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Joeran Beel
Ussher Assistant Professor, Computer Science

Biography

Dr Joeran Beel is a tenure-track Ussher Assistant Professor in Intelligent Systems at the School of Computer Science and Statistics at Trinity College Dublin. He is also affiliated with the ADAPT Centre, an interdisciplinary research centre that closely cooperates with industry partners including Google, Deutsche Bank, Huawei, and Novartis. Joeran is further a Visiting Professor at the National Institute of Informatics in Tokyo, where he also completed his postdoctoral research. Joeran obtained a PhD in Computer Science from the Otto-von-Guericke University Magdeburg. During his PhD studies, he completed three research visits at the University of California, Berkeley and one at the University of Cyprus. Joeran has industry experience as a product manager at HRS.de / HRS Holidays.

Joeran"s research focuses on machine learning, text mining, natural language processing, the blockchain and other technologies, in areas including recommender systems, search engines, news analysis, plagiarism detection, document engineering, and machine translation. Domains he is particularly interested in include digital libraries & digital humanities, eHealth, tourism, law, fintech, and mobility. Joeran published more than 60 peer-reviewed publications that have received over 1,500 citations. He acts as a reviewer for SIGIR, ECIR, RecSys, UMAP, ACM TiiS, and JASIST and he is serving as general co-chair of the upcoming 26th Irish Conference on Artificial Intelligence and Cognitive Science. He acquired more than 1 million Euro in funding for his research, prototype development, and two business start-ups, which both received several awards at business plan competitions such as start2grow and BPW. Joeran is currently preparing to spin out his third business start-up, this time in the field of recommender systems and machine learning.

Please visit https://www.scss.tcd.ie/joeran.beel/ for more details

Publications and Further Research Outputs

Peer-Reviewed Publications

Collier M. and Beel J., EMANN: A Novel Extended-Memory-Augmented Neural Network for Machine Translation, ECIR, 2019 Conference Paper, 2019

Joeran Beel, Andrew Collins, and Akiko Aizawa, The Architecture of Mr. DLib's Scientific Recommender-System API, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), Dublin, 2018, pp78 - 89 Conference Paper, 2018

Mark Collier and Joeran Beel, An Empirical Comparison of Syllabuses for Curriculum Learning, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp150 - 161 Conference Paper, 2018

Dominika Tkaczyk, Paraic Sheridan, Joeran Beel, ParsRec: A Meta-Learning Recommender System for Bibliographic Reference Parsing Tools, 12th ACM Conference on Recommender Systems, 2018, pp101 - 102 Poster, 2018

Mark Collier and Joeran Beel, Implementing Neural Turing Machines, International Conference on Artificial Neural Networks, 2018-10-05, 2018, pp10-18 Conference Paper, 2018 DOI

Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti and Joeran Beel, ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp162 - 173 Conference Paper, 2018

Dominika Tkaczyk, Andrew Collins and Joeran Beel, Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes, ACM Joint Conference on Digital Libraries (JCDL), 2018, pp387-388 Poster, 2018 DOI

Dominika Tkaczyk, Andrew Collins, Paraic Sheridan and Joeran Beel, Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers, ACM Joint Conference on Digital Libraries (JCDL), 2018, pp99-108 Conference Paper, 2018 DOI

Andrew Collins, Dominika Tkaczyk, and Joeran Beel, A Novel Approach to Recommendation Algorithm Selection using Meta-Learning, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp162 - 173 Conference Paper, 2018

Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, and Joeran Beel, Position Bias in Recommender Systems for Digital Libraries, iConference , 2018, pp335-344 Conference Paper, 2018 DOI

Joeran Beel, Corinna Breitinger, and Stefan Langer, Evaluating the CC-IDF citation-weighting scheme: How effectively can "Inverse Document Frequency" (IDF) be applied to references, Proceedings of the 12th iConference, 2017, pp1-11 Conference Paper, 2017

Bela Gipp, Corinna Breitinger, Norman Meuschke, and Joeran Beel, CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback using the Blockchain, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017, pp1-4 Conference Paper, 2017 DOI

Joeran Beel, Real-World Recommender Systems for Academia: The Gain and Pain in Developing, Operating, and Researching them, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp6-17 Conference Paper, 2017 URL URN

Stefan Feyer, Sophie Siebert, Bela Gipp, Akiko Aizawa, and Joeran Beel, Integration of the Scientific Recommender System Mr. DLib into the Reference Manager JabRef, Proceedings of the 39th European Conference on Information Retrieval (ECIR), 2017, pp770-774 Poster, 2017 DOI

Stefan Langer and Joeran Beel, Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp85-92 Conference Paper, 2017

Joeran Beel, Siddharth Dinesh, Philipp Mayr, Zeljko Carevic, and Jain Raghvendra, Stereotype and Most-Popular Recommendations in the Digital Library Sowiport, Proceedings of the 15th International Symposium of Information Science, 2017, pp96-108 Conference Paper, 2017

Felix Beierle, Akiko Aizawa, and Joeran Beel, Exploring Choice Overload in Related-Article Recommendations in Digital Libraries, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp51-61 Conference Paper, 2017 URL URN

Joeran Beel, Bela Gipp, and Akiko Aizawa, Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017, pp313-314 Poster, 2017 DOI URL

Joeran Beel, Stefan Langer, and Bela Gipp, TF-IDuF: A Novel Term-Weighting Scheme for User Modeling based on Users' Personal Document Collections, Proceedings of the 12th iConference, 2017, pp34-40 Conference Paper, 2017

Joeran Beel, Zeljko Carevic, Johann Schaible, and Gabor Neusch, RARD: The Related-Article Recommendation Dataset, D-Lib Magazine, 2017, p1-14 Journal Article, 2017 DOI

Bela Gipp, Norman Meuschke, Joeran Beel, and Corinna Breitinger, Using the Blockchain of Cryptocurrencies for Timestamping Digital Cultural Heritage, Bulletin of IEEE Technical Committee on Digital Libraries (TCDL), 2017, p12-14 Journal Article, 2017

Beel, Joeran and Breitinger, Corinna and Langer, Stefan and Lommatzsch, Andreas and Gipp, Bela, Towards Reproducibility in Recommender-Systems Research, User Modeling and User-Adapted Interaction (UMUAI), 26, (1), 2016, p69-101 Journal Article, 2016 DOI

Stability Evaluation of Event Detection Techniques for Twitter in, editor(s)Bostrm, Henrik and Knobbe, Arno and Soares, Carlos and Papapetrou, Panagiotis , Advances in Intelligent Data Analysis XV, Springer, 2016, pp368--380 , [Weiler, Andreas and Beel, Joeran and Gipp, Bela and Grossniklaus, Michael] Book Chapter, 2016 DOI

Joeran Beel and Bela Gipp and Stefan Langer and Corinna Breitinger, Research Paper Recommender Systems: A Literature Survey, International Journal on Digital Libraries, (4), 2016, p305-338 Journal Article, 2016 DOI

Joeran Beel and Stefan Langer, A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems, Proceedings of the 19th International Conference on Theory and Practice of Digital Libraries (TPDL), edited by Kapidakis, Sarantos and Mazurek, Cezary and Werla, Marcin , 9316, 2015, pp153-168 Conference Paper, 2015 DOI

Beel, Joeran and Langer, Stefan and Kapitsaki, Georgia M. and Breitinger, Corinna and Gipp, Bela, Exploring the Potential of User Modeling based on Mind Maps, Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), edited by Francesco Ricci and Kalina Bontcheva and Owen Conlan and Séamus Lawless , 9146, Springer, 2015, pp3-17 Conference Paper, 2015 DOI

Joeran Beel, Utilizing Mind-Maps for Information Retrieval and User Modelling, Proceedings of the 22nd Conference on User Modelling, Adaption, and Personalization (UMAP), edited by Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben , 8538, Springer, 2014, pp301-313 Conference Paper, 2014 DOI

Stefan Langer and Joeran Beel, The Comparability of Recommender System Evaluations and Characteristics of Docear's Users, Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (REDD) at the 2014 ACM Conference Series on Recommender Systems (RecSys), CEUR-WS, 2014, pp1--6 Conference Paper, 2014

Joeran Beel and Stefan Langer and Bela Gipp and Andreas NÃ"rnberger, The Architecture and Datasets of Docear's Research Paper Recommender System, D-Lib Magazine, 20, (11/12), 2014 Journal Article, 2014 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr, Docear4Word: Reference Management for Microsoft Word based on BibTeX and the Citation Style Language (CSL), Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp445-446 Poster, 2013 DOI

Beel, Joeran and Langer, Stefan and Genzmehr, Marcel, Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 2013, pp395--399 Poster, 2013

Joeran Beel and Stefan Langer and Marcel Genzmehr and Christoph MÃ"ller, Docears PDF Inspector: Title Extraction from PDF files, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp443-444 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃ"rnberger, Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 8092, Springer, 2013, pp390--394 Poster, 2013

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Andreas NÃ"rnberger, A Comparative Analysis of Offline and Online Evaluations and Discussion of Research Paper Recommender System Evaluation, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), 2013, pp7-14 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Andreas NÃ"rnberger and Marcel Genzmehr, The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , Springer, 2013, pp400--404 Poster, 2013

Mario Lipinski and Kevin Yao and Corinna Breitinger and Joeran Beel and Bela Gipp, Evaluation of Header Metadata Extraction Approaches and Tools for Scientific PDF Documents, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL'13), Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL'13), 2013, pp385-386 Conference Paper, 2013

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃŒrnberger, Introducing Docear's Research Paper Recommender System, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp459-460 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Corinna Breitinger and Andreas NÃ"rnberger, Research Paper Recommender System Evaluation: A Quantitative Literature Survey, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), ACM, 2013, pp15-22 Conference Paper, 2013 DOI

Beel, Joeran and Gipp, Bela, Academic search engine spam and Google Scholar's resilience against it, Journal of Electronic Publishing, 13, (3), 2010 Journal Article, 2010 DOI

Bela Gipp and Joeran Beel and Ivo Roessling, ePassport: The World's New Electronic Passport, Scotts Valley (USA), Createspace, 2007 Book, 2007

Non-Peer-Reviewed Publications

Joeran Beel, Please visit my Google Scholar profile for a complete list of publications, https://scholar.google.de/citations?user=jyXACVcAAAAJ&hl=en, 2020 Journal Article, 2020

Joeran Beel, Darwin & Goliath: Micro & Macro Recommendations as-a-Service, Machine Learning Meetup Dublin, Dublin, 2018-04, 2018 Invited Talk, 2018

Joeran Beel, Machine Learning und die Bibliothek - Chancen und Grenzen, 18. BVB-Verbundkonferenz, Weiden, Germany, 2018 Invited Talk, 2018

Joeran Beel, Reference Parsing, Author Role Extraction, and Micro Recommendations, NII Weekly Seminar, Media Devision, Tokyo, 2018-03-30, 2018 Invited Talk, 2018

Joeran Beel, The Potential of Meta Recommender-Systems at Macro- and Micro Level, Machine Learning Dublin Meetup, Ireland, Dublin, 2018 Invited Talk, 2018

Rob Brennan, Joeran Beel, Ruth Byrne and Jeremy Debattista, Preface: The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2018), 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS, 2018, pp1 - 7 Conference Paper, 2018

Joeran Beel, Keynote: Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them, BIR Workshop, co-located with ECIR, UK, 2017 Invited Talk, 2017

Joeran Beel, Real-World Recommender Systems for Academia The Pain and Gain in Building, Operating and Researching them, UCD Recommender System Seminar, Dublin, 2017-11-21, 2017 Invited Talk, 2017

Joeran Beel, My Life as a PhD Student and Researcher, Research Methods Seminar, Dublin, 2017-10-18, 2017, School of Computer Science, TCD Invited Talk, 2017

Joeran Beel, Virtual Citation Proximity (VCP): Calculating Co-Citation-Proximity-Based Document Relatedness for Uncited Documents with Machine Learning, 2017 Working Paper, 2017 DOI

Joeran Beel, Mr. DLib: Literature Recommendations as a Service, Deutsch-Japanischer Wirtschaftskreis, Tokyo, 2016-10-04, 2016 Invited Talk, 2016

Joeran Beel, Work & Life in Tokyo as a Researcher, DAAD Butterbrot & Bier, Tokyo, 2016-10-24, 2016 Invited Talk, 2016

Joeran Beel, Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps, 2015 Thesis, 2015

Research Expertise

Description

Recommender Systems, User Modelling, Information Retrieval, Machine Learning, Artificial Intelligence, Information Extraction, Natural Language Processing, Text Mining, Citation Analysis, Bibliometrics, Altmetrics, Scientometrics, Plagiarism Detection, Blockchain, Digital Libraries, Digital Humanities, Finance (FinTech), Legal, Tourism, Medical

Projects

  • Title
    • Darwin & Goliath: Meta-Learning Recommendations As-a-Service
  • Summary
    • Recommender systems like on Amazon generate up to 30% of enterprises' consumption. Consequently, many large enterprises employ hundreds of software engineers to develop recommender systems and make them as effective as possible. Small and medium-sized enterprises (SME) often do not have the skills and resources to develop recommender systems from scratch. Instead, they typically use recommendations-as-a-service (RaaS). RaaS providers operate recommender systems on their servers, and SMEs pay a monthly subscription fee to request recommendations from the RaaS' API. Current RaaS providers offer suboptimal recommendation effectiveness because their systems do not adapt optimally to the characteristics of their SME partners. This is due to three reasons. 1. RaaS providers use only a single recommendation-framework. This limits both the number of potential algorithms to use and the type of data they can process. 2. RaaS providers follow a one-size-fits-all approach and optimise their systems globally, i.e. they use one algorithm for all their SMEs. However, research shows that recommendation algorithms perform significantly different with different SMEs; hence one algorithm for all SMEs will deliver suboptimal effectiveness for at least some of them. 3. RaaS providers do not optimise the presentation of recommendations. However, the presentation of recommendations is important as, for instance, the number of recommendations and the displayed information has a strong impact on how users perceive the quality of recommendations. We create the spin-out company Darwin & Goliath, which will offer a unique and highly adaptable meta-recommendation framework as a recommendations-as-a-service. The proposed solution features three USPs. 1. Darwin & Goliath uses multiple state-of-the-art recommendation frameworks (Apache Mahout, TensorFlow, and others). Each of these frameworks provides dozens of pre-implemented algorithms. Consequently, Darwin & Goliath chooses from a wide range of algorithms when it comes to identifying the best algorithm for each SME. 2. Darwin & Goliath applies a novel technology for algorithm selection, which we call "micro optimization". Not only identifies Darwin & Goliath the best algorithm for each SME, but for each recommendation request. This means that even for the same SME, Darwin & Goliath may use a different algorithm for different users, items, and contexts. 3. Darwin & Goliath automatically finds the most favourable way of presenting recommendations for each SME.
  • Funding Agency
    • Enterprise Ireland
  • Date From
    • 2018-05-30
  • Date To
    • 2020-05-29
  • Title
    • Domain-Independent Semantic Annotation of the Text
  • Summary
    • Domain-Independent Semantic Annotation of the Text (DISCANT): Surrounded by huge and exponentially growing volume of information of various nature, every day we face challenges with keeping track of the latest news in a domain of interest or finding good-quality answers to specific questions. The problem of information overload has been addressed by modern information systems and search engines, however, their capabilities are strongly limited by using unstructured textual formats for exchanging and storing information. Textual documents often contain a lot of concrete facts and quantifiable information, but the communication of these facts and information through human languages renders them inaccessible to machines. This "semantic bottleneck" problem substantially slows down communication and knowledge propagation in the world. In order to equip machines with the ability of effective processing of the text, DISCANT ("Domain-Independent SemantiC ANnotation of the Text") aims at creating a comprehensive framework for semantic annotation of textual documents of arbitrary domains, such as scientific papers, legal documents, customer reviews or clinical trial reports. We will develop approaches, methods and tools for two classes of solutions: an environment for discovering entity and relation types in a given domain and a system for automated semantic annotation of the text. The project proposes a novel approach based on a combination of unsupervised natural language processing and machine learning techniques. DISCANT will advance machine understanding of the text, contributing to the release of important knowledge buried in textual documents, the creation of machine-readable knowledge repositories and more effective solutions for semantic search and personal recommendations. As a consequence, DISCANT will equip the consumers of textual documents with better tools for overcoming data deluge and information overload, enabling them to make better-quality, data-driven decisions.
  • Funding Agency
    • EU/SFI
  • Date From
    • 2017-07
  • Date To
    • 2019-06
  • Title
    • SciPlore
  • Summary
    • See http://sciplore.org
  • Funding Agency
    • ego.START
  • Date From
    • 2012
  • Date To
    • 2013
  • Title
    • Mr. DLib and Recommender System Reproducibility
  • Summary
    • Fellowship to conduct research at Tokyo's National Institute of Informatics (NII) for two years in the field of recommender systems.
  • Funding Agency
    • DAAD (German Academic Exchange Service)
  • Date From
    • 2015
  • Date To
    • 2016
  • Title
    • Docear: Literature Management & Mind-Mapping Based User Modelling
  • Summary
    • Docear is a unique solution to academic literature management, i.e. it helps you organizing, creating, and discovering academic literature. Among others, Docear offers: 1. A single-section user-interface that allows the most comprehensive organization of your literature. With Docear, you can sort documents into categories; you can sort annotations (comments, bookmarks, and highlighted text from PDFs) into categories; you can sort annotations within PDFs; and you can view multiple annotations of multiple documents, in multiple categories - at once. 2. A "literature suite concept" that combines several tools in a single application (pdf management, reference management, mind mapping, ...). This allows you to draft your own papers, assignments, thesis, etc. directly in Docear and copy annotations and references from your collection directly into your draft. 3. A recommender system that helps to discover new literature: Docear recommends papers which are free, in full-text, instantly to download, and tailored to your information needs.
  • Funding Agency
    • German Federal Ministry of Economics
  • Date From
    • 2011
  • Date To
    • 2012
  • Title
    • SciPlore
  • Summary
    • See http://sciplore.org
  • Funding Agency
    • German Federal Ministry of Economics
  • Date From
    • 2011
  • Date To
    • 2012
  • Title
    • Docear: Literature Management
  • Summary
    • Docear is a unique solution to academic literature management, i.e. it helps you organizing, creating, and discovering academic literature. Among others, Docear offers: 1. A single-section user-interface that allows the most comprehensive organization of your literature. With Docear, you can sort documents into categories; you can sort annotations (comments, bookmarks, and highlighted text from PDFs) into categories; you can sort annotations within PDFs; and you can view multiple annotations of multiple documents, in multiple categories - at once. 2. A "literature suite concept" that combines several tools in a single application (pdf management, reference management, mind mapping, ...). This allows you to draft your own papers, assignments, thesis, etc. directly in Docear and copy annotations and references from your collection directly into your draft. 3. A recommender system that helps to discover new literature: Docear recommends papers which are free, in full-text, instantly to download, and tailored to your information needs.
  • Funding Agency
    • ego.START
  • Date From
    • 2012
  • Date To
    • 2014

Keywords

ARTIFICIAL INTELLIGENCE; Bibliometrics; Bitcoin; Blockchain; computer-assisted language learning; Cross-Language Information Retrieval; Digital Humanities; Digital Libraries; FinTech; INFORMATION EXTRACTION; INFORMATION-RETRIEVAL; MACHINE LEARNING; Natural Language Processing; Personalisation; Personalisation and User-Centric Adaptivity; Personalised Information Retrieval; Plagiarism Detection; Recommender Systems; Scientometrics; Text Mining; TOURISM; User Modeling

Recognition

Representations

Scientific advisor on the board of iris.ai 2018

Advisor on the board of Originstamp 2016

Awards and Honours

Best-paper award for the paper "Implementing Neural Turing Machines" at the 27th International Conference on Artificial Neural Networks. 7th of October 2018

Best-Reviewer Nominee at 12th ACM Conference on Recommender Systems 5th of October 2018

5th prize in B-P-W business plan contest 2013

Award for exceptional achievements in the field of technology by the Heinz and Gisela Friederichs Foundation 2002

2nd winner at Jugend-forscht, Germany's national wide research contest 2002

Honoring for outstanding research work by German's Chancellor Gerhard Schröder 2002

4th prize in nationwide business plan contest start2grow 2003

4th winner of the business plan contest futureSAX in Saxony 2003

2nd prize in nationwide business plan contest B-P-W 2003

1st / prize in ego.BUSINESS business plan contest 2012

Best Master's graduate of the computer science department 2008

Award for an outstanding microelectronic equipment development by the Association of German Electrical Engineers 2001