A Fragment of Me, in 13774 characters.

Segmnts01/2025 - current

CEO
Co-Founder

Developed Segmnts, an AI-native workspace that replaces traditional office tools with a topic-centric workflow, enabling seamless collaboration between humans and AI. Designed Pinnacle, an AI assistant that transforms, refines, and automates content creation, enhanced by Pins, reusable AI-powered functions for workflow automation. Led the Publishing Pin initiative, enabling content creators to publish, remix, and track contributions dynamically, forming a collaborative content network.

Sourceflow Computational Intelligence Ltd.07/2022 - 01/2025

CEO
Lead Machine Learning Engineer
Lead Software Engineering

Consulting startups and spearheading the early stages of AI-driven product research and development, with projects ranging from developing an AI-based testing suite for virtual worlds—leveraging low-level screen and input device data—to conducting R&D on high-fidelity, shape-preserving polygon mesh generation from implicit surfaces for Unbound.

Epoch ML02/2022 - 02/2024

Lead Machine Learning Engineer
Senior Full-Stack Developer

Researching and developing automated game testing solutions, including capture-replay engines, localization testing, and an agent-based testing framework using imitation learning. Designing and deploying data and machine learning pipelines while also building essential platform features for mobile, web, and desktop applications, such as video streaming, dashboarding infrastructure, and collaborative team tools for real-time feedback.

Smarter Ecommerce GmbH07/2020 - 02/2022

Senior Data Scientist
Senior Machine Learning Engineer

Leading the data science product and architectural development of the bidding engine at Smarter Ecommerce, the core component of the company's highest revenue-generating product. Co-developing the data science chapter while leading a data science sub-team, driving innovation and strategic decision-making. Responsible for technical hiring and the growth and development of data scientists within the organization.

Johannes Kepler University Linz2016 - 06/2020

Senior Reseacher for Probabilistic Software Modeling
Co-Supervisor for theses
Lecturer for Software Development 1
Lecturer for Software Engineering
Lecturer for Seminar in Software Engineering
Lecturer for Model-Driven Engineering

Researching, developing, and publishing concepts in Probabilistic Software Modeling while supervising multiple theses and teaching exercise tracks in software development, software engineering, and model-driven engineering. Mentoring junior PhD candidates, guiding them through their initial steps in research and academia.

Johannes Kepler University Linz2015 - 2016

Student Researcher in the field of Code Clones
Developer for the ECCO Project

Researching, developing, and analyzing code clones in large-scale industrial IEC 61131-3 software systems. Contributing as a developer to the Extraction and Composition for Clone-and-Own (ECCO) project, a feature-oriented and distributed configuration management and version control system. Contributed to the early ECCO UI, integrating it into Eclipse to manage software versions and variants efficiently.

Johannes Kepler University Linz2013 - 2016

Student teaching assistant for Software Development 1 & 2
Student teaching assistant for Software Engineering
Student teaching assistant for Software Testing
Student teaching assistant for Alogrithm and Data Structures 1
Student teaching assistant for Multimedia Systems

Supporting teaching activities across various courses, including Software Development, Software Engineering, Software Testing, Algorithms and Data Structures, and Multimedia Systems. Assisting students in understanding course material, preparing coursework, and providing feedback on assignments and projects. Teaching contracts were awarded based on academic performance and professor recommendations, each lasting for a semester.

ecx.io (an IBM Company)2012 - 2014

Multiple internships focused on Software Development

Supporting various internal projects over the course of multiple summer internships at ecx.io.

Johannes Kepler University Linz2016 - 10/2020

Doctorate Degree Program in Computer Science
Graduated with distinction

Researching, developing, and publishing concepts in Probabilistic Software Modeling (ML/AI for software) and general software analysis. By the time of graduation, published nine papers in top-tier conferences and journals, accumulating over 100 citations.

Johannes Kepler University Linz2014 - 2016

Master of Science in Computer Science
Graduated with distinction

Researching software analysis concepts with a focus on detecting design patterns and architectural structures in software systems. Graduated with distinction and published my master's thesis in a top-tier conference, earning over 40 citations.

Johannes Kepler University Linz2011 - 2014

Bachelor in Science in Computer Science

Graduated with a focus on machine learning and software engineering, building a strong foundation in machine learning, computational intelligence, software architecture, and software development methodologies.

Merit Scholarship2016

Faculty of Engineering and Natural Sciences at the Johannes Kepler University Linz

Awarded for outstanding academic performance and research achievements in the field of computer science.

Technical Skills

Computer Science

Extensive experience with a wide range of programming languages, primarily focusing on imperative and functional languages such as C++, Python, TypeScript, Kotlin, and Rust. Deep understanding of software engineering principles, software architecture, and development methodologies. Expertise in machine learning, deep learning, and probabilistic modeling, with a strong emphasis on software analysis and software testing.

Leadership Skills

Entrepreneurship and Lead Engineering

Proven track record of leading and managing teams of engineers and data scientists, driving innovation and strategic decision-making. Strong ability to communicate complex technical concepts to non-technical stakeholders. Experienced in mentoring and coaching junior team members, guiding their professional development and growth.

English

Conversationally fluent (C2)

German

Native

Semantic Clone Detection via Probabilistic Software Modeling03/2022

Johnsen, E.B., Wimmer, M. (eds) Fundamental Approaches to Software Engineering. FASE 2022. Lecture Notes in Computer Science, vol 13241. Springer

Developed Semantic Clone Detection via Probabilistic Software Modeling (SCD-PSM), a novel approach for identifying semantically equivalent program elements with 0% syntactic similarity. SCD-PSM builds probabilistic models of programs to evaluate runtime behavior and applies statistical significance testing to detect clones with high precision (MCC > 0.9). Demonstrated effectiveness on classical and complex coding competition problems.DOI 10.1007/978-3-030-99429-7_16PDF

Towards Semantic Clone Detection via Probabilistic Software Modeling03/2022

2020 IEEE 14th International Workshop on Software Clones (IWSC)

Developed a robust semantic clone detection method using Probabilistic Software Modeling (PSM) to identify semantically equivalent methods with 0% syntactic similarity. The approach builds probabilistic models to analyze program structure and runtime behavior, achieving high precision and low error rates. Demonstrated effectiveness in detecting complex semantic clones.DOI 10.1109/IWSC50091.2020.9047635PDF

Towards Fault Localization via Probabilistic Software Modeling02/2020

2020 IEEE Workshop on Validation, Analysis and Evolution of Software Tests (VST)

Developed a fault localization approach using Probabilistic Software Modeling (PSM) to accurately identify buggy program components. PSM builds probabilistic models to analyze program structure and runtime behavior, leveraging likelihood evaluations to pinpoint fault locations and assess their impact. Demonstrated high accuracy and robustness in fault localization.DOI 10.1109/VST50071.2020.9051635PDF

An Empirical Evaluation for Object Initialization of Member Variables in Unit Testing02/2020

2020 IEEE Workshop on Validation, Analysis and Evolution of Software Tests (VST)

Conducted a large-scale analysis of 110 open-source Java systems to evaluate how settable class fields are for automated test case generation. Identified setter methods and other means of modifying field values, finding that 66.5% of fields are fully settable, 31.5% have partial constraints, and only 2% are immutable. Provided insights into controllability of internal object state for improving test coverage.DOI 10.1109/VST50071.2020.9051634PDF

Feature Maps: A Comprehensible Software Representation for Design Pattern Detection02/2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

Developed Feature Maps, a human- and machine-comprehensible software representation for detecting design patterns in source code. Introduced Feature-Role Normalization to transform high-dimensional micro-structures into structured feature maps, enabling robust classification using both classical machine learning and deep learning. Achieved strong performance even on imbalanced datasets, enhancing software analysis capabilities.DOI 10.1109/SANER.2019.8667978PDF

Benefits and Drawbacks of Representing and Analyzing Source Code and Software Engineering Artifacts with Graph Databases12/2018

Winkler, D., Biffl, S., Bergsmann, J. (eds) Software Quality: The Complexity and Challenges of Software Engineering and Software Quality in the Cloud. SWQD 2019. Lecture Notes in Business Information Processing

Developed and applied graph database solutions for analyzing source code and software engineering artifacts across five industry and research use cases. Utilized Neo4j to model dependencies, support rapid prototyping, and enable flexible querying, scaling up to systems with 44 million lines of code. Evaluated benefits such as enhanced dependency analysis and architectural insights, while identifying challenges related to frontend limitations and time series data support.DOI 10.1007/978-3-030-05767-1_9PDF

Probabilistic Software Modeling07/2018

2018 ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), Amsterdam, 2018, ECOOP and ISSTA Doc Symposium

Proposed Probabilistic Software Modeling (PSM), a novel paradigm for software analysis that builds statistical models from runtime observations without altering abstraction levels. PSM constructs hierarchical probabilistic models reflecting both static structure and dynamic behavior, enabling applications such as test-case generation, anomaly detection, and probabilistic program simulation. This approach enhances automation in software testing and analysis, improving efficiency in large-scale software development.PDFPoster

Exploring Code Clones in Programmable Logic Controller Software09/2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

Conducted an in-depth study on code clones in Programmable Logic Controller (PLC) software, analyzing a real-world system using IEC 61131-3 Structured Text and C/C++. Extended a widely used clone detection tool with normalization support to improve detection accuracy. Evaluated clone types, their impact on maintenance, and the benefits of tailored detection tools for industrial automation systems.DOI 10.1109/ETFA.2017.8247574PDF

Subliminal Visual Information to Enhance Driver Awareness and Induce Behavior Change09/2014

AutomotiveUI '14: Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Investigated the use of subliminal visual stimuli to induce non-conscious behavioral changes in drivers, aiming to mitigate accidents caused by cognitive overload. Conducted a driving simulator study using a Lane Change Task (LCT) similar to ISO 26022-2010, demonstrating the feasibility of subliminal cues for steering assistance. While results were mostly not statistically significant, they suggest potential benefits for future subliminally driven automotive interfaces.DOI 10.1145/2667317.2667328PDF

Probabilistic Software Modeling10/2021

Doctoral Thesis to obtain the academic degree of Doctor of technical Sciences

Developed Probabilistic Software Modeling (PSM), a novel paradigm for software analysis that transforms programs into probabilistic models to simulate and evaluate their behavior. Demonstrated PSM's effectiveness in semantic clone detection, fault localization, and static code analysis using graph databases, achieving state-of-the-art performance. Conducted feasibility studies showcasing PSM’s scalability and applicability for improving software comprehension, prediction, and generative modeling.PDF

Towards Deep Learning-Driven Design Pattern Detection10/2016

Master Thesis to obtain the academic degree of Master of Science in Computer Science

Developed a deep learning-based approach for detecting design patterns in software systems, restoring valuable architectural information for developers and maintainers. Utilized micro-structures projected onto feature maps and analyzed them with a convolutional neural network, achieving robust pattern inference. Results demonstrate the potential of deep learning for automated design pattern detection.PDF

Driving Performance Manipulation via Visual Subliminal Cues04/2014

Bachelor Thesis to obtain the academic degree of Bachelor of Science in Computer Science

Conducted a Lane Change Test (LCT) experiment to evaluate the effectiveness of visual subliminal cues in guiding or warning drivers. Investigated their impact on driving performance and cognitive load in critical situations. Findings suggest that visual priming may be ineffective for improving driving performance.PDF