Dr.-Ing. Reinhard Munz

Chief Technology Officer @ Daiki | Responsible AI

Professional

I am a software systems engineer and build AI/ML systems. My AI/ML systems are ethical and responsible and some enable others to build their own AI/ML systems in ethical and responsible ways. Daiki's SaaS solution falls into the latter category. It combines AI strategy, governance, and knowledge into a wholistic platform for teams to successfully complete any AI project. Technically the platform rests on my expertise in large-scale distributed systems, large language models, retrieval-augmented generation, and systems security. Content-wise it further benefits from my background in private data analytics and differential privacy.

Before Daiki, I worked at Gradient0 on copious other AI/ML projects. For further details, please see my CV.

Of course I have not been working alone. Kudos to the amiable and outstanding teams of both, Daiki and Gradient0!

Special Thanks go to Felix, Jona, Artur, Wolfgang, Kevin, Sandra, Julia, Paolo, Craig, Dominik, Dominic, Karim, and everyone else!

Research

My research interests revolved around systems and how they can be improved to benefit users, providers, and the environment. I was working in the area of system security and privacy, with a particular focus on private data analytics. 

In my thesis I presented the first steps towards usable privacy protection mechanisms for data analytics systems. Usable in that context meant more queries and less distortion of answers than previous systems. I developed the UniTraX system, the first differentially private analytics system that is able to support personalized privacy budgets without giving up on answer accuracy.

With my thesis work I further laid the foundations for Diffix, a once commercially deployed private analytics system. Diffix used a defense in depth approach to privacy protection. It provided guarantees of usability to analysts and was one of the first systems to focus on usability in order to foster industry adoption of private analytics systems. Diffix later continued as Open Diffix.

Before my work on usable privacy with Paul, Deepak, and Matteo, I improved user satisfaction during peak system loads with Allen and devised energy efficient and failure resilient mechanisms for large-scale distributed environments with Umut and Dave.

During my time in different research groups I always had the pleasure to work with amazing people.

Some non-exhaustive sample: Arthur, Ekin, Ezgi, Fabienne, Imran, Iulian, Matt, Mike, Mustafa, Nancy, and Natacha.

Thanks folks! And thanks to all my other friends at MPI-SWS, CMU, and Uni Würzburg.

Publications

“Towards Usability in Private Data Analytics”. PhD thesis. Department of Computer Science, Technische Universität Kaiserslautern, 2019. [University publication server]

UniTraX: Protecting Data Privacy with Discoverable Biases. With Fabienne Eigner, Matteo Maffei, Paul Francis, and Deepak Garg. Conference on Principles of Security and Trust (POST), 2018. [Conference paper][Technical report with proofs][Gitlab repository]

Diffix: High-Utility Database Anonymization. With Paul Francis and Sebastian Probst Eide. Privacy Technologies and Policy (APF), 2017. [Conference paper][Preprint Diffix-Birch: Extending Diffix-Aspen 2019]

Persistent, Protected and Cached: Building Blocks for Main Memory Data Stores. With Iulian Moraru, David G. Andersen, Michael Kaminsky, Nathan Binkert, Niraj Tolia, and Parthasarathy Ranganathan. Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-11-114v2, Nov 2012. [v2 Nov 2012][v1 Dec 2011]