Energy

Gunnar CarlssonPresident & Co-Founder, Ayasdi

2000 2005 2008 2010 2013

AYASDI Company Timeline

Ayasdi’s approach is using Topological Data Analysis one of the top 10 innovations developed at DARPA in the last decade.

“ ”Tony Tether, Director

Defense Advanced Research Projects Agency (2001-2009)

Data has shape, Shape has meaning.

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Linear Methods

Clustering

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Circular

Limitations with Current Methods

Machine Learning

Statistics • Hypothesis focused• Model Driven

• Formula Driven• Black-box

analytics

Miss subtle signals

Missed systematic phenomena

Real Data = Real Complex

Real World Data does not adhere to models

Deep analysis requires taking the “model” assumption out of the equation

Ayasdi Core analyzes the data you have, not the data you want to have.

Ayasdi’s Approach

Key Takeaways

1. Segmentation• Ex: Understanding how

differences in completion can impact recovery

2. Subtle Feature Extraction• Ex: identifying additional

geological features that play a role in predicting recovery

3. Anomaly Detection• Ex: Understanding state

changes in SAGD wells

Machine Learning

Statistics

Topological Data Analysis

Key Properties of TDA

Coordinate Freeness1

Source Agnostic

Key Properties of TDA

Deformation Invariance2

Noise & Null Tolerant

Key Properties of TDA

Compressed Representation3

Expose All Signals

Mapping

F

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Network Orientation

Nodes are groups of similar objects

Edges connect similar nodes

Colors let you see values of interest

Position of a node on the screen doesn’t matter

The shape of the network shows underlying properties of data that yield insights and meaning

Relationships between diabetic, pre-diabetic and healthy populations

Glucose Level

InsulinResponse

Healthy Pre-Diabetic Overt-Diabetic

Analyzing Breast Cancer Data

Death

Survived

Relapsed

No Relapse

Model ValidationUnderstanding failure and improving performance

Colored by model prediction Colored by actual outcomes

SurvivalLow High