Here’s a link to download logos in full resolutions:

https://qm.unileoben.ac.at/en/qm-documents/q4-communication

Put your documents at TravelApplications/drafts

Here: https://cloud.cps.unileoben.ac.at/index.php/f/977844

Create the folder with your name

Travel Planning Checklist

Approval and Registration

• Initial Planning: Check for a reasonable flight itinerary. Check if 1-2 days before and after the event have a substantially lower price. 
• Obtain Approval: Secure trip approval from Elmar. Argue according to the initial planning.
• Travel System Entry: Request Regina to input the trip details into the travel system. Specify which days are for official duties (e.g., conference, lab visits) and which are for personal stay. Provide Regina with the proof of acceptance, or reason to travel.

Booking Essentials

• Accommodation and Commute Options: Provide a comparison spreadsheet of different options within the budget. Opt for reasonable over the cheapest options.
• Booking Approval: Get approved by Elmar.
• Accommodations and Commute: After obtaining approval, book your stay, conference registration, accommodations, etc. 

Travel Insurance

• Carry Insurance Documentation: If traveling abroad, particularly outside the EU, bring a printed copy of the university’s or other relevant insurance policy

Visa Requirements

• Include Embassy Commute: If a visa is necessary, incorporate the embassy commute in the travel system and communicate this to the secretary for travel cost reimbursement.
• Visa Application Time: Visa application efforts are recognized as working hours.

After the Travel

• Receipts: After the end of the trip, provide Regina with all the receipts, invoices, and tickets from:

• • • Airplanes, trains, buses, and boats: tickets, invoices, bank statement
    • Accommodation: invoice, bank statement

• Registrations: invoice, bank statement

• etc.

Important Notes

• OEBB Trains: The chair has a membership with OBB, please book the ticket in the user’s name. You can obtain the user’s login information from Regina.

• After the travel: Keep all original receipts and submit them to Regina after returning.
• Report Everything: Due to Austrian law for work insurance coverage, you must inform Regina by email if you will be outside the university zone during working hours, even for a few hours.
• Private Stay: A private stay cannot exceed 50% of the duration of the working days. For example, if a conference is for six days, your private stay must be a maximum of three days. Otherwise, the university will cover only 50% of the flight tickets and hotel.

Tips:

• Credit card with travel coverage (check if hospitalization is included for overseas)

Here’s a guide on how to label your categories effectively:

1. wiki_phds: This category should encompass all aspects of your day-to-day life as a PhD student.

2. wiki_road_to_thesis: Include guidelines, tips, and resources related to various stages of thesis writing, from proposal development to final defense preparations.

3. wiki_hard_software: Use this category to share information, tutorials, and updates about the hardware and software used in your research projects.

4. wiki_scientific_research_aspects: Discuss methodologies, data analysis techniques, experimental setups, and anything else related to the scientific rigor of your work.

5. wiki_teaching_aspects: This category is dedicated to sharing insights, strategies, and resources for effective teaching, whether it’s leading a seminar, designing a course, or mentoring undergraduates.

6. wiki_career_aspects: This category covers everything related to career development and professional growth.

The category label determines where the post will appear in its respective section.

Date & Location: 24.05.2024 17:00-21:00

We expect many visitors and will prepare some beverages. Please let us know if you plan to join! 

Chair of Cyber-Physical-Systems 
Metallurgiegebäude 1.Stock
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria

https://youtu.be/jLAUo_Kk01E

Impressions of the last open lab day in 2024.  

English: Immerse yourself in the fascinating world of artificial intelligence and robotics. We present self-learning robots, mobile robot guides and how deep neural networks are learned. Children can experiment with our Lego EV3 robots and try to deliver snacks autonomously. Catering will be provided.

Deutsch: Tauchen Sie ein in die faszinierende Welt der künstlichen Intelligenz und Robotik. Wir präsentieren selbstlernende Roboter, mobile Roboterguides und wie tiefe neuronale Netze gelernt werden. Kinder können mit unseren Lego EV3 Robotern experimentieren und versuchen Snacks autonom auszuliefern. Für Verpflegung ist gesorgt.

The pictures above are from October 2023 and will be updated after the event. 

Program

Short Bio

Ozan Özdenizci is a research group leader at the Chair of Cyber-Physical-Systems at the Montanuniversität Leoben in Austria, since April 2024. Prior to joining CPS, he was a postdoctoral researcher at the Institute of Theoretical Computer Science at Graz University of Technology. He received his PhD in electrical engineering from Northeastern University (Boston, MA, USA) in 2020, and his BSc and MSc degrees from Sabancı University (Istanbul, Turkey). His research is focused in the domain of robust, secure and efficient deep learning algorithms for reliable artificial intelligence systems, and statistical signal processing with biomedical applications.

• Google Scholar
• GitHub
• LinkedIn
• Twitter(X)
• Web

Research Interests​

Machine learning, security and privacy in deep learning, adversarial machine learning, resource-efficient learning algorithms, brain-inspired neural computation, generative artificial intelligence, statistical signal processing, biomedical and neural data analysis, neuroinformatics.

Contact

Dr. Ozan Özdenizci
Research Group Leader at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1903
Email: ozan.oezdenizci@unileoben.ac.at
Chat: WEBEX

Selected Publications

[1] O. Özdenizci, R. Legenstein, “Adversarially robust spiking neural networks through conversion”, Transactions on Machine Learning Research, 2024.

[2] O. Özdenizci, R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

[3] O. Özdenizci, R. Legenstein, “Improving robustness against stealthy weight bit-flip attacks by output code matching”, CVPR 2022.

[4] O. Özdenizci, R. Legenstein, “Training adversarially robust sparse networks via Bayesian connectivity sampling”, ICML 2021.

[5] O. Özdenizci, Y. Wang, T. Koike-Akino, D. Erdogmus, “Learning invariant representations from EEG via adversarial inference”, IEEE Access, 2020.

[6] O. Özdenizci, D. Erdogmus, “Information theoretic feature transformation learning for brain interfaces”, IEEE Transactions on Biomedical Engineering, 2019.

[7] O. Özdenizci, M. Yalcin, A. Erdogan, V. Patoglu, M. Grosse-Wentrup, M. Cetin, “Electroencephalographic identifiers of motor adaptation learning”, Journal of Neural Engineering, 2017.

A complete list of publications can be found on Google Scholar.

You have no prior experience with deep learning or robots but would like to work with them?

If so, this hands-on project will enable you to build and control your state-of-the-art robotic devices, such as compliant robot arms, five-fingered robot hands, mobile robots, legged robots, or tactile and visual sensors.

You will use Python for programming. Prior experience is beneficial but not mandatory. 

At the end of the practical project, we discuss your achievements and what you have learnt.

You can work on your own or build a team of up to three people at most. We provide a student lab with high-performance pcs with RTX 4090 graphics cards and student rooms.

The project is based on code examples, wiki pages and video tutorials for non-experts.

Links and Resources

• MUOnline 
• Have a look at our robots and hardware
• Programming in Python Slides by Univ.-Prof. Dr. Elmar Rueckert

Location & Time

• Location: Laboratories at the Chair of Cyber-Physical-Systems.
• Dates:
  • Kick-off 11.03.2024 12:00 -14:00 CR IL/IT.
  • Regular (typically weekly meetings) at the chair or online. 
  • Projects using real hardware will be conducted at the chair.

Learning objectives / qualifications

• Students get a practical experience in working, programming and understanding autonomous robots in navigation and obstacle avoidance tasks.
• Students understand and can apply classical robot path planning and navigation algorithms.
• Students learn how to present their implementation, assumptions and achievements.

Robot serial number:20225300304

Passwords:

• safety: 0000

Supervisor: Vedant Dave, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 15th August 2023

Theoretical difficulty: Mid
Practical difficulty: High

Abstract

The aim of this Thesis is to predict the electricity price for the Hydrogen plants from open-sourced Energy data provided by the European Network of Transmission System Operators (ENTSO-E) [1]. We explore multiple machine learning techniques to achieve this aim. At the end, a standalone GUI is provided, that can be used in the industry with ease. This work was done in collaboration HyCenta Research GmbH.

Further, this thesis seeks to address the following research questions:

• How do different determinants such as the electricity mix (the proportion of energy from various generation sources), in-house generation, and gas prices, influence the cost of electricity?
• Which machine learning approaches/algorithms are most suitable for accurately predicting future electricity price trends, particularly in Austria or other European countries? 
• To what extent does the sensitivity of our model to inputs, like solar and wind energy, affect its overall accuracy and reliability in predicting electricity prices?

Thesis

Deep Neural Energy Price Forecasting for the Hydrogen Industry

Tentative Work Plan

To achieve the objectives, the following concrete tasks will be focused on:

• Literature review
• Evaluation of SOTA methods
• Designing network and hyperparameter tuning
• Evaluation on unseen country’s data
• Development of Standalone GUI

Related Work

[1]  Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. “The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform,” Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.

Supervisor: Fotios Lygerakis and Prof. Elmar Rueckert

Start Date: 1st March 2023

Theoretical difficulty: low
Practical difficulty: mid

Abstract

As the interaction with robots becomes an integral part of our daily lives, there is an escalating need for more human-like communication methods with these machines. This surge in robotic integration demands innovative approaches to ensure seamless and intuitive communication. Incorporating sign language, a powerful and unique form of communication predominantly used by the deaf and hard-of-hearing community, can be a pivotal step in this direction. 

By doing so, we not only provide an inclusive and accessible mode of interaction but also establish a non-verbal and non-intrusive way for everyone to engage with robots. This evolution in human-robot interaction will undoubtedly pave the way for more holistic and natural engagements in the future.

Thesis

ROS2-based Human-Robot Interaction Framework with Sign Language

Project Description

The implementation of sign language in human-robot interaction will not only improve the user experience but will also advance the field of robotics and artificial intelligence.

This project will encompass 4 crucial elements.

1. Human Gesture Recognition with CNNs and/or Transformers – Recognizing human gestures in sign language through the development of deep learning methods utilizing a camera.
   • Letter-level
   • Word/Gloss-level
2. Chat Agent with Large Language Models (LLMs) – Developing a gloss chat agent.
3. Finger Spelling/Gloss gesture with Robot Hand/Arm-Hand –
   
   • Human Gesture Imitation
   • Behavior Cloning
   • Offline Reinforcement Learning
4. Software Engineering – Create a seamless human-robot interaction framework using sign language.
   • Develop a ROS-2 framework
   • Develop a robot digital twin on simulation
5. Human-Robot Interaction Evaluation – Evaluate and adopt the more human-like methods for more human-like interaction with a robotic signer.

Quelle: Obersteirische Rundschau (www.rundschau-medien.at)