Ishanu Chattopadhyay PRO
ML Data Science Biomedicine Social Science Faculty
Ishanu Chattopadhyay
Assistant Professor of
Data Science
University of Chicago
Rare and Extreme Events In Complex Dynamical Systems
Urban Crime
Extreme weather phenomena
Seismic events
Long-range memory
Non-trivial stochastic effects
Can we learn stochastic phenomena non-parametrically?
Can we learn stochastic phenomena non-parametrically?
?
State structure :
self-similarity in dynamical systems
The Fractalnet architecture
No back-propagation
Deep Learning without Neural Networks
Lets Predict Crime
Philadelphia
The Problem:
>3 days in advance
Within ~2 city blocks
ONLY Past eventlog as input
Mean AUC
Property crime: 81%
Violent crime: 84%
Spatial tiles:
0.003 deg latitude, 0.003 deg longitude
0.25 miles across
Time-period:
Training: Jan 1 2016 - Dec 31 2018
Out-of-sample test: Jan 1 2019 - April 1 2019
Prediction Performance (Philadelphia)
sensitivity 0.90
ppv 0.87
100 crimes
Raise 103 flags
90 correct flags
13 false positives
10 missed
3 day ahead prediction
Jan 1 2019
to
April 1 2019
Play Movie
Triangles: actual events
heatmap: predicted risk 3 days ahead
Triple homicide incident
Jan 7 2019
https://www.inquirer.com/crime/kensington-triple-shooting-homicide-philadelphia-police-20190107.html
Triangles: actual events
heatmap: predicted risk 3 days ahead
Predicting
extreme weather
Outperforms pure physics-based models at longer horizons
Next:
Model and predict world events
Nearly
Hands-free
No feature-engineering
The Issue with Neural Networks
only certain specific structures may be identified
By Ishanu Chattopadhyay
Predictive modeling of crime and rare phenomena using fractal nets
ML Data Science Biomedicine Social Science Faculty