Prof. Dr. Rainer Gemulla

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.

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

• Kerstin Beckersjürgen
• Samuel Broscheit
• Simon Forbat
• Julie Naegelen

Former PhD students

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

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 (HWS26)

• CS 560: Large-Scale Data Management (Master Course)
• IE 675b: Machine Learning (Master course)
• SM 445/CS707: Seminar “Machine Learning on Structured Data”  (Bachelor/Master seminar)
• Data Analytics Team Project: Your Project, Your Team
• Colloquium (for PhD candidates)

This semester (FSS26)

I am on sabbatical leave. The course “CS 303: Praktische Informatik II” is taught by Prof. Frederik Armknecht. The course “IE 678 Deep Learning” is taught by Prof. Margret Keuper. We are still offering:

• SM445/CS 707: Seminar “Machine Learning on Structured Data” (Bachelor/Master seminar)
• Data Analytics Team Project: Your Project, Your Team (3 projects accepted)
• Colloquium (for PhD candidates)

Previous semester (HWS 2025)

• CS 560: Large-Scale Data Management (Master Course)
• IE 675b: Machine Learning (Master course)
• SM 445/CS707: Seminar “Machine Learning at Scale”  (Bachelor/Master seminar)
• Data Analytics Team Project: Your Project, Your Team
• Colloquium (for PhD candidates)

And the one before (FSS 2025)

• CS 303: Praktische Informatik II (Bachelor course)
• IE 678: Deep Learning (Master course)
• SM445/CS 707: Seminar “Machine Learning and Time” (Bachelor/Master seminar)
• Data Analytics Team Project: Your Project, Your Team
• Colloquium (for PhD candidates)

Previous courses (not taught anymore)

• Hot Topics in Machine Learning (UMA, MSc, 2016–2017); pre-cursor to IE675b Machine Learning
• Database Systems II (UMA, MSc, 2015); pre-cursor to CS560 Large-Scale Data Management
• Algorithmen und Datenstrukturen (PDF, 20 MB) (UMA, BSc, 2014)
• Data Mining and Matrices (PDF, 17 MB) (MPII/UMA, MSc, 2013–2018)
• Scalable Uncertainty Management (PDF, 10 MB) (MPII, MSc, 2012–2013)
• Datenbankprogrammierung (PDF, 3 MB) (TUD, Diploma, DB2 certification course, 2005–2007)

• Distinguished Area Chair Award at ICDE, 2026
• Distinguished Reviewer Award at SIGMOD, 2025
• 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)}

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

• Chair of study program at BTW23 and BTW25 conferences
• LWDA 2018 conference
• GI Dagstuhl seminar: Informatik@Schule 2016 – Das Verhältnis von informatischer Bildung und “Digitaler Bildung” (2016)
• GI Dagstuhl seminar: Informatik@Schule – Agenda für informatische Bildung in der Schule (2014)
• Member of organizing board and head of jury of BTW11's demonstration program

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: 2027, 2021, 2019, 2015
• ICDE: 2026
• IJCAI: 2025
• TKDE: 2015–2018
• DASP: 2018
• EDBT: 2017
• JISA: 2016
• CIKM: 2014

PC member / reviewer (since 2011)

• 2027: SIGMOD
• 2026: EDBT, ICLR, ICML, SIGMOD, TMLR, VLDB
• 2025: DEEM@SIGMOD, EDBT, ICLR, ICML, LLM+Graph@VLDB,NeurIPS, 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

• 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