Ongoing PhDs

• Lena BAUER: Mining High-Dimensional Data with Applications in Medicine (supervised by Claudia Plant und Philipp Grohs)
• Xandro BAYER: Machine Learning meets Financial Econometrics (supervised by Nikolaus Hautsch)
• Aleksandar DOKNIC: Explainable models: Visual parameter space analysis in neural networks (supervised by Torsten Möller and Claudia Plant)
• Leon GERARD: Solving the electronic Schrödinger equation using Deep Neural Networks (supervised by Philipp Grohs)
• Guojun LAI: Efficient Data Analysis and Processing on Morden Hardware (supervised by Claudia Plant)
• Timothée SCHMUDE: Interpretability and Explainability as Drivers to Democracy (supervised by Sebastian Tschiatschek and Torsten Möller)

Former PhDs

• Alexander KINAST: Combining metaheuristics and process mining to improve
  manufacturing processes (2023, supervised by Karl Dörner and Stefanie Rinderle-Ma)
• Sebastian RATZENBÖCK: Finding needles in the Galactic haystack - Towards interpretable machine learning methods to identify stellar structures in the Milky Way (2022, supervised by João Alves and Torsten Möller)
• Michael SCHERBELA: Solving the Schrödinger Equation using Deep Neural Networks (supervised by Philipp Grohs)
• Michael SEDLMAYER: Convergence rate analysis of optimisation and minimax algorithms for machine learning (2023, supervised by Radu Bot)

Former Post-Docs:

• Pavol HARÁR
• Rafael REISENHOFER

Business Analytics

• Eduardo BAGGIO: Data mining for efficient request bundle generation in auction-based transportation collaborations (2023, supervised by Jan Ehmke)
• Anja BOHATSCHEK: Concepts for spatial clustering of passenger rail booking data on a case study of the Austrian railway network (2023, supervised by Immanuel Bomze, co-supervised by Bertram Wassermann)
• Kseniia BOKOVAIA: Analyzing banking data by designing a Machine Learning tool for selecting the best model (2024, supervised by Yllka Velaj)
• Pavel CIMILI: Deep learning for automated damage detection of trailers at intermodal terminals (2023, supervised by Karl Dörner)
• Florian FAHRNGRUBER: Prediction of transfer passenger streams at an airport using machine learning (2022, supervised by Jan Ehmke)
• Stefan FILIPOVIĆ: SaaS business development through design thinking. A user-centered approach (2024, supervised by Arndt Niebisch)
• Soojeong JANG: Named entity recognition in government domain with reports from the city of Vienna court of audit (2023, supervised by Benjamin Roth)
• Carmen Maria KALUZINSKI: A time series churn analysis integrating the generation of product recommendations for customers (2023, supervised by Jan Ehmke)
• Marina KOZHEVNIKOVA: Applying predictive analytics to order book data of financial spot markets (2022, supervised by Roland Braune)
• Mykola LAZARENKO: Clustering brain regions by similar interaction patterns based on multivariate neural signals for identifying the response to antidepressants (2023, supervised by Claudia Plant, co-supervised by Katerina Schindlerova)
• Anastasiia MINAEVA: Development of metrics to measure and evaluate cities in terms of their implementation of the 15 minute city concept (2023, supervised by Roland Braune)
• Gabriela-Nicoleta PETROV: Robust crew scheduling problem using the Tabu Search metaheuristic (2023, supervised by Jan Ehmke)
• Sofiia Ihorivna PIVEN: Text analytics for customer relationship management in tourism (2023, supervised by Werner Winiwarter)
• Rebekka PRADER: Novel matheuristic for two echelon inventory routing problems in lower-middle income countries (2023, supervised by Karl Dörner)
• Theresa Sophie WOLF: A dynamic dial-a-ride problem with predicted return transports (2023, supervised by Karl Dörner)
• Johannes ZISCHG: Copositivity testing: a novel decomposition procedure for arbitrary matrices and an investigation of gradient-based search algorithms for finding violating vectors (2023, supervised by Immanuel Bomze)

Data Science

• Jonas BRUGGER: A two-step approach for analysing time-to-event data under non-proportional hazards (2023, supervised by Franz König)
• Louis CAUMEL MORALES: Clustering of Wind Related Time Series in a Wind Turbine Farm (2024, supervised by Claudia Plant, co-supervised by Katerina Schindlerova)
• Simon Noel-Marie FOURMAN: Estimation of sets with Barron boundaries under margin conditions (2023, supervised by Philipp Petersen)
• Konstantin HAAS: Generalized concentration protocols for gas sensing applications (2023, supervised by Moritz Grosse-Wentrup)
• Hannes JANKER: Conceiving, designing and implementing an energy dashboard on the example of the industrial company Constantia Teich (2024, supervised by Torsten Möller)
• Melanija KRALJEVSKA: Classification of treatment response in depression patients using motif discovery (2024, supervised by Claudia Plant)
• Pavla KROTKÁ: Model-based adjustments for non-concurrent comparisons in platform trials (2023, supervised by Martin Posch)
• Patrick POLLEK: Interpreting Neural Networks Under Latent Confounding (2024, supervised by Sebastian Tschiatschek)
• Michael RAFFELSBERGER: Variational autoencoders with structured missingness (2023, supervised by Sebastian Tschiatschek)
• Marlene STEINER: Analysis of gene expression data for precision medicine in ulcerative colitis (2023, supervised by Thomas Rattei)
• Sarah THILL: Unsupervised root cause analysis of anomalies in multivariate time series signals (2022, supervised by Sebastian Tschiatschek)
• Vojtech VORÁCEK: Learning with Reduced Molecular Graphs (2023, supervised by Nils Kriege)
• Susanna Maria WEINBERGER: Imitation learning from multiple perspectives (2024, supervised by Sebastian Tschiatschek)
• Elisabeth Katharina WIMMER: An analysis of dashboard usability and design for rowing coaches (2024, supervised by Torsten Möller)
• Lena ZELLINGER: Gradient matching for learning with noisy data (2022, supervised by Benjamin Roth)

Digital Humanities

• Christos BINTSIS: Applying Data Analytics and Natural Language Processing Methods in Acta Sanctorum (2024, supervised by Thomas Wallnig)
• Dasha EVSINA: Modelling power dynamics in piratical networks (2022, supervised by Tara Andrews)
• Nora LINSER: Exploring the potential of digital and computational tools for analysis, research and visualization of art collections: a case study of MoMA's datasets (2023, supervised by Rebecca Kahn)
• Lisa Maria NUßBAUMER: Sensitive information leakage in neural taggers (2023, supervised by Benjamin Roth)
• Emilie SITTER: Predicting keywords in a corpus of field post letters (2024, supervised by Benjamin Roth)

DS Day Posters

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