SIMON WALK

SENIOR DATA SCIENTIST


Detego GmbH

Hans-Resel-Gasse 17a
A-8020 Graz
Email: web@simonwalk.info
Interests: Machine Learning & Neural Networks, Recommender Systems, Network Science, Knowledge & Data Mining
About Me
Simon Walk currently works as Team Lead and Senior Data Scientist at Detego GmbH. From 2012 to 2013, he was working as a scientific developer at the Know-Center GmbH and as a project assistant at the Knowledge Management Institute at Graz University of Technology. From 2013 to 2014, Simon Walk worked as a scientific developer at Virtual World Services GmbH. Additionally, he has been a visiting researcher at the Stanford Center for Biomedical Informatics Research from November 2011 to February 2012 and September 2013 to December 2013. In 2014, he started to work as University Assistant at the Institute for Information Systems and Computer Media at Graz University of Technology where he received his PhD in 2016. From 2016 to 2017, he worked as Post-Doctoral Researcher at the Stanford Center for Biomedical Informatics Research at Stanford University. In 2017, he returned to Austria and started to work as Post-Doctoral Scholar at the Institute of Interactive Systems and Data Science at Graz University of Technology from June 2017 to January 2018.

The main research interests of Simon Walk include Data Science, Machine Learning & Neural Networks, Recommender Systems, Network Science and Knowledge & Data Mining.
Publications

JOURNAL PUBLICATIONS

  1. T. Hasler, M. Wölbitsch, M. Goller and S. Walk (2020). Relative Tag Locations Based on Time-Differences in Read Events for Practical Applications. IEEE Journal of Radio Frequency Identification (IEEE RFID). [PDF] [IEEE Xplore DL]
  2. T. Santos, S. Walk, R. Kern, M. Strohmaier and D. Helic (2019). Activity Archetypes in in Questions-and-Answers Websites - A Study of 50 Stack Exchange Instances. ACM Transactions on Social Computing. [PDF] [ACM DL]
  3. M. Vitiello, S. Walk, D. Helic, V. Chang and C. Gütl (2018). User Behavioral Patterns and Early Dropouts Detection: Improved Users Profiling through Analysis of Successive Offering of MOOC. Journal of Universal Computer Science (J.UCS). [PDF]
  4. M. R. Kamdar, S. Walk, T. Tudorache and M. A. Musen (2017). Analyzing user interactions with biomedical ontologies: A visual perspective. Journal of Web Semantics: Science, Services and Agents on the World Wide Web (JWS). [PDF] [Dataset] [Web-App]
  5. S. Walk, D. Helic, F. Geigl and M. Strohmaier (2016). Activity Dynamics in Collaboration Networks. ACM Transactions on the Web (TWEB) 10(2):11. [PDF] [arXiv] [ACM DL]
  6. S. Walk, P. Singer, M. Strohmaier, D. Helic, N. F. Noy and M. A. Musen (2015). How to apply Markov chains for modeling sequential edit patterns in collaborative ontology-engineering projects. International Journal of Human-Computer Studies 84, 51-66. [PDF] [arXiv]
  7. S. Walk, P. Singer, M. Strohmaier, T. Tudorache, M. A. Musen and N. F. Noy (2014). Discovering Beaten Paths in Collaborative Ontology-Engineering Projects Using Markov Chains. Journal of biomedical informatics 51, 254-271. [PDF] [arXiv]
  8. S. Walk, J. Pöschko, M. Strohmaier, K. Andrews, T. Tudorache, N. F. Noy, C. Nyulas and M. A. Musen (2013). Pragmatix: An Interactive Tool for Visualizing the Creation Process Behind Collaboratively Engineered Ontologies. International journal on Semantic Web and information systems 9 (1), 45. [PDF]
  9. M. Strohmaier, S. Walk, J. Pöschko, D. Lamprecht, T. Tudorache, C. Nyulas, M. A. Musen and N. F. Noy (2013). How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology-Engineering Projects. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 20, 18-34. [PDF]

CONFERENCE PUBLICATIONS

  1. L. Eberhard, S. Walk and D. Helic (2020). Tell Me What You Want: Embedding Narratives for Movie Recommendations. In Proceedings of 31st ACM Conference on Hypertext and Social Media (HT'20). [PDF]
  2. M. Wölbitsch, T. Hasler, D. Helic and S. Walk (2020). Show Me the Money: RFID-based Article-to-Fixture Predictions for Fashion Retail Stores. In Proceedings of 14th IEEE International Conference on RFID. [PDF] [Dataset]
  3. M. Wölbitsch, T. Hasler, S. Walk and D. Helic (2020). Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels. In Proceedings of 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP'20). [PDF] [Dataset]
  4. M. Wölbitsch, T. Hasler, M. Goller, C. Gütl, S. Walk and D. Helic (2019). RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. In Proceedings of 6th IEEE International Conference on Internet of Things: Systems, Management and Security IOTSMS2019 [PDF] [Dataset]
  5. M. Wölbitsch, S. Walk, M. Goller and D. Helic (2019). Beggars Can't Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores. In Proceedings of 27th ACM International Conference on User Modelling, Adaptation and Personalization UMAP2019. [PDF] [Dataset]
  6. T. Santos, S. Walk, R. Kern, M. Strohmaier and D. Helic (2019). Self- and Cross-Excitation in StackExchange Question & Answer Communities. In Proceedings of 28th World Wide Web Conference WWW'19. [PDF]
  7. L. Espín-Noboa, F. Lemmerich, S. Walk, M. Strohmaier and M. A. Musen (2019). HopRank: How Semantic Structure Influences Teleportation in PageRank (A case study on BioPortal). In Proceedings of 28th World Wide Web Conference WWW'19. [PDF]
  8. T. Hasler, M. Wölbitsch, M. Goller and S. Walk (2019). Estimating Relative Tag Locations based on Time-Differences in Read Events. In Proceedings of 13th IEEE International Conference on RFID. [PDF] [Dataset]
  9. L. Eberhard, S. Walk, L. Posch and D. Helic (2019). Evaluating Narrative-Driven Movie Recommendations on Reddit. In Proceedings of 24th International Conference on Intelligent User Interfaces IUI2019. [PDF] [Dataset] [ACM DL]
  10. M. Wölbitsch, S. Walk and D. Helic (2017). Modeling Peer Influence in Time-Varying Networks. In Proceedings of 6th International Conference on Complex Networks and Their Applications [PDF]
  11. M. R. Kamdar, S. Walk, T. Tudorache and M. A. Musen (2017). BiOnIC: A Catalog of User Interactions with Biomedical Ontologies. In Proceedings of 16th International Semantic Web Conference ISWC'17 [PDF] [Dataset]
  12. M. Vitiello, S. Walk, D. Helic, V. Chang and C. Gütl (2017). Predicting dropouts on the successive offering of a MOOC. In Proceedings of International MOOC-MAKER Conference 2017 moocmaker17 [PDF]
  13. M. Vitiello, S. Walk, V. Chang, R. Hernandez, D. Helic and C. Gütl (2017). MOOC droputs: A multi-system classifier. In Proceedings of 12th European Conference on Technology Enhanced Learning, EC-TEL 2017 [PDF]
  14. S. Walk, L. Espín-Noboa, D. Helic, M. Strohmaier and M. A. Musen (2017). How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs. In Proceedings of 26th International World Wide Web Conference WWW'17 [PDF] [arXiv] [Slides]
  15. F. Geigl, S. Walk, M. Strohmaier and D. Helic (2016). Steering the Random Surfer on Directed Webgraphs. In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence WI2016 [PDF]
  16. F. Geigl, K. Lerman, S. Walk, M. Strohmaier and D. Helic (2016). Assessing the Navigational Effects of Click Biases and Link Insertion on the Web. In Proceedings of the 27th ACM Conference on Hypertext and Social Media HT '16 [PDF] [arXiv]
  17. M. Vitiello, S. Walk, R. Hernández, D. Helic and C. Gütl (2016). Classifying Students to improve MOOC dropout rates. In Proceedings of the European Stakeholder Summit on experiences and best practices in and around MOOCs (EMOOCS 2016), 501-508. [PDF]
  18. D. Lamprecht, F. Geigl, T. Karas, S. Walk, D. Helic and M. Strohmaier (2015). Improving Recommender System Navigability Through Diversification: A Case Study of IMDb. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, 21:1-21:8. [PDF]
  19. F. Geigl, D. Lamprecht, R. Hofmann-Wellenhof, S. Walk, M. Strohmaier and D. Helic (2015). Random Surfers on a Web Encyclopedia. In Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, 5:1-5:8. [PDF]
  20. S. Walk, P. Singer, L. Espín-Noboa, T. Tudorache, M. A. Musen and M. Strohmaier (2015). Understanding How Users Edit Ontologies: Comparing Hypotheses About Four Real-World Projects. In Proceedings of the 14th International Semantic Web Conference 2015, 551-568. [PDF]
  21. S. Walk, P. Singer, M. Strohmaier (2014). Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), 1349-1358. [PDF]
  22. S. Walk, M. Strohmaier, T. Tudorache, N. F. Noy, C. Nyulas and M. A. Musen (2012). Recommending Concepts to Experts: An Exploration of Recommender Techniques for Collaborative Ontology Engineering Platforms in the Biomedical Domain. In Proceedings of the 3rd International Conference on Biomedical Ontology (ICBO 2012), Graz, Austria, 2012. [PDF]

WORKSHOP PUBLICATIONS

  1. T. Santos, S. Walk and D. Helic (2017). Nonlinear Characterization of Activity Dynamics in Online Collaboration Websites. In Proceedings of the 7th Temporal Web Analytics Workshop. [PDF]
  2. P. Koncar, S. Walk, D. Helic and M. Strohmaier (2017). Exploring the Impact of Trolls on Activity Dynamics in Real-World Collaboration Networks. In Proceedings of the 7th Temporal Web Analytics Workshop. [PDF]
  3. S. Walk, T. Tudorache and M. A. Musen (2016). Visualizing User Editing Behavior in Collaborative Ontology-Engineering Projects. In Proceedings of the 2nd International Workshop on Visualization and Interaction for Ontologies and Linked Data. [PDF] [Slides]
  4. S. Walk and M. Strohmaier (2014). Characterizing and Predicting Activity in Semantic MediaWiki Communities. Proceedings of the Third International Conference on Semantic Web Collaborative Spaces, 1275:54-67. [PDF]
  5. D. Kowald, S. Dennerlein, D. Theiler, S. Walk and C. Trattner (2013). The Social Semantic Server: A Framework to Provide Services on Social Semantic Network Data. I-Semantics, Graz, Austria, 2013. [PDF]

BOOK CHAPTERS

  1. P. Kasper, P. Koncar, S. Walk, M. Wölbitsch, T. Santos, M. Strohmaier and D. Helic (2019). Modeling User Dynamics in Collaboration Websites. In Dynamics On and Of Complex Networks. [PDF]

TALKS & PRESENTATIONS

  1. S. Walk (2020). Big Data and Machine Learning to monitor and improve the Supply Chain (Global Track and Trace). Presented as Detego Webinar.
  2. S. Walk (2020). RFID Tag Localization for AI-based use-cases in Retail Presented at 2019 National Retail Federation (NRF2020), Javits Convention Center, New York City, USA.
  3. S. Walk (2019). AI-based use-cases in Retail Presented as Detego Webinar.
  4. S. Walk (2019). Smart Planograms to improve Article Availability in Retail Presented as Detego Webinar.
  5. S. Walk (2019). AI-based use-cases in Retail Presented at The Future of AI in Retail (2019), TheRetailBulletin, London, UK.
  6. S. Walk (2017). How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs. 26th International World Wide Web Conference (WWW'17), Perth, Australia.
  7. S. Walk (2016). Activity Dynamics in Collaboration Networks. Presented at 2016 Conference on Complex Systems (CCS2016), Beurs van Berlage, Amsterdam, NL. [Slides]
  8. S. Walk (2016). Activity Dynamics in Collaboration Networks. Presented at GESIS, Cologne, GER.
  9. S. Walk (2016). Extracting and Analyzing Sequential Interaction-Patterns. Presented at Protégé Research Meeting, Stanford, USA. [Slides]
  10. S. Walk (2016). Visualizing User Editing Behavior in Collaborative Ontology-Engineering Projects. 2nd Workshop on Visualization and Interaction for Ontologies and Linked Data (VOILA 2016), Kobe, Japan.
  11. S. Walk (2015). Understanding How Users Edit Ontologies: Comparing Hypotheses About Four Real-World Projects. 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA
  12. S. Walk (2014). Characterizing and Predicting Activity in Semantic MediaWiki Communities. Third International Workshop on Semantic Web Collaborative Spaces, Riva del Garda, Italy.
  13. S. Walk (2014). Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain. International Conference on Information and Knowledge Managemen (CIKM 2014), Shanghai, China.
  14. S. Walk (2012). Recommending Concepts to Experts: An Exploration of Recommender Techniques for Collaborative Ontology-Engineering Platforms in the Biomedical Domain. Presented at 3rd International Conference on Biomedical Ontology (ICBO 2012), Graz, Austria.
Datasets & Materials

Tag Localization - RFID Reads and Fixture Mappings

For our paper titled "Show Me the Money: RFID-based Article-to-Fixture Predictions for Fashion Retail Stores", we created multiple datasets consisting of RFID read-events in S0 and S1 during daily stocktaking processes, our calculated article to fixture mappings, as well as the corresponding ground truth. to compare retail and ecom buying behavior and enriched it with Smart Fitting Room data in the paper "Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels". Specifically, the dataset contains the following real-world and lab data for the presented experiments:

  1. EPC to article to fixture mappings.
  2. Reference tag to fixture mappings.
  3. Raw RFID read event data with Timestamp, EPC, RSSI and RFID-reader session.

A more detailed description of the dataset can be found in our publication below. Note that the dataset is free to use for research purposes but requires citing our paper as the source of the data.

  • M. Wölbitsch, T. Hasler, D. Helic and S. Walk (2020). Show Me the Money: RFID-based Article-to-Fixture Predictions for Fashion Retail Stores. In Proceedings of 14th IEEE International Conference on RFID (IEEE RFID 2020) [PDF] [Dataset]

Large-Scale Retail vs. Ecom Sales Dataset

We used this dataset to compare retail and ecom buying behavior and enriched it with Smart Fitting Room data in the paper "Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels". The dataset contains the following data:

  1. E-com shopping baskets.
  2. Retail store shopping baskets.
  3. Smart Fitting Room fitting baskets (i.e., articles brought together into the fitting room).

A more detailed description of the dataset can be found in our publication below. Note that the dataset is free to use for research purposes but requires citing our paper as the source of the data.

  • M. Wölbitsch, T. Hasler, S. Walk and D. Helic (2020). Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels. In Proceedings of 28th ACM Conference on User Modeling, Adaptation and Personaization (UMAP 2020) [PDF] [Dataset]

Large-Scale Stocktake RFID Read-Events Dataset

This dataset (>2GB!) was introduced in the paper "RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products", and consists of two parts:

  1. Information about the individual stocktakes.
  2. The read events of individual stocktakes.

Data Structure

Specifically, the dataset contains the following information about individual stocktakes in the sample_stocktakes.csv file:

  • InventoryId: the unique identifier of a stocktake
  • Store: the unique identifier of the store in which the stocktake was performed
  • Region: the region in which the store is located in (i.e., US, Europe, or Asia)
  • Expected: the number of items which were expected by the stock management system for the stocktake
  • Unexpected: the number of items which were not expected by the stock management system for the stocktake
  • Missing: the number of items which were expected by the stock management system but not found for the stocktake
  • Actual: the number of items which were actually read during the stocktake
  • TimeStampStart and TimeStampEnd: the timestamps in UTC time when the stocktake started/was finished
  • Accuracy: the achieved accuracy of the stocktake (determined by the item quantities)

Furthermore, for each stocktake all recorded items are available as well in the sample_read_events.csv file. It contains:

  • InventoryId: the unique identifier of a stocktake
  • EpcSerial: the unique identifier of an item (EPC without company prefix)
  • Product: the identifier of the product the item is associated with

A more detailed description of the dataset can be found in our publication below. Note that the dataset is free to use for research purposes but requires citing our paper as the source of the data.

  • M. Wölbitsch, T. Hasler, M. Goller, C. Gütl, S. Walk and D. Helic (2019). RFID in the Wild - Analyzing Stocktake Data to Determine Detection Probabilities of Products. In Proceedings of 6th IEEE International Conference on Internet of Things: Systems, Management and Security IOTSMS2019 [PDF] [Dataset]

Narrative-Driven Recommendations Dataset

This dataset contains crowdsourced and manually curated annotations for submissions and comments to r/MovieSuggestions. Specifically, the annotations include movies (IMDb IDs), keywords, actors and genres for more than 1,400 submissions and 20,000 comments.

The dataset was generated for the purpose of analyzing narrative-driven recommendations, using data dumps available at pushshift.io/reddit/.

Data Structure
  • submissions.csv: contains several different crowdsourced and manually curated annotations for movie suggestion requests on r/MovieSuggestion. Specifically, the file includes the reddit submission id, positively mentioned movie ids (IMDb), negatively mentioned movie ids (IMDb) as well as desired and undesired keywords, genres and actors.
  • comments.csv: contains annotations for comments posted on r/MovieSuggestions. Each line in comments.csv contains the reddit submission is was posted under, the individual reddit comment id as well as the IMDb movie ids annotated in each comment.
  • movie_titles.csv: includes a mapping between IMDb movie ids and their original titles (both found on IMDb)

A more detailed description of the dataset can be found in our publication below. Note that the dataset is free to use for research purposes but requires citing our paper as the source of the data.

  • L. Eberhard, S. Walk, L. Posch and D. Helic (2019). Evaluating Narrative-Driven Movie Recommendations on Reddit. In Proceedings of 24th International Conference on Intelligent User Interfaces IUI2019. [PDF] [Dataset] [ACM DL]

RFID Tag Localization Dataset

This dataset includes CSVs with all read events we collected for the experiments conducted for our paper "Estimating Relative Tag Locations based on Time-Differences in Read Events".

Specifically, the dataset contains the following fields:

  • experiment_id: identifier of the experiment
  • group: groups experiments with the same properties (setup, experiment, tags, iterations)
  • setup: "2d" for 2d-setup, "2da" for 2d-asymmetric-setup, "3d" for 3d-setup
  • experiment: either "walking" or "random"
  • tags: number of tags involved in the experiment
  • iterations: number of iterations
  • milliseconds: milliseconds since beginning of the experiment
  • serial: the serial number extracted from the epc
  • rssi: the measured rssi value

The corresponding ground truth dataset are located in the files 2d.npy, 2d_asymmetric.npy, and 3d.npy. The files contain the ground truth coordinates of the tags, relative to the tag with serial number 0.

The dataset is free to use for research purposes but requires citing our paper as the source of the data.

  • T. Hasler, M. Wölbitsch, M. Goller and S. Walk (2019). Estimating Relative Tag Locations based on Time-Differences in Read Events. In Proceedings of 13th IEEE International Conference on RFID. [PDF] [Dataset]

Shopping-Baskets Dataset

The dataset consists of roughly half a million shopping baskets from 20 retail fashion stores located in four different cities. The data was collected between November 2016 and December 2018.

The dataset csv file contains the following fields:

  • TransactionId: the transaction identifier, which can be used for grouping (i.e., generating shopping baskets)
  • ProductId: the product identifier (anonymized product number)
  • Date: the date on which a product was sold
  • City: the city which a product was sold (anonymized)

When using the dataset please cite our paper as the source of the data.

  • M. Wölbitsch, S. Walk, M. Goller and D. Helic (2019). Beggars Can't Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores. In Proceedings of 27th ACM International Conference on User Modelling, Adaptation and Personalization UMAP2019. [PDF] [Dataset]
Activities

RESEARCH VISITS

TEACHING

CONFERENCE PROGRAM COMMITTEE MEMBER

  • The Web Conference 2021 - Semantics and Knowledge Track WWW21
  • 2nd Workshop on Recommender Systems in Fashion fashionXrecsys2020
  • Workshop on Recommender Systems in Fashion - Special Issue fashionXrecsysSI2019
  • International Symposium on Methodologies for Intelligent Systems ISMIS20
  • The Web Conference 2020 - Semantics and Knowledge Track WWW20
  • 1st Workshop on Recommender Systems in Fashion fashionXrecsys2019
  • 18th International Semantic Web Conference - Research Track ISWC2019
  • The Web Conference 2019 - Semantics and Knowledge Track WWW19
  • 4th International Workshop on Visualization and Interaction for Ontologies and Linked Data VOILA! 2018
  • 17th International Semantic Web Conference - Research Track ISWC2018
  • Opinion Mining, Summarization and Diversification Workshop RevOpiD 2018
  • 29th ACM Conference on Hyptertext and Social Media ACM Hypertext 2018
  • The Web Conference 2018 - Web Content Analysis, Semantics, and Knowledge Track WWW18
  • International MOOC-MAKER Conference 2017 moocmaker17
  • 3rd International Workshop on Visualization and Interaction for Ontologies and Linked Data VOILA! 2017
  • 16th International Semantic Web Conference - Research Track ISWC2017
  • 16th International Semantic Web Conference - Resources Track ISWC2017
  • 28th ACM Conference on Hyptertext and Social Media - Social Networks & Digital Humanities Track ACM Hypertext 2017
  • 28th ACM Conference on Hyptertext and Social Media - Publicity Chair ACM Hypertext 2017
  • 14th Extended/European Semantic Web Conference - Research Track ESWC2017
  • 25th World Wide Web Conference - Posters & Demos WWW16
  • 15th International Semantic Web Conference - Resources Track ISWC2016
  • 14th International Semantic Web Conference - Evaluation Track ISWC2015
  • 14th International Semantic Web Conference - Posters & Demos ISWC2015
  • WebSci Conference 2014 WebSci 2014
  • 13th International Semantic Web Conference - Posters & Demos ISWC2014

SUBREVIEWER

  • 15th International Semantic Web Conference - Research Track ISWC2016
  • 4th Workshop on USage Analysis and the Web of Data USEWOD2014
  • 23rd International World Wide Web Conference - Web Science Track WWW WebSci 2014
  • 11th European Semantic Web Conference ESWC 2014
  • 14th International Conference on Web Engineering ICWE 2014
  • 8th International Conference on Weblogs and Social Media ICWSM-14
  • 13th International Semantic Web Conference ISWC2014
  • 24th ACM Conference on Hypertext and Social Media Hypertext2013
  • 7th ACM Conference on Recommender Systems RecSys 2013
  • 3rd International Workshop on Mining Ubiquitous and Social Environments MUSE2012
Research
My main research interests include Machine Learning & Neural Networks, Recommender Systems, Network Science, Knowledge & Data Mining and Semantic Web & Ontologies.

STUDENTS


I would like to thank all the students who currently work or worked with me on interesting research topics: Lukas Eberhard, Thomas Hasler, Clemens Hofer, Tomas Karas, Patrick Kasper, Philipp Koncar, Dietmar Maurer, Thomas Niedermair, Tiago Santos, Massimo Vitiello and Matthias Wölbitsch.
Grants & Awards
I was involved in the acquisition of the following grants:
  • 2019 DOC Stipend for Tiago Santos - Austrian Academy of Sciences
  • 2018 Dissertation Grant "Industrienahe Dissertation" for Matthias Wölbitsch - FFG Austria
  • 2016 Student Travel Grant (ISWC 2016) - National Institutes of Health ($2,020)
  • 2015 Competitive Initial Funding Program F&T Haus - Graz University of Technology (10,000€)
  • 2013 Marshallplan Stipend (6,000€)