Prof. Dr. Rainer Gemulla

Chair of Practical Computer Science I: Data Analytics

Universität Mannheim
B6, 26, Room B 016
D-68159 Mannheim

Tel.: +49 621 181 2480

rgemullamail-uni-mannheim.de
My PGP public key (id 0x81405E0B30302532)

I am heading the Chair of Practical Computer Science I: Data Analytics at the University of Mannheim. The chair is part of the Data and Web Science Group.


Go to: Research Interests, CV, PhD Students, Teaching, Awards, Professional Activities, Data and Software, Publications


If you consider applying to our lab, please read:

  • Applications should include CV, transcripts, and a short (!) cover letter or will be ignored.
  • We generally do not offer short interships (3 months or less). Applications for such internships will be ignored.
  • If you are a BSc or MSc student here, have very good transcripts, and are interested in a student job with us, contact me directly.

Research Interests

My research interests are mainly in machine learning and data processing. In particular:

  • Machine learning with structured data (such as relational data)
  • Machine learning with semi-structured data (such as multi-relational graphs)
  • Combining the above with unstructured knowledge (such as text)
  • Efficient and scalable methods and systems for data-intensive processing

News

Curriculum Vitae

Since 2014   W3-Professor for Practical Computer Science I, Universität Mannheim, Germany
2010 – 2014   Senior researcher / group leader, Max-Planck-Institut für Informatik, Saarbrücken, Germany
2008 – 2010   Postdoctoral researcher, IBM Almaden Research Center, San Jose, CA, USA
2004 – 2008   PhD in Computer Science, Technische Universität Dresden, Germany

PhD Students

Former PhD students

Kaustubh Beedkar, Luciano del Corro, Kiril Gashteovski, Stefan Kain, Faraz Makari Manshadi, Alexander Renz-Wieland, Christina Teflioudi, Yanjie Wang

Teaching

If you are interested in writing a seminar, Bachelor or Master thesis with us, please read the following guidelines.

If you are within the intranet of University of Mannheim, you can access lecture videos / materials here. If not, ask me.

Upcoming semester (FSS 2024)

Current semester (HWS 2024)

Previous courses (not taught anymore)

      Awards

      • Distinguished PC Member Award at EDBT, 2023
      • Outstanding Reviewer Award at NeurIPS, 2021
      • Named Distinguished PC Member for SIGMOD, 2017
      • Junior-Fellow of the Gesellschaft für Informatik (GI), 2013
      • AWS in Education Research Grant Award, 2013
      • Busy Beaver teaching award (winter term 2012/2013)
        for Non-Traditional Data Management (NoSQL and more)
      • IBM's 2011 Pat Goldberg Memorial best paper award in CS, EE and Math
        (for “Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent” with P. J. Haas, E. Nijkamp, and Y. Sismanis; KDD 2011)
      • Best paper of NIPS 2011 Biglearn workshop
        (for “Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent” with P. J. Haas, Y. Sismanis, C. Teflioudi, and F. Makari)
      • Google Focused Research Award 2011: Robust and Scalable Fact Discovery from Web Sources
        (with G. Weikum and M. Theobald)
      • Research Highlight in Communications of the ACM
        (for “Distinct-Value Synopses for Multiset Operations” with K. Beyer, P.J. Haas, B. Reinwald, Y. Sismanis)
      • The VLDB Journal, Special Issue: Best Papers of VLDB 2006
        (for “A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets” with W. Lehner and P.J. Haas)

      Professional Activities

      Administration

      • Head of examination board: MSc Business Informatics (since 2017)
      • Member of examination board: BSc Business Informatics (since 2017 or earlier), Mannheim Master in Data Science (since 2017)
      • Member of selection commitee: BSc and MSc Business Informatics (since 2017 or earlier), Mannheim Master in Data Science (since 2024)
      • Information officer of the WIM faculty: since 2022
      • Embassador of the German Informatics Society (GI): since 2017
      • CIO of University of Mannheim: 2022–2024
      • Study dean of the WIM faculty: 2016–2019
      • Member of the selection committee: Max Planck International Research School (2010–2014)

      Organizer

      Young academics

      • One-day workshop “Machine Learning Systems” at Jugendforum BW 2024
      • Mentor of Junior Professors: Roland Leißa (2021–2025), Margret Keuper (2017–2021), Goran Glavaš (2017–2021)
      • Mentor in the mentoring program of the German Informatics Society (GI, since 2020)
      • Member of task committee and final-round jury of BWINF (since 2010)
      • Member of the board of BWINF (2014–2022)

      Associate editor / area chair

      • VLDB: 2021, 2019, 2015
      • TKDE: 2015–2018
      • DASP: 2018
      • EDBT: 2017
      • JISA: 2016
      • CIKM: 2014

      PC member / reviewer (since 2011)

      • 2025: EDBT, ICLR, SIGMOD, STACS, TMLR, VLDB
      • 2024: ARR, DAMI, DEEM, ICLR, IJCAI, LLM+KG, NeurIPS, SIGMOD, TMLR, VLDBJ
      • 2023: ARR, Artificial Intelligence, BTW, DEEM, EDBT, ICLR, IJCAI, Neural Networks, Repl4NLP, SIGMOD, TMLR, VLDB, VLDBJ
      • 2022: Artificial Intelligence, DEEM, EDBT, ICLR, IJCAI, KDML, Machine Learning, Pattern Recognition, SDM, SIGMOD, VLDBJ
      • 2021: DEEM, EDBT, ICDM, IJCAI, KDML, NeurIPS, SIGMOD (demo), Repl4NLP
      • 2020: AKBC, EDBT, IJCAI, LWDA, Repl4NLP, SUM, TPDS
      • 2019: AKBC, BTW, DEEM, IJCAI, INFORMATIK, LWDA, SUM
      • 2018: VLDB, EDBT, DEEM
      • 2017: SIGMOD, BTW, DEEM, TALG, VLDB, ECML-PKDD
      • 2016: SIGMOD, ECML-PKDD
      • 2015: BTW, DAMI, GvDB, IS, JODS, JWS, PODS
      • 2014: BDDC, BigData, Buda, DMC, JMLR, SIGMOD, VLDB
      • 2013: AKBC, BigData, BTW, CIKM, DMC, ICALP, TKDE, TML, VLDB
      • 2012: KDD, JMLR, TODS
      • 2011: BTW, IS, VLDB, VLDBJ

        Data and Software

        • GraSH: Multi-fidelity HPO for graph learning
        • DistKGE: A knowledge graph embedding library for multi-GPU and multi-machine training
        • AdaPM: A fully adaptive parameter manager
        • LibKGE: A knowledge graph embedding library
        • Lapse: A parameter server with dynamic parameter allocation
        • OPIEC: An open information extraction corpus
        • MinIE: Open information extractor (spiritual successor to ClausIE)
        • DSGDpp: Various parallel algorithms for matrix factorization (including DSGD++)
        • DESQ: Frequent sequence mining with subsequence constraints
        • Rounding rank: algorithms for computing rounding-rank decompositions
        • CORE: Context-aware open relation extraction with factorization machines
        • FINET: Context-aware fine-grained named entity typing
        • Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning
        • ClausIE: Clause-Based Open Information Extraction
        • LEMP: Fast Retrieval of Large Entries in a Matrix Product
        • LASH: Large-Scale Sequence Mining with Hierarchies
        • MG-FSM: Large-Scale Frequent Sequence Mining

        Publications

        See also Google Scholar and DBLP.

        2023   A. Kochsiek, R. Gemulla
        A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs [pdfresources]
        In EMNLP Findings, 2023
         A. Kochsiek, A. Saxena, I. Nair, R. Gemulla
        Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction [pdfresources]
        In Repl4NLP workshop, 2023
         A. Renz-Wieland, A. Kieslinger, R. Gericke, R. Gemulla, Z. Kaoudi, V. Markl
        Good Intentions: Adaptive Parameter Management via Intent Signaling [pdfresources]
        In CIKM, 2023
        2022   A. Kochsiek, F. Niesel, R. Gemulla
        Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings [pdfresources]
        In ECML-PKDD, 2022
         A. Saxena, A. Kochsiek, R. Gemulla
        Sequence-to-Sequence Knowledge Graph Completion and Question Answering [pdf, video resources]
        In ACL, pp. 2814-2828, 2022
         A. Renz-Wieland, R. Gemulla, Z. Kaoudi, V. Markl
        NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access [pdfsource code]
        In SIGMOD, pp. 481–495, 2022
        2021   A. Kochsiek, R. Gemulla
        Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques [pdfresources]
        In PVLDB, 15(3), 2021
         A. Renz-Wieland, T. Drobisch, R. Gemulla, Z. Kaoudi, V. Markl
        Just Move It! Dynamic Parameter Allocation in Action [pdfdemo]
        In PVLDB (demo), 14(12), 2021.
        2020   A. Renz-Wieland, R. Gemulla, S. Zeuch, V. Markl
        Dynamic Parameter Allocation in Parameter Servers [pdfsource code]
        In PVLDB, 13(12), pp. 1877-1890, 2020
         S. Broscheit, K. Gashteovski, Y. Wang, Rainer Gemulla
        Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction [pdfresources]
        In ACL, 2020
         D. Ruffinelli, S. Broscheit, R. Gemulla
        You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings [pdfvideoresourcesOpenReview]
        In ICLR, 2020
         S. Broscheit, D. Ruffinelli, A. Kochsiek, P. Betz, R. Gemulla
        LibKGE – A knowledge graph embedding library for reproducible research [pdfsource]
        In EMNLP (demo), 2020
         K. Gashteovski, R. Gemulla, B. Kotnis, S. Hertling, C. Meilicke
        On Aligning OpenIE Extractions with Knowledge Bases: A Case Study [pdfslides, resources]
        In Eval4NLP, 2020
        2019   Y. Wang, D. Ruffinelli, R. Gemulla, S. Broscheit, C. Meilicke
        On Evaluating Embedding Models for Knowledge Base Completion [pdf]
        In RepL4NLP workshop, 2019
         K. Beedkar, R. Gemulla, W. Martens
        A Unified Framework for Frequent Sequence Mining with Subsequence Constraints [pdf (journal version), pdf (author version), resources]
        In TODS, 2019
         K. Gashteovski, S. Wanner, S. Hertling, S. Broscheit, R. Gemulla
        OPIEC: An Open Information Extraction Corpus [pdfposterresourcesOpenReview]
        In AKBC, 2019
         A. Renz-Wieland, M. Bertsch, R. Gemulla
        Scalable Frequent Sequence Mining With Flexible Subsequence Constraints [pdfposter]
        In ICDE, 2019
        Preprints
        (2019)
           
        Y. Wang, S. Broscheit, R. Gemulla
        A Relational Tucker Decomposition for Multi-Relational Link Prediction [arXiv]
        2019
        2018   C. Meilicke, M. Fink, Y. Wang, D. Ruffinelli, R. Gemulla, and H. Stuckenschmidt
        Fine-grained Evaluation of Rule- and Embedding-based Systems for Knowledge Graph Completion [pdfresources]
        In ISWC, 2018
         J. Pfeiffer, S. Broscheit, R. Gemulla, M. Göschl
        A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval [pdf]
        In BioNLP workshop, 2018
         S. Broscheit, R. Gemulla, M. Keuper
        Learning Distributional Token Representations from Visual Features [pdf]
        In RepL4NLP workshop, 2018
         Y. Wang, R. Gemulla, H. Li
        On Multi-Relational Link Prediction with Bilinear Models [pdfresources]
        In AAAI, 2018
        2017   K. Gashteovski, R. Gemulla, L. del Corro
        MinIE: Minimizing Facts in Open Information Extraction [pdfposterresources]
        In EMNLP, pp. 2620-2630, 2017
         C. Teflioudi, R. Gemulla
        Exact and Approximate Maximum Inner Product Search with LEMP [pdf (journal version)pdf (author version)resources]
        In TODS, 42(1) Art. 5, 2017
        2016   S. Neumann, R. Gemulla, P. Miettinen
        What You Will Gain By Rounding: Theory and Algorithms for Rounding Rank [pdftech reportresources]
        In ICDM, pp. 380–389, 2016
         K. Beedkar, R. Gemulla
        DESQ: Frequent Sequence Mining with Subsequence Constraints [pdftech reportresources]
        In ICDM (short paper), pp. 793–798, 2016
        2015   L. Del Corro, A. Abujabal, R. Gemulla, G. Weikum
        FINET: Context-Aware Fine-Grained Named Entity Typing [pdfslidesresources]
        In EMNLP, pp. 868–878, 2015
         F. Petroni, L. Del Corro, R. Gemulla
        CORE: Context-Aware Open Relation Extraction with Factorization Machines [pdfslidesresources]
        In EMNLP, pp. 1763-1773, 2015
         K. Beedkar, K. Berberich, R. Gemulla, I. Miliaraki
        Closing the Gap: Sequence Mining at Scale [pdf (journal version)pdf (author version)resources]
        In TODS, 40(2) Art. 8, 2015
         C. Teflioudi, R. Gemulla, O. Mykytiuk
        LEMP: Fast Retrieval of Large Entries in a Matrix Product [pdfslidesresources]
        In SIGMOD, pp. 107–122, 2015
         K. Beedkar, R. Gemulla
        LASH: Large-Scale Sequence Mining with Hierarchies [pdfslidessource code]
        In SIGMOD, pp. 491–503, 2015
         R. Gemulla
        A Self-Portrayal of GI Junior Fellow Rainer Gemulla: Data Analysis at Scale [pdf (journal version), pdf (author version)]
        it – Information Technology 57(2), pp. 130–132 , 2015
        2014   L. Del Corro, R. Gemulla, G. Weikum
        Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning [pdfresources]
        In EMNLP, pp. 374–385, 2014
         P. Roy, J. Teubner, R. Gemulla
        Low-Latency Handshake Join [pdf]
        In PVLDB, 7(9), pp. 709–720, 2014
         L. Qu, Y. Zhang, R. Wang, L. Jiang, R. Gemulla, G. Weikum
        Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM [pdf]
        In TACL, 2, pp. 155–168, 2014
         D. Erdös, R. Gemulla, E. Terzi
        Reconstructing Graphs from Neighborhood Data [pdf (author version)pdf (journal version)]
        In TKDD, 8(4), 2014
        2013   F. Makari, C. Teflioudi, R. Gemulla, P. J. Haas, Y. Sismanis
        Shared-Memory and Shared-Nothing Stochastic Gradient Descent Algorithms for Matrix Completion [pdf (author version)pdf (journal version)source code]
        In KAIS (special issue: best papers of ICDM 2012), pp. 1–31, 2013
         F. Makari, R. Gemulla
        A Distributed Approximation Algorithm for Mixed Packing-Covering Linear Programs [pdf]
        In NIPS 2013 Biglearn workshop (poster), 2013
         F. Makari, B. Awerbuch, R. Gemulla, R. Khandekar, J. Mestre, M. Sozio
        A Distributed Algorithm for Large-Scale Generalized Matching [pdfslides]
        The analysis of the number of binary search steps (Lemma 2) contains a bug; see our Biglearn paper for a corrected version.
        In PVLDB, 6(9), pp. 613–624, 2013
         I. Miliaraki, K. Berberich, R. Gemulla, S. Zoupanos
        Mind the Gap: Large-Scale Frequent Sequence Mining [pdfslidesresources]
        In SIGMOD, pp. 797–808, 2013
         L. Del Corro, R. Gemulla
        ClausIE: Clause-Based Open Information Extraction [pdfslidesresources]
        In WWW, pp. 355–366, 2013
         R. Gemulla, P. J. Haas, W. Lehner
        Non-Uniformity Issues and Workarounds in Bounded-Size Sampling [pdf (author version)pdf (journal version)source code]
        In The VLDB Journal, 22(6), pp. 753–772, 2013
         K. Beedkar, L. Del Corro, R. Gemulla
        Fully Parallel Inference in Markov Logic Networks [pdf]
        In BTW, pp. 205–224, 2013
        2012   D. Erdös, R. Gemulla, E. Terzi
        Reconstructing Graphs from Neighborhood Data [pdfslides]
        In ICDM, pp. 231–240, 2012
         C. Teflioudi, F. Makari, R. Gemulla
        Distributed Matrix Completion [pdfslidessource code]
        In ICDM, pp. 655–664, 2012
         L. Qu, R. Gemulla, G. Weikum
        A Weakly Supervised Model for Sentence-Level Semantic Orientation Analysis with Multiple Experts [pdf]
        In EMNLP-CoNLL, pp. 149–159, 2012
        2011   R. Gemulla, P. J. Haas, Y. Sismanis, C. Teflioudi, F. Makari
        Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdfslidessource code]
        In NIPS 2011 Biglearn workshop, 2011 (best paper award)
         R. Gemulla, E. Nijkamp, P. J. Haas, Y. Sismanis
        Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdfslidessource code]
        In KDD, pp. 69–77, 2011
         K. Beyer, V. Ercegovac, R. Gemulla, A. Balmin, M. Eltabakh, C.C. Kanne, F. Ozcan, E. Shekita
        Jaql: A Scripting Language for Large Scale Semistructured Data Analysis [pdf]
        In PVLDB (industrial track), 4(11), pp. 1272-1283, 2011
         M. Y. Eltabakh, Y. Tian, F. Özcan, R. Gemulla, A. Krettek, J. McPherson
        CoHadoop: Flexible Data Placement and Its Exploitation in Hadoop [pdf]
        In PVLDB, 4(9), pp. 575–585, 2011
         R. Gemulla, P. J. Haas, E. Nijkamp, Y. Sismanis
        Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent [pdf]
        IBM Research Report RJ10481, March 2011 Revised February, 2013
         B. Schlegel, R. Gemulla, W. Lehner
        Memory-Efficient Frequent-Itemset Mining [pdf]
        In EDBT, pp. 461–472, 2011
        2010   S. Das, Y. Sismanis, K. S. Beyer, R. Gemulla, P. J. Haas, J. McPherson.
        Ricardo: Integrating R and Hadoop [pdf]
        In SIGMOD (industrial track), pp. 987–998, 2010
         B. Schlegel, R. Gemulla, W. Lehner.
        Fast Integer Compression using SIMD Instructions [pdf]
        In DAMON, pp. 34–40, 2010
        2009   K. Beyer, R. Gemulla. P. J. Haas, B. Reinwald, Y. Sismanis.
        Distinct-Value Synopses for Multiset Operations [pdftechnical perspective by Surajit Chaudhuri]
        In Commun. ACM, 52(10), pp. 87–95, 2009
         B. Schlegel, R. Gemulla, W. Lehner.
        k-Ary Search on Modern Processors [pdfslides]
        In DAMON, pp. 52–60, 2009
        2008   R. Gemulla.
        Sampling Algorithms for Evolving Datasets [pdfsummaryslides]
        Ph.D. thesis, Technische Universität Dresden, 2009
        URL for citations: nbn-resolving.de/urn:nbn:de:bsz:14-ds-1224861856184-11644
         R. Gemulla, P. Rösch and W. Lehner.
        Linked Bernoulli Synopses: Sampling Along Foreign Keys [pdfslides]
        In SSDBM, pp. 6–23, 2008
         R. Gemulla and W. Lehner.
        Sampling Time-Based Sliding Windows in Bounded Space [pdfslides]
        As observed by Hu et al., the lower bound of Ω(k log N) stated in Theorem 1 should read Ω(k log(N/k)).
        In SIGMOD, pp. 379–392, 2008
         P. Rösch, R. Gemulla and W. Lehner.
        Designing Random Sample Synopses with Outliers [pdfposter]
        In ICDE (poster), pp. 1400-1402, 2008
        2007   R. Gemulla, W. Lehner and P.J. Haas.
        Maintaining Bounded-Size Sample Synopses of Evolving Datasets [pdf]
        The resizing algorithm proposed in this article contains a bug; see my Ph.D. thesis or our 2013 VLDB Journal paper for a corrected version.
        In The VLDB Journal, Special Issue: Best Papers of VLDB 2006, pp. 173–201, 2007
         K. Beyer, P. J. Haas, B. Reinwald, Y. Sismanis and R. Gemulla.
        On Synopses for Distinct-Value Estimation Under Multiset Operations [pdfslides]
        In SIGMOD, pp. 199–210, 2007
         R. Gemulla, W. Lehner and P. J. Haas.
        Maintaining Bernoulli Samples over Evolving Multisets [pdfslides]
        In PODS, pp. 93–102, 2007
        2006   R. Gemulla, W. Lehner and P. J. Haas.
        A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets [pdfslides]
        In VLDB, pp. 595–606, 2006
         A. Klein, R. Gemulla, P. Rösch and W. Lehner.
        Derby/S: A DBMS for Sample-Based Query Answering [pdfposter1poster2]
        In SIGMOD (demo), pp. 757–759, 2006
         R. Gemulla and W. Lehner.
        Deferred Maintenance of Disk-Based Random Samples [pdfslides]
        In EDBT, pp. 423–441, 2006

        Kontakt

        Prof. Dr. Rainer Gemulla

        Prof. Dr. Rainer Gemulla

        Chair of Practical Computer Science I: Data Analytics
        University of Mannheim
        School of Business Informatics and Mathematics
        B 6, 26 – Room B 0.16
        68159 Mannheim