Co-Founder & CEO
Co-Founder & CTO
Co-Founder & Chief Scientist
VP R&D
VP product
Senior Strategic Advisor
Director of MedTech
Scientific Content Advisor
Tech Lead
Senior Data Scientist
Product & Operations Manager
Software Engineer
Data Scientist
ML-Ops Engineer
Software Engineer
Sr. Algorithms Developer
Data Scientist
Knowledge Engineer
Data Engineer
Co-Founder &
Chief Scientist
Co-Founder &
CEO
Co-Founder &
CTO
VP R&D
VP product
Senior Strategic Advisor
Director of MedTech
Scientific Content Advisor
Tech Lead
Software Engineer
Data Engineer
Data Scientist
Product & Operations Manager
Senior Data Scientist
Data Scientist
ML-Ops Engineer
Data Engineer
Data Engineer
Knowledge Engineer
Treat data itself as a product. Have dedicated algorithms improve it before the ML parts.
Assess the impact of your data properties regarding size, noise and sparseness levels.
Employ various simulation techniques in order to overcome data challenges.
Transform raw data into more meaningful representations to enhance machine learning.
Reduce big data jungles to the optimal combination of the most informative features.
Generate new, creative feature relations by automatic mathematical manipulations.
Instead of having one model to treat the whole dataset, split data into several packages.
Run a variety of algorithms, each on its own data package, to combine their advantages.
Create new boosting mechanisms of various datasets and algorithms working in parallel.
Evolve traditional structures of classification models to express complex behaviors.
Improve ML training processes to reach the globally-optimal tuned model.
Create more free-form classification rules to increase inference accuracy.
Free your algorithm from restricted centroids or linkage methods.
Integrate more domain expertise into cluster definitions.
Match data points based on real proximity rather than clustering procedures.
Catch hidden phenomena by considering novel pattern definitions.
Discover more authentic associations between patterns.
Formulate new, more predictive distance metrics between patterns.
Evolve your current AI system automatically by tuning its parameters for better results.
Run a powerful trial-and-error search of multiple parameters and combinations at once.
Repeat this process over and over again (24/7 GA), to always strive for perfection.
Project time series into either less or more complex dimensions.
Align historical events to each other according to flexible rules.
Find hidden relationships between features to better understand data process behavior.
Mix regression models with themselves or with classification models.
Make large-scale expeditions to draw insights from raw data.
Predict better by finding unexpected relationships between hypotheses.