How I Computer Science – Travis Hoppe, PhD – C.S. (mis)conceptions
How I Computer Science – Travis Hoppe, PhD – C.S. (mis)conceptions
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Presentation_GWU_CSintro
Invited lecture to GWU Introductory CS classOn Github thoppe / Presentation_GWU_CSintro
How I Computer Science
Travis Hoppe, PhD
@metasemanticPostdoctoral Fellow, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
C.S. (mis)conceptions
What I thought it was... What it actually is...
Outline
Science
Machine Learning
Public Relations
Science
My Background: PhD, MS Physics, BS Mathematics
Protein "Physics"
Folding, interfaces, aggregation, electrostatics, statistical mechanics, ...
Protein Structure
Primary structure (sequence)
GSIGAASMEF CFDVFKELKV HHANENIFYC PIAIMSALAM VYLGAKDSTR TQINKVVRFD KLPGFGDEIE AQCGTSVNVH SSLRDILNQI TKPNDVYSFS LASRLYAEER YPILPEYLQC VKELYRGGLE PINFQTAADQ ARELINSWVE SQTNGIIRNV LQPSSVDSQT AMVLVNAIVF KGLWEKAFKD EDTQAMPFRV TEQESKPVQM MYQIGLFRVA SMASEKMKIL ELPFASGTMS MLVLLPDEVS GLEQLESIIN FEKLTEWTSS NVMEERKIKV YLPRMKMEEK YNLTSVLMAM GITDVFSSSA NLSGISSAES LKISQAVHAA HAEINEAGRE VVGGAEAGVD AASVSEEFRA DHPFLFCIKH IATNAVLFFG RCVSPSecondary structurehelices [red], sheets [blue Tertiary structure3D structure Higher-order structurecomplexes, aggregation
Protein interactions
Folding Binding, dimerization Aggregation, Fibril formation
How do we do it?
Statistical Potentials: Residue-residue interactions
Residue-residue interaction matrix, MJ
MJ matrix reveals biophysical structure
Higher order structure
Phase separations lead to sudden changes in liquid structure. Leibler, Nature 2004 Tanaka, Phys. Rev. E 2005
How do we model many protein-protein interactions?Can we predict aggregates from experimental structure?
Human serum albuminPDB:1AO6 OvalbuminPDB:1OVA LysozymePDB:1W6Z Bovine Serum AlbuminPDB:3V03
Where does CS come into play?
Be able to say what is possible, and what isn't!
Algorithmic design, ex. linear algebra, molecular dynamics...
Hardware design, specialized hardware, ex. Anton, GRAPE.
Predicting run-time (non-trivial at model stage!).
Scaling up!
Machine Learning
Meet The Man Who Gamed Reddit With A Bot
The goal
Train a machine to find
new & interesting things
Supervised learning
r/TIL, a subreddit short for Today I Learned
Keep only Wikipedia data
Filter for consistent writing style...
Data collection
Download WikipediaDownload all posts with score>1000 for 2013 and 2014 (~5000)Cross-reference each post to the correct Wikipedia paragraphBuild True positives (known TIL's)Build Decoys (other paragraphs in TIL's)Build unknown samples (rest of Wikipedia*)
from python import science
sqlite3, requests, bs4, pandas, numpy, scikit-learn,gensim, praw, wikipedia, nltk, stemmming.porter2
Data Wrangling
Tokenize
>> "Good muffins cost $3.88\n in New York" ['Good', 'muffins', 'cost', 'TOKEN_MONEY', 'in', 'New', 'York', 'TOKEN_EOS']
Remove "stop words"
>> "I sat on the rock" ['I', 'sat', 'on', 'rock']
Stem words
>> stem("factionally")
'faction'
"Entropy" vectors
counts the uniqueness of each word to the rest of the entry,local TF-IDF (term frequency-inverse document frequency)
Feature generation
Used Word2Vec (developed by Google),weighted by local article TF-IDF>>> model.most_similar(positive=['woman', 'king'], negative=['man'])
[('queen', 0.50882536), ...]
>>> model.doesnt_match("breakfast cereal dinner lunch".split())
'cereal'
>>> model.similarity('woman', 'man')
0.73723527
>>> model['computer'] # raw numpy vector of a word
array([-0.00449447, -0.00310097, 0.02421786, ...], dtype=float32)
Uses far fewer features to store relationships between words!
Modeling training
Used Extremely Randomized Trees, variant of Random Tree classifier.Training classifier Test Accuracy: 0.878; Test Accuracy on TP: 0.116; Test Accuracy on TN: 0.998Receiver Operating Characteristic
Does it work?
yes! look at all that sweet front-page karma...TIL The Founder Of Japans Mcdonalds Stated | 4726TIL Mike Kurtz An American Burglar Found Out That | 4123TIL A Woman That Reported 100 Incidents Of | 2899TIL During The Sentencing Of His War Crimes Trial | 1551TIL That Art Spiegelman The Creator Of Maus A | 1144TIL That Once Officially Labeled As Retarded | 640TIL Before World War Ii It Was Very Rare For | 498TIL That A Study Showed Those With A Distressed | 142TIL Frankie Fraser A Notorious English Gangster | 135TIL Rafael Quintero A Mexican Drug Trafficker | 68...
AI vs. Human (Turing test pt. 1)
I can do (almost) anything you can do better...
Turing test pt. 2
After three months and 60 submissions, I revealed to Redditthe true nature of /u/possible_urban_king.The account was promptly banned from r/todayIlearned ...thanks anonymous moderator for helping prove the test!
Where does CS come into play?
Natural language parsing, NLP.
Supervised and unsupervised learning.
Knowing the right algorithm and its limitations...
Validation and statistics.
Public Relations
Build a portfolio
Network with others
Advertise yourself!
Learn from others!
... computer science is more than just code ...
Learn from others / help others!
Stack Overflow
Challenge yourself!
PE: Math challenges that require coding. Kaggle: Machine learning for profit! TC: Mini-Hackathons and prizes! HR: Used in interviews.
Share your code!
github
Meetups and Hackathons
Meetup
Shameless plug and Extra Credit!
DC Hack && Tell
Next event October 13th!
Thanks, you!
For class participation credit, fill out this questionnaire: