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

There is currently an opening for a PhD position in our lab. 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.

• Data mining and machine learning, esp. involving structured data
• Efficient and scalable methods and systems for data-intensive processing
• Approximation techniques

• Samuel Broscheit
• Kiril Gashteovski
• Adrian Kochsiek
• Daniel Ruffinelli
• Alexander Renz-Wieland (external; cosupervision with Volker Markl)

Former PhD students

Kaustubh Beedkar, Luciano del Corro, Stefan Kain, Faraz Makari Manshadi, Christina Teflioudi, Yanjie Wang

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

Current semester (FSS 2023)

• CS 303: Praktische Informatik II (Bachelor course)
• IE 678: Deep Learning (Master course)
• CS 707: Data and Web Science Seminar (Master seminar)
• Colloquium (for PhD candidates)

Previous semester (HWS 2022)

• CS 560: Large-Scale Data Management (Master Course)
• IE 675b: Machine Learning (Master course)
• SM 445/CS 707: Data and Web Science Seminar (Bachelor/Master seminar)
• Data Analytics Team Project: Your Project, Your Team
• Colloquium (for PhD candidates)

• 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)}

Associate editor / area chair

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

PC member / reviewer (since 2011)

• 2022: EDBT, IJCAI, ICLR, SDM, SIGMOD
• 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

Organizer

• 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)

• Lapse: A parameter server with dynamic parameter allocation
• LibKGE: A knowledge graph embedding library
• 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