input bits is 1, and a 0 otherwise; another rule is to output a 0 if the string has an even number of 1s, and a 1 otherwise. Every computer circuit is some combination of Boolean functions, making them “the bricks and mortar of whatever you’re doing in computer science,” said Rocco Servedio of Columbia University.
Click on the article to read the solution
https://www.quantamagazine.org/mathematician-solves-computer-science-conjecture-in-two-pages-20190725/

Self-Supervised Learning for the win!
Literally. For winning cash.
FASSL is not facile.

https://sites.google.com/view/fb-ssl-challenge-iccv19/home

The Nature of Machine Learning (evolution vs. experience vs. abstract) - Video by Art of the Problem

https://www.youtube.com/watch?v=yLAwDEfzqRw

Release of 27 pretrained models for NLP / NLU for PyTorch

Hugging Face open sources a new library that contains up to 27 pretrained models to conduct state-of-the-art NLP/NLU tasks.

Link: https://medium.com/dair-ai/pytorch-transformers-for-state-of-the-art-nlp-3348911ffa5b

#SOTA #NLP #NLU #PyTorch #opensource

Approximate Bayesian inference for a "steps and turns" continuous-time random walk observ... arxiv.org/abs/1907.10115

Lifelong GAN: Continual Learning for Conditional Image Generation. arxiv.org/abs/1907.10107

Exploring Factors for Improving Low Resolution Face Recognition. arxiv.org/abs/1907.10104

Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein... arxiv.org/abs/1907.10087

Meet Your AI-Generated Dream Anime Girl

https://medium.com/syncedreview/meet-your-ai-generated-dream-anime-girl-5dc149c5eab4

ACL 2019 acceptance rates

https://acl2019pcblog.fileli.unipi.it/?p=161

New fast.ai course: A Code-First Introduction to Natural Language Processing

https://www.fast.ai/2019/07/08/fastai-nlp/

Github: https://github.com/fastai/course-nlp

Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9

Artificial Intelligence can generate interesting story endings. It picks important phrases of a story and creates more “diverse” endings.

Great work on WriterForcing by Carnegie Mellon University, which takes Seq2Seq models to the next level.

Read arxiv.org/pdf/1907.08259

A Neural Network Based On-device Learning Anomaly Detector for Edge Devices. arxiv.org/abs/1907.10147