Research Team

Our team at the Smart Analytics & Reinforcement Learning (SARL) lab is dedicated to advancing AI through research spanning from theoretical foundations to real-world applications.

PhD Students

Primary supervision of doctoral candidates working on cutting-edge RL and AI research.

SP
Sabrina Pochaba
Data Science PhD
Multi-Agent Reinforcement Learning for Resource Allocation in Wireless Network Communication
ST
Sarah Trausner
PhD Student
FOCUS: Forecasting and optimization under constraints and uncertainty for sustainable industrial energy systems
CS
Christoph Schranz
PhD Student
Contactless Monitoring Beyond Ballistocardiography
RK
Reuf Kozlica
Data Science PhD
Hierarchical reinforcement learning in assembly line optimization
GS
Georg Schaefer
PhD Student
Improving Trajectory Tracking by Augmenting States with Future Targets
MD
Markus Dygruber
PhD Student
INSPIRE: Intelligent Novel Support for Personalized Instruction and Robust Evaluation in STEM

Master Students

LX
Laya Shibu Xavior
Master Student
Regelungsoptimierung von Piezoantrieben
SD
Sahan Warnakulasooriya Dabarera
Data Science Master
Adaptive PID Tuning via Meta-Reinforcement Learning
OM
Olga Mironova
Data Science Master
Causal GP-MPC: Where Structure, Safety, and Online Learning Meet
BH
Benjamin Halilovic
Data Science Master
Robust Real-Time Optimization of SIS18 Injection using Gaussian Process MPC
JL
Julian Langschwert
Master Student
Online Parameter Identification via Reinforcement Learning Integrated with Model Predictive Control

Bachelor Students

MT
Maximilian Tengler
AI Bachelor
Reinforcement Learning Beyond Greedy Optimisation
KG
Kevin Gajic
AI Bachelor
Agentic AI
SR
Stefan Reiter
AI Bachelor
Contracts and AI – Risk, Regulation, and Strategic Impact
FM
Fabio Matanza
AI Bachelor
Curriculum-Guided PPO for LUMEN Engine
AB
Armin Brückl
CS Bachelor
TBD topic in Reinforcement Learning
MP
Maria Pape
Bachelor
Welche Methoden ermöglichen die Einbettung domänenspezifischen Unternehmenswissens in KI-Systeme?
KB
Kajsa Bjoerkbom
Bachelor
Reinforcement Learning Beyond Greedy Optimisation for Delayed-Consequence Accelerator Control

Secondary Supervision

Co-supervision and advisory roles for doctoral students.

  • Raoul Kutil — Data Science PhD: Knowledge Graphs in medicine
  • Juan Manuel Montoya Bayardo — Data Science PhD: Binary Trigger Signals for Deep RL in Equity Trading / Nautical Robotics
  • Olivia Zechner — Towards AI-Driven Adaptive Virtual Environments, based on Biosignals: XR training to improve decision-making and acting in stressful situations
  • Jakob Uhl — Tangible XR for Training of Challenging Occupations: Increasing Presence and Sensory

Alumni

Former students who have successfully completed their theses.

  • Lukas Lamminger (finished May 2023) — Data Science Master: Model based and Meta reinforcement learning in accelerator physics
  • Sascha Schuster (finished March 2023) — Data Science Master: Reinforcement learning in medicine (DTR of insulin dosing)

Interested in joining our team?
We welcome motivated students interested in Reinforcement Learning, AI safety, and applications in healthcare, robotics, and industrial systems. Feel free to reach out to discuss potential research opportunities.