🚀 QuantPi Joins HPE’s Unleash AI Partner Program to Accelerate AI Outcomes by Ensuring Trustworthy AI at Scale

Collaborative Research Hub

Innovation Unleashed, Trust Ensured

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Why is Research so important to us?

Innovative Research for Advanced AI Systems

Collaborative research validates the foundational technology which QuantPi runs on, ensuring the trustworthiness of AI system assessments and reinforcing the integrity of our solutions.

Comprehensive AI Risk Assessment and Analysis

The sheer scope of assessing AI systems is so broad (tests, risk dimensions, use cases) that a holistic approach fundamentally requires collaboration with partners across scientific communities.

Collaborative Efforts for Cutting-Edge Technology Development

As a deep tech company, our team depends on research. Collaborative efforts with partners fuel innovation, ensuring our solutions remain on the cutting edge and our products consistently evolve.

In cooperation with

Portfolio

Our commitment to advancing artificial intelligence responsibly

This dedication is reflected in our diverse portfolio of ongoing and completed research projects

Test Criteria and Certification of AI Systems

Zertifizierte KI: QuantPi is an associated partner in the German Lighthouse Project on the development of testing criteria for AI systems.

Auditing Synthetic Data Quality

Jointly with Hewlett Packard Enterprise, CISPA and DZNE, we analyze authenticity metrics and methods to detect mode deficiencies.

Operationalization of AI Tests

DIN, DKE: QuantPi co-authors the second version of the German AI Standardization Roadmap defining requirements for AI testing tools.

Documenting Explainability Methods for AI

DIN: QuantPi co-authors DIN SPEC 92001-3 which provides a domain-independent guide on explainability methods of AI system life cycle stages.

Enhancing Malware Detection System Robustness

ELSA, EU: QuantPi seeks to identify the strengths and vulnerabilities of AI-based malware detection systems using subgroup discovery.

Shape Partition of Tensors and Eigenvalue Problem

SIAM Review: QuantPi Co-Founder co-authors a paper on the novel applications of the nonlinear Perron-Frobenius theory, published in SIGEST.

Model-Agnostic Assessments of ML Models

Federal Ministry for Economic Affairs and Climate Action: QuantPi develops an efficient and extensible framework to assess ML models.

Defending API-Based Attacks on ML Models

Federal Ministry of Education and Research: QuantPi developed a technological framework to optimize the utility-privacy trade-off.

Machine Learning Security in Practice

IEEE Transactions on Information Forensics and Security: QuantPi co-authors a study on the occurrence of attacks on AI systems in industry.

Trustworthy AI Tool Support

OECD.AI Policy Observatory: QuantPi's AI Trust Platform is in the catalogue of tools and metrics designed to help with trustworthy AI.

Trustworthiness of Generative AI

European Innovation Council: QuantPi is tasked to develop the first automated risk management platform for generative AI systems.

Mental Models of Adversarial Machine Learning

SOUPS: QuantPi co-authored and presented a study on practitioners’ perception of vulnerabilities in AI systems.