Daniel Svonava

Superlinked

superlinked.com makes it easier to turn data into vector embeddings

Co-Founder & CEO

creable.io

creable.com is the world's biggest influencer database (300M creators)

Advisory Board Member

Tater Games

tatergames.com is a data-driven game studio

Board Member

Primeflow

primeflow.com built tools for super-connectors and their network-driven business

Founder & CTO

Google

2016 - 2018
Tech-lead for YouTube Reservation Ads forecasting (Can a reservation be booked or not?) and pricing (How much should we charge for it?).

2015
Tech-lead for YouTube Video Ads traffic prediction and cross-Google Ads reach prediction, enabling ad buying in Adwords and DBM.
Focus on making Google Ads useful for Brand advertisers.

2013 - 2014
Within YouTube monetization I'm constantly switching between being an analyst who organizes and makes sense of data, builds prototype visualizations and spreads around the insights to help decision making and product design and being an engineer who uses these insights to build useful production systems.

Senior Software Engineer

47pixels

We were a software startup in the Story-telling space. Major products:
47posters - B2B SaaS for Print Service Providers launched in 7 countries
memoarc - B2C storytelling photogallery
dofsome - B2C minimalistic, high-quality photo hosting.
Reach out to me for a complete portfolio.

Co-Founder

Exploratory large-scale information mining from unstructured data. Patent granted.

Software Engineering Intern

IBM Research

Joined the Information analytics group within the Mathematics and Computer Science department again to continue my work on matrix reordering and co-clustering remotely via IBM SK.

Research Internship

Joined the Information analytics group within the Mathematics and Computer Science department. Co-authored a research paper involving graph theory, clustering and visualization. Worked on a web-based marketing intelligence tool employing data-mining techniques and back-end infrastructure optimization.

"Great Minds" Research Internship

Slovak University of Technology

The course is called Development of effective algorithms and programs. I prepared problem statements and solutions for assignments and competitions. I also set problems for ProFIIT, national programming competition for High school students.

Teaching assistant

Vector Compute @ Superlinked | xYouTube

➡️ Building a compute framework for turning data into vector embeddings.
🤗 Connect your data infrastructure to your vector database with Superlinked.

Join our product preview:
📋 Try it out in a Python notebook with no dependencies.
↗️ Design the perfect vectors for your retrieval use-case.
🔧 Mix and match streaming, batch, structured and unstructured data.
🚀 Launch to production with your favorite Vector Database.

➡️ Let's sort out information retrieval across your organization!
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Previous:

Daniel is a Senior Software Engineer at Google, where he tech-leads, designs and builds forecasting & pricing systems for ads worth billions of dollars. These are distributed systems that run on thousands of machines, process terabytes of data per query and combine Machine Learning modeling with game-like simulation of tens of thousands ad campaigns - all happening in real-time to enable thousands of concurrent users of buying tools like AdWords and DBM.

Daniel wields a rare combination of deep technical skills, a user-centric drive and an ability to align multiple teams towards a common goal - launching and landing high quality cutting edge products.

Daniel co-founded a tech startup that brought machine vision to the consumer photo space 10 years ago, he was an invited speaker at an ML dev conference in Zurich and speaks at startup events regularly. He holds multiple patents with Google, is published in a peer-reviewed ML journal from his time at IBM Research and is a Google Code Jam on-site finalist.

On weekends he flies, climbs, skis and bikes in the Alps, see: http://instagram.com/mountain.supo

Stealth Startup

Counterfactual-reasoning augmented multi-agent activties reinforcement algorithms (fundamental to our technology value proposition) is broadly a branch of experimental mathematics focusing on iterative approximations to continuous functions. More specifically, it is a derivative of generative synthetic data from discriminative models.

Co-developing with data modeling partners an AI-powered product analytics platform with automatic features recommendation by interdependencies between marketing science modeling and PLG strategies.

Working with self-serve technology partner to develop proprietary self-serve platform that would allow users to combine fast, scalable proprietary ML algorithms with the capabilities of LLMs, we use proprietary algorithms in conjunction with, or instead of, conventional MLlib algorithms. We create XAI with particular emphasis on feature impact, prediction explanations and cluster insights, and A | B test experiments to validate stability of time-series models.

The self-serve platform created would be directly applicable to each industry’s requirements with a unique proposition for successfully standardising use cases and deeply automating and optimising features engineering by industry vertical.

Developing an AI-powered product analytics platform with automatic features recommendation by interdependencies between marketing science modelling and PLG strategies. Validating if it will be meaningful to prompt activities and events with product analytics than the usual way of activities, events and metrics feeding into analytical modelling.

Founder

Independent Consultant

We have been helping digital transforming / transformed companies with their market research, customer experience and data analytics life cycle from innovation to deployment to scalability and sustainability, throughout their product development process.

We have been customising ML marketing science modeling solution by source of data and type of data contextualised to the customer’s business requirements for predictive sales analytics results that can more representative of the nature of their business and industry. 

We specialise in applying creative contextual thinking to data strategy design and data acquisition planning to customise the iteration of each existing digital product and to personalise the building of each new digital product. We seek to challenge business projection estimates and product performance assumptions with value-added measurement tools, ranging from market research, marketing science modeling to data analytics.

Turning proprietary ML marketing science analytical models into XAI algorithms. To accelerate market traction of first targeted group of enterprise users at e-commerce, retail, and other consumer durables and consumer services companies. Experimenting with privacy-preserving technology to exploit opportunities in Retail Media Networks (RMNs).

Contributing to data architecture engineering decisions to support analytics with data strategy oversight of data sourcing, ETL, data modeling, data transformation, data aggregation and data optimization.

Independent contractor

Working towards standardising for industry-specific solutions for what we have been doing for customising ML marketing science modelling solution by source of data and type of data contextualised to the customer’s business requirements by the nature of their business and industry. 

Turning proprietary ML marketing science analytical models into XAI algorithms. To accelerate market traction of first targeted group of enterprise users at e-commerce, retail, and other consumer durables and consumer services companies, then going on to other industry verticals, such as blockchain analytics.

Turning proprietary ML marketing science analytical models into XAI algorithms. Introducing inductive-reasoning based ML models that should reinforce the validity of dependable models and add to the robustness of the results derived from inter-dependent models, so as to create XAI with emphasis on feature impact, prediction explanations and cluster insights, and to create A | B test experiments that can validate the stability of time-series models. MMF for XAI algorithms has been created.

In the future, we are looking to build an AI-powered product analytics platform with automatic features recommendation by interdependencies between marketing science modeling and PLG strategies created and to be validated, and data collection methods to be enhanced with privacy preserving ML with profit-sharing technology partner.

taplio

The Battle

Daniel Svonava

Huimin 慧敏 Liu 刘

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