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TARO (Taming Robots)
research in the area of artificial intelligence and machine learning

Main Research Areas

  • Applied Artificial Intelligence
  • Optimization/Automation with AI Technologies
  • Trust & Transparency
  • Social Responsibility
  • Privacy & Data Protection
  • Reproducibility & Accountability

Topics for Projects / Bachelor Theses

Bachelor Thesis, Computer Science Project: Mobile Dashcam App using Open-Source ML Frameworks

The goal of this Project is to build a mobile application that uses the camera to extract traffic information in real time.

Tasks include but are not limited to:

  • Find open-source models or train models to recognize various objects e.g. traffic signs
  • Evaluate and optimize the performance of model inference on mobile devices
  • Evaluate and optimize the model for mobile devices in terms of speed, battery usage and other criteria

Variations of the above topic are also possible.

Bachelor Thesis, Computer Science Project: Evaluation of State-of-the-Art Natural Language Processing (NLP) Libraries and Frameworks

NLP is currently one of the major research areas in machine learning. Ground breaking new architectures and techniques are constantly being published, achieving major improvements.

The goal of this project and/or thesis is to explore the current state of the art of NLP. Possible topics include but are not limited to:

  • Overview of NLP architectures (shallow algorithms as well as deep learning)
  • Current state-of-the-art high performance models (ELMO, BERT, GPT-2, ALBERT, ..)
  • Overview of current NLP libraries and supported architectures
  • Comparison of the models for various NLP tasks
Variations of the above topics or own topics in the field of NLP are also possible.

Bachelor Thesis, Computer Science Project: Machine Learning Platform with Docker and Kubernetes

Design an implement a state-of-the-art machine learning platform using open-source tools.

  • Compare current state-of-the-art machine learning platforms and define a minimal set of required modules
  • Implement a pipeline using those modules with open-source tools
  • Examples Modules: Storage, Data Ingestion, Data Preparation, Model Training, Model Versioning, Model Deployment, Monitoring, Verification

Computer Science Project: Learn Computers play Arcade Games with Reinforced Learning

Arcade games form the 80s are fun and have very handy features: The graphics are simple, the controls are very limited and the game mechanic is in general very simple. This makes it perfect for a computer to teach itself playing a game.

Teach the computer to play a arcade game.

  • find a game suitable to be controlled by a computer
  • research on reinforcement learning for games
  • select framework (e.g. [1], [2], [3])
  • implement learning and training (e.g. [4])
  1. https://github.com/google/dopamine
  2. https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-sagemaker-announces-support-for-reinforcement-learning/
  3. https://github.com/uber-research/atari-model-zoo
  4. https://deepmind.com/research/dqn/

Bachelor Thesis, Computer Science Project: Understanding of Business Documents

Invoices, orders, credit notes and similar business documents contain information needed for trade to occur between companies, much of it on paper or in semi-structured formats such as PDFs. These documents carry information not only in text, but also in the spacial location. Ordinary Natural Language Processing or Computer Vision methods are not sufficient to understand these kind of documents.

In particular invoices carry lots of information in the spacial location of the text and other characters like lines and signs e.g. for tables. Different approaches can be followed for different problems. Simple pattern matchings or mask techniques can already achieve good results. But these approaches does not work well on unseen data. Deep learning is a different approach to understand these invoices.

The problem is divided in two to major areas:
  • Preprocessing and OCR for text extraction
  • Document understand and information extraction

Preprocessing and OCR for text extraction

This project focuses on the OCR part of the overall problem to understand business documents:
  • Identify and document requirements to an OCR system to be used for understanding business documents
  • Research and select appropriate libraries or algorithms
  • Document preprocessing
  • Actual extraction of text information while preserving the spacial position
  • Post-processing, error correction and optimization for further document understanding processing

Document understand and information extraction

In this project and/or thesis focuses on the document understanding and information extraction.
  • State of the art analysis - new papers on this topic are released on monthly basis
  • Analyse currently available products or libraries (such as rossum.ai)
  • Develop different approaches
  • Implement and verify selected approaches

Other Topics

  • Software Defined Storage in the Cloud Native area
  • Streaming in conjunction with AI
  • Debugging and Verification of AI Output
  • Moral Responsibility/Ethics & Bias of AI
  • Continuous Delivery with Machine Learning
  • ... or ... bring your own topic!

Office location: Wiedner Hauptstraße 76, Stiege 2, 2. Stock; 1040 Wien
Office hours: by appointment per e-mail
Fax: 01 / 5872198
E-Mail: taro@inso.tuwien.ac.at