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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

…

Observations

Density 1.94× 1017 g/cm^3

Velocity 78.9474 km/s

Τ1 2 Height-Width 12.5251

…

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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