Thesis / Practical

Supervision by Karin Anna Hummel.

Collaborative Decision Support System

Develop a decision support system that integrates input from multiple users and utilizes machine learning algorithms to provide informed suggestions or recommendations. This could be applied in group decision-making scenarios.

Emotion-aware Communication Platform

Develop a communication platform that utilizes facial recognition and natural language processing to detect and respond to users' emotions. This can enhance online collaboration by adapting communication strategies based on emotional cues.

Group-based Learning Analytics

Implement a learning analytics system that tracks the progress of individuals within a group learning environment. Use machine learning to identify patterns and provide personalized feedback to each group member.

AI-assisted Group Fitness App

Create a fitness app that encourages group workouts by leveraging AI to tailor exercise routines based on the fitness levels and preferences of each participant. The system could adapt workouts dynamically based on real-time feedback.

Collaborative Music Playlist Generation

Design a music playlist generation system that takes input from multiple users and employs collaborative filtering to create playlists that suit the preferences of the entire group.

Supervision by Gabriele Kotsis / Ismail Khalil.

Thesis / Practical

Supervision by Karin Anna Hummel.

Design research on artefacts for communication, collaboration, and coordination.

Thesis / Practical

Supervision by Gabriele Kotsis.

Thesis / Practical

Supervision by Karin Anna Hummel.

Feasibility studies on mobile technologies in application domains including Arts, Medicine, or Education.

Thesis / Practical

Supervision by Gabriele Kotsis.

Gesture Recognition for Human-Robot Collaboration:

Develop a system that allows humans to communicate with robots using gestures. Train a machine learning model to recognize and interpret different hand gestures, enabling seamless collaboration between humans and robots.

Human-AI Collaboration in Image Editing:

Develop an image editing tool that combines human creativity with AI assistance. Train a model to understand and implement user instructions in the image editing process, making the collaboration more intuitive.

Human-Machine Cooperation in Autonomous Vehicles:

Develop a system for autonomous vehicles that integrates human input for decision-making. Explore scenarios where humans and AI collaborate to enhance safety and efficiency in navigation.

Predicting Traffic Flow with Machine Learning:

Build a machine learning model that predicts traffic flow patterns based on historical data, weather conditions, and events. This system can assist in optimizing traffic management and providing real-time navigation recommendations.

AI-assisted Language Translation

Create a language translation system that combines human input with machine translation capabilities. Train the model to understand context and user preferences, improving the accuracy and naturalness of translations.

Supervision by Gabriele Kotsis / Ismail Khalil.

Thesis / Practical

Supervision by Karin Anna Hummel.

MARL coordination

The project on coordination languages like Linda for multi-agent reinforcement learning (MARL) aims to leverage tuple-based coordination models to enhance the collaboration and communication among agents. By integrating Linda-like coordination mechanisms into MARL, the project seeks to create a structured and flexible environment where agents can efficiently share state information, synchronize actions, and achieve collective goals. This approach addresses key challenges in MARL such as reducing communication overhead, ensuring scalability, and improving the robustness of the system. The ultimate goal is to develop more efficient and scalable multi-agent systems capable of solving complex tasks in dynamic environments, such as robotics, smart grids, and collaborative problem-solving scenarios.

Multi-Agent Reinforcement Learning for Traffic Control

Design a traffic control system using multi-agent reinforcement learning, where intelligent agents control traffic signals to optimize traffic flow, reduce congestion, and improve overall transportation efficiency.

Distributed Image Recognition Network

Develop a distributed image recognition system where multiple machines collaborate to process and analyze large sets of images. Each machine can specialize in recognizing specific objects or patterns.

Energy Optimization in Smart Grids

Build a machine learning-based system for optimizing energy consumption in a smart grid. Machines within the grid can collaborate to predict demand, manage renewable energy sources, and balance the load efficiently.

Multi-Robot Exploration in Unknown Environments

Develop a multi-robot system where robots collaborate to explore and map unknown environments. Utilize machine learning algorithms for path planning, obstacle avoidance, and information sharing.

Dynamic Resource Allocation in Cloud Computing

Create a system for dynamic resource allocation in a cloud computing environment. Machines can collaborate to optimize resource allocation based on workload, improving overall system performance.

Supervision by Gabriele Kotsis / Ismail Khalil.

Thesis / Practical

Supervision by Karin Anna Hummel.

Performance evaluation of distributed and mobile systems, Web-Architectures, ad-hoc networks, …

Thesis / Practical

Supervision by Gabriele Kotsis.

Prototypical development and evaluation of collaborative systems.

Thesis / Practical

Supervision by Gabriele Kotsis.

Thesis / practical

Supervision by Karin Anna Hummel

Rene Gabner

Betreut von Karin Anna Hummel

Johannes Mittendorfer, Betreut von Karin Anna Hummel

Manuela Pollak

Betreut von Karin Anna Hummel, Co-Betreuung durch Gabriele Kotsis

Markus Hiesmair, Betreut von Karin Anna Hummel

Kai Knabl, Betreut von Karin Anna Hummel, Co-Betreuung durch Prof. M. Sonntag

Alexander Ritt, betreut von Karin Anna Hummel