Artificial Intelligence and Machine Learning in Research

P U B L I C S E C T O R
S U M M I T
Washingt on, DC

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Artificial Intelligence and Machine Learning in Research
Sanjay Padhi
AWS Research Initiatives
Amazon Web Services
S e s s i o n I D 3 0 1 0 6 9
Cornelia Caragea
Professor of Computer Science
University of Illinois at Chicago
Lise St. Denis
Research Scientist
UC Colorado Boulder, CIRES Earthlab

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Artificial Intelligence
Amazon AI: https://aws.amazon.com/amazon-ai/
Amazon Machine Learning: https://aws.amazon.com/machine-learning/
A system or service which can perform tasks that usually require human intelligence

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Machine Learning
Supervised Learning
Unsupervised Learning
Supervised Learning:
- Learning from “labelled data”
- Classification, Regression, Prediction, Function Approx.
Unsupervised Learning:
- Method to find similar groups in the data clusters
- Groups that are similar to near clusters
- Groups different far away from each other
Reinforcement Learning

S U M M I T
• Detecting Financial Market Manipulation: An Integrated Data- and Model-Driven Approach
University of Michigan, Georgia Tech
• Scalable and Interpretable machine learning: bridging mechanistic and data-driven modeling in the biological sciences
University of California, Berkeley
• Taming Big Networks via Embedding
University of Virginia, University of Illinois at Urbana-Champaign
• Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media
Kansas State University, University of North Texas and Pennsylvania State University
• Distributed Semi-Supervised Training of Deep Models and Its Applications in Video Understanding
University of Central Florida
Examples of Research projects funded by NSF with Big Data Awards on AWS
“In today's era of data-driven science and engineering, we are pleased to work with the AWS Research Initiative via the NSF
BIGDATA program, to provide cloud resources for our Nation’s researchers to foster and accelerate discovery and innovation."
Dr. Jim Kurose, Assistant Director, CISE, National Science Foundation (NSF)

S U M M I T
• Automating Analysis and Feedback to Improve Mathematics Teachers' Classroom
University of Colorado, Boulder
• Collaborative Research: Protecting Yourself from Wildfire Smoke: Big Data Driven Adaptive Air Quality Prediction
Methodologies
University of Nevada, Reno
• Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data
Emory University
• Collaborative Research: Optimizing Log-Structured-Merge-Based Big Data Management Systems
University of California, Riverside
• Collaborative Research: Intelligent Solutions for Navigating Big Data from the Arctic and Antarctic
Texas A&M University Corpus Christi
Examples of Research projects funded by NSF with 2018 Big Data Award on AWS
“This NSF big data award, coupled with AWS’s advanced computational and analytic services, is expected to help unlock the
secrets of interactions among biomolecules that drive human and animal biological processes.”
Dr. Bin Yu, Chancellor’s Professor at University of California,

S U M M I T
Big Data Market Manipulation Project
• Collaboration between U.Michigan and Georgia Tech
• Sponsored by NSF BIGDATA program, computational support from AWS
• Interdisciplinary: Computer Science (AI/ML), Finance, Law & Public Policy
Goal: New techniques for detecting and mitigating manipulation

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Market Manipulation

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Spoofing
Definition: Practice of
submitting large spurious orders
to buy or sell some security to
mislead other traders about
market state
True buy
order
True sell
order
Spoofing sell
order
Spoofing buy
order
Source: UK Financial Conduct Authority

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Approach
Model spoofing and other manipulation strategies
Develop new surveillance and audit algorithm based on signatures extracted from model-augmented data streams
background
trading model
market
data
calibration
spoofing
strategy
optimization
signature
extractor
surveillance/audit
algorithms
machine learning
Generate realistic financial order streams

S U M M I T
An Agent-Based Model of Spoofing
• Simulate financial markets as complex multi-agent
systems.
• Elucidate strategic choices faced by market
participants.
• Evaluate market performance and the impact of
spoofing given agent interactions at Nash equilibrium.

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Machine Learning

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Generative Adversarial Network (GAN)
Interleave training of two deep Neural Net

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Order Book Evolution
Generator outputs next order, conditional on order book state and history

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Generator NN Architecture
LSTM layer
Noise input
Convolutional layers
after a single
fully connected layer
Pre-trained
CDA network
Time
History xj of length

Managed Distributed Training with Flexibility
Training code
• Matrix Factorization
• Regression
• Principal Component Analysis
• K-Means Clustering
• Gradient Boosted Trees
• And More!
Amazon provided Algorithms
Bring Your Own Script (IM builds the Container)
Bring Your Own Algorithm (You build the Container)
I
ML Training Service
Fetch Training data
Save Model Artifacts
Fully
managed –
Secured
–
Amazon ECR
Save Inference Image
IM Estimators in
Apache Spark
CPU GPU HPO
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Results
Fake means generated Order
Results compare - Real and
Fake Order distributions
Training
Simulated financial Market
Thinly traded stock
- PN, 20k orders/day
Thickly traded stock:
- GOOG, 230k order/day
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M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
Vision | Documents | Speech | Language | Chatbots | Forecasting | Recommendations
M L S E R V I C E S Data labeling | Pre-built algorithms & notebooks | One-click training and deployment
Build, train, and deploy machine learning models fast
Easily add intelligence to applications without machine
learning skills
Flexibility & choice, highest-performing infrastructure
Support for ML frameworks | Compute options purpose-built for ML
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M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3 E C 2 C 5 F P G A s G R E E N G R A S S
E L A S T I C
I N F E R E N C E
Language Forecasting Recommendations
T E X T R A C T C O M P R E H E N D &
C O M P R E H E N D
M E D I C A L
F O R E C A S T P E R S O N A L I Z E
A M A Z O N
S A G E M A K E R
G R O U N D T R U T H
N O T E B O O K S
A W S M A R K E T P L A C E
A L G O R I T H M S
R E I N F O R C E M E N T
L E A R N I N G
T R A I N I N G
O P T I M I Z A T I O N
( N E O )
D E P L O Y M E N T
H O S T I N G
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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
S U M M I T

• Optical Nerve Damage & Macular
Degeneration
• Radiology Image Recognition
• Readmission Risk
• Clinical Decision Support (Diagnosis &
Treatment)
• Fitness Incentives
• Hospital Stay Length & Re-admittance
Predictions
• Pharmaceutical Sales Force
Optimization
• Personalized Medicine
• Structure-based Drug Design
• Knowledge Curation for Drug Discovery
• Cohort Selection
• Etiology of Disease
• Disease Onset Likelihood
• Clinical Trial Intelligence
• Outbreak Prediction
• Anomaly Detection
• Computational Drug Design
• Pictogram Classification
• Patient Condition Forecasting
D i a g n o s i s & O u t c o m e s
Health and Life Sciences
D r u g D i s c o v e r y & O p s
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Stanford: Automatic Grading of Diabetic Retinopathy through Deep Learning using AWS
S U M M I T

FDA-Approved Medical Imaging
S U M M I T

HeartFlow creates personalized medical technology
using deep learning to help diagnose heart disease.
• Analyzes CT scans to create accurate 3D model
of a patient’s heart and coronary arteries
• Simulates the flow of blood in each vessel to
provide details on blockages and blood flow with
which physicians can base a diagnosis
• The 100% non-invasive solution means 60% of
patients can avoid an angiogram, reducing
healthcare system costs by 25%
Detecting Heart Disease with Deep Learning
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Computer Vision/NLP
• Amazon Rekognition provides
computer vision
• Amazon Comprehend Medical
provides natural language
processing (NLP)
• No algorithm development or model
training required
• Implemented in less than 100 lines of
Python
S U M M I T

Medical Image Classification
• Use machine learning to detect diseases
shown by medical images
• ML models created using Amazon
SageMaker
• Train a model on over 100,000 images in
about 9 hours using large GPU instances
• Generalized pattern for model training and
threshold selection can be applied to any
image modality
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Research for Social Good (Collaboration: AWS, NSF and University of Nevada)
https://www.unr.edu/nevada-today/news/2019/big-data-wildfires
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Machine Learning for improving Disaster Management and Response usin
Hurricane Irma predicted path Hurricane Irma real path
Source: Weather Channel
S U M M I T

S U M M I T
Deep Learning for
Disaster Management and Response
Cornelia Caragea
Professor of Computer Science
University of Illinois at Chicago
S e s s i o n I D 3 0 1 0 6 9

S U M M I T
Acknowledgements
NSF BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches
for Classifying Crisis Related Data on Social Media

S U M M I T
Social Networking Sites
• Connect people all around the world.
• Have become part of our daily lives and
everyday communication patterns.

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Big (Disaster) Data
• The use of social media is on a rise, and so is the use of social
media in disaster events.
• Deadly disasters happen all the time -> source of Big (Disaster)
Data

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Big (Disaster) Data – Hurricane Sandy

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Big (Disaster) Data - 2011 Tohoku Earthquake

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Importance of Social Media Data in Disasters
• Micro-blogging data from crowds of
non-professional participants during
disasters are of significant value.
Researchers assert that bystanders “on the ground are uniquely positioned to
share information that may not yet be available elsewhere in the information
space...” [Starbird et al., 2010].

S U M M I T
Challenges with Using Social Media Data
• Scholars of disasters see hope in social media, and argue that can
produce accurate results, often in advance of official communications.
• Still, there has not been much uptake of message data by large-scale,
disaster response organizations.
• Response organizations operate in conditions of extreme uncertainty
• With the exponential increase in social media data, so comes the increase in irrelevant data
• Diminish people’s ability to find information that they need in order to organize relief efforts, find help
and potentially save lives.

S U M M I T
How To Identify Useful Content in Twitter?
• Direct Twitter search:
• Keyword-based search
• “oklahoma tornado"
• "oklahoma" or "tornado"
• #oklahomatornado
• Location-based search
• collect postings containing geographical coordinates inside the affected areas
Irrelevant tweet: “I’ve lived in Oklahoma since I was born.”
• Manual selection: too time consuming
• Hence, there is an increasing need to automatically extract
appropriate information, which could make improvements in the
response process.

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Deep Learning

S U M M I T
• Use Twitter Streaming API to crawl tweets
posted during crisis events
• Parse the tweet JSON objects to extract
tweet text, hashtags, media information,
user information, and geo-location (when
available)
• Perform text classification, natural language
processing and text analytics on the tweet
text
Data collection and analysis

S U M M I T
Tweet classification

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Classes of machine learning algorithms
Supervised learning [Imran et al., 2013; Ashktorab et al., 2014; Caragea et al., 2014;
Imran et al., 2018]
• Labeled tweets needed, but not readily available for an emergent disaster
Domain adaptation [Li et al., 2015; Li et al., 2017, Alam et al., 2018, Mazloom et al.,
2018]
• Knowledge from a prior source disaster is transferred to a target disaster
Unsupervised learning, e.g., topic modeling [Resch et al., 2017]
• Topic modeling can help associate topics/categories with tweets

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How it works

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Comprehend workflow
Identify a tweet’s language
Identify key phrases to help categorize tweets
Identify topics in a collection of tweets
• Use them to categorize tweets
• Enhance situational awareness
Identify entities in actionable tweets
• The goal is to understand who, where, when, what, why
Identify the sentiment of the tweets
• Enhance situational awareness

S U M M I T
Example: topics
Hurricane degenerates Hurricane regenerates
Harvey degenerates into tropical wave
RT Hurricane Hunter plane finds that the
remnants of Harvey have not regenerated
into tropical cyclone
RT Harvey showing signs of regeneration over western Caribbean Sea
amp will produce heavy rainfall storms this week hit
RT Active tropics continue redvlpmt of Harvey likely as well as
the potential for two new tropical cyclones next days on

S U M M I T
Deep Learning for Identifying Informative Tweets
We study the effectiveness of deep neural networks in comparison
with traditional machine learning classifiers for identifying
informative messages from social media streams (Twitter).

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Results

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Using locations identified by Comprehend to track hurricane path
Source: Wikipedia
Source: Weather Channel

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Mining Social Media During Disasters using
AWS and Machine Learning
Lise St. Denis
Research Scientist
University of Colorado Boulder, CIRES Earth Lab
S e s s i o n I D : 3 0 1 0 6 9

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Social Media in Emergency Response

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2014 Carlton Complex Wildfire, Eastern Washington

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S U M M I T
“In the thick of it …what I really want
to know is what I don’t know.”
Kris Eriksen
PIO, Portland NIMO

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Filtering Concept

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Filtered Content

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Data Collection Process
• Real-time
• Flexible
• Integrated

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Future Data Collection
• Independent collections
• Multiple processes
• Collection User Interface

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Account Classification

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Confusion Matrix: Account Classification

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Tweet Content Analysis: filtered out

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Tweet Content Analysis: prioritized
• Life safety
• Hazards
• Criticism
• Misinformation
• Rumor
• Information gaps
• Situational awareness

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Next Steps
• Collaborative effort
• Early feedback*
• Flexible framework

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S U M M I T

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Thank you!
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Sanjay Padhi: sanpadhi@amazon.com
Cornelia Caragea: cornelia@uic.edu
Lise St. Denis: Lise.St.Denis@Colorado.edu

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