TEAM - eventpower-res.cloudinary.com
Transcript of TEAM - eventpower-res.cloudinary.com
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TEAMCraig Michoski
CEO, PhD
Founder
Matthieu Vitse
CCO, PhD
Co-Founder
Dongyang Kuang
Dir. of ML, PhD
David Hatch
CSO, PhD
Co-Founder
Steph-Yves Louis
ML Lead, PhD
Todd Oliver
Chief Computational
Engr, PhD
Co-Founder
Siwei Luo
Physicist/Data
Scientist, PhD
Webpage
Core
1. Background
2. MLaaS Model
3. Example Capabilities & Applications
Anomaly Detectors
Audio/Visual Learning
Control Optimization
Machine Intuition
Spectral Optimization
Analysis Tools
Optimal Design & Statistical Inference
Database Design3
Overview
⇌
You
Background
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Active and current collaborations:
Background
● is a startup based in Austin TX
● Specializing in Customizable/Bespoke MLaaS
(Machine Learning as a Service), B2B context
● 100+ years of combined experience
● Founders from Oden Institute for
Computational Engr & Science (UT Austin) Number 1 Ranked Computational Engr Institute in the
World! – Center for World University Rankings (CWUR)
● PhDs, high level expertise in ML, AI,
computational engineering, computer science,
mathematics, physics, chemistry, engineering –
Multidisciplinary 200+ peer reviewed publications in computer science,
engineering, statistics, applied math, physics, chemistry,
machine learning, computational numerics, pattern
recognition, hardware performance and optimization,
BCI/HCI, bioinformatics, ….5
Broad Working Background
● Large datasets and databases
○ HPC, billions of CPU hours experience
○ Development
● Industrial Scale Optimization and
Anomaly Detection algorithms
● Domain Knowledge: Engineering,
Computer Science, Plasma Physics, and
Chemistry
● Software Engineering
● Audio/Visual (Machine) Learning
● Deep Learning, Reinforcement Learning,
Active Learning, Genetic Programming,
Bayesian Networks & Bayesian Inference,
Unsupervised Learning, Cluster Analysis,
….
● The Ousai platform6
MLaaS Model
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Machine Learning as a Service
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Machine Learning, A.I. Expertise
Computational Science, Engineering, Physics,
Chemistry
Applied Mathematics & Statistics
Rapid effective prototyping and scaling of
ML/AI requires extensive:
1. Breadth of knowledge
2. Technical experience
3. Domain knowledge
4. Integral systems understanding
Ousai Platform
Data scientists/ML in relatively short
supply, tend to have paucity of domain
knowledge → require training
Rare species in the data science
ecosystem:
Example Architectures
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Example Capabilities & Applications
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Results Per Signal
Accuracy Precision Recall
98.05% 0.97 0.97
Composite Shot Score
Shot e.g. 36 Gun Sync Score
5055 92%
Anomaly
Anomaly
NormalOusai Neural Network Classifier Tools:
1. Identify anomalous performance
2. Throw alarm and provide insight
3. Optimize calibration / Real time operation
4. Save $$ via shot efficiency
e.g. Multichannel Rogowski sync in MIF reactors
• Assure switches fire in sync
• Identify gun failures and anomalies
Anomaly Detectors
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Neural Network Engagement Diagnostic Tool
Audio
Video
Python Backend
Neural Networks
Remote/Hybrid
Classroom Tech
1. Real-time Student
Engagement Indicators
2. Student Engagement
Incentivization Tools
3. Post Class Data Analytics
ICCD
Camera
Predict/Classify
Implosion:
1. Symmetry
2. Strength
3. Quality
4. 3D Reconstruction
Audio/Visual attention tool for
online/remote learning environments
Plasma liner identification tool and
stagnation point analyzer
Audio / Visual Learning in Ousai
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Control Optimization Ousai – Control Optimization Tools
Controls Plasma output
Controls / Settings
Valve settings 45.0 psig
Press Gun Switch 23.0 psig
Trigger Switch 12.0 psig
Chamber Pressure 7e-5 Torr
Charge Voltage 4.3 kV
Time 1122.0 μs
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Observations
Density 1.94× 1017 g/cm^3
Velocity 78.9474 km/s
Τ1 2 Height-Width 12.5251
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Significance Analysis
NormalizationData Driven Encoding
Optimization
Accurate Output
Significance Analysis
NormalizationActive Learning
Optimization
Accurate Prediction
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Real time capabilitiesRemote controlled
Automation
Adaptability
Transfer Learning
L R
B
F
Wrist movement
e.g. Forward Prediction
1. Data with high noise/signal ratios
2. Various time length: seconds to
minutes
3. Weakly spatially/temporally
related features
Activation
Maximization
Ousai includes BCI/HCI tools for extracting patterns from
complex data streams, to develop Machine Intuition.
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Machine Intuition Interfaces
Achieved
1. State-of-the-art, > 90% Accuracy
2. Minimum preprocessing, < 3K
parameters
3. Small variation in prediction
Dongyang Kuang and Craig Michoski
2021 J. Neural Eng. 18 016006
Emotion Recognition from functional
neuroimaging signal (MEG, EEG, MET,
fNIRS, etc)
Backward Analysis – ‘Symmetry of concept’
Att
enti
on
wei
ghts
Activation maximization Temporal attention
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Brain / Human Computer Interfaces
Ousai includes BCI/HCI tools for extracting patterns from
complex data streams, to develop Machine Intuition.
Dongyang Kuang and Craig Michoski 2021
Pattern Recognition (In Review)
Sample observed spectrum
Infer & Optimize – e.g. temperature, density,
chemical constituent profiles, etc., in chemicals /
plasmas from spectra:○ Automate/Optimize
○ Reproducibility/Consistency
○ Uncertainty Characterization
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Suite of Spectral Optimization Tools
* Can identify metallic/organic/inorganic contaminants
Given emission characteristics
𝐹𝑊𝐻𝑀 = −
0.999126 + 0.031432𝑝𝑚𝑠 𝑣𝑚
𝑠
𝑝𝑔𝑠 − 0.0026
𝑝𝑔𝑝𝑔𝑠
𝑣𝑔𝑠
0.0275
In general, a symbolic expression with higher accuracy is achieved by increasing the
number of terms and/or order of nonlinearity.
System discovery from data
1. Variational relations
2. Stochasticities
3. Model enhancement
1. Model discovery
2. Model extraction
3. Symbolic regression
4. Optimization
Ousai – Analysis Tools
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* Can be made real time capable!
Ousai incorporates statistical inference
models – GPR, VNN, Bayesian
Optimization, Active Learning, etc.
Example in Derived Features:
• Propagating uncertainty often done
inconsistently
• Must preserve basic statistical
principles (e.g. Bayes)
• Black box approaches can produce
non-predictive, non-informative, over-
interpreted models
• Model priors must be understood
• Full Bayesian requires systems
understanding, e.g. integrating
engineering, physics, chemistry,
uncertainties/statistics, diagnostic
systems …
e.g. Inference of edge plasmas in tokamaksOptimal Design & Statistical Inference
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Experience with:
1. Database creation, management, ETL
2. Cloud-based solutions
3. High Performance Computing and Storage
4. Database optimization
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Database Experience
Database
Thank You for Your Time!
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