Kuhan Wang developed a machine learning pipeline to analyze textual features on content URLs and optimize advertisement placement for a company. The pipeline involved scraping URL data, processing text, modeling feature importance, and extracting top keywords. It was delivered to the company in Python code. Wang's dissertation background involved searching for signatures of microscopic black holes and exotic gravity states using data from the Large Hadron Collider particle detector.

1. Renaissance Technologies Presentation
-
Insight Data Science
Kuhan Wang
October 21th, 2015
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2. Introduction
Insight Data Science: developed
a machine learning pipeline in a
consulting project.
PhD Particle Physics, McGill
University, researcher on the Large
Hadron Collider.
Lead the search for microscopic
black holes and exotic gravity
states in the ATLAS Collaboration.
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3. Consulting Scenario
Company X wishes to maximize user engagement through
optimal placement of advertisements on content URLs.
Ad Type: Tourism
Keyword: Cuba
Keyword:
Package Tour
Keyword: Airplane
Ad Type X
Keyword 1
Keyword 2
Keyword 3
Keyword N
.
.
.
Example: Tourism ads not ideal on investment content URL.
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4. A Pipeline to Analyze Textual Features
Developed and implemented a pipeline to analyze
importance of textual feature on content URLs relative to
engagement.
Scrape
URL
Process
Text
Model
Features
Extract
Keywords
Update
Keywords
Collect Data, Reiterate
Begin
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5. User Engagement Data
Occurrences
Counts
Summary of Engagement Data
Page Loaded
Ad Viewed
Ad Clicked
Summary of Engagement Data
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6. Modeling
Attempted linear regression.
Classify engagement as yes/no.
Word Count
0 1 2 3 4 5 6 7 8 9 10
Probability[%]
0
0.2
0.4
0.6
0.8
1
Logistic Classification Model
Ad Clicked
Ad Not Clicked
Logistic Classification Model
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7. Validation
Randomly split data into training/test sets.
- Distribution of validation scores (shown for 50/50 split).
Precision
0.55 0.6 0.65 0.7 0.75 0.8 0.85
Recall
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
NumberofMCToys
0
10
20
30
40
50
60
70
80
Ad Type 1
Distribution of Precision vs Recall for 50.0% Test/Train
〉Precision, Recall〈
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8. Deliverables
Extracted keywords:
Rank Ad Type 1 Ad Type 2 Ad Type 3 Ad Type 4
1 debt coordinator mortgage gold
2 gift administrative home 0
3 profit minimum procurement stock
4 check minimum wage loan fund
5 balance reports trustee event
Pipeline in Python is delivered to company for
implementation.
Project details: http://kuhanw.zohosites.com/.
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9. 9 / 20

10. Dissertation Project
Particle Colliders recreate conditions in the early universe.
Searched for signatures of microscopic gravity at the Large Hadron
Collider.
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11. The Large Hadron Collider
27 km ring, most powerful particle accelerator built to date.
- 13 TeV collisions.
ATLAS: a giant particle detector.
Produced black holes leave debris due to evaporation inside detector.
2008JINST3S08001
Figure 2.1: Schematic layout of the LHC (Beam 1- clockwise, Beam 2 — anticlockwise).
systems. The insertion at Point 4 contains two RF systems: one independent system for each LHC
beam. The straight section at Point 6 contains the beam dump insertion, where the two beams are
vertically extracted from the machine using a combination of horizontally deflecting fast-pulsed
(’kicker’) magnets and vertically-deflecting double steel septum magnets. Each beam features an
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12. Data Processing
Developed complete analysis
pipeline in C++.
- Processed ∼10 TB of LHC data
using distributed computing
methods.
Raw Data From Detector
Processed Data with Objects
Analysis Data Structure
Histogram Data for Final Fitting
~ TB
~ 100 GB
~ GB
~ TB
~ MB
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13. Technical Analysis
~Energy
of event
Black Hole
Signals
Background
Prediction
Quantify compatibility with likelihood model.
L(ns|µ, b, θ) = P(ns|s, µ, b, θ) ×
i
Nsyst(θ0, θ, σθ)i . (1)
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14. Results
Placed leading constraints on models of microscopic gravity physics.
Models of n
extra
dimensions
Planck
mass of
theory
95% CL
Exclusion
Contours
Black Hole
Mass
Model Type
Public results: JHEP 07 (2015) 032, arXiv:1503.08988 [hep-ex]
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15. ATLAS Detector
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16. Thank you for your time.
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17. Large Extra Spatial Dimensions
The size and number of extra spatial dimensions suppress the
observed gravitational strength.
Observed gravity is weaker than intrinsic gravity within the
bulk.
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18. Backup
Feature Frequency/Documents
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
RelativeNumberofDocuments[%]
4−
10
3−
10
2−
10
1−
10
1
Ad Type 1Ad Type 1
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19. FeatureRank
Kuhan Wang1
1. Insight Data Science
October 2, 2015
Abstract
FeatureRank is a software tool for extracting correlations between text
ngram features and user engagement, thereby optimizing the placement
of financial widgets on URL articles.
1 Directory Structure
• /
processing.py
Pre-processing to parse relevant information from engagement csv files.
crawl.py
A simple web crawler that pulls the title and < p > tag text from URLs.
FeatureRank.py
Driver file to execute main functions.
feature_extraction_model.py
The core program that contains the machine learning algorithms.
post_processing.py
Post processing to produce evaluation metrics and ngram rankings.
web_text_data_set_1_2.json 19 / 20

20. Precision
0.55 0.6 0.65 0.7 0.75 0.8 0.85
Recall
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
NumberofMCToys
0
5
10
15
20
25
Ad Type 1
Distribution of Precision vs Recall for 0.33% Test/Train
〉Precision, Recall〈
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